VariantAnnotation/build/0000755000175100017510000000000014614361054016334 5ustar00biocbuildbiocbuildVariantAnnotation/build/vignette.rds0000644000175100017510000000051314614361054020672 0ustar00biocbuildbiocbuildR=O0uPhT|sFXD&qFUm!1{{)oBHz=Z4 I[L,Ϣ8E)ϐ(NkUDBHȥ|GX\|L#Xr"Fl}h@ SN. U29g7w*i,ӸuGEm9晳cx ߼nSխiH hiH ;H\*,܃u6d"qT:Vra]}PϮ8SNB:DVariantAnnotation/DESCRIPTION0000644000175100017510000000415314614361055016747 0ustar00biocbuildbiocbuildPackage: VariantAnnotation Type: Package Title: Annotation of Genetic Variants Description: Annotate variants, compute amino acid coding changes, predict coding outcomes. Version: 1.50.0 Authors@R: c( person("Valerie", "Oberchain", role="aut"), person("Martin", "Morgan", role="aut"), person("Michael", "Lawrence", role="aut"), person("Stephanie", "Gogarten", role="ctb"), person("Bioconductor Package Maintainer", role="cre", email="maintainer@bioconductor.org")) License: Artistic-2.0 Depends: R (>= 4.0.0), methods, BiocGenerics (>= 0.37.0), MatrixGenerics, GenomeInfoDb (>= 1.15.2), GenomicRanges (>= 1.41.5), SummarizedExperiment (>= 1.19.5), Rsamtools (>= 2.19.1) Imports: utils, DBI, zlibbioc, Biobase, S4Vectors (>= 0.27.12), IRanges (>= 2.23.9), XVector (>= 0.29.2), Biostrings (>= 2.57.2), AnnotationDbi (>= 1.27.9), rtracklayer (>= 1.39.7), BSgenome (>= 1.47.3), GenomicFeatures (>= 1.31.3) Suggests: RUnit, AnnotationHub, BSgenome.Hsapiens.UCSC.hg19, TxDb.Hsapiens.UCSC.hg19.knownGene, SNPlocs.Hsapiens.dbSNP144.GRCh37, SIFT.Hsapiens.dbSNP132, SIFT.Hsapiens.dbSNP137, PolyPhen.Hsapiens.dbSNP131, snpStats, ggplot2, BiocStyle, knitr, magick, jsonlite, httr, rjsoncons LinkingTo: S4Vectors, IRanges, XVector, Biostrings, Rhtslib (>= 2.99.0) SystemRequirements: GNU make LazyLoad: yes biocViews: DataImport, Sequencing, SNP, Annotation, Genetics, VariantAnnotation Video: https://www.youtube.com/watch?v=Ro0lHQ_J--I&list=UUqaMSQd_h-2EDGsU6WDiX0Q RoxygenNote: 7.3.1 VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/VariantAnnotation git_branch: RELEASE_3_19 git_last_commit: 7805cec git_last_commit_date: 2024-04-30 Repository: Bioconductor 3.19 Date/Publication: 2024-04-30 NeedsCompilation: yes Packaged: 2024-05-01 06:33:17 UTC; biocbuild Author: Valerie Oberchain [aut], Martin Morgan [aut], Michael Lawrence [aut], Stephanie Gogarten [ctb], Bioconductor Package Maintainer [cre] Maintainer: Bioconductor Package Maintainer VariantAnnotation/inst/0000755000175100017510000000000014614361054016212 5ustar00biocbuildbiocbuildVariantAnnotation/inst/CITATION0000644000175100017510000000335014614305321017343 0ustar00biocbuildbiocbuildbibentry(bibtype = "Article", key = "Obenchain15072014", author = c(person(given = "Valerie", family = "Obenchain"), person(given = "Michael", family = "Lawrence"), person(given = "Vincent", family = "Carey"), person(given = "Stephanie", family = "Gogarten"), person(given = "Paul", family = "Shannon"), person(given = "Martin", family = "Morgan")), title = "VariantAnnotation: a Bioconductor package for exploration and annotation of genetic variants", volume = "30", number = "14", pages = "2076-2078", year = "2014", doi = "10.1093/bioinformatics/btu168", abstract = "Summary: VariantAnnotation is an R / Bioconductor package for the exploration and annotation of genetic variants. Capabilities exist for reading, writing and filtering variant call format (VCF) files. VariantAnnotation allows ready access to additional R / Bioconductor facilities for advanced statistical analysis, data transformation, visualization and integration with diverse genomic resources. Availability and implementation: This package is implemented in R and available for download at the Bioconductor Web site (http://bioconductor.org/packages/2.13/bioc/html/VariantAnnotation.html). The package contains extensive help pages for individual functions and a 'vignette' outlining typical work flows; it is made available under the open source 'Artistic-2.0' license. Version 1.9.38 was used in this article. Contact: valerie.obenchain@roswellpark.org", journal = "Bioinformatics") VariantAnnotation/inst/doc/0000755000175100017510000000000014614361054016757 5ustar00biocbuildbiocbuildVariantAnnotation/inst/doc/ensemblVEP.html0000644000175100017510000246235314614360751021667 0ustar00biocbuildbiocbuild ensemblVEP: using the REST API with Bioconductor

Contents

1 Introduction

Ensembl’s Variant Effect Predictor is described in McLaren et al. (2016).

Prior to Bioconductor 3.19, the ensemblVEP package provided access to Ensembl’s predictions through an interface between Perl and MySQL.

In 3.19 VariantAnnotation supports the use of the VEP component of the REST API at https://rest.ensembl.org.

2 Acquire annotation on variants from a VCF file

The function vep_by_region will accept a VCF object as defined in VariantAnnotation.

library(VariantAnnotation)
fl <- system.file("extdata", "chr22.vcf.gz", package="VariantAnnotation")
r22 = readVcf(fl)
r22
## class: CollapsedVCF 
## dim: 10376 5 
## rowRanges(vcf):
##   GRanges with 5 metadata columns: paramRangeID, REF, ALT, QUAL, FILTER
## info(vcf):
##   DataFrame with 22 columns: LDAF, AVGPOST, RSQ, ERATE, THETA, CIEND, CIPOS,...
## info(header(vcf)):
##              Number Type    Description                                        
##    LDAF      1      Float   MLE Allele Frequency Accounting for LD             
##    AVGPOST   1      Float   Average posterior probability from MaCH/Thunder    
##    RSQ       1      Float   Genotype imputation quality from MaCH/Thunder      
##    ERATE     1      Float   Per-marker Mutation rate from MaCH/Thunder         
##    THETA     1      Float   Per-marker Transition rate from MaCH/Thunder       
##    CIEND     2      Integer Confidence interval around END for imprecise var...
##    CIPOS     2      Integer Confidence interval around POS for imprecise var...
##    END       1      Integer End position of the variant described in this re...
##    HOMLEN    .      Integer Length of base pair identical micro-homology at ...
##    HOMSEQ    .      String  Sequence of base pair identical micro-homology a...
##    SVLEN     1      Integer Difference in length between REF and ALT alleles   
##    SVTYPE    1      String  Type of structural variant                         
##    AC        .      Integer Alternate Allele Count                             
##    AN        1      Integer Total Allele Count                                 
##    AA        1      String  Ancestral Allele, ftp://ftp.1000genomes.ebi.ac.u...
##    AF        1      Float   Global Allele Frequency based on AC/AN             
##    AMR_AF    1      Float   Allele Frequency for samples from AMR based on A...
##    ASN_AF    1      Float   Allele Frequency for samples from ASN based on A...
##    AFR_AF    1      Float   Allele Frequency for samples from AFR based on A...
##    EUR_AF    1      Float   Allele Frequency for samples from EUR based on A...
##    VT        1      String  indicates what type of variant the line represents 
##    SNPSOURCE .      String  indicates if a snp was called when analysing the...
## geno(vcf):
##   List of length 3: GT, DS, GL
## geno(header(vcf)):
##       Number Type   Description                      
##    GT 1      String Genotype                         
##    DS 1      Float  Genotype dosage from MaCH/Thunder
##    GL G      Float  Genotype Likelihoods

In this example we confine attention to single nucleotide variants.

There is a limit of 200 locations in a request, and 55000 requests per hour. We’ll base our query on 100 positions in the chr22 VCF.

dr = which(width(rowRanges(r22))!=1)
r22s = r22[-dr]
res = vep_by_region(r22[1:100], snv_only=FALSE, chk_max=FALSE)
jans = toJSON(content(res))

There are various ways to work with the result of this query to the API. We’ll use the rjsoncons JSON processing infrastructure to dig in and understand aspects of the API behavior.

First, the top-level concepts produced for each variant can be retrieved using

library(rjsoncons)
names(jsonlite::fromJSON(jmespath(jans, "[*]")))
##  [1] "strand"                          "end"                            
##  [3] "regulatory_feature_consequences" "id"                             
##  [5] "seq_region_name"                 "start"                          
##  [7] "most_severe_consequence"         "transcript_consequences"        
##  [9] "assembly_name"                   "input"                          
## [11] "allele_string"                   "motif_feature_consequences"     
## [13] "colocated_variants"

Annotation of the most severe consequence known will typically be of interest:

table(jsonlite::fromJSON(jmespath(jans, "[*].most_severe_consequence")))
## 
##   5_prime_UTR_variant        intron_variant splice_region_variant 
##                     1                    98                     1

There is variability in the structure of data returned for each query.

head(fromJSON(jmespath(jans, "[*].regulatory_feature_consequences")))
## [[1]]
##   regulatory_feature_id variant_allele consequence_terms   impact  biotype
## 1          ENSR0000....              G      regulato.... MODIFIER promoter
## 
## [[2]]
##   consequence_terms  biotype   impact regulatory_feature_id variant_allele
## 1      regulato.... promoter MODIFIER          ENSR0000....              T
## 
## [[3]]
##     impact consequence_terms  biotype variant_allele regulatory_feature_id
## 1 MODIFIER      regulato.... promoter              A          ENSR0000....
## 
## [[4]]
##     impact consequence_terms  biotype variant_allele regulatory_feature_id
## 1 MODIFIER      regulato.... promoter              T          ENSR0000....
## 
## [[5]]
##   variant_allele regulatory_feature_id consequence_terms   impact  biotype
## 1              T          ENSR0000....      regulato.... MODIFIER promoter
## 
## [[6]]
##   consequence_terms  biotype   impact regulatory_feature_id variant_allele
## 1      regulato.... promoter MODIFIER          ENSR0000....              A

Furthermore, the content of the motif feature consequences field seems very peculiar.

table(unlist(fromJSON(jmespath(jans, "[*].motif_feature_consequences"))))
## 
##                  -0.003                  -0.022                  -0.087 
##                       1                       1                       1 
##                       1                       2                       6 
##                       3                       1                       1 
##                       9                       A         ENSM00205804739 
##                       1                       1                       1 
##         ENSM00494167763         ENSM00522532781              ENSPFM0238 
##                       1                       1                       1 
##              ENSPFM0506              ENSPFM0597               GCM1::MAX 
##                       1                       1                       1 
##                MODIFIER                       N                       T 
##                       3                       2                       2 
##             TEAD4::ELF1             TEAD4::ELK1             TEAD4::SPIB 
##                       1                       1                       1 
## TF_binding_site_variant             TFAP2C::MAX                       Y 
##                       3                       1                       1

3 Transforming the API response to GRanges

We’ll consider the following approach to converting the API response to a GenomicRanges GRanges instance. Eventually this may become part of the package.

library(GenomicRanges)
.make_GRanges = function( vep_response ) {
  stopifnot(inherits(vep_response, "response"))  # httr
  nested = fromJSON(toJSON(content(vep_response)))
  ini = GRanges(seqnames = unlist(nested$seq_region_name),
    IRanges(start=unlist(nested$start), end=unlist(nested$end)))
  dr = match(c("seq_region_name", "start", "end"), names(nested))
  mcols(ini) = DataFrame(nested[,-dr])
  ini
}
tstg = .make_GRanges( res )
tstg[,1]  # full print is unwieldy
## GRanges object with 100 ranges and 1 metadata column:
##         seqnames    ranges strand | strand
##            <Rle> <IRanges>  <Rle> | <list>
##     [1]       22  50300078      * |      1
##     [2]       22  50300086      * |      1
##     [3]       22  50300101      * |      1
##     [4]       22  50300113      * |      1
##     [5]       22  50300166      * |      1
##     ...      ...       ...    ... .    ...
##    [96]       22  50304748      * |      1
##    [97]       22  50304805      * |      1
##    [98]       22  50304935      * |      1
##    [99]       22  50304943      * |      1
##   [100]       22  50305084      * |      1
##   -------
##   seqinfo: 1 sequence from an unspecified genome; no seqlengths
names(mcols(tstg))
##  [1] "strand"                          "regulatory_feature_consequences"
##  [3] "id"                              "most_severe_consequence"        
##  [5] "transcript_consequences"         "assembly_name"                  
##  [7] "input"                           "allele_string"                  
##  [9] "motif_feature_consequences"      "colocated_variants"

Now information about variants can be retrieved with range operations. Deep annotation requires nested structure of the metadata columns.

mcols(tstg)[1, "transcript_consequences"]
## [[1]]
##     impact transcript_id gene_symbol      gene_id consequence_terms
## 1 MODIFIER  ENST0000....      PLXNB2 ENSG0000....      intron_v....
## 2 MODIFIER  ENST0000....      PLXNB2 ENSG0000....      intron_v....
## 3 MODIFIER  ENST0000....      PLXNB2 ENSG0000....      intron_v....
## 4 MODIFIER  ENST0000....      PLXNB2 ENSG0000....      intron_v....
##        biotype gene_symbol_source   hgnc_id strand variant_allele      flags
## 1 protein_....               HGNC HGNC:9104     -1              G           
## 2 protein_....               HGNC HGNC:9104     -1              G cds_end_NF
## 3 protein_....               HGNC HGNC:9104     -1              G cds_end_NF
## 4 protein_....               HGNC HGNC:9104     -1              G

4 Further work

An important element of prior work in ensemblVEP supports feeding annotation back into the VCF used to generate the effect prediction query. This seems feasible but concrete use cases are of interest.

References

McLaren, William, Laurent Gil, Sarah E. Hunt, Harpreet Singh Riat, Graham R. S. Ritchie, Anja Thormann, Paul Flicek, and Fiona Cunningham. 2016. “The Ensembl Variant Effect Predictor.” Genome Biology 17 (1): 122. https://doi.org/10.1186/s13059-016-0974-4.

VariantAnnotation/inst/doc/ensemblVEP.R0000644000175100017510000000355614614360750021115 0ustar00biocbuildbiocbuild## ----setup,echo=FALSE,results="hide",message=FALSE---------------------------- library(BiocStyle) library(VariantAnnotation) library(jsonlite) library(httr) ## ----dodemo,message=FALSE----------------------------------------------------- library(VariantAnnotation) fl <- system.file("extdata", "chr22.vcf.gz", package="VariantAnnotation") r22 = readVcf(fl) r22 ## ----lksnv-------------------------------------------------------------------- dr = which(width(rowRanges(r22))!=1) r22s = r22[-dr] res = vep_by_region(r22[1:100], snv_only=FALSE, chk_max=FALSE) jans = toJSON(content(res)) ## ----doj1, message=FALSE------------------------------------------------------ library(rjsoncons) names(jsonlite::fromJSON(jmespath(jans, "[*]"))) ## ----doj2--------------------------------------------------------------------- table(jsonlite::fromJSON(jmespath(jans, "[*].most_severe_consequence"))) ## ----doj3--------------------------------------------------------------------- head(fromJSON(jmespath(jans, "[*].regulatory_feature_consequences"))) ## ----lktaaaa------------------------------------------------------------------ table(unlist(fromJSON(jmespath(jans, "[*].motif_feature_consequences")))) ## ----lkmakeg, message=FALSE--------------------------------------------------- library(GenomicRanges) .make_GRanges = function( vep_response ) { stopifnot(inherits(vep_response, "response")) # httr nested = fromJSON(toJSON(content(vep_response))) ini = GRanges(seqnames = unlist(nested$seq_region_name), IRanges(start=unlist(nested$start), end=unlist(nested$end))) dr = match(c("seq_region_name", "start", "end"), names(nested)) mcols(ini) = DataFrame(nested[,-dr]) ini } tstg = .make_GRanges( res ) tstg[,1] # full print is unwieldy names(mcols(tstg)) ## ----lkmc--------------------------------------------------------------------- mcols(tstg)[1, "transcript_consequences"] VariantAnnotation/inst/doc/ensemblVEP.Rmd0000644000175100017510000000712514614305321021423 0ustar00biocbuildbiocbuild--- title: "ensemblVEP: using the REST API with Bioconductor" author: "Vincent J. Carey, stvjc at channing.harvard.edu" date: "`r format(Sys.time(), '%B %d, %Y')`" vignette: > %\VignetteEngine{knitr::rmarkdown} %\VignetteIndexEntry{ensemblVEP: using the REST API with Bioconductor} %\VignetteEncoding{UTF-8} output: BiocStyle::html_document: highlight: pygments number_sections: yes theme: united toc: yes bibliography: ens.bib --- ```{r setup,echo=FALSE,results="hide",message=FALSE} library(BiocStyle) library(VariantAnnotation) library(jsonlite) library(httr) ``` # Introduction Ensembl's Variant Effect Predictor is described in @McLaren2016. Prior to Bioconductor 3.19, the ensemblVEP package provided access to Ensembl's predictions through an interface between Perl and MySQL. In 3.19 VariantAnnotation supports the use of the VEP component of the REST API at [https://rest.ensembl.org](https://rest.ensembl.org/). # Acquire annotation on variants from a VCF file The function `vep_by_region` will accept a VCF object as defined in `r Biocpkg("VariantAnnotation")`. ```{r dodemo,message=FALSE} library(VariantAnnotation) fl <- system.file("extdata", "chr22.vcf.gz", package="VariantAnnotation") r22 = readVcf(fl) r22 ``` In this example we confine attention to single nucleotide variants. There is a limit of 200 locations in a request, and 55000 requests per hour. We'll base our query on 100 positions in the chr22 VCF. ```{r lksnv} dr = which(width(rowRanges(r22))!=1) r22s = r22[-dr] res = vep_by_region(r22[1:100], snv_only=FALSE, chk_max=FALSE) jans = toJSON(content(res)) ``` There are various ways to work with the result of this query to the API. We'll use the `r CRANpkg('rjsoncons')` JSON processing infrastructure to dig in and understand aspects of the API behavior. First, the top-level concepts produced for each variant can be retrieved using ```{r doj1, message=FALSE} library(rjsoncons) names(jsonlite::fromJSON(jmespath(jans, "[*]"))) ``` Annotation of the most severe consequence known will typically be of interest: ```{r doj2} table(jsonlite::fromJSON(jmespath(jans, "[*].most_severe_consequence"))) ``` There is variability in the structure of data returned for each query. ```{r doj3} head(fromJSON(jmespath(jans, "[*].regulatory_feature_consequences"))) ``` Furthermore, the content of the motif feature consequences field seems very peculiar. ```{r lktaaaa} table(unlist(fromJSON(jmespath(jans, "[*].motif_feature_consequences")))) ``` # Transforming the API response to GRanges We'll consider the following approach to converting the API response to a GenomicRanges GRanges instance. Eventually this may become part of the package. ```{r lkmakeg, message=FALSE} library(GenomicRanges) .make_GRanges = function( vep_response ) { stopifnot(inherits(vep_response, "response")) # httr nested = fromJSON(toJSON(content(vep_response))) ini = GRanges(seqnames = unlist(nested$seq_region_name), IRanges(start=unlist(nested$start), end=unlist(nested$end))) dr = match(c("seq_region_name", "start", "end"), names(nested)) mcols(ini) = DataFrame(nested[,-dr]) ini } tstg = .make_GRanges( res ) tstg[,1] # full print is unwieldy names(mcols(tstg)) ``` Now information about variants can be retrieved with range operations. Deep annotation requires nested structure of the metadata columns. ```{r lkmc} mcols(tstg)[1, "transcript_consequences"] ``` # Further work An important element of prior work in ensemblVEP supports feeding annotation back into the VCF used to generate the effect prediction query. This seems feasible but concrete use cases are of interest. # References VariantAnnotation/inst/doc/filterVcf.html0000644000175100017510000236446614614361005021612 0ustar00biocbuildbiocbuild 2. Using filterVcf() to Select Variants from VCF Files

Contents

1 Introduction

Whole genome Variant Call Format (VCF) files are very large, typically containing millions of called variants, one call per line of text. The actual number of variants relevant to a particular study (of a disease such as breast cancer, for instance) will often be far fewer. Thus the first task one faces when analyzing a whole genome VCF file is to identify and extract the relatively few variants which may be of interest, excluding all others. This vignette illustrates several techniques for doing this.

We demonstrate three methods: filtering by genomic region, filtering on attributes of each specific variant call, and intersecting with known regions of interest (exons, splice sites, regulatory regions, etc.). We are primarily concerned with the latter two. However, in order to create the small VCF data file we use here for demonstration purposes, we employed genomic region filtering, reducing a very large whole genome two-sample breast cancer VCF file of fourteen million calls, to a file containing fewer than ten thousand calls in a one million base pair region of chromosome 7. For the sake of reproducibility, and for completeness of exposition, we will illustrate this first step also.

2 The Data: Paired Tumor/Normal Breast Cancer Variants

Complete Genomics Inc. states:

To provide the scientific community with public access to data generated from two paired tumor/normal cancer samples, Complete Genomics sequenced and analyzed cell-line samples of patients with breast cancer (invasive ductal carcinomas). The cell line-derived DNA are housed at ATCC. Samples have been sequenced to an average genome-wide coverage of 123X for three of the samples, and 92X for for the fourth sample.1 Data generated withversion 2.0.0.32 of the Complete Genomics assembly software, on high molecular weight genomic DNA isolated from the HCC1187 breast carcinoma cell line ATCC CRL 2322. Sequencing methods documented in (Drmanac et al., 2010)

A small (1M base) subset of this data is included in the current package, and used in the code presented below.

3 Filter by Genomic Region

We identified this subset in a prior exploration of the full data set (work not shown), learning that variants of biological interest suitable for our purpose are found in a 1M base pair region of chromosome seven. The appendix to this document (see below) shows the few lines of code required to extract variant calls in that small region, from the very large file obtained from Complete Genomics (somaticVcfBeta-HCC1187-H-200-37-ASM-T1-N1.vcf.gz) and write them to a new, small VCF file.

4 Introducing the filterVcf Method

The filterVcf method reads (by chunks, about which more below) through a possibly very large VCF file to write a new, smaller VCF file which includes only those variant calling rows which meet the criteria specified in prefilters and filters.

Reading “by chunks” is accomplished using a tabix (Li, 2011) file. The a yieldSize argument specifies how many variant lines are read into memory at each iteration.

tabix.file <- TabixFile(file.gz, yieldSize=10000)
filterVcf(tabix.file, genome, destination.file,
              prefilters=prefilters, filters=filters)

in which

  1. file.gz: a gzipped vcf file with an accompanying Tabix index file.
  2. yieldSize: the number of text (call variant) lines to read at a time.
  3. genome: a string indicating the genome assembly, e.g., “hg19”.
  4. prefilters: one or more simple string-based filtering functions, each of which returns a logical vector corresponding to the vcf rows it will be passed (as simple character strings).
  5. filters: one or more filtering function, each of which returns a logical vector, corresponding to the list of parsed vcf structures it will be passed.

4.1 Prefilters

Prefilters are conceptually very simple. They are functions which will be called with a single argument, a vector of character strings, each of which is an unparsed variant call line, as read from the input VCF file. We use grepl to return a logical vector equal in length to the incoming vector of unparsed VCF lines. Each prefilter and filter is called repeatedly, with lines supplied on each invocation. filterVcf calls these functions repeatedly until the input file is exhausted.

Notice how the logic of these prefilters is very simple, using grepl to do fast, simple, fixed pattern matching:

isGermlinePrefilter <- function(x) {
    grepl("Germline", x, fixed=TRUE)
}

notInDbsnpPrefilter <- function(x) {
    !(grepl("dbsnp", x, fixed=TRUE))
}

4.2 Filters

Filters are more sophisticated than prefilters in that they assess parsed variant call lines for possible inclusion. Such parsing is intrinsically expensive but will be performed only on those lines which passed the prefilters. Therefore it pays to eliminate as many lines as possible using prefilters. Filters are useful when there exists detailed criteria for inclusion and exclusion. This can be seen below, especially in the allelicDepth function. Each filter must be written to return a logical vector as long as the number of rows in the input VCF argument; be sure your filter works with 0-row VCF instances.

## We will use isSNV() to filter only SNVs

allelicDepth <- function(x) {
    ##  ratio of AD of the "alternate allele" for the tumor sample
    ##  OR "reference allele" for normal samples to total reads for
    ##  the sample should be greater than some threshold (say 0.1,
    ##  that is: at least 10% of the sample should have the allele
    ##  of interest)
    ad <- geno(x)$AD
    tumorPct <- ad[,1,2,drop=FALSE] / rowSums(ad[,1,,drop=FALSE])
    normPct <- ad[,2,1, drop=FALSE] / rowSums(ad[,2,,drop=FALSE])
    test <- (tumorPct > 0.1) | (normPct > 0.1)
    as.vector(!is.na(test) & test)
}

4.3 FilterRules

FilterRules allow you to combine a list of filters, or of prefilters so that they may be passed as parameters to filterVcf. We use them here to combine the isGermlinePrefilter with the notInDbsnpPrefilter, and the isSNV with the AD filter.

library(VariantAnnotation)
prefilters <- FilterRules(list(germline=isGermlinePrefilter,
                               dbsnp=notInDbsnpPrefilter))
filters <- FilterRules(list(isSNV=isSNV, AD=allelicDepth))

4.4 Create the Filtered file

file.gz     <- system.file("extdata", "chr7-sub.vcf.gz",
                           package="VariantAnnotation")
file.gz.tbi <- system.file("extdata", "chr7-sub.vcf.gz.tbi",
                           package="VariantAnnotation")
destination.file <- tempfile()
tabix.file <- TabixFile(file.gz, yieldSize=10000)
filterVcf(tabix.file,  "hg19", destination.file,
          prefilters=prefilters, filters=filters, verbose=TRUE)

5 Look for SNPs in Regulatory Regions

We have now created a file containing 29 novel, Germline, SNP variant calls, each with a reasonable allelic depth, extracted from the 3808 calls in chr7-sub.vcf. We examine those variants for overlap with regulatory regions, turning to the ENCODE project and using Bioconductor’s AnnotationHub.

The ENCODE project is a large, long-term effort to build a “parts list” of all the functional elements in the human genome. They have recently focused on regulatory elements. We use the Bioconductor AnnotationHub to download regulatory regions reported for a breast cancer cell line, with which to identify possibly functional, and possibly clinically relevant SNVs in the breast cancer tumor/normal genome we have been examining.

The AnnotationHub is a recent addition to Bioconductor that facilitates access to genome-scale resources like ENCODE.

5.1 Load CTCF Transcription Factor Binding Regions Identified in MCF-7 Breast Cancer Cell Line

The MCF-7 Breast Cancer Cell line was established forty years ago, and has since played a dominant role in breast cancer cell line studies. The University of Washington reports transcription factor binding sites (TFBS) for the CTCF protein, which often acts as a negative regulator of transcription via chromatin structure modifications Though the MCF-7 cell line is an imperfect match to the HCC1187 cell line sequenced by Complete Genomics, we combine these two breast-cancer related data sets here, didactically, in this exercise, to highlight the importance of cell-type-specific regulatory regions, and of the availability of such data from ENCODE. We shall see a SNP in the intronic binding region of the cancer-related gene, EGFR.

library(AnnotationHub)
hub <- AnnotationHub()
id <- names(query(hub, "wgEncodeUwTfbsMcf7CtcfStdPkRep1.narrowPeak"))
mcf7.gr <- hub[[tail(id, 1)]]

5.2 Find SNPs in CTCF Binding Regions

vcf <- readVcf(destination.file, "hg19")
seqlevels(vcf) <- paste("chr", seqlevels(vcf), sep="")
ov.mcf7 <- findOverlaps(vcf, mcf7.gr)

There is just one SNV which overlaps with the MCF-7 regulatory regions. Find out where, if anywhere, it fits within a gene model.

library(TxDb.Hsapiens.UCSC.hg19.knownGene)
txdb <- TxDb.Hsapiens.UCSC.hg19.knownGene
locateVariants(vcf[6,], txdb, AllVariants())
## GRanges object with 10 ranges and 9 metadata columns:
##                  seqnames    ranges strand | LOCATION  LOCSTART    LOCEND
##                     <Rle> <IRanges>  <Rle> | <factor> <integer> <integer>
##   7:55212133_C/T     chr7  55212133      + |   intron    168915    168915
##   7:55212133_C/T     chr7  55212133      + |   intron    162109    162109
##   7:55212133_C/T     chr7  55212133      + |   intron    136465    136465
##   7:55212133_C/T     chr7  55212133      + |   intron    124739    124739
##   7:55212133_C/T     chr7  55212133      + |   intron    124739    124739
##   7:55212133_C/T     chr7  55212133      + |   intron    124739    124739
##   7:55212133_C/T     chr7  55212133      + |   intron    124739    124739
##   7:55212133_C/T     chr7  55212133      + |   intron    124739    124739
##   7:55212133_C/T     chr7  55212133      + |   intron    124739    124739
##   7:55212133_C/T     chr7  55212133      + |   intron     34146     34146
##                    QUERYID        TXID         CDSID      GENEID
##                  <integer> <character> <IntegerList> <character>
##   7:55212133_C/T         1       25129                      2898
##   7:55212133_C/T         1       25130                      2898
##   7:55212133_C/T         1       25131                      2898
##   7:55212133_C/T         1       25132                    389421
##   7:55212133_C/T         1       25133                    389421
##   7:55212133_C/T         1       25134                    154442
##   7:55212133_C/T         1       25135                       639
##   7:55212133_C/T         1       25136                       639
##   7:55212133_C/T         1       25138                       202
##   7:55212133_C/T         1       25139                       202
##                        PRECEDEID        FOLLOWID
##                  <CharacterList> <CharacterList>
##   7:55212133_C/T                                
##   7:55212133_C/T                                
##   7:55212133_C/T                                
##   7:55212133_C/T                                
##   7:55212133_C/T                                
##   7:55212133_C/T                                
##   7:55212133_C/T                                
##   7:55212133_C/T                                
##   7:55212133_C/T                                
##   7:55212133_C/T                                
##   -------
##   seqinfo: 1 sequence from an unspecified genome; no seqlengths

6 Conclusion

This case study begins, somewhat artificially, with a very short region of chromosome seven, a section which we knew, from previous exploration, held an intronic regulatory SNV for EGFR, a receptor tryosine kinase implicated in some cancers. Though artificial, the case study illustrates all of the steps needed for broader, realistic surveys of whole genome variation data:

  1. Filter by genomic coordinates.
  2. Filter to extract only those variant calls which meet these criteria: Germline, novel (not in dbSnp), consisting of a single nucleotide, of sufficient allelic depth.
  3. Intersect these variants with recognized DNA elements. In our case, we used short TFBS regulatory regions, but the same method can be used with exons, splice sites, DNaseI footprints, methylation sites, etc.

7 Appendix: Filter by Genomic Region

The most basic form of VCF file filtering is by genomic region. We demonstrate that here, extracting variant calls in a 1M base region of chromosome 7, writing them to a new file, compressing and then indexing that file. These steps created the small VCF file which accompanies this vignette, and is used in the code shown above.

Note that this code is NOT executed during the creation of this vignette: we do not supply the very large VCF file that it operates on. This code is here for tutorial purposes only, showing how you can filter by genomic region with a possibly large VCF file of your own.

library(VariantAnnotation)
file.gz <- "somaticVcfBeta-HCC1187-H-200-37-ASM-T1-N1.vcf.gz"
stopifnot(file.exists(file.gz))
file.gz.tbi <- paste(file.gz, ".tbi", sep="")
if(!(file.exists(file.gz.tbi)))
    indexTabix(file.gz, format="vcf")
start.loc <- 55000000
end.loc   <- 56000000
chr7.gr <- GRanges("7", IRanges(start.loc, end.loc))
params <- ScanVcfParam(which=chr7.gr)
vcf <- readVcf(TabixFile(file.gz), "hg19", params)
writeVcf(vcf, "chr7-sub.vcf")
bgzip("chr7-sub.vcf", overwrite=TRUE)
indexTabix("chr7-sub.vcf.gz", format="vcf")

This code creates the small gzipped vcf and index files used in the examples above.

Bibliography

Drmanac,R. et al. (2010) Human genome sequencing using unchained base reads on self-assembling dna nanoarrays. Science, 327, 78–81.

Li,H. (2011) Tabix: Fast retrieval of sequence features from generic tab-delimited files. Bioinformatics, 27, 718–719.

VariantAnnotation/inst/doc/filterVcf.R0000644000175100017510000000630614614361004021026 0ustar00biocbuildbiocbuild## ----eval=FALSE--------------------------------------------------------------- # tabix.file <- TabixFile(file.gz, yieldSize=10000) # filterVcf(tabix.file, genome, destination.file, # prefilters=prefilters, filters=filters) ## ----prefilters--------------------------------------------------------------- isGermlinePrefilter <- function(x) { grepl("Germline", x, fixed=TRUE) } notInDbsnpPrefilter <- function(x) { !(grepl("dbsnp", x, fixed=TRUE)) } ## ----filters------------------------------------------------------------------ ## We will use isSNV() to filter only SNVs allelicDepth <- function(x) { ## ratio of AD of the "alternate allele" for the tumor sample ## OR "reference allele" for normal samples to total reads for ## the sample should be greater than some threshold (say 0.1, ## that is: at least 10% of the sample should have the allele ## of interest) ad <- geno(x)$AD tumorPct <- ad[,1,2,drop=FALSE] / rowSums(ad[,1,,drop=FALSE]) normPct <- ad[,2,1, drop=FALSE] / rowSums(ad[,2,,drop=FALSE]) test <- (tumorPct > 0.1) | (normPct > 0.1) as.vector(!is.na(test) & test) } ## ----createFilterRules, message=FALSE, warning=FALSE-------------------------- library(VariantAnnotation) prefilters <- FilterRules(list(germline=isGermlinePrefilter, dbsnp=notInDbsnpPrefilter)) filters <- FilterRules(list(isSNV=isSNV, AD=allelicDepth)) ## ----createFilteredFile, message=FALSE, warning=FALSE------------------------- file.gz <- system.file("extdata", "chr7-sub.vcf.gz", package="VariantAnnotation") file.gz.tbi <- system.file("extdata", "chr7-sub.vcf.gz.tbi", package="VariantAnnotation") destination.file <- tempfile() tabix.file <- TabixFile(file.gz, yieldSize=10000) filterVcf(tabix.file, "hg19", destination.file, prefilters=prefilters, filters=filters, verbose=TRUE) ## ----mcf7regulatoryRegions, message=FALSE, warning=FALSE---------------------- library(AnnotationHub) hub <- AnnotationHub() id <- names(query(hub, "wgEncodeUwTfbsMcf7CtcfStdPkRep1.narrowPeak")) mcf7.gr <- hub[[tail(id, 1)]] ## ----findOverlaps------------------------------------------------------------- vcf <- readVcf(destination.file, "hg19") seqlevels(vcf) <- paste("chr", seqlevels(vcf), sep="") ov.mcf7 <- findOverlaps(vcf, mcf7.gr) ## ----locateVariant, message=FALSE, warning=FALSE------------------------------ library(TxDb.Hsapiens.UCSC.hg19.knownGene) txdb <- TxDb.Hsapiens.UCSC.hg19.knownGene locateVariants(vcf[6,], txdb, AllVariants()) ## ----eval=FALSE--------------------------------------------------------------- # library(VariantAnnotation) # file.gz <- "somaticVcfBeta-HCC1187-H-200-37-ASM-T1-N1.vcf.gz" # stopifnot(file.exists(file.gz)) # file.gz.tbi <- paste(file.gz, ".tbi", sep="") # if(!(file.exists(file.gz.tbi))) # indexTabix(file.gz, format="vcf") # start.loc <- 55000000 # end.loc <- 56000000 # chr7.gr <- GRanges("7", IRanges(start.loc, end.loc)) # params <- ScanVcfParam(which=chr7.gr) # vcf <- readVcf(TabixFile(file.gz), "hg19", params) # writeVcf(vcf, "chr7-sub.vcf") # bgzip("chr7-sub.vcf", overwrite=TRUE) # indexTabix("chr7-sub.vcf.gz", format="vcf") VariantAnnotation/inst/doc/filterVcf.Rmd0000644000175100017510000002756714614305321021363 0ustar00biocbuildbiocbuild--- title: "2. Using filterVcf() to Select Variants from VCF Files" author: "Paul Shannon" date: |+ Created: 20 February, 2013 Last Modified: `r format(Sys.Date(), "%B %d, %Y")` vignette: > %\VignetteIndexEntry{2. Using filterVcf to Select Variants from VCF Files} %\VignetteEngine{knitr::rmarkdown} %\VignetteEncoding{UTF-8} output: BiocStyle::html_document: number_sections: yes toc: yes toc_depth: 4 bibliography: filterVcf.bib csl: bioinformatics.csl link-citations: true --- # Introduction Whole genome Variant Call Format (VCF) files are very large, typically containing millions of called variants, one call per line of text. The actual number of variants relevant to a particular study (of a disease such as breast cancer, for instance) will often be far fewer. Thus the first task one faces when analyzing a whole genome VCF file is to identify and extract the relatively few variants which may be of interest, excluding all others. This vignette illustrates several techniques for doing this. We demonstrate three methods: filtering by genomic region, filtering on attributes of each specific variant call, and intersecting with known regions of interest (exons, splice sites, regulatory regions, etc.). We are primarily concerned with the latter two. However, in order to create the small VCF data file we use here for demonstration purposes, we employed genomic region filtering, reducing a very large whole genome two-sample breast cancer VCF file of fourteen million calls, to a file containing fewer than ten thousand calls in a one million base pair region of chromosome 7. For the sake of reproducibility, and for completeness of exposition, we will illustrate this first step also. # The Data: Paired Tumor/Normal Breast Cancer Variants [Complete Genomics Inc.](http://www.completegenomics.com/public-data/cancer-data) states: > To provide the scientific community with public access to data > generated from two paired tumor/normal cancer samples, Complete > Genomics sequenced and analyzed cell-line samples of patients with > breast cancer (invasive ductal carcinomas). The cell line-derived DNA > are housed at ATCC. Samples have been sequenced to an average > genome-wide coverage of 123X for three of the samples, and 92X for for > the fourth sample.^[Data generated withversion 2.0.0.32 of the Complete Genomics assembly software, on high molecular weight genomic DNA isolated from the HCC1187 breast carcinoma cell line ATCC CRL 2322. Sequencing methods documented in [@drmanac2010human]] A small (1M base) subset of this data is included in the current package, and used in the code presented below. # Filter by Genomic Region We identified this subset in a prior exploration of the full data set (work not shown), learning that variants of biological interest suitable for our purpose are found in a 1M base pair region of chromosome seven. The appendix to this document (see below) shows the few lines of code required to extract variant calls in that small region, from the very large file obtained from Complete Genomics (*somaticVcfBeta-HCC1187-H-200-37-ASM-T1-N1.vcf.gz*) and write them to a new, small VCF file. # Introducing the `filterVcf` Method The `filterVcf ` method reads (by chunks, about which more below) through a possibly very large VCF file to write a new, smaller VCF file which includes only those variant calling rows which meet the criteria specified in `prefilters` and `filters`. Reading "by chunks" is accomplished using a tabix [@li2011tabix] file. The a `yieldSize` argument specifies how many variant lines are read into memory at each iteration. ```{r eval=FALSE} tabix.file <- TabixFile(file.gz, yieldSize=10000) filterVcf(tabix.file, genome, destination.file, prefilters=prefilters, filters=filters) ``` in which 1. *file.gz*: a gzipped vcf file with an accompanying Tabix index file. 2. *yieldSize*: the number of text (call variant) lines to read at a time. 3. *genome*: a string indicating the genome assembly, e.g., "hg19". 4. *prefilters*: one or more simple string-based filtering functions, each of which returns a logical vector corresponding to the vcf rows it will be passed (as simple character strings). 5. *filters*: one or more filtering function, each of which returns a logical vector, corresponding to the list of parsed vcf structures it will be passed. ## Prefilters Prefilters are conceptually very simple. They are functions which will be called with a single argument, a vector of character strings, each of which is an *unparsed* variant call line, as read from the input VCF file. We use `grepl` to return a logical vector equal in length to the incoming vector of unparsed VCF lines. Each prefilter and filter is called repeatedly, with lines supplied on each invocation. `filterVcf` calls these functions repeatedly until the input file is exhausted. Notice how the logic of these prefilters is very simple, using `grepl` to do fast, simple, fixed pattern matching: ```{r prefilters} isGermlinePrefilter <- function(x) { grepl("Germline", x, fixed=TRUE) } notInDbsnpPrefilter <- function(x) { !(grepl("dbsnp", x, fixed=TRUE)) } ``` ## Filters Filters are more sophisticated than prefilters in that they assess *parsed* variant call lines for possible inclusion. Such parsing is intrinsically expensive but will be performed only on those lines which passed the prefilters. Therefore it pays to eliminate as many lines as possible using prefilters. Filters are useful when there exists detailed criteria for inclusion and exclusion. This can be seen below, especially in the `allelicDepth` function. Each filter must be written to return a logical vector as long as the number of rows in the input VCF argument; be sure your filter works with 0-row VCF instances. ```{r filters} ## We will use isSNV() to filter only SNVs allelicDepth <- function(x) { ## ratio of AD of the "alternate allele" for the tumor sample ## OR "reference allele" for normal samples to total reads for ## the sample should be greater than some threshold (say 0.1, ## that is: at least 10% of the sample should have the allele ## of interest) ad <- geno(x)$AD tumorPct <- ad[,1,2,drop=FALSE] / rowSums(ad[,1,,drop=FALSE]) normPct <- ad[,2,1, drop=FALSE] / rowSums(ad[,2,,drop=FALSE]) test <- (tumorPct > 0.1) | (normPct > 0.1) as.vector(!is.na(test) & test) } ``` ## FilterRules `FilterRules` allow you to combine a list of filters, or of prefilters so that they may be passed as parameters to *filterVcf*. We use them here to combine the *isGermlinePrefilter* with the *notInDbsnpPrefilter*, and the *isSNV* with the *AD* filter. ```{r createFilterRules, message=FALSE, warning=FALSE} library(VariantAnnotation) prefilters <- FilterRules(list(germline=isGermlinePrefilter, dbsnp=notInDbsnpPrefilter)) filters <- FilterRules(list(isSNV=isSNV, AD=allelicDepth)) ``` ## Create the Filtered file ```{r createFilteredFile, message=FALSE, warning=FALSE} file.gz <- system.file("extdata", "chr7-sub.vcf.gz", package="VariantAnnotation") file.gz.tbi <- system.file("extdata", "chr7-sub.vcf.gz.tbi", package="VariantAnnotation") destination.file <- tempfile() tabix.file <- TabixFile(file.gz, yieldSize=10000) filterVcf(tabix.file, "hg19", destination.file, prefilters=prefilters, filters=filters, verbose=TRUE) ``` # Look for SNPs in Regulatory Regions We have now created a file containing 29 novel, Germline, SNP variant calls, each with a reasonable allelic depth, extracted from the 3808 calls in *chr7-sub.vcf*. We examine those variants for overlap with regulatory regions, turning to the ENCODE project and using Bioconductor's `r Biocpkg("AnnotationHub")`. The ENCODE project is a large, long-term effort to build a "parts list" of all the functional elements in the human genome. They have recently focused on regulatory elements. We use the Bioconductor `r Biocpkg("AnnotationHub")` to download regulatory regions reported for a breast cancer cell line, with which to identify possibly functional, and possibly clinically relevant SNVs in the breast cancer tumor/normal genome we have been examining. The `r Biocpkg("AnnotationHub")` is a recent addition to Bioconductor that facilitates access to genome-scale resources like ENCODE. ## Load CTCF Transcription Factor Binding Regions Identified in MCF-7 Breast Cancer Cell Line The [MCF-7](http://en.wikipedia.org/wiki/MCF-7) Breast Cancer Cell line was established forty years ago, and has since played a dominant role in breast cancer cell line studies. The University of Washington reports transcription factor binding sites (TFBS) for the CTCF protein, which often acts as a negative regulator of transcription via chromatin structure modifications Though the MCF-7 cell line is an imperfect match to the HCC1187 cell line sequenced by Complete Genomics, we combine these two breast-cancer related data sets here, didactically, in this exercise, to highlight the importance of cell-type-specific regulatory regions, and of the availability of such data from ENCODE. We shall see a SNP in the intronic binding region of the cancer-related gene, EGFR. ```{r mcf7regulatoryRegions, message=FALSE, warning=FALSE} library(AnnotationHub) hub <- AnnotationHub() id <- names(query(hub, "wgEncodeUwTfbsMcf7CtcfStdPkRep1.narrowPeak")) mcf7.gr <- hub[[tail(id, 1)]] ``` ## Find SNPs in CTCF Binding Regions ```{r findOverlaps} vcf <- readVcf(destination.file, "hg19") seqlevels(vcf) <- paste("chr", seqlevels(vcf), sep="") ov.mcf7 <- findOverlaps(vcf, mcf7.gr) ``` There is just one SNV which overlaps with the MCF-7 regulatory regions. Find out where, if anywhere, it fits within a gene model. ```{r locateVariant, message=FALSE, warning=FALSE} library(TxDb.Hsapiens.UCSC.hg19.knownGene) txdb <- TxDb.Hsapiens.UCSC.hg19.knownGene locateVariants(vcf[6,], txdb, AllVariants()) ``` # Conclusion This case study begins, somewhat artificially, with a very short region of chromosome seven, a section which we knew, from previous exploration, held an intronic regulatory SNV for EGFR, a receptor tryosine kinase implicated in some cancers. Though artificial, the case study illustrates all of the steps needed for broader, realistic surveys of whole genome variation data: 1. Filter by genomic coordinates. 2. Filter to extract only those variant calls which meet these criteria: Germline, novel (not in dbSnp), consisting of a single nucleotide, of sufficient allelic depth. 3. Intersect these variants with recognized DNA elements. In our case, we used short TFBS regulatory regions, but the same method can be used with exons, splice sites, DNaseI footprints, methylation sites, etc. # Appendix: Filter by Genomic Region The most basic form of VCF file filtering is by genomic region. We demonstrate that here, extracting variant calls in a 1M base region of chromosome 7, writing them to a new file, compressing and then indexing that file. These steps created the small VCF file which accompanies this vignette, and is used in the code shown above. Note that this code is *NOT* executed during the creation of this vignette: we do not supply the very large VCF file that it operates on. This code is here for tutorial purposes only, showing how you can filter by genomic region with a possibly large VCF file of your own. ```{r eval=FALSE} library(VariantAnnotation) file.gz <- "somaticVcfBeta-HCC1187-H-200-37-ASM-T1-N1.vcf.gz" stopifnot(file.exists(file.gz)) file.gz.tbi <- paste(file.gz, ".tbi", sep="") if(!(file.exists(file.gz.tbi))) indexTabix(file.gz, format="vcf") start.loc <- 55000000 end.loc <- 56000000 chr7.gr <- GRanges("7", IRanges(start.loc, end.loc)) params <- ScanVcfParam(which=chr7.gr) vcf <- readVcf(TabixFile(file.gz), "hg19", params) writeVcf(vcf, "chr7-sub.vcf") bgzip("chr7-sub.vcf", overwrite=TRUE) indexTabix("chr7-sub.vcf.gz", format="vcf") ``` This code creates the small gzipped vcf and index files used in the examples above. # Bibliography {-}VariantAnnotation/inst/doc/VariantAnnotation.html0000644000175100017510000301340214614361054023307 0ustar00biocbuildbiocbuild 1. Introduction to VariantAnnotation

Contents

1 Introduction

This vignette outlines a work flow for annotating and filtering genetic variants using the VariantAnnotation package. Sample data are in VariantCall Format (VCF) and are a subset of chromosome 22 from 1000 Genomes. VCF text files contain meta-information lines, a header line with column names, data lines with information about a position in the genome, and optional genotype information on samples for each position. Samtools organisation and repositories describes the VCF format in detail.

Data are read in from a VCF file and variants identified according to region such as coding, intron, intergenic, spliceSite etc. Amino acid coding changes are computed for the non-synonymous variants and SIFT and PolyPhen databases provide predictions of how severly the coding changes affect protein function.

2 Variant Call Format (VCF) files

2.1 Data import and exploration

Data are parsed into a VCF object with readVcf.

library(VariantAnnotation)
fl <- system.file("extdata", "chr22.vcf.gz", package="VariantAnnotation")
vcf <- readVcf(fl, "hg19")
vcf
## class: CollapsedVCF 
## dim: 10376 5 
## rowRanges(vcf):
##   GRanges with 5 metadata columns: paramRangeID, REF, ALT, QUAL, FILTER
## info(vcf):
##   DataFrame with 22 columns: LDAF, AVGPOST, RSQ, ERATE, THETA, CIEND, CIPOS,...
## info(header(vcf)):
##              Number Type    Description                                        
##    LDAF      1      Float   MLE Allele Frequency Accounting for LD             
##    AVGPOST   1      Float   Average posterior probability from MaCH/Thunder    
##    RSQ       1      Float   Genotype imputation quality from MaCH/Thunder      
##    ERATE     1      Float   Per-marker Mutation rate from MaCH/Thunder         
##    THETA     1      Float   Per-marker Transition rate from MaCH/Thunder       
##    CIEND     2      Integer Confidence interval around END for imprecise var...
##    CIPOS     2      Integer Confidence interval around POS for imprecise var...
##    END       1      Integer End position of the variant described in this re...
##    HOMLEN    .      Integer Length of base pair identical micro-homology at ...
##    HOMSEQ    .      String  Sequence of base pair identical micro-homology a...
##    SVLEN     1      Integer Difference in length between REF and ALT alleles   
##    SVTYPE    1      String  Type of structural variant                         
##    AC        .      Integer Alternate Allele Count                             
##    AN        1      Integer Total Allele Count                                 
##    AA        1      String  Ancestral Allele, ftp://ftp.1000genomes.ebi.ac.u...
##    AF        1      Float   Global Allele Frequency based on AC/AN             
##    AMR_AF    1      Float   Allele Frequency for samples from AMR based on A...
##    ASN_AF    1      Float   Allele Frequency for samples from ASN based on A...
##    AFR_AF    1      Float   Allele Frequency for samples from AFR based on A...
##    EUR_AF    1      Float   Allele Frequency for samples from EUR based on A...
##    VT        1      String  indicates what type of variant the line represents 
##    SNPSOURCE .      String  indicates if a snp was called when analysing the...
## geno(vcf):
##   List of length 3: GT, DS, GL
## geno(header(vcf)):
##       Number Type   Description                      
##    GT 1      String Genotype                         
##    DS 1      Float  Genotype dosage from MaCH/Thunder
##    GL G      Float  Genotype Likelihoods

2.1.1 Header information

Header information can be extracted from the VCF with header(). We see there are 5 samples, 1 piece of meta information, 22 info fields and 3 geno fields.

header(vcf)
## class: VCFHeader 
## samples(5): HG00096 HG00097 HG00099 HG00100 HG00101
## meta(1): fileformat
## fixed(2): FILTER ALT
## info(22): LDAF AVGPOST ... VT SNPSOURCE
## geno(3): GT DS GL

Data can be further extracted using the named accessors.

samples(header(vcf))
## [1] "HG00096" "HG00097" "HG00099" "HG00100" "HG00101"
geno(header(vcf))
## DataFrame with 3 rows and 3 columns
##         Number        Type            Description
##    <character> <character>            <character>
## GT           1      String               Genotype
## DS           1       Float Genotype dosage from..
## GL           G       Float   Genotype Likelihoods

2.1.2 Genomic positions

rowRanges contains information from the CHROM, POS, and ID fields of the VCF file, represented as a GRanges. The paramRangeID column is meaningful when reading subsets of data and is discussed further below.

head(rowRanges(vcf), 3)
## GRanges object with 3 ranges and 5 metadata columns:
##               seqnames    ranges strand | paramRangeID            REF
##                  <Rle> <IRanges>  <Rle> |     <factor> <DNAStringSet>
##     rs7410291       22  50300078      * |           NA              A
##   rs147922003       22  50300086      * |           NA              C
##   rs114143073       22  50300101      * |           NA              G
##                              ALT      QUAL      FILTER
##               <DNAStringSetList> <numeric> <character>
##     rs7410291                  G       100        PASS
##   rs147922003                  T       100        PASS
##   rs114143073                  A       100        PASS
##   -------
##   seqinfo: 1 sequence from hg19 genome; no seqlengths

Individual fields can be pulled out with named accessors. Here we see REF is stored as a DNAStringSet and qual is a numeric vector.

ref(vcf)[1:5]
## DNAStringSet object of length 5:
##     width seq
## [1]     1 A
## [2]     1 C
## [3]     1 G
## [4]     1 C
## [5]     1 C
qual(vcf)[1:5]
## [1] 100 100 100 100 100

ALT is a DNAStringSetList (allows for multiple alternate alleles per variant) or a DNAStringSet. When structural variants are present it will be a CharacterList.

alt(vcf)[1:5]
## DNAStringSetList of length 5
## [[1]] G
## [[2]] T
## [[3]] A
## [[4]] T
## [[5]] T

2.1.3 Genotype data

Genotype data described in the FORMAT fields are parsed into the geno slot. The data are unique to each sample and each sample may have multiple values variable. Because of this, the data are parsed into matrices or arrays where the rows represent the variants and the columns the samples. Multidimentional arrays indicate multiple values per sample. In this file all variables are matrices.

geno(vcf)
## List of length 3
## names(3): GT DS GL
sapply(geno(vcf), class)
##      GT       DS       GL      
## [1,] "matrix" "matrix" "matrix"
## [2,] "array"  "array"  "array"

Let’s take a closer look at the genotype dosage (DS) variable. The header provides the variable definition and type.

geno(header(vcf))["DS",]
## DataFrame with 1 row and 3 columns
##         Number        Type            Description
##    <character> <character>            <character>
## DS           1       Float Genotype dosage from..

These data are stored as a 10376 x 5 matrix. Each of the five samples (columns) has a single value per variant location (row).

DS <-geno(vcf)$DS
dim(DS)
## [1] 10376     5
DS[1:3,]
##             HG00096 HG00097 HG00099 HG00100 HG00101
## rs7410291         0       0       1       0       0
## rs147922003       0       0       0       0       0
## rs114143073       0       0       0       0       0

DS is also known as ‘posterior mean genotypes’ and range in value from [0, 2]. To get a sense of variable distribution, we compute a five number summary of the minimum, lower-hinge (first quartile), median, upper-hinge (third quartile) and maximum.

fivenum(DS)
## [1] 0 0 0 0 2

The majority of these values (86%) are zero.

length(which(DS==0))/length(DS)
## [1] 0.8621627

View the distribution of the non-zero values.

hist(DS[DS != 0], breaks=seq(0, 2, by=0.05),
    main="DS non-zero values", xlab="DS")

2.1.4 Info data

In contrast to the genotype data, the info data are unique to the variant and the same across samples. All info variables are represented in a single DataFrame.

info(vcf)[1:4, 1:5]
## DataFrame with 4 rows and 5 columns
##                  LDAF   AVGPOST       RSQ     ERATE     THETA
##             <numeric> <numeric> <numeric> <numeric> <numeric>
## rs7410291      0.3431    0.9890    0.9856     2e-03    0.0005
## rs147922003    0.0091    0.9963    0.8398     5e-04    0.0011
## rs114143073    0.0098    0.9891    0.5919     7e-04    0.0008
## rs141778433    0.0062    0.9950    0.6756     9e-04    0.0003

We will use the info data to compare quality measures between novel (i.e., not in dbSNP) and known (i.e., in dbSNP) variants and the variant type present in the file. Variants with membership in dbSNP can be identified by using the appropriate SNPlocs package for the hg19 genome (GRCh37).

library(SNPlocs.Hsapiens.dbSNP144.GRCh37)
vcf_rsids <- names(rowRanges(vcf))
chr22snps <- snpsBySeqname(SNPlocs.Hsapiens.dbSNP144.GRCh37, "22")
chr22_rsids <- mcols(chr22snps)$RefSNP_id
in_dbSNP <- vcf_rsids %in% chr22_rsids
table(in_dbSNP) 
## in_dbSNP
## FALSE  TRUE 
##  1114  9262

Info variables of interest are ‘VT’, ‘LDAF’ and ‘RSQ’. The header offers more details on these variables.

info(header(vcf))[c("VT", "LDAF", "RSQ"),]
## DataFrame with 3 rows and 3 columns
##           Number        Type            Description
##      <character> <character>            <character>
## VT             1      String indicates what type ..
## LDAF           1       Float MLE Allele Frequency..
## RSQ            1       Float Genotype imputation ..

Create a data frame of quality measures of interest …

metrics <- data.frame(QUAL=qual(vcf), in_dbSNP=in_dbSNP,
    VT=info(vcf)$VT, LDAF=info(vcf)$LDAF, RSQ=info(vcf)$RSQ)

and visualize the distribution of qualities using ggplot2. For instance, genotype imputation quality is higher for the known variants in dbSNP.

library(ggplot2)
ggplot(metrics, aes(x=RSQ, fill=in_dbSNP)) +
    geom_density(alpha=0.5) +
    scale_x_continuous(name="MaCH / Thunder Imputation Quality") +
    scale_y_continuous(name="Density") +
    theme(legend.position="top")

2.2 Import data subsets

When working with large VCF files it may be more efficient to read in subsets of the data. This can be accomplished by selecting genomic coordinates (ranges) or by specific fields from the VCF file.

2.2.1 Select genomic coordinates

To read in a portion of chromosome 22, create a GRanges with the regions of interest.

rng <- GRanges(seqnames="22", ranges=IRanges(
           start=c(50301422, 50989541), 
           end=c(50312106, 51001328),
           names=c("gene_79087", "gene_644186")))

When ranges are specified, the VCF file must have an accompanying Tabix index file. See indexTabix for help creating an index.

tab <- TabixFile(fl)
vcf_rng <- readVcf(tab, "hg19", param=rng)

The paramRangesID column distinguishes which records came from which param range.

head(rowRanges(vcf_rng), 3)
## GRanges object with 3 ranges and 5 metadata columns:
##                   seqnames    ranges strand | paramRangeID            REF
##                      <Rle> <IRanges>  <Rle> |     <factor> <DNAStringSet>
##       rs114335781       22  50301422      * |   gene_79087              G
##         rs8135963       22  50301476      * |   gene_79087              T
##   22:50301488_C/T       22  50301488      * |   gene_79087              C
##                                  ALT      QUAL      FILTER
##                   <DNAStringSetList> <numeric> <character>
##       rs114335781                  A       100        PASS
##         rs8135963                  C       100        PASS
##   22:50301488_C/T                  T       100        PASS
##   -------
##   seqinfo: 1 sequence from hg19 genome; no seqlengths

2.2.2 Select VCF fields

Data import can also be defined by the fixed, info and geno fields. Fields available for import are described in the header information. To view the header before reading in the data, use ScanVcfHeader.

hdr <- scanVcfHeader(fl)
## e.g., INFO and GENO fields
head(info(hdr), 3)
## DataFrame with 3 rows and 3 columns
##              Number        Type            Description
##         <character> <character>            <character>
## LDAF              1       Float MLE Allele Frequency..
## AVGPOST           1       Float Average posterior pr..
## RSQ               1       Float Genotype imputation ..
head(geno(hdr), 3)
## DataFrame with 3 rows and 3 columns
##         Number        Type            Description
##    <character> <character>            <character>
## GT           1      String               Genotype
## DS           1       Float Genotype dosage from..
## GL           G       Float   Genotype Likelihoods

To subset on “LDAF” and “GT” we specify them as character vectors in the info and geno arguments to ScanVcfParam. This creates a ScanVcfParam object which is used as the param argument to readVcf.

## Return all 'fixed' fields, "LAF" from 'info' and "GT" from 'geno'
svp <- ScanVcfParam(info="LDAF", geno="GT")
vcf1 <- readVcf(fl, "hg19", svp)
names(geno(vcf1))
## [1] "GT"

To subset on both genomic coordinates and fields the ScanVcfParam object must contain both.

svp_all <- ScanVcfParam(info="LDAF", geno="GT", which=rng)
svp_all
## class: ScanVcfParam 
## vcfWhich: 1 elements
## vcfFixed: character() [All] 
## vcfInfo: LDAF 
## vcfGeno: GT 
## vcfSamples:

3 Locating variants in and around genes

Variant location with respect to genes can be identified with the locateVariants function. Regions are specified in the region argument and can be one of the following constructors: CodingVariants, IntronVariants, FiveUTRVariants, ThreeUTRVariants, IntergenicVariants, SpliceSiteVariants or PromoterVariants. Location definitions are shown in Table 1.


Table 1: Variant locations
Location Details
coding falls within a coding region
fiveUTR falls within a 5’ untranslated region
threeUTR falls within a 3’ untranslated region
intron falls within an intron region
intergenic does not fall within a transcript associated with a gene
spliceSite overlaps any portion of the first 2 or last 2
promoter falls within a promoter region of a transcript

For overlap methods to work properly the chromosome names (seqlevels) must be compatible in the objects being compared. The VCF data chromosome names are represented by number, i.e., ‘22’, but the TxDb chromosome names are preceded with ‘chr’. Seqlevels in the VCF can be modified with the seqlevels function.

library(TxDb.Hsapiens.UCSC.hg19.knownGene)
txdb <- TxDb.Hsapiens.UCSC.hg19.knownGene
seqlevels(vcf) <- "chr22"
rd <- rowRanges(vcf)
loc <- locateVariants(rd, txdb, CodingVariants())
head(loc, 3)
## GRanges object with 3 ranges and 9 metadata columns:
##                   seqnames    ranges strand | LOCATION  LOCSTART    LOCEND
##                      <Rle> <IRanges>  <Rle> | <factor> <integer> <integer>
##       rs114335781    chr22  50301422      - |   coding       939       939
##         rs8135963    chr22  50301476      - |   coding       885       885
##   22:50301488_C/T    chr22  50301488      - |   coding       873       873
##                     QUERYID        TXID         CDSID      GENEID
##                   <integer> <character> <IntegerList> <character>
##       rs114335781        24       75253        218562       79087
##         rs8135963        25       75253        218562       79087
##   22:50301488_C/T        26       75253        218562       79087
##                         PRECEDEID        FOLLOWID
##                   <CharacterList> <CharacterList>
##       rs114335781                                
##         rs8135963                                
##   22:50301488_C/T                                
##   -------
##   seqinfo: 1 sequence from an unspecified genome; no seqlengths

Locate variants in all regions with the AllVariants() constructor,

allvar <- locateVariants(rd, txdb, AllVariants())

To answer gene-centric questions data can be summarized by gene reguardless of transcript.

## Did any coding variants match more than one gene?
splt <- split(mcols(loc)$GENEID, mcols(loc)$QUERYID) 
table(sapply(splt, function(x) length(unique(x)) > 1))
## 
## FALSE  TRUE 
##   965    15
## Summarize the number of coding variants by gene ID.
splt <- split(mcols(loc)$QUERYID, mcols(loc)$GENEID)
head(sapply(splt, function(x) length(unique(x))), 3)
## 113730   1890  23209 
##     22     15     30

4 Amino acid coding changes

predictCoding computes amino acid coding changes for non-synonymous variants. Only ranges in query that overlap with a coding region in the subject are considered. Reference sequences are retrieved from either a BSgenome or fasta file specified in seqSource. Variant sequences are constructed by substituting, inserting or deleting values in the varAllele column into the reference sequence. Amino acid codes are computed for the variant codon sequence when the length is a multiple of 3.

The query argument to predictCoding can be a GRanges or VCF. When a GRanges is supplied the varAllele argument must be specified. In the case of a VCF, the alternate alleles are taken from alt(<VCF>) and the varAllele argument is not specified.

The result is a modified query containing only variants that fall within coding regions. Each row represents a variant-transcript match so more than one row per original variant is possible.

library(BSgenome.Hsapiens.UCSC.hg19)
coding <- predictCoding(vcf, txdb, seqSource=Hsapiens)
coding[5:7]
## GRanges object with 3 ranges and 17 metadata columns:
##                   seqnames    ranges strand | paramRangeID            REF
##                      <Rle> <IRanges>  <Rle> |     <factor> <DNAStringSet>
##   22:50301584_C/T    chr22  50301584      - |           NA              C
##       rs114264124    chr22  50302962      - |           NA              C
##       rs149209714    chr22  50302995      - |           NA              C
##                                  ALT      QUAL      FILTER      varAllele
##                   <DNAStringSetList> <numeric> <character> <DNAStringSet>
##   22:50301584_C/T                  T       100        PASS              A
##       rs114264124                  T       100        PASS              A
##       rs149209714                  G       100        PASS              C
##                      CDSLOC    PROTEINLOC   QUERYID        TXID         CDSID
##                   <IRanges> <IntegerList> <integer> <character> <IntegerList>
##   22:50301584_C/T       777           259        28       75253        218562
##       rs114264124       698           233        57       75253        218563
##       rs149209714       665           222        58       75253        218563
##                        GENEID   CONSEQUENCE       REFCODON       VARCODON
##                   <character>      <factor> <DNAStringSet> <DNAStringSet>
##   22:50301584_C/T       79087 synonymous               CCG            CCA
##       rs114264124       79087 nonsynonymous            CGG            CAG
##       rs149209714       79087 nonsynonymous            GGA            GCA
##                           REFAA         VARAA
##                   <AAStringSet> <AAStringSet>
##   22:50301584_C/T             P             P
##       rs114264124             R             Q
##       rs149209714             G             A
##   -------
##   seqinfo: 1 sequence from hg19 genome; no seqlengths

Using variant rs114264124 as an example, we see varAllele A has been substituted into the refCodon CGG to produce varCodon CAG. The refCodon is the sequence of codons necessary to make the variant allele substitution and therefore often includes more nucleotides than indicated in the range (i.e. the range is 50302962, 50302962, width of 1). Notice it is the second position in the refCodon that has been substituted. This position in the codon, the position of substitution, corresponds to genomic position 50302962. This genomic position maps to position 698 in coding region-based coordinates and to triplet 233 in the protein. This is a non-synonymous coding variant where the amino acid has changed from R (Arg) to Q (Gln).

When the resulting varCodon is not a multiple of 3 it cannot be translated. The consequence is considered a frameshift and varAA will be missing.

## CONSEQUENCE is 'frameshift' where translation is not possible
coding[mcols(coding)$CONSEQUENCE == "frameshift"]
## GRanges object with 2 ranges and 17 metadata columns:
##                       seqnames    ranges strand | paramRangeID            REF
##                          <Rle> <IRanges>  <Rle> |     <factor> <DNAStringSet>
##   22:50317001_G/GCACT    chr22  50317001      + |           NA              G
##   22:50317001_G/GCACT    chr22  50317001      + |           NA              G
##                                      ALT      QUAL      FILTER      varAllele
##                       <DNAStringSetList> <numeric> <character> <DNAStringSet>
##   22:50317001_G/GCACT              GCACT       233        PASS          GCACT
##   22:50317001_G/GCACT              GCACT       233        PASS          GCACT
##                          CDSLOC    PROTEINLOC   QUERYID        TXID
##                       <IRanges> <IntegerList> <integer> <character>
##   22:50317001_G/GCACT       808           270       359       74357
##   22:50317001_G/GCACT       628           210       359       74358
##                               CDSID      GENEID CONSEQUENCE       REFCODON
##                       <IntegerList> <character>    <factor> <DNAStringSet>
##   22:50317001_G/GCACT        216303       79174  frameshift            GCC
##   22:50317001_G/GCACT        216303       79174  frameshift            GCC
##                             VARCODON         REFAA         VARAA
##                       <DNAStringSet> <AAStringSet> <AAStringSet>
##   22:50317001_G/GCACT        GCACTCC                            
##   22:50317001_G/GCACT        GCACTCC                            
##   -------
##   seqinfo: 1 sequence from hg19 genome; no seqlengths

5 SIFT and PolyPhen Databases

From predictCoding we identified the amino acid coding changes for the non-synonymous variants. For this subset we can retrieve predictions of how damaging these coding changes may be. SIFT (Sorting Intolerant From Tolerant) and PolyPhen (Polymorphism Phenotyping) are methods that predict the impact of amino acid substitution on a human protein. The SIFT method uses sequence homology and the physical properties of amino acids to make predictions about protein function. PolyPhen uses sequence-based features and structural information characterizing the substitution to make predictions about the structure and function of the protein.

Collated predictions for specific dbSNP builds are available as downloads from the SIFT and PolyPhen web sites. These results have been packaged into SIFT.Hsapiens.dbSNP132 and PolyPhen.Hsapiens.dbSNP131 and are designed to be searched by rsid. Variants that are in dbSNP can be searched with these database packages. When working with novel variants, SIFT and PolyPhen must be called directly. See references for home pages.

Identify the non-synonymous variants and obtain the rsids.

nms <- names(coding) 
idx <- mcols(coding)$CONSEQUENCE == "nonsynonymous"
nonsyn <- coding[idx]
names(nonsyn) <- nms[idx]
rsids <- unique(names(nonsyn)[grep("rs", names(nonsyn), fixed=TRUE)])

Detailed descriptions of the database columns can be found with ?SIFTDbColumns and ?PolyPhenDbColumns. Variants in these databases often contain more than one row per variant. The variant may have been reported by multiple sources and therefore the source will differ as well as some of the other variables.

It is important to keep in mind the pre-computed predictions in the SIFT and PolyPhen packages are based on specific gene models. SIFT is based on Ensembl and PolyPhen on UCSC Known Gene. The TxDb we used to identify the coding snps was based on UCSC Known Gene so we will use PolyPhen for predictions. PolyPhen provides predictions using two different training datasets and has considerable information about 3D protein structure. See ?PolyPhenDbColumns or the PolyPhen web site listed in the references for more details.

Query the PolyPhen database,

library(PolyPhen.Hsapiens.dbSNP131)
pp <- select(PolyPhen.Hsapiens.dbSNP131, keys=rsids,
          cols=c("TRAININGSET", "PREDICTION", "PPH2PROB"))
head(pp[!is.na(pp$PREDICTION), ]) 
##          RSID TRAININGSET     OSNPID     OACC OPOS OAA1 OAA2      SNPID
## 13  rs8139422      humdiv  rs8139422 Q6UXH1-5  182    D    E  rs8139422
## 14  rs8139422      humvar  rs8139422     <NA> <NA> <NA> <NA>  rs8139422
## 15 rs74510325      humdiv rs74510325 Q6UXH1-5  189    R    G rs74510325
## 16 rs74510325      humvar rs74510325     <NA> <NA> <NA> <NA> rs74510325
## 21 rs73891177      humdiv rs73891177 Q6UXH1-5  207    P    A rs73891177
## 22 rs73891177      humvar rs73891177     <NA> <NA> <NA> <NA> rs73891177
##         ACC POS AA1 AA2  NT1  NT2        PREDICTION   BASEDON EFFECT PPH2CLASS
## 13 Q6UXH1-5 182   D   E    T    A possibly damaging alignment   <NA>   neutral
## 14 Q6UXH1-5 182   D   E <NA> <NA> possibly damaging      <NA>   <NA>      <NA>
## 15 Q6UXH1-5 189   R   G    C    G possibly damaging alignment   <NA>   neutral
## 16 Q6UXH1-5 189   R   G <NA> <NA> possibly damaging      <NA>   <NA>      <NA>
## 21 Q6UXH1-5 207   P   A    C    G            benign alignment   <NA>   neutral
## 22 Q6UXH1-5 207   P   A <NA> <NA>            benign      <NA>   <NA>      <NA>
##    PPH2PROB PPH2FPR PPH2TPR PPH2FDR SITE REGION PHAT DSCORE SCORE1 SCORE2 NOBS
## 13    0.228   0.156   0.892   0.258 <NA>   <NA> <NA>  0.951  1.382  0.431   37
## 14    0.249   0.341   0.874    <NA> <NA>   <NA> <NA>   <NA>   <NA>   <NA> <NA>
## 15    0.475   0.131   0.858   0.233 <NA>   <NA> <NA>  1.198  1.338   0.14   36
## 16    0.335   0.311   0.851    <NA> <NA>   <NA> <NA>   <NA>   <NA>   <NA> <NA>
## 21    0.001    0.86   0.994    0.61 <NA>   <NA> <NA> -0.225  -0.45 -0.225    1
## 22    0.005   0.701   0.981    <NA> <NA>   <NA> <NA>   <NA>   <NA>   <NA> <NA>
##    NSTRUCT NFILT PDBID PDBPOS PDBCH IDENT LENGTH NORMACC SECSTR MAPREG DVOL
## 13       0  <NA>  <NA>   <NA>  <NA>  <NA>   <NA>    <NA>   <NA>   <NA> <NA>
## 14    <NA>  <NA>  <NA>   <NA>  <NA>  <NA>   <NA>    <NA>   <NA>   <NA> <NA>
## 15       0  <NA>  <NA>   <NA>  <NA>  <NA>   <NA>    <NA>   <NA>   <NA> <NA>
## 16    <NA>  <NA>  <NA>   <NA>  <NA>  <NA>   <NA>    <NA>   <NA>   <NA> <NA>
## 21       0  <NA>  <NA>   <NA>  <NA>  <NA>   <NA>    <NA>   <NA>   <NA> <NA>
## 22    <NA>  <NA>  <NA>   <NA>  <NA>  <NA>   <NA>    <NA>   <NA>   <NA> <NA>
##    DPROP BFACT HBONDS AVENHET MINDHET AVENINT MINDINT AVENSIT MINDSIT TRANSV
## 13  <NA>  <NA>   <NA>    <NA>    <NA>    <NA>    <NA>    <NA>    <NA>      1
## 14  <NA>  <NA>   <NA>    <NA>    <NA>    <NA>    <NA>    <NA>    <NA>   <NA>
## 15  <NA>  <NA>   <NA>    <NA>    <NA>    <NA>    <NA>    <NA>    <NA>      1
## 16  <NA>  <NA>   <NA>    <NA>    <NA>    <NA>    <NA>    <NA>    <NA>   <NA>
## 21  <NA>  <NA>   <NA>    <NA>    <NA>    <NA>    <NA>    <NA>    <NA>      1
## 22  <NA>  <NA>   <NA>    <NA>    <NA>    <NA>    <NA>    <NA>    <NA>   <NA>
##    CODPOS  CPG MINDJNC PFAMHIT IDPMAX IDPSNP IDQMIN          COMMENTS
## 13      2    0    <NA>    <NA> 18.261 18.261 48.507 chr22:50315363_CA
## 14   <NA> <NA>    <NA>    <NA>   <NA>   <NA>   <NA> chr22:50315363_CA
## 15      0    1    <NA>    <NA> 19.252 19.252 63.682 chr22:50315382_CG
## 16   <NA> <NA>    <NA>    <NA>   <NA>   <NA>   <NA> chr22:50315382_CG
## 21      0    0    <NA>    <NA>  1.919   <NA> 60.697 chr22:50315971_CG
## 22   <NA> <NA>    <NA>    <NA>   <NA>   <NA>   <NA> chr22:50315971_CG

6 Other operations

6.1 Create a SnpMatrix

The ‘GT’ element in the FORMAT field of the VCF represents the genotype. These data can be converted into a SnpMatrix object which can then be used with the functions offered in snpStats and other packages making use of the SnpMatrix class.

The genotypeToSnpMatrix function converts the genotype calls in geno to a SnpMatrix. No dbSNP package is used in this computation. The return value is a named list where ‘genotypes’ is a SnpMatrix and ‘map’ is a DataFrame with SNP names and alleles at each loci. The ignore column in ‘map’ indicates which variants were set to NA (missing) because they met one or more of the following criteria,

  • variants with >1 ALT allele are set to NA
  • only single nucleotide variants are included; others are set to NA
  • only diploid calls are included; others are set to NA

See ?genotypeToSnpMatrix for more details.

res <- genotypeToSnpMatrix(vcf) 
res
## $genotypes
## A SnpMatrix with  5 rows and  10376 columns
## Row names:  HG00096 ... HG00101 
## Col names:  rs7410291 ... rs114526001 
## 
## $map
## DataFrame with 10376 rows and 4 columns
##         snp.names       allele.1           allele.2    ignore
##       <character> <DNAStringSet> <DNAStringSetList> <logical>
## 1       rs7410291              A                  G     FALSE
## 2     rs147922003              C                  T     FALSE
## 3     rs114143073              G                  A     FALSE
## 4     rs141778433              C                  T     FALSE
## 5     rs182170314              C                  T     FALSE
## ...           ...            ...                ...       ...
## 10372 rs187302552              A                  G     FALSE
## 10373   rs9628178              A                  G     FALSE
## 10374   rs5770892              A                  G     FALSE
## 10375 rs144055359              G                  A     FALSE
## 10376 rs114526001              G                  C     FALSE

In the map DataFrame, allele.1 represents the reference allele and allele.2 is the alternate allele.

allele2 <- res$map[["allele.2"]]
## number of alternate alleles per variant
unique(elementNROWS(allele2))
## [1] 1

In addition to the called genotypes, genotype likelihoods or probabilities can also be converted to a SnpMatrix, using the snpStats encoding of posterior probabilities as byte values. To use the values in the ‘GL’ or ‘GP’ FORMAT field instead of the called genotypes, use the uncertain=TRUE option in genotypeToSnpMatrix.

fl.gl <- system.file("extdata", "gl_chr1.vcf", package="VariantAnnotation")
vcf.gl <- readVcf(fl.gl, "hg19")
geno(vcf.gl)
## List of length 3
## names(3): GT DS GL
## Convert the "GL" FORMAT field to a SnpMatrix
res <- genotypeToSnpMatrix(vcf.gl, uncertain=TRUE)
res
## $genotypes
## A SnpMatrix with  85 rows and  9 columns
## Row names:  NA06984 ... NA12890 
## Col names:  rs58108140 ... rs200430748 
## 
## $map
## DataFrame with 9 rows and 4 columns
##     snp.names       allele.1           allele.2    ignore
##   <character> <DNAStringSet> <DNAStringSetList> <logical>
## 1  rs58108140              G                  A     FALSE
## 2 rs189107123              C                         TRUE
## 3 rs180734498              C                  T     FALSE
## 4 rs144762171              G                         TRUE
## 5 rs201747181             TC                         TRUE
## 6 rs151276478              T                         TRUE
## 7 rs140337953              G                  T     FALSE
## 8 rs199681827              C                         TRUE
## 9 rs200430748              G                         TRUE
t(as(res$genotype, "character"))[c(1,3,7), 1:5]
##             NA06984     NA06986     NA06989     NA06994     NA07000    
## rs58108140  "Uncertain" "Uncertain" "A/B"       "Uncertain" "Uncertain"
## rs180734498 "Uncertain" "Uncertain" "Uncertain" "Uncertain" "Uncertain"
## rs140337953 "Uncertain" "Uncertain" "Uncertain" "Uncertain" "Uncertain"
## Compare to a SnpMatrix created from the "GT" field
res.gt <- genotypeToSnpMatrix(vcf.gl, uncertain=FALSE)
t(as(res.gt$genotype, "character"))[c(1,3,7), 1:5]
##             NA06984 NA06986 NA06989 NA06994 NA07000
## rs58108140  "A/B"   "A/B"   "A/B"   "A/A"   "A/A"  
## rs180734498 "A/B"   "A/A"   "A/A"   "A/A"   "A/B"  
## rs140337953 "B/B"   "B/B"   "A/B"   "B/B"   "A/B"
## What are the original likelihoods for rs58108140?
geno(vcf.gl)$GL["rs58108140", 1:5]
## $NA06984
## [1] -4.70 -0.58 -0.13
## 
## $NA06986
## [1] -1.15 -0.10 -0.84
## 
## $NA06989
## [1] -2.05  0.00 -3.27
## 
## $NA06994
## [1] -0.48 -0.48 -0.48
## 
## $NA07000
## [1] -0.28 -0.44 -0.96

For variant rs58108140 in sample NA06989, the "A/B" genotype is much more likely than the others, so the SnpMatrix object displays the called genotype.

6.2 Write out VCF files

A VCF file can be written out from data stored in a VCF class.

fl <- system.file("extdata", "ex2.vcf", package="VariantAnnotation")
out1.vcf <- tempfile()
out2.vcf <- tempfile()
in1 <- readVcf(fl, "hg19")
writeVcf(in1, out1.vcf)
in2 <- readVcf(out1.vcf, "hg19")
writeVcf(in2, out2.vcf)
in3 <- readVcf(out2.vcf, "hg19")
identical(rowRanges(in1), rowRanges(in3))
## [1] TRUE
identical(geno(in1), geno(in2))
## [1] TRUE

7 Performance

Targeted queries can greatly improve the speed of data input. When all data from the file are needed define a yieldSize in the TabixFile to iterate through the file in chunks.

readVcf(TabixFile(fl, yieldSize=10000))

readVcf can be used with a to select any combination of INFO and GENO fields, samples or genomic positions.

readVcf(TabixFile(fl), param=ScanVcfParam(info='DP', geno='GT'))

While readvcf offers the flexibility to define combinations of INFO, GENO and samples in the ScanVcfParam, sometimes only a single field is needed. In this case the lightweight read functions (readGT, readInfo and readGeno) can be used. These functions return the single field as a matrix instead of a VCF object.

readGT(fl)

The table below highlights the speed differences of targeted queries vs reading in all data. The test file is from 1000 Genomes and has 494328 variants, 1092 samples, 22 INFO, and 3 GENO fields and is located at http://ftp.1000genomes.ebi.ac.uk/vol1/ftp/release/20101123/. yieldSize is used to define chunks of 100, 1000, 10000 and 100000 variants. For each chunk size three function calls are compared: readGT reading only GT, readVcf reading both GT and ALT and finally readVcf reading in all the data.

library(microbenchmark)
fl <- "ALL.chr22.phase1_release_v3.20101123.snps_indels_svs.genotypes.vcf.gz"
ys <- c(100, 1000, 10000, 100000)

## readGT() input only 'GT':
fun <- function(fl, yieldSize) readGT(TabixFile(fl, yieldSize))
lapply(ys, function(i) microbenchmark(fun(fl, i), times=5)

## readVcf() input only 'GT' and 'ALT':
fun <- function(fl, yieldSize, param) 
           readVcf(TabixFile(fl, yieldSize), "hg19", param=param)
param <- ScanVcfParam(info=NA, geno="GT", fixed="ALT")
lapply(ys, function(i) microbenchmark(fun(fl, i, param), times=5)

## readVcf() input all variables:
fun <- function(fl, yieldSize) readVcf(TabixFile(fl, yieldSize), "hg19")
lapply(ys, function(i) microbenchmark(fun(fl, i), times=5))

Table 2: Targeted queries (time in seconds)
n records readGT readVcf (GT and ALT) readVcf (all)
100 0.082 0.128 0.501
1000 0.609 0.508 5.878
10000 5.972 6.164 68.378
100000 78.593 81.156 693.654

8 References

Appendix

Wang K, Li M, Hakonarson H, (2010), ANNOVAR: functional annotation of genetic variants from high-throughput sequencing data. Nucleic Acids Research, Vol 38, No. 16, e164.

McLaren W, Pritchard B, RiosD, et. al., (2010), Deriving the consequences of genomic variants with the Ensembl API and SNP Effect Predictor. Bioinformatics, Vol. 26, No. 16, 2069-2070.

SIFT home page: http://sift.bii.a-star.edu.sg/

PolyPhen home page: http://genetics.bwh.harvard.edu/pph2/

A Session Information

## R version 4.4.0 beta (2024-04-15 r86425)
## Platform: x86_64-pc-linux-gnu
## Running under: Ubuntu 22.04.4 LTS
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## BLAS:   /home/biocbuild/bbs-3.19-bioc/R/lib/libRblas.so 
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## attached base packages:
## [1] stats4    stats     graphics  grDevices utils     datasets  methods  
## [8] base     
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## other attached packages:
##  [1] snpStats_1.54.0                         
##  [2] Matrix_1.7-0                            
##  [3] survival_3.6-4                          
##  [4] PolyPhen.Hsapiens.dbSNP131_1.0.2        
##  [5] RSQLite_2.3.6                           
##  [6] ggplot2_3.5.1                           
##  [7] SNPlocs.Hsapiens.dbSNP144.GRCh37_0.99.20
##  [8] TxDb.Hsapiens.UCSC.hg19.knownGene_3.2.2 
##  [9] GenomicFeatures_1.56.0                  
## [10] AnnotationDbi_1.66.0                    
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## [12] BSgenome_1.72.0                         
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## [19] httr_1.4.7                              
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## [21] VariantAnnotation_1.50.0                
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## [34] tidyselect_1.2.1         abind_1.4-5              yaml_2.3.8              
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## [40] tibble_3.2.1             withr_3.0.0              KEGGREST_1.44.0         
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## [64] dplyr_1.1.4              gtable_0.3.5             sass_0.4.9              
## [67] digest_0.6.35            SparseArray_1.4.0        rjson_0.2.21            
## [70] farver_2.1.1             memoise_2.0.1            htmltools_0.5.8.1       
## [73] lifecycle_1.0.4          mime_0.12                bit64_4.0.5
VariantAnnotation/inst/doc/VariantAnnotation.R0000644000175100017510000001747514614361053022556 0ustar00biocbuildbiocbuild## ----readVcF,message=FALSE---------------------------------------------------- library(VariantAnnotation) fl <- system.file("extdata", "chr22.vcf.gz", package="VariantAnnotation") vcf <- readVcf(fl, "hg19") vcf ## ----readVcf_showheader------------------------------------------------------- header(vcf) ## ----headeraccessors---------------------------------------------------------- samples(header(vcf)) geno(header(vcf)) ## ----readVcf_rowRanges-------------------------------------------------------- head(rowRanges(vcf), 3) ## ----readVcf_fixed------------------------------------------------------------ ref(vcf)[1:5] qual(vcf)[1:5] ## ----readVcf_ALT-------------------------------------------------------------- alt(vcf)[1:5] ## ----geno_hdr----------------------------------------------------------------- geno(vcf) sapply(geno(vcf), class) ## ----explore_geno------------------------------------------------------------- geno(header(vcf))["DS",] ## ----dim_geno----------------------------------------------------------------- DS <-geno(vcf)$DS dim(DS) DS[1:3,] ## ----fivenum------------------------------------------------------------------ fivenum(DS) ## ----DS_zero------------------------------------------------------------------ length(which(DS==0))/length(DS) ## ----DS_hist, fig=TRUE-------------------------------------------------------- hist(DS[DS != 0], breaks=seq(0, 2, by=0.05), main="DS non-zero values", xlab="DS") ## ----info--------------------------------------------------------------------- info(vcf)[1:4, 1:5] ## ----examine_dbSNP, message=FALSE, warning=FALSE------------------------------ library(SNPlocs.Hsapiens.dbSNP144.GRCh37) vcf_rsids <- names(rowRanges(vcf)) chr22snps <- snpsBySeqname(SNPlocs.Hsapiens.dbSNP144.GRCh37, "22") chr22_rsids <- mcols(chr22snps)$RefSNP_id in_dbSNP <- vcf_rsids %in% chr22_rsids table(in_dbSNP) ## ----header_info-------------------------------------------------------------- info(header(vcf))[c("VT", "LDAF", "RSQ"),] ## ----examine_quality---------------------------------------------------------- metrics <- data.frame(QUAL=qual(vcf), in_dbSNP=in_dbSNP, VT=info(vcf)$VT, LDAF=info(vcf)$LDAF, RSQ=info(vcf)$RSQ) ## ----examine_ggplot2, message=FALSE, warning=FALSE, fig=TRUE------------------ library(ggplot2) ggplot(metrics, aes(x=RSQ, fill=in_dbSNP)) + geom_density(alpha=0.5) + scale_x_continuous(name="MaCH / Thunder Imputation Quality") + scale_y_continuous(name="Density") + theme(legend.position="top") ## ----subset_ranges------------------------------------------------------------ rng <- GRanges(seqnames="22", ranges=IRanges( start=c(50301422, 50989541), end=c(50312106, 51001328), names=c("gene_79087", "gene_644186"))) ## ----subset_TabixFile--------------------------------------------------------- tab <- TabixFile(fl) vcf_rng <- readVcf(tab, "hg19", param=rng) ## ----------------------------------------------------------------------------- head(rowRanges(vcf_rng), 3) ## ----subset_scanVcfHeader----------------------------------------------------- hdr <- scanVcfHeader(fl) ## e.g., INFO and GENO fields head(info(hdr), 3) head(geno(hdr), 3) ## ----subset_ScanVcfParam------------------------------------------------------ ## Return all 'fixed' fields, "LAF" from 'info' and "GT" from 'geno' svp <- ScanVcfParam(info="LDAF", geno="GT") vcf1 <- readVcf(fl, "hg19", svp) names(geno(vcf1)) ## ----subset_ScanVcfParam_new-------------------------------------------------- svp_all <- ScanVcfParam(info="LDAF", geno="GT", which=rng) svp_all ## ----locate_rename_seqlevels, message=FALSE, warning=FALSE-------------------- library(TxDb.Hsapiens.UCSC.hg19.knownGene) txdb <- TxDb.Hsapiens.UCSC.hg19.knownGene seqlevels(vcf) <- "chr22" rd <- rowRanges(vcf) loc <- locateVariants(rd, txdb, CodingVariants()) head(loc, 3) ## ----AllVariants, eval=FALSE-------------------------------------------------- # allvar <- locateVariants(rd, txdb, AllVariants()) ## ----locate_gene_centric------------------------------------------------------ ## Did any coding variants match more than one gene? splt <- split(mcols(loc)$GENEID, mcols(loc)$QUERYID) table(sapply(splt, function(x) length(unique(x)) > 1)) ## Summarize the number of coding variants by gene ID. splt <- split(mcols(loc)$QUERYID, mcols(loc)$GENEID) head(sapply(splt, function(x) length(unique(x))), 3) ## ----predictCoding, warning=FALSE--------------------------------------------- library(BSgenome.Hsapiens.UCSC.hg19) coding <- predictCoding(vcf, txdb, seqSource=Hsapiens) coding[5:7] ## ----predictCoding_frameshift------------------------------------------------- ## CONSEQUENCE is 'frameshift' where translation is not possible coding[mcols(coding)$CONSEQUENCE == "frameshift"] ## ----nonsynonymous------------------------------------------------------------ nms <- names(coding) idx <- mcols(coding)$CONSEQUENCE == "nonsynonymous" nonsyn <- coding[idx] names(nonsyn) <- nms[idx] rsids <- unique(names(nonsyn)[grep("rs", names(nonsyn), fixed=TRUE)]) ## ----polyphen, message=FALSE, warning=FALSE----------------------------------- library(PolyPhen.Hsapiens.dbSNP131) pp <- select(PolyPhen.Hsapiens.dbSNP131, keys=rsids, cols=c("TRAININGSET", "PREDICTION", "PPH2PROB")) head(pp[!is.na(pp$PREDICTION), ]) ## ----snpMatrix, message=FALSE------------------------------------------------- res <- genotypeToSnpMatrix(vcf) res ## ----snpMatrix_ALT------------------------------------------------------------ allele2 <- res$map[["allele.2"]] ## number of alternate alleles per variant unique(elementNROWS(allele2)) ## ----message=FALSE------------------------------------------------------------ fl.gl <- system.file("extdata", "gl_chr1.vcf", package="VariantAnnotation") vcf.gl <- readVcf(fl.gl, "hg19") geno(vcf.gl) ## Convert the "GL" FORMAT field to a SnpMatrix res <- genotypeToSnpMatrix(vcf.gl, uncertain=TRUE) res t(as(res$genotype, "character"))[c(1,3,7), 1:5] ## Compare to a SnpMatrix created from the "GT" field res.gt <- genotypeToSnpMatrix(vcf.gl, uncertain=FALSE) t(as(res.gt$genotype, "character"))[c(1,3,7), 1:5] ## What are the original likelihoods for rs58108140? geno(vcf.gl)$GL["rs58108140", 1:5] ## ----writeVcf, message=FALSE, warning=FALSE----------------------------------- fl <- system.file("extdata", "ex2.vcf", package="VariantAnnotation") out1.vcf <- tempfile() out2.vcf <- tempfile() in1 <- readVcf(fl, "hg19") writeVcf(in1, out1.vcf) in2 <- readVcf(out1.vcf, "hg19") writeVcf(in2, out2.vcf) in3 <- readVcf(out2.vcf, "hg19") identical(rowRanges(in1), rowRanges(in3)) identical(geno(in1), geno(in2)) ## ----eval=FALSE--------------------------------------------------------------- # readVcf(TabixFile(fl, yieldSize=10000)) ## ----eval=FALSE--------------------------------------------------------------- # readVcf(TabixFile(fl), param=ScanVcfParam(info='DP', geno='GT')) ## ----eval=FALSE--------------------------------------------------------------- # readGT(fl) ## ----eval=FALSE--------------------------------------------------------------- # library(microbenchmark) # fl <- "ALL.chr22.phase1_release_v3.20101123.snps_indels_svs.genotypes.vcf.gz" # ys <- c(100, 1000, 10000, 100000) # # ## readGT() input only 'GT': # fun <- function(fl, yieldSize) readGT(TabixFile(fl, yieldSize)) # lapply(ys, function(i) microbenchmark(fun(fl, i), times=5) # # ## readVcf() input only 'GT' and 'ALT': # fun <- function(fl, yieldSize, param) # readVcf(TabixFile(fl, yieldSize), "hg19", param=param) # param <- ScanVcfParam(info=NA, geno="GT", fixed="ALT") # lapply(ys, function(i) microbenchmark(fun(fl, i, param), times=5) # # ## readVcf() input all variables: # fun <- function(fl, yieldSize) readVcf(TabixFile(fl, yieldSize), "hg19") # lapply(ys, function(i) microbenchmark(fun(fl, i), times=5)) ## ----sessionInfo, echo=FALSE-------------------------------------------------- sessionInfo() VariantAnnotation/inst/doc/VariantAnnotation.Rmd0000644000175100017510000004753314614305321023071 0ustar00biocbuildbiocbuild--- title: "1. Introduction to VariantAnnotation" author: "Valerie Obenchain" date: "`r format(Sys.time(), '%B %d, %Y')`" vignette: > %\VignetteEngine{knitr::rmarkdown} %\VignetteIndexEntry{1. Introduction to VariantAnnotation} %\VignetteEncoding{UTF-8} output: BiocStyle::html_document: number_sections: yes toc: yes toc_depth: 4 --- # Introduction This vignette outlines a work flow for annotating and filtering genetic variants using the `r Biocpkg("VariantAnnotation")` package. Sample data are in VariantCall Format (VCF) and are a subset of chromosome 22 from [1000 Genomes](http://ftp.1000genomes.ebi.ac.uk/vol1/ftp/release/20110521/). VCF text files contain meta-information lines, a header line with column names, data lines with information about a position in the genome, and optional genotype information on samples for each position. Samtools organisation and repositories describes the [VCF format](http://samtools.github.io/hts-specs/VCFv4.4.pdf) in detail. Data are read in from a VCF file and variants identified according to region such as `coding`, `intron`, `intergenic`, `spliceSite` etc. Amino acid coding changes are computed for the non-synonymous variants and SIFT and PolyPhen databases provide predictions of how severly the coding changes affect protein function. # Variant Call Format (VCF) files ## Data import and exploration Data are parsed into a `VCF` object with `readVcf`. ```{r readVcF,message=FALSE} library(VariantAnnotation) fl <- system.file("extdata", "chr22.vcf.gz", package="VariantAnnotation") vcf <- readVcf(fl, "hg19") vcf ``` ### Header information Header information can be extracted from the VCF with `header()`. We see there are 5 samples, 1 piece of meta information, 22 info fields and 3 geno fields. ```{r readVcf_showheader} header(vcf) ``` Data can be further extracted using the named accessors. ```{r headeraccessors} samples(header(vcf)) geno(header(vcf)) ``` ### Genomic positions `rowRanges` contains information from the CHROM, POS, and ID fields of the VCF file, represented as a `GRanges`. The `paramRangeID` column is meaningful when reading subsets of data and is discussed further below. ```{r readVcf_rowRanges} head(rowRanges(vcf), 3) ``` Individual fields can be pulled out with named accessors. Here we see `REF` is stored as a `DNAStringSet` and `qual` is a numeric vector. ```{r readVcf_fixed} ref(vcf)[1:5] qual(vcf)[1:5] ``` `ALT` is a `DNAStringSetList` (allows for multiple alternate alleles per variant) or a `DNAStringSet`. When structural variants are present it will be a `CharacterList`. ```{r readVcf_ALT} alt(vcf)[1:5] ``` ### Genotype data Genotype data described in the `FORMAT` fields are parsed into the geno slot. The data are unique to each sample and each sample may have multiple values variable. Because of this, the data are parsed into matrices or arrays where the rows represent the variants and the columns the samples. Multidimentional arrays indicate multiple values per sample. In this file all variables are matrices. ```{r geno_hdr} geno(vcf) sapply(geno(vcf), class) ``` Let's take a closer look at the genotype dosage (DS) variable. The header provides the variable definition and type. ```{r explore_geno} geno(header(vcf))["DS",] ``` These data are stored as a 10376 x 5 matrix. Each of the five samples (columns) has a single value per variant location (row). ```{r dim_geno} DS <-geno(vcf)$DS dim(DS) DS[1:3,] ``` DS is also known as 'posterior mean genotypes' and range in value from [0, 2]. To get a sense of variable distribution, we compute a five number summary of the minimum, lower-hinge (first quartile), median, upper-hinge (third quartile) and maximum. ```{r fivenum} fivenum(DS) ``` The majority of these values (86%) are zero. ```{r DS_zero} length(which(DS==0))/length(DS) ``` View the distribution of the non-zero values. ```{r DS_hist, fig=TRUE} hist(DS[DS != 0], breaks=seq(0, 2, by=0.05), main="DS non-zero values", xlab="DS") ``` ### Info data In contrast to the genotype data, the info data are unique to the variant and the same across samples. All info variables are represented in a single `DataFrame`. ```{r info} info(vcf)[1:4, 1:5] ``` We will use the info data to compare quality measures between novel (i.e., not in dbSNP) and known (i.e., in dbSNP) variants and the variant type present in the file. Variants with membership in dbSNP can be identified by using the appropriate SNPlocs package for the hg19 genome (GRCh37). ```{r examine_dbSNP, message=FALSE, warning=FALSE} library(SNPlocs.Hsapiens.dbSNP144.GRCh37) vcf_rsids <- names(rowRanges(vcf)) chr22snps <- snpsBySeqname(SNPlocs.Hsapiens.dbSNP144.GRCh37, "22") chr22_rsids <- mcols(chr22snps)$RefSNP_id in_dbSNP <- vcf_rsids %in% chr22_rsids table(in_dbSNP) ``` Info variables of interest are 'VT', 'LDAF' and 'RSQ'. The header offers more details on these variables. ```{r header_info} info(header(vcf))[c("VT", "LDAF", "RSQ"),] ``` Create a data frame of quality measures of interest ... ```{r examine_quality} metrics <- data.frame(QUAL=qual(vcf), in_dbSNP=in_dbSNP, VT=info(vcf)$VT, LDAF=info(vcf)$LDAF, RSQ=info(vcf)$RSQ) ``` and visualize the distribution of qualities using `r CRANpkg("ggplot2")`. For instance, genotype imputation quality is higher for the known variants in dbSNP. ```{r examine_ggplot2, message=FALSE, warning=FALSE, fig=TRUE} library(ggplot2) ggplot(metrics, aes(x=RSQ, fill=in_dbSNP)) + geom_density(alpha=0.5) + scale_x_continuous(name="MaCH / Thunder Imputation Quality") + scale_y_continuous(name="Density") + theme(legend.position="top") ``` ## Import data subsets When working with large VCF files it may be more efficient to read in subsets of the data. This can be accomplished by selecting genomic coordinates (ranges) or by specific fields from the VCF file. ### Select genomic coordinates To read in a portion of chromosome 22, create a `GRanges` with the regions of interest. ```{r subset_ranges} rng <- GRanges(seqnames="22", ranges=IRanges( start=c(50301422, 50989541), end=c(50312106, 51001328), names=c("gene_79087", "gene_644186"))) ``` When ranges are specified, the VCF file must have an accompanying Tabix index file. See `indexTabix` for help creating an index. ```{r subset_TabixFile} tab <- TabixFile(fl) vcf_rng <- readVcf(tab, "hg19", param=rng) ``` The `paramRangesID` column distinguishes which records came from which param range. ```{r} head(rowRanges(vcf_rng), 3) ``` ### Select VCF fields Data import can also be defined by the `fixed`, `info` and `geno` fields. Fields available for import are described in the header information. To view the header before reading in the data, use `ScanVcfHeader`. ```{r subset_scanVcfHeader} hdr <- scanVcfHeader(fl) ## e.g., INFO and GENO fields head(info(hdr), 3) head(geno(hdr), 3) ``` To subset on "LDAF" and "GT" we specify them as `character` vectors in the `info` and `geno` arguments to `ScanVcfParam`. This creates a `ScanVcfParam` object which is used as the `param` argument to `readVcf`. ```{r subset_ScanVcfParam} ## Return all 'fixed' fields, "LAF" from 'info' and "GT" from 'geno' svp <- ScanVcfParam(info="LDAF", geno="GT") vcf1 <- readVcf(fl, "hg19", svp) names(geno(vcf1)) ``` To subset on both genomic coordinates and fields the `ScanVcfParam` object must contain both. ```{r subset_ScanVcfParam_new} svp_all <- ScanVcfParam(info="LDAF", geno="GT", which=rng) svp_all ``` # Locating variants in and around genes Variant location with respect to genes can be identified with the `locateVariants` function. Regions are specified in the `region` argument and can be one of the following constructors: CodingVariants, IntronVariants, FiveUTRVariants, ThreeUTRVariants, IntergenicVariants, SpliceSiteVariants or PromoterVariants. Location definitions are shown in Table \@ref(tab:table). | Location | Details | |------------|------------------------------------------------------------| | coding | falls *within* a coding region | | fiveUTR | falls *within* a 5' untranslated region | | threeUTR | falls *within* a 3' untranslated region | | intron | falls *within* an intron region | | intergenic | does not fall *within* a transcript associated with a gene | | spliceSite | overlaps any portion of the first 2 or last 2 | | promoter | falls *within* a promoter region of a transcript | : (\#tab:table) Variant locations For overlap methods to work properly the chromosome names (seqlevels) must be compatible in the objects being compared. The VCF data chromosome names are represented by number, i.e., '22', but the TxDb chromosome names are preceded with 'chr'. Seqlevels in the VCF can be modified with the `seqlevels` function. ```{r locate_rename_seqlevels, message=FALSE, warning=FALSE} library(TxDb.Hsapiens.UCSC.hg19.knownGene) txdb <- TxDb.Hsapiens.UCSC.hg19.knownGene seqlevels(vcf) <- "chr22" rd <- rowRanges(vcf) loc <- locateVariants(rd, txdb, CodingVariants()) head(loc, 3) ``` Locate variants in all regions with the `AllVariants()` constructor, ```{r AllVariants, eval=FALSE} allvar <- locateVariants(rd, txdb, AllVariants()) ``` To answer gene-centric questions data can be summarized by gene reguardless of transcript. ```{r locate_gene_centric} ## Did any coding variants match more than one gene? splt <- split(mcols(loc)$GENEID, mcols(loc)$QUERYID) table(sapply(splt, function(x) length(unique(x)) > 1)) ## Summarize the number of coding variants by gene ID. splt <- split(mcols(loc)$QUERYID, mcols(loc)$GENEID) head(sapply(splt, function(x) length(unique(x))), 3) ``` # Amino acid coding changes `predictCoding` computes amino acid coding changes for non-synonymous variants. Only ranges in query that overlap with a coding region in the `subject` are considered. Reference sequences are retrieved from either a `BSgenome` or fasta file specified in `seqSource`. Variant sequences are constructed by substituting, inserting or deleting values in the `varAllele` column into the reference sequence. Amino acid codes are computed for the variant codon sequence when the length is a multiple of 3. The query argument to `predictCoding` can be a `GRanges` or `VCF`. When a `GRanges` is supplied the `varAllele` argument must be specified. In the case of a `VCF`, the alternate alleles are taken from `alt()` and the `varAllele` argument is not specified. The result is a modified `query` containing only variants that fall within coding regions. Each row represents a variant-transcript match so more than one row per original variant is possible. ```{r predictCoding, warning=FALSE} library(BSgenome.Hsapiens.UCSC.hg19) coding <- predictCoding(vcf, txdb, seqSource=Hsapiens) coding[5:7] ``` Using variant rs114264124 as an example, we see varAllele `A` has been substituted into the `refCodon` `CGG` to produce `varCodon` `CAG.` The `refCodon` is the sequence of codons necessary to make the variant allele substitution and therefore often includes more nucleotides than indicated in the range (i.e. the range is 50302962, 50302962, width of 1). Notice it is the second position in the `refCodon` that has been substituted. This position in the codon, the position of substitution, corresponds to genomic position 50302962. This genomic position maps to position 698 in coding region-based coordinates and to triplet 233 in the protein. This is a non-synonymous coding variant where the amino acid has changed from `R` (Arg) to `Q` (Gln). When the resulting `varCodon` is not a multiple of 3 it cannot be translated. The consequence is considered a `frameshift` and `varAA` will be missing. ```{r predictCoding_frameshift} ## CONSEQUENCE is 'frameshift' where translation is not possible coding[mcols(coding)$CONSEQUENCE == "frameshift"] ``` # SIFT and PolyPhen Databases From `predictCoding` we identified the amino acid coding changes for the non-synonymous variants. For this subset we can retrieve predictions of how damaging these coding changes may be. SIFT (Sorting Intolerant From Tolerant) and PolyPhen (Polymorphism Phenotyping) are methods that predict the impact of amino acid substitution on a human protein. The SIFT method uses sequence homology and the physical properties of amino acids to make predictions about protein function. PolyPhen uses sequence-based features and structural information characterizing the substitution to make predictions about the structure and function of the protein. Collated predictions for specific dbSNP builds are available as downloads from the SIFT and PolyPhen web sites. These results have been packaged into `r Biocpkg("SIFT.Hsapiens.dbSNP132")` and `r Biocpkg("PolyPhen.Hsapiens.dbSNP131")` and are designed to be searched by rsid. Variants that are in dbSNP can be searched with these database packages. When working with novel variants, SIFT and PolyPhen must be called directly. See references for home pages. Identify the non-synonymous variants and obtain the rsids. ```{r nonsynonymous} nms <- names(coding) idx <- mcols(coding)$CONSEQUENCE == "nonsynonymous" nonsyn <- coding[idx] names(nonsyn) <- nms[idx] rsids <- unique(names(nonsyn)[grep("rs", names(nonsyn), fixed=TRUE)]) ``` Detailed descriptions of the database columns can be found with `?SIFTDbColumns` and `?PolyPhenDbColumns`. Variants in these databases often contain more than one row per variant. The variant may have been reported by multiple sources and therefore the source will differ as well as some of the other variables. It is important to keep in mind the pre-computed predictions in the SIFT and PolyPhen packages are based on specific gene models. SIFT is based on Ensembl and PolyPhen on UCSC Known Gene. The `TxDb` we used to identify the coding snps was based on UCSC Known Gene so we will use PolyPhen for predictions. PolyPhen provides predictions using two different training datasets and has considerable information about 3D protein structure. See `?PolyPhenDbColumns` or the PolyPhen web site listed in the references for more details. Query the PolyPhen database, ```{r polyphen, message=FALSE, warning=FALSE} library(PolyPhen.Hsapiens.dbSNP131) pp <- select(PolyPhen.Hsapiens.dbSNP131, keys=rsids, cols=c("TRAININGSET", "PREDICTION", "PPH2PROB")) head(pp[!is.na(pp$PREDICTION), ]) ``` # Other operations ## Create a SnpMatrix The 'GT' element in the `FORMAT` field of the VCF represents the genotype. These data can be converted into a `SnpMatrix` object which can then be used with the functions offered in `r Biocpkg("snpStats")` and other packages making use of the `SnpMatrix` class. The `genotypeToSnpMatrix` function converts the genotype calls in `geno` to a `SnpMatrix`. No dbSNP package is used in this computation. The return value is a named list where 'genotypes' is a `SnpMatrix` and 'map' is a `DataFrame` with SNP names and alleles at each loci. The `ignore` column in 'map' indicates which variants were set to NA (missing) because they met one or more of the following criteria, - variants with >1 ALT allele are set to NA - only single nucleotide variants are included; others are set to NA - only diploid calls are included; others are set to NA See ?`genotypeToSnpMatrix` for more details. ```{r snpMatrix, message=FALSE} res <- genotypeToSnpMatrix(vcf) res ``` In the map DataFrame, allele.1 represents the reference allele and allele.2 is the alternate allele. ```{r snpMatrix_ALT} allele2 <- res$map[["allele.2"]] ## number of alternate alleles per variant unique(elementNROWS(allele2)) ``` In addition to the called genotypes, genotype likelihoods or probabilities can also be converted to a `SnpMatrix`, using the `r Biocpkg("snpStats")` encoding of posterior probabilities as byte values. To use the values in the 'GL' or 'GP' `FORMAT` field instead of the called genotypes, use the `uncertain=TRUE` option in `genotypeToSnpMatrix`. ```{r message=FALSE} fl.gl <- system.file("extdata", "gl_chr1.vcf", package="VariantAnnotation") vcf.gl <- readVcf(fl.gl, "hg19") geno(vcf.gl) ## Convert the "GL" FORMAT field to a SnpMatrix res <- genotypeToSnpMatrix(vcf.gl, uncertain=TRUE) res t(as(res$genotype, "character"))[c(1,3,7), 1:5] ## Compare to a SnpMatrix created from the "GT" field res.gt <- genotypeToSnpMatrix(vcf.gl, uncertain=FALSE) t(as(res.gt$genotype, "character"))[c(1,3,7), 1:5] ## What are the original likelihoods for rs58108140? geno(vcf.gl)$GL["rs58108140", 1:5] ``` For variant rs58108140 in sample NA06989, the \"A/B\" genotype is much more likely than the others, so the `SnpMatrix` object displays the called genotype. ## Write out VCF files A VCF file can be written out from data stored in a `VCF` class. ```{r writeVcf, message=FALSE, warning=FALSE} fl <- system.file("extdata", "ex2.vcf", package="VariantAnnotation") out1.vcf <- tempfile() out2.vcf <- tempfile() in1 <- readVcf(fl, "hg19") writeVcf(in1, out1.vcf) in2 <- readVcf(out1.vcf, "hg19") writeVcf(in2, out2.vcf) in3 <- readVcf(out2.vcf, "hg19") identical(rowRanges(in1), rowRanges(in3)) identical(geno(in1), geno(in2)) ``` # Performance Targeted queries can greatly improve the speed of data input. When all data from the file are needed define a `yieldSize` in the `TabixFile` to iterate through the file in chunks. ```{r eval=FALSE} readVcf(TabixFile(fl, yieldSize=10000)) ``` `readVcf` can be used with a to select any combination of INFO and GENO fields, samples or genomic positions. ```{r eval=FALSE} readVcf(TabixFile(fl), param=ScanVcfParam(info='DP', geno='GT')) ``` While `readvcf` offers the flexibility to define combinations of INFO, GENO and samples in the `ScanVcfParam`, sometimes only a single field is needed. In this case the lightweight `read` functions (`readGT`, `readInfo` and `readGeno`) can be used. These functions return the single field as a matrix instead of a `VCF` object. ```{r eval=FALSE} readGT(fl) ``` The table below highlights the speed differences of targeted queries vs reading in all data. The test file is from 1000 Genomes and has 494328 variants, 1092 samples, 22 INFO, and 3 GENO fields and is located at http://ftp.1000genomes.ebi.ac.uk/vol1/ftp/release/20101123/. `yieldSize` is used to define chunks of 100, 1000, 10000 and 100000 variants. For each chunk size three function calls are compared: `readGT` reading only GT, `readVcf` reading both `GT` and `ALT` and finally `readVcf` reading in all the data. ```{r eval=FALSE} library(microbenchmark) fl <- "ALL.chr22.phase1_release_v3.20101123.snps_indels_svs.genotypes.vcf.gz" ys <- c(100, 1000, 10000, 100000) ## readGT() input only 'GT': fun <- function(fl, yieldSize) readGT(TabixFile(fl, yieldSize)) lapply(ys, function(i) microbenchmark(fun(fl, i), times=5) ## readVcf() input only 'GT' and 'ALT': fun <- function(fl, yieldSize, param) readVcf(TabixFile(fl, yieldSize), "hg19", param=param) param <- ScanVcfParam(info=NA, geno="GT", fixed="ALT") lapply(ys, function(i) microbenchmark(fun(fl, i, param), times=5) ## readVcf() input all variables: fun <- function(fl, yieldSize) readVcf(TabixFile(fl, yieldSize), "hg19") lapply(ys, function(i) microbenchmark(fun(fl, i), times=5)) ``` n records readGT readVcf (GT and ALT) readVcf (all) ----------- -------- ---------------------- --------------- 100 0.082 0.128 0.501 1000 0.609 0.508 5.878 10000 5.972 6.164 68.378 100000 78.593 81.156 693.654 : (\#tab:performance) Targeted queries (time in seconds) # References Wang K, Li M, Hakonarson H, (2010), ANNOVAR: functional annotation of genetic variants from high-throughput sequencing data. Nucleic Acids Research, Vol 38, No. 16, e164. McLaren W, Pritchard B, RiosD, et. al., (2010), Deriving the consequences of genomic variants with the Ensembl API and SNP Effect Predictor. Bioinformatics, Vol. 26, No. 16, 2069-2070. 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T . 47 PASS NS=3;DP=13;AA=T GT:GQ:DP:HQ 0|0:54:7:56,60 0|0:48:4:51,51 0/0:61:2 20 1234567 microsat1 GTC G,GTCT 50 PASS NS=3;DP=9;AA=G GT:GQ:DP 0/1:35:4 0/2:17:2 1/1:40:3 VariantAnnotation/inst/extdata/gl_chr1.vcf0000644000175100017510000006036314614305321021666 0ustar00biocbuildbiocbuild##fileformat=VCFv4.1 ##INFO= ##INFO= ##INFO= ##INFO= ##INFO= ##INFO= ##INFO= ##INFO= ##INFO= ##INFO= ##INFO= ##INFO= ##INFO= ##INFO= ##ALT= ##FORMAT= ##FORMAT= ##FORMAT= ##INFO= ##INFO= ##INFO= ##INFO= ##INFO= ##INFO= ##INFO= ##INFO= ##reference=GRCh37 ##source_20120923.1=/nfs/public/rw/ensembl/vcftools/bin/vcf-subset -c NA06984,NA06986,NA06989,NA06994,NA07000,NA07037,NA07048,NA07051,NA07056,NA07347,NA07357,NA10847,NA10851,NA11829,NA11830,NA11831,NA11843,NA11892,NA11893,NA11894,NA11919,NA11920,NA11930,NA11931,NA11932,NA11933,NA11992,NA11993,NA11994,NA11995,NA12003,NA12004,NA12006,NA12043,NA12044,NA12045,NA12046,NA12058,NA12144,NA12154,NA12155,NA12249,NA12272,NA12273,NA12275,NA12282,NA12283,NA12286,NA12287,NA12340,NA12341,NA12342,NA12347,NA12348,NA12383,NA12399,NA12400,NA12413,NA12489,NA12546,NA12716,NA12717,NA12718,NA12748,NA12749,NA12750,NA12751,NA12761,NA12763,NA12775,NA12777,NA12778,NA12812,NA12814,NA12815,NA12827,NA12829,NA12830,NA12842,NA12843,NA12872,NA12873,NA12874,NA12889,NA12890 /net/isilonP/public/rw/ensembl/1000genomes/release-12/tmp/slicer/1.1-50000.ALL.chr1.phase1_release_v3.20101123.snps_indels_svs.genotypes.vcf.gz #CHROM POS ID REF ALT QUAL FILTER INFO FORMAT NA06984 NA06986 NA06989 NA06994 NA07000 NA07037 NA07048 NA07051 NA07056 NA07347 NA07357 NA10847 NA10851 NA11829 NA11830 NA11831 NA11843 NA11892 NA11893 NA11894 NA11919 NA11920 NA11930 NA11931 NA11932 NA11933 NA11992 NA11993 NA11994 NA11995 NA12003 NA12004 NA12006 NA12043 NA12044 NA12045 NA12046 NA12058 NA12144 NA12154 NA12155 NA12249 NA12272 NA12273 NA12275 NA12282 NA12283 NA12286 NA12287 NA12340 NA12341 NA12342 NA12347 NA12348 NA12383 NA12399 NA12400 NA12413 NA12489 NA12546 NA12716 NA12717 NA12718 NA12748 NA12749 NA12750 NA12751 NA12761 NA12763 NA12775 NA12777 NA12778 NA12812 NA12814 NA12815 NA12827 NA12829 NA12830 NA12842 NA12843 NA12872 NA12873 NA12874 NA12889 NA12890 1 10583 rs58108140 G A 100 PASS AA=.;AC=37;AF=0.14;AFR_AF=0.04;AMR_AF=0.17;AN=170;ASN_AF=0.13;AVGPOST=0.7707;ERATE=0.0161;EUR_AF=0.21;LDAF=0.2327;RSQ=0.4319;SNPSOURCE=LOWCOV;THETA=0.0046;VT=SNP GT:DS:GL 0|1:1.250:-4.70,-0.58,-0.13 0|1:0.750:-1.15,-0.10,-0.84 0|1:1.000:-2.05,-0.00,-3.27 0|0:0.450:-0.48,-0.48,-0.48 0|0:0.400:-0.28,-0.44,-0.96 0|0:0.550:-0.48,-0.48,-0.48 0|1:1.000:-1.93,-0.02,-1.45 0|0:0.100:-0.02,-1.44,-5.00 0|1:1.150:-2.72,-0.46,-0.19 0|0:0.400:-0.48,-0.48,-0.48 0|0:0.000:-0.05,-0.96,-5.00 0|1:0.550:-0.48,-0.18,-2.28 0|0:0.050:-0.01,-1.50,-5.00 0|0:0.200:-0.05,-0.94,-4.70 0|1:1.000:-0.90,-0.06,-5.00 0|0:0.350:-0.48,-0.17,-4.40 0|0:0.400:-0.48,-0.48,-0.48 0|0:0.150:-0.11,-0.64,-4.10 0|0:0.100:-0.04,-1.08,-5.00 0|0:0.250:-0.48,-0.48,-0.48 0|1:1.050:-0.93,-0.39,-0.32 0|0:0.150:-0.48,-0.48,-0.48 0|1:0.850:-0.76,-0.08,-5.00 0|1:0.550:-0.48,-0.48,-0.48 0|0:0.100:-0.11,-0.64,-3.40 0|1:0.850:-0.62,-0.12,-5.00 0|0:0.550:-0.48,-0.48,-0.48 0|0:0.150:-0.13,-0.58,-2.73 0|0:0.000:-0.07,-0.85,-5.00 0|1:0.850:-1.34,-0.08,-0.92 0|0:0.500:-0.29,-0.43,-0.95 0|0:0.550:-0.48,-0.48,-0.48 0|1:1.000:-3.15,-0.01,-1.70 0|0:0.350:-0.48,-0.48,-0.48 0|1:0.900:-1.16,-0.10,-0.89 0|0:0.350:-0.18,-0.48,-1.76 0|0:0.550:-0.477139,-0.477113,-0.477113 0|1:1.000:-4.70,-0.00,-5.00 0|0:0.100:-0.04,-1.04,-5.00 0|1:1.000:-2.80,-0.00,-2.27 0|0:0.150:-0.08,-0.78,-5.00 0|0:0.450:-0.48,-0.48,-0.48 0|1:0.750:-0.48,-0.48,-0.48 0|0:0.400:-0.18,-0.48,-2.05 0|0:0.200:-0.19,-0.47,-2.13 0|0:0.050:-0.0744919,-0.802609,-4.09691 0|1:0.800:-1.34,-0.02,-3.52 0|1:0.550:-0.46,-0.19,-5.00 0|1:0.800:-0.48,-0.48,-0.48 0|0:0.700:-0.48,-0.48,-0.48 0|0:0.250:-0.11,-0.65,-3.92 0|1:1.000:-5.00,-0.00,-3.05 0|1:1.000:-3.09,-0.00,-5.00 0|1:1.000:-5.00,0.00,-5.00 0|0:0.250:-0.12,-0.63,-3.74 0|0:0.350:-0.19,-0.46,-2.34 0|1:0.750:-0.477139,-0.477113,-0.477113 0|1:0.800:-1.38,-0.02,-4.70 0|0:0.200:-0.11,-0.65,-3.36 0|1:1.000:-5.00,-0.00,-3.80 0|1:0.650:-0.48,-0.48,-0.48 0|0:0.550:-0.48,-0.48,-0.48 0|1:1.000:-5.00,-0.00,-2.37 0|0:0.150:-0.06,-0.92,-5.00 0|0:0.000:-0.01,-1.54,-5.00 0|1:1.100:-1.78,-0.40,-0.23 0|0:0.500:-0.48,-0.48,-0.48 0|0:0.500:-0.48,-0.48,-0.48 0|0:0.700:-0.48,-0.48,-0.48 0|1:1.150:-4.70,-0.07,-0.85 0|0:0.100:-0.06,-0.87,-5.00 0|1:1.100:-4.70,-0.14,-0.55 0|0:0.350:-0.14,-0.56,-2.82 0|0:0.050:-0.05,-0.97,-5.00 0|0:0.000:-0.11,-0.65,-3.40 0|1:0.800:-0.70,-0.10,-5.00 0|1:1.000:-3.22181,-0.00425079,-2.03905 0|1:1.000:-5.00,0.00,-5.00 0|0:0.150:-0.14,-0.55,-3.52 0|1:1.000:-1.62,-0.01,-2.66 0|1:1.200:-2.36,-0.21,-0.42 0|0:0.100:-0.08,-0.77,-4.70 0|0:0.100:-0.02,-1.27,-5.00 0|1:1.000:-4.10,-0.00,-4.40 0|0:0.150:-0.05,-0.99,-5.00 1 10611 rs189107123 C . 100 PASS AA=.;AC=0;AF=0.02;AFR_AF=0.01;AMR_AF=0.03;AN=170;ASN_AF=0.01;AVGPOST=0.9330;ERATE=0.0048;EUR_AF=0.02;LDAF=0.0479;RSQ=0.3475;SNPSOURCE=LOWCOV;THETA=0.0077;VT=SNP GT:DS:GL 0|0:0.100:-0.48,-0.48,-0.48 0|0:0.000:-0.24,-0.41,-1.44 0|0:0.050:-0.19,-0.45,-2.25 0|0:0.050:-0.48,-0.48,-0.48 0|0:0.000:-0.12,-0.63,-3.10 0|0:0.000:-0.19,-0.45,-2.23 0|0:0.000:-0.19,-0.45,-2.04 0|0:0.000:-0.12,-0.62,-4.40 0|0:0.050:-0.48,-0.48,-0.48 0|0:0.050:-0.48,-0.48,-0.48 0|0:0.050:-0.41,-0.45,-0.59 0|0:0.000:-0.19,-0.46,-2.27 0|0:0.000:-0.04,-1.06,-5.00 0|0:0.000:-0.19,-0.48,-1.56 0|0:0.000:-0.11,-0.65,-4.10 0|0:0.000:-0.20,-0.45,-1.96 0|0:0.050:-0.48,-0.48,-0.48 0|0:0.050:-0.48,-0.48,-0.48 0|0:0.000:-0.07,-0.85,-5.00 0|0:0.150:-0.48,-0.48,-0.48 0|0:0.000:-0.24,-0.41,-1.44 0|0:0.100:-0.48,-0.48,-0.48 0|0:0.000:-0.11,-0.66,-4.40 0|0:0.000:-0.48,-0.48,-0.48 0|0:0.050:-0.40,-0.42,-0.66 0|0:0.000:-0.01,-1.59,-5.00 0|0:0.100:-0.48,-0.48,-0.48 0|0:0.000:-0.19,-0.47,-1.81 0|0:0.000:-0.04,-1.08,-5.00 0|0:0.000:-0.19,-0.47,-1.69 0|0:0.000:-0.48,-0.48,-0.48 0|0:0.000:-0.19,-0.46,-1.80 0|0:0.000:-0.16,-0.52,-2.28 0|0:0.000:-0.48,-0.48,-0.48 0|0:0.050:-0.22,-0.44,-1.41 0|0:0.200:-0.48,-0.48,-0.48 0|0:0.000:-0.165516,-0.505484,-2.33348 0|0:0.000:-0.07,-0.81,-5.00 0|0:0.050:-0.13,-0.58,-2.79 0|0:0.000:-0.13,-0.60,-2.98 0|0:0.050:-0.14,-0.56,-3.05 0|0:0.100:-0.48,-0.48,-0.48 0|0:0.100:-0.48,-0.48,-0.48 0|0:0.050:-0.18,-0.48,-2.04 0|0:0.100:-0.48,-0.48,-0.48 0|0:0.000:-0.0666747,-0.846856,-4.39794 0|0:0.000:-0.12,-0.63,-3.59 0|0:0.000:-0.01,-1.48,-5.00 0|0:0.000:-0.48,-0.48,-0.48 0|0:0.050:-0.48,-0.48,-0.48 0|0:0.000:-0.20,-0.45,-2.01 0|0:0.000:-0.19,-0.46,-2.39 0|0:0.000:-0.11,-0.66,-4.00 0|0:0.000:-0.01,-1.70,-5.00 0|0:0.100:-0.48,-0.48,-0.48 0|0:0.050:-0.48,-0.48,-0.48 0|0:0.000:-0.477139,-0.477113,-0.477113 0|0:0.000:-0.03,-1.12,-5.00 0|0:0.050:-0.18,-0.48,-1.99 0|0:0.050:-0.07,-0.85,-5.00 0|0:0.150:-0.48,-0.48,-0.48 0|0:0.100:-0.48,-0.48,-0.48 0|0:0.000:-0.01,-1.71,-5.00 0|0:0.000:-0.09,-0.73,-4.22 0|0:0.050:-0.20,-0.47,-1.57 0|0:0.100:-0.48,-0.48,-0.48 0|0:0.150:-0.48,-0.48,-0.48 0|0:0.050:-0.48,-0.48,-0.48 0|0:0.150:-0.48,-0.48,-0.48 0|0:0.000:-0.12,-0.63,-3.66 0|0:0.000:-0.48,-0.48,-0.48 0|0:0.000:-0.07,-0.82,-5.00 0|0:0.000:-0.18,-0.48,-2.27 0|0:0.000:-0.14,-0.56,-2.82 0|0:0.000:-0.13,-0.58,-2.97 0|0:0.000:-0.03,-1.13,-5.00 0|0:0.000:-0.0503466,-0.960745,-5 0|0:0.000:-0.00,-4.40,-5.00 0|0:0.050:-0.20,-0.45,-2.25 0|0:0.150:-0.20,-0.45,-1.94 0|0:0.050:-0.13,-0.58,-2.84 0|0:0.150:-0.18,-0.48,-2.21 0|0:0.000:-0.05,-0.97,-5.00 0|0:0.050:-0.19,-0.46,-2.12 0|0:0.000:-0.05,-1.00,-5.00 1 13302 rs180734498 C T 100 PASS AA=.;AC=20;AF=0.11;AFR_AF=0.21;AMR_AF=0.08;AN=170;ASN_AF=0.02;AVGPOST=0.8895;ERATE=0.0058;EUR_AF=0.14;LDAF=0.1573;RSQ=0.6281;SNPSOURCE=LOWCOV;THETA=0.0048;VT=SNP GT:DS:GL 0|1:1.000:-2.01,-0.40,-0.23 0|0:0.000:-0.04,-1.08,-5.00 0|0:0.000:-0.08,-0.77,-5.00 0|0:0.100:-0.20,-0.45,-1.73 0|1:0.950:-1.68,-0.40,-0.24 0|1:0.850:-0.42,-0.21,-5.00 0|0:0.200:-0.48,-0.48,-0.48 0|0:0.100:-0.13,-0.60,-4.10 1|0:0.850:-1.15,-0.03,-5.00 0|0:0.100:-0.26,-0.41,-1.17 0|0:0.100:-0.08,-0.79,-4.70 0|0:0.200:-0.20,-0.45,-2.12 0|1:0.950:-2.54,-0.00,-5.00 0|1:1.000:-5.00,0.00,-5.00 0|0:0.000:-0.00,-2.09,-5.00 0|1:0.750:-0.63,-0.12,-5.00 0|0:0.000:-0.07,-0.84,-5.00 0|0:0.350:-0.26,-0.34,-5.00 0|1:1.000:-4.22,-0.00,-5.00 0|0:0.150:-0.31,-0.41,-0.92 0|0:0.200:-0.20,-0.45,-2.02 0|0:0.150:-0.12,-0.61,-3.27 0|0:0.000:-0.01,-1.49,-5.00 0|0:0.400:-0.48,-0.48,-0.48 0|0:0.250:-0.48,-0.48,-0.48 0|1:1.000:-1.93,-0.03,-1.29 0|0:0.050:-0.11,-0.65,-3.80 0|0:0.300:-0.16,-0.52,-5.00 0|0:0.000:-0.05,-1.01,-5.00 0|0:0.500:-0.48,-0.48,-0.48 0|0:0.450:-0.22,-0.40,-3.92 0|0:0.050:-0.04,-1.03,-4.40 1|0:1.000:-3.55,-0.00,-5.00 0|0:0.250:-0.21,-0.46,-1.39 0|0:0.250:-0.22,-0.46,-1.31 0|0:0.200:-0.19,-0.45,-2.11 0|0:0.050:-0.130463,-0.587405,-3.04576 0|0:0.150:-0.20,-0.44,-2.36 0|0:0.300:-0.20,-0.46,-1.76 0|0:0.100:-0.03,-1.18,-5.00 1|0:1.000:-4.40,-0.00,-2.95 0|0:0.300:-0.48,-0.48,-0.48 0|0:0.300:-0.48,-0.48,-0.48 0|0:0.400:-0.48,-0.48,-0.48 0|1:1.250:-4.70,-0.62,-0.12 0|0:0.000:-0.0146544,-1.47912,-5 0|0:0.100:-0.11,-0.66,-3.85 0|0:0.100:-0.05,-0.95,-5.00 0|0:0.200:-0.38,-0.43,-0.67 0|0:0.300:-0.48,-0.48,-0.48 0|0:0.350:-0.20,-0.45,-2.02 1|0:1.000:-4.00,-0.02,-1.37 1|0:0.650:-0.86,-0.06,-5.00 0|0:0.000:-0.00,-2.05,-5.00 0|0:0.000:-0.02,-1.46,-5.00 0|0:0.150:-0.48,-0.48,-0.48 0|0:0.000:-0.00528701,-1.91721,-5 0|0:0.100:-0.09,-0.74,-5.00 0|1:0.700:-0.80,-0.07,-4.70 0|1:0.850:-1.28,-0.02,-2.69 0|0:0.300:-0.48,-0.48,-0.48 0|0:0.300:-0.48,-0.48,-0.48 0|0:0.050:-0.03,-1.24,-5.00 0|0:0.300:-0.21,-0.44,-1.66 0|0:0.150:-0.21,-0.46,-1.51 1|0:0.900:-1.40,-0.38,-0.26 0|0:0.700:-0.48,-0.48,-0.48 0|0:0.150:-0.48,-0.48,-0.48 0|0:0.100:-0.15,-0.54,-2.92 0|0:0.150:-0.23,-0.42,-1.56 0|0:0.000:-0.01,-1.61,-5.00 1|0:0.750:-1.07,-0.04,-4.10 0|0:0.350:-0.67,-0.10,-5.00 0|1:1.000:-2.36,-0.00,-4.70 0|1:1.000:-3.52,-0.00,-3.10 0|0:0.100:-0.08,-0.78,-5.00 0|0:0.000:-0.00338326,-2.11014,-5 0|0:0.100:-0.18,-0.48,-2.37 0|0:0.000:-0.04,-1.01,-5.00 0|0:0.100:-0.05,-0.94,-5.00 0|0:0.050:-0.06,-0.90,-3.74 0|0:0.250:-0.32,-0.28,-5.00 0|0:0.250:-0.10,-0.70,-2.78 0|0:0.550:-0.48,-0.48,-0.48 0|0:0.050:-0.13,-0.59,-2.97 1 13327 rs144762171 G . 100 PASS AA=.;AC=0;AF=0.03;AFR_AF=0.02;AMR_AF=0.03;AN=170;ASN_AF=0.02;AVGPOST=0.9698;ERATE=0.0012;EUR_AF=0.04;LDAF=0.0359;RSQ=0.6482;SNPSOURCE=LOWCOV;THETA=0.0204;VT=SNP GT:DS:GL 0|0:0.100:-0.48,-0.48,-0.48 0|0:0.000:-0.00,-3.07,-5.00 0|0:0.000:-0.10,-0.68,-4.70 0|0:0.050:-0.21,-0.45,-1.67 0|0:0.050:-0.48,-0.48,-0.48 0|0:0.000:-0.00,-3.36,-5.00 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L۫L裯oo>^]xk}gnϸnׯg7[Snke߽,5w˖^-szjit˖1̈́^-ր ?tgjY-y[&sd2W[빖3y*e*<2[f\s\Ce( ղϖܲ2k32k1ne@ݲ[tM|2kK-]c!]Khko<[˸Dk5Ik| 9[eM2=.켺o߳,21]++e0=[jkIm 5HFkYjٚ56khd]ּdVֶdT5*Y5'[wBCVariantAnnotation/inst/extdata/structural.vcf0000644000175100017510000000576414614305321022563 0ustar00biocbuildbiocbuild##fileformat=VCFv4.1 ##fileDate=20100501 ##reference=1000GenomesPilot-NCBI36 ##assembly=ftp://ftp-trace.ncbi.nih.gov/1000genomes/ftp/release/sv/breakpoint_assemblies.fasta ##INFO= ##INFO= ##INFO= ##INFO= ##INFO= ##INFO= ##INFO= ##INFO= ##INFO= ##INFO= ##ALT= ##ALT= ##ALT= ##ALT= ##ALT= ##ALT= ##ALT= ##ALT= ##ALT= ##ALT= ##FORMAT= ##FORMAT= ##FORMAT= ##FORMAT= #CHROM POS ID REF ALT QUAL FILTER INFO FORMAT NA00001 1 13220 . T 6 PASS IMPRECISE;SVTYPE=DEL;END=13221;SVLEN=-105;CIPOS=-56,20;CIEND=-10,62 GT:GQ 0/1:12 1 2827693 . CCGTGGATGCGGGGACCCGCATCCCCTCTCCCTTCACAGCTGAGTGACCCACATCCCCTCTCCCCTCGCA C . PASS SVTYPE=DEL;END=2827680;BKPTID=Pindel_LCS_D1099159;HOMLEN=1;HOMSEQ=C;SVLEN=-66 GT:GQ 1/1:13.9 2 321682 . T 6 PASS IMPRECISE;SVTYPE=DEL;END=321887;SVLEN=-105;CIPOS=-56,20;CIEND=-10,62 GT:GQ 0/1:12 2 14477084 . C 12 PASS IMPRECISE;SVTYPE=DEL;END=14477381;SVLEN=-297;MEINFO=AluYa5,5,307,+;CIPOS=-22,18;CIEND=-12,32 GT:GQ 0/1:12 3 9425916 . C 23 PASS IMPRECISE;SVTYPE=INS;END=9425916;SVLEN=6027;CIPOS=-16,22;MEINFO=L1HS,1,6025,- GT:GQ 1/1:15 3 12665100 . A 14 PASS IMPRECISE;SVTYPE=DUP;END=12686200;SVLEN=21100;CIPOS=-500,500;CIEND=-500,500 GT:GQ:CN:CNQ ./.:0:3:16.2 4 18665128 . T 11 PASS IMPRECISE;SVTYPE=DUP;END=18665204;SVLEN=76;CIPOS=-10,10;CIEND=-10,10 GT:GQ:CN:CNQ ./.:0:5:8.3 VariantAnnotation/inst/scripts/0000755000175100017510000000000014614305321017674 5ustar00biocbuildbiocbuildVariantAnnotation/inst/scripts/test_Rplinkseq.R0000644000175100017510000002524214614305321023033 0ustar00biocbuildbiocbuild### ====================================================================== ### Testing Rplinkseq and VariantAnnotation ### ====================================================================== ### ### February 2014 ### 'rhino03' Ubuntu server, 387 Gb RAM ### 16 processors with the following configuration: ### ### vendor_id : GenuineIntel ### cpu family : 6 ### model : 45 ### model name : Intel(R) Xeon(R) CPU E5-2690 0 @ 2.90GHz ### stepping : 7 ### microcode : 0x70d ### cpu MHz : 2900.142 ### cache size : 20480 KB ### physical id : 1 ### siblings : 8 ### core id : 0 ### cpu cores : 8 ### apicid : 32 ### initial apicid : 32 ### fpu : yes ### fpu_exception : yes ### cpuid level : 13 ### wp : yes ### flags : fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov ### pat pse36 clflush dts acpi mmx fxsr sse sse2 ss ht tm pbe syscall nx pdpe1gb ### rdtscp lm constant_tsc arch_perfmon pebs bts rep_good nopl xtopology nonstop_tsc ### aperfmperf pni pclmulqdq dtes64 monitor ds_cpl vmx smx est tm2 ssse3 cx16 xtpr ### pdcm pcid dca sse4_1 sse4_2 x2apic popcnt tsc_deadline_timer aes xsave avx ### lahf_lm ida arat xsaveopt pln pts dtherm tpr_shadow vnmi flexpriority ept vpid ### bogomips : 5799.87 ### clflush size : 64 ### cache_alignment : 64 ### address sizes : 46 bits physical, 48 bits virtual ### power management: ### -------------------------------------------------------------------------- ### Objectives ### -------------------------------------------------------------------------- ### Compare runtimes between Rplinkseq and VariantAnnotation packages. ### ### ### Test functions: ### ### load.vcf: Data are loaded from a vcf file into a list of lists ### and accessed with x.consensus* functions. (Rplinkseq) ### ### var.fetch: Data are loaded from a PLINK/Seq 'project' into a list of ### lists and accessed with x.consensus* functions. (Rplinkseq) ### ### meta.fetch: Data are loaded from a PLINK/Seq 'project' and parsed into ### a data.frame. (Rplinkseq) ### ### var.iterate: Applies a function to data from a PLINK/Seq 'project' ### while iterating. Result of function is returned. ### (Rplinkseq) ### ### scanVcf: Data are loaded from a vcf file. Info and geno fields are ### parsed into a list of lists; other 'core' fields are ### returned as a GRAnges object. (VariantAnnotation) ### ### ### Test cases: ### ### Test I: Query by range. ### Import data by randomly chosen genomic range. ### (Functions: load.vcf, var.fetch, scanVcf) ### ### Test II: Query by range and fields. ### Import data by genomic range and 4 randomly ### chosen info (2) and geno (2) fields. ### (Functions: meta.fetch, scanVcf) ### ### Test III. Iterate through file. ### A simple function is applied to all records in the ### file in an iterative fashion. ### (Functions: var.iterate, scanVcf) ### ### -------------------------------------------------------------------------- ### Data ### -------------------------------------------------------------------------- ### Test file: ### Size on disk: 1.8G compressed ### Contents: 494328 variants, 1092 samples, 22 INFO, and 3 GENO fields ### Download location: ### ftp://ftp-trace.ncbi.nih.gov/1000genomes/ftp/release/20110521/ALL.chr22.phase1_release_v3.20101123.snps_indels_svs.genotypes.vcf.gz ### ### Test subset: ### An arbitrary subset of data was chosen for testing because the full file ### requires >250G in memory. The subset range is 2e7 to 2.5e7 and contains ### 63088 records. This subset is the 'range' in the mask and param objects. ### ### PLINK/Seq 'project': ### From the raw vcf on disk we creted a PLINK/Seq 'project' so we could use ### the var.iterate function. A 'project' creates compressed, indexed, SQLite ### database from input files. This one took ~30 minutes to create. ### pseq proj new-project ### pseq proj load-vcf --vcf 'ALL.chr22.phase1_release_v3.20101123.snps_indels_svs.genotypes.vcf.gz' ### -------------------------------------------------------------------------- ### Set up ### -------------------------------------------------------------------------- library(microbenchmark) library(VariantAnnotation) library(Rplinkseq) ## version 0.08 fl <- "/loc/no-backup/vobencha/ALL.chr22.phase1_release_v3.20101123.snps_indels_svs.genotypes.vcf.gz" ### Attach the 'project' to the R session: pseq.project("proj") ### -------------------------------------------------------------------------- ### Test I. Query by range ### -------------------------------------------------------------------------- ### load.vcf: mask <- "reg=22:20000000..25000000" loadvcf_1 <- function() load.vcf(fl, mask=mask, limit=200000) ### var.fetch: varfetch_1 <- function() var.fetch(mask=mask, limit=200000) ### scanVcf: tfile <- TabixFile(fl) which <- GRanges("22", IRanges(2e7, 2.5e7)) param <- ScanVcfParam(which=which) scanvcf_1 <- function() scanVcf(tfile, param=param) micro <- microbenchmark(loadvcf_1(), varfetch_1(), scanvcf_1(), times=5) micro ###Unit: seconds ### expr min lq median uq max neval ### loadvcf_1() 313.1729 335.5738 359.7979 368.4054 369.9397 5 ### varfetch_1() 264.4101 283.2388 291.7533 305.7028 318.7879 5 ### scanvcf_1() 300.5585 308.5507 359.0814 400.2049 678.2296 5 ### -------------------------------------------------------------------------- ### Test II. Query by range and fields ### -------------------------------------------------------------------------- ### Randomly select 2 INFO and 2 GENO fields: info_var <- c("RSQ", "THETA") geno_var <- c("GT", "DS") ### Other fields parsed by scanVcf: other_var <- c("CHROM", "POS", "ID", "REF", "ALT", "QUAL", "FILTER") ### load.vcf: To the best of our knowledge, this function can import ### by range but not by select fields. To isolate a field, ### all fields are read in and parsed with the x.consensus ### functions. There is no equivalent comparision for ### load.vcf in this test case. ### meta.fetch: mask <- "reg=22:20000000..25000000" metafetch_2 <- function() meta.fetch(c(info_var, geno_var, other_var), mask=mask) ### scanVcf: NOTE: Fields in 'other_var' are imported and parsed by ### scanVcf() under names of 'rowRanges' and 'fixed'. tfile <- TabixFile(fl) which <- GRanges("22", IRanges(2e7, 2.5e7)) param <- ScanVcfParam(which=which, info=info_var, geno=geno_var) scanvcf_2 <- function() scanVcf(tfile, param=param) micro <- microbenchmark(metafetch_2(), scanvcf_2(), times=5) micro ###Unit: seconds ### expr min lq median uq max neval ### metafetch_2() 118.14090 120.65009 120.87346 120.88615 121.13340 5 ### scanvcf_2() 35.45512 35.46179 35.51305 35.59804 37.41809 5 ### -------------------------------------------------------------------------- ### Test III. Iterate through file ### -------------------------------------------------------------------------- ### load.vcf: To the best of our knowledge Rplinkseq cannot iterate on ### raw vcf files. Iteration must be done on a 'project' with ### var.iterate. There is no equivalent comparision for load.vcf ### in this test case. ### var.iterate: simple_ct <- function(v) ct <<- ct + length(v$ID) variterate_3 <- function() { ct <<- 0 var.iterate(simple_ct) } ### scanVcf: tfile <- TabixFile(fl, yieldSize=10000) param <- ScanVcfParam(fixed=c("ALT"), info=NA, geno=NA) scanvcf_3 <- function() { ct2 <<- 0 open(tfile) while((len <- length(scanVcf(tfile, param=param)[[1]]$rowRanges)) > 0) ct2 <<- ct2 + len close(tfile) } micro <- microbenchmark(variterate_3(), scanvcf_3(), times=5) micro ##Unit: seconds ## expr min lq median uq max neval ## variterate_3() 1552.88520 1576.55856 1583.13120 1585.65281 1591.96375 5 ## scanvcf_3() 50.04566 50.06945 50.27194 50.56145 50.70112 5 ### -------------------------------------------------------------------------- ### Scaling with number of variants and samples (scanVcf only) ### -------------------------------------------------------------------------- ### Linear scaling by variants: tfile <- TabixFile(fl) yieldSize <- c(100000, 200000, 300000, 400000, 500000) param <- ScanVcfParam(info=NA, geno=NA) fun0 <- function(tfile, param) scanVcf(tfile, param=param) res <- lapply(yieldSize, function(i) { tf <- TabixFile(fl, yieldSize=i) microbenchmark(fun0(tf, param), times=5)}) res ### [[1]] ### Unit: seconds ### expr min lq median uq max neval ### fun0(tf, param) 9.883719 10.13574 10.15165 10.17318 10.70124 5 ### ### [[2]] ### Unit: seconds ### expr min lq median uq max neval ### fun0(tf, param) 19.385 19.6715 19.71968 19.72206 20.23362 5 ### ### [[3]] ### Unit: seconds ### expr min lq median uq max neval ### fun0(tf, param) 29.24268 29.33945 29.36927 29.38207 30.01012 5 ### ### [[4]] ### Unit: seconds ### expr min lq median uq max neval ### fun0(tf, param) 38.71675 38.73608 38.77965 38.8696 39.27382 5 ### ### [[5]] ### Unit: seconds ### expr min lq median uq max neval ### fun0(tf, param) 47.94605 48.76884 49.03272 49.03859 49.08618 5 ### Linear scaling by sample: tf <- TabixFile(fl) ids <- samples(scanVcfHeader(fl)) sampleSize <- c(200, 400, 600, 800, 1000) which <- GRanges("22", IRanges(2e7, 2.5e7)) ## 63088 records fun0 <- function(tf, param) scanVcf(tf, param=param) res <- lapply(sampleSize, function(i) { param <- ScanVcfParam(which=which, samples=ids[1:i]) microbenchmark(fun0(tf, param), times=5)}) res ### [[1]] ### Unit: seconds ### expr min lq median uq max neval ### fun0(tf, param) 61.80377 61.91141 69.62993 70.10218 103.1949 5 ### ### [[2]] ### Unit: seconds ### expr min lq median uq max neval ### fun0(tf, param) 128.3595 132.1819 135.7386 135.966 150.7588 5 ### ### [[3]] ### Unit: seconds ### expr min lq median uq max neval ### fun0(tf, param) 204.0067 211.0546 219.2069 232.3388 249.188 5 ### ### [[4]] ### Unit: seconds ### expr min lq median uq max neval ### fun0(tf, param) 266.7383 269.8461 273.7986 299.7805 330.6505 5 ### ### [[5]] ### Unit: seconds ### expr min lq median uq max neval ### fun0(tf, param) 329.6348 337.1841 348.7968 359.5972 364.2781 5 VariantAnnotation/inst/unitTests/0000755000175100017510000000000014614305321020207 5ustar00biocbuildbiocbuildVariantAnnotation/inst/unitTests/cases/0000755000175100017510000000000014614305321021305 5ustar00biocbuildbiocbuildVariantAnnotation/inst/unitTests/cases/banded_gvcf.vcf0000644000175100017510000000716414614305321024237 0ustar00biocbuildbiocbuild##fileformat=VCFv4.1 ##ALT= ##FILTER= ##FORMAT= ##FORMAT= ##FORMAT= ##FORMAT= ##FORMAT= ##FORMAT= ##FORMAT= ##GVCFBlock=minGQ=0(inclusive),maxGQ=5(exclusive) ##GVCFBlock=minGQ=20(inclusive),maxGQ=60(exclusive) ##GVCFBlock=minGQ=5(inclusive),maxGQ=20(exclusive) ##GVCFBlock=minGQ=60(inclusive),maxGQ=2147483647(exclusive) ##INFO= ##INFO= ##INFO= ##INFO= ##INFO= ##INFO= ##INFO= ##INFO= ##INFO= ##INFO= ##INFO= ##INFO= ##INFO= ##contig= ##reference=file:///humgen/1kg/reference/human_g1k_v37.fasta #CHROM POS ID REF ALT QUAL FILTER INFO FORMAT NA12878 20 10000000 . T . . END=10000116 GT:DP:GQ:MIN_DP:PL 0/0:44:99:38:0,89,1385 20 10000117 . C T, 612.77 . BaseQRankSum=0.000;ClippingRankSum=-0.411;DP=38;MLEAC=1,0;MLEAF=0.500,0.00;MQ=221.39;MQ0=0;MQRankSum=-2.172;ReadPosRankSum=-0.235 GT:AD:DP:GQ:PL:SB 0/1:17,21,0:38:99:641,0,456,691,519,1210:6,11,11,10 20 10000118 . T . . END=10000210 GT:DP:GQ:MIN_DP:PL 0/0:42:99:38:0,80,1314 20 10000211 . C T, 638.77 . BaseQRankSum=0.894;ClippingRankSum=-1.927;DP=42;MLEAC=1,0;MLEAF=0.500,0.00;MQ=221.89;MQ0=0;MQRankSum=-1.750;ReadPosRankSum=1.549 GT:AD:DP:GQ:PL:SB 0/1:20,22,0:42:99:667,0,566,728,632,1360:9,11,12,10 20 10000212 . A . . 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AR:RR:DP:AAP:RAP 2:21:23:1:1 VariantAnnotation/inst/unitTests/cases/fewer-FORMAT-than-GENO.vcf0000644000175100017510000000114714614305321025564 0ustar00biocbuildbiocbuild##fileformat=VCFv4.1 ##source=VariantTools 1.3.2 ##phasing=unphased ##project=example ##fileDate=20130419 ##FORMAT= ##FORMAT= ##FORMAT= ##FORMAT= #CHROM POS ID REF ALT QUAL FILTER INFO FORMAT H1993 TP53 1011392 . C G . . . AD:DP:AP 41,540:582:1,1 TP53 1012027 . T C . . . AD:DP:AP 0,2:2:0:1 TP53 1011392 . C G . . . AD:DP:AP 41,540:582:1,1 VariantAnnotation/inst/unitTests/cases/FORMAT_header_no_SAMPLEs.vcf0000644000175100017510000000113214614305321026222 0ustar00biocbuildbiocbuild##fileformat=VCFv4.1 ##INFO= ##ALT= ##FORMAT= ##FORMAT= ##FORMAT= ##reference=GRCh37 #CHROM POS ID REF ALT QUAL FILTER INFO 1 10523 . TCCG T 62 PASS AN=2184 1 10583 rs58108140 G A 100 PASS AN=2184 1 10611 rs189107123 C G 100 PASS AN=2184 1 10616 . CCGCCGTTGCAAAGGCGCGCCG C 172 PASS AN=2184 1 11540 . T TA 30 PASS AN=2184 VariantAnnotation/inst/unitTests/cases/meta_header.vcf0000644000175100017510000000250014614305321024240 0ustar00biocbuildbiocbuild##fileformat=VCFv4.3 ##Tassel= ##FORMAT= ##FORMAT= ##FORMAT= ##FORMAT= ##FORMAT= ##INFO= ##INFO= ##INFO= ##META= ##META= ##SAMPLE= ##SAMPLE= #CHROM POS ID REF ALT QUAL FILTER INFO FORMAT Sam1 Sam2 Chr01 1000 S01_1000 G A 100 PASS DP=20 GT:AD 0/0:10,0 0/1:5,5 VariantAnnotation/inst/unitTests/cases/missing-FORMAT-metadata-elt.vcf0000644000175100017510000000105214614305321027002 0ustar00biocbuildbiocbuild##fileformat=VCFv4.1 ##source=VariantTools 1.3.2 ##phasing=unphased ##project=example ##fileDate=20130419 ##FORMAT= ##FORMAT= ##FORMAT= #CHROM POS ID REF ALT QUAL FILTER INFO FORMAT H1993 TP53 1011392 . C G . . . AD:DP:AP 41,540:582:1,1 TP53 1012027 . T C . . . AD:DP:XP 0,2:2:1,1 TP53 1011392 . C G . . . AD:DP:AP 41,540:582:1,1 VariantAnnotation/inst/unitTests/cases/mixedStructural.vcf0000644000175100017510000000563214614305321025212 0ustar00biocbuildbiocbuild##fileformat=VCFv4.1 ##fileDate=20100501 ##reference=1000GenomesPilot-NCBI36 ##assembly=ftp://ftp-trace.ncbi.nih.gov/1000genomes/ftp/release/sv/breakpoint_assemblies.fasta ##INFO= ##INFO= ##INFO= ##INFO= ##INFO= ##INFO= ##INFO= ##INFO= ##INFO= ##INFO= ##ALT= ##ALT= ##ALT= ##ALT= ##ALT= ##ALT= ##ALT= ##ALT= ##ALT= ##ALT= ##FORMAT= ##FORMAT= ##FORMAT= ##FORMAT= #CHROM POS ID REF ALT QUAL FILTER INFO FORMAT NA00001 1 13220 . T 6 PASS IMPRECISE;SVTYPE=DEL;END=13221;SVLEN=-105;CIPOS=-56,20;CIEND=-10,62 GT:GQ 0/1:12 1 2827693 . C G,T . PASS BKPTID=Pindel_LCS_D1099159;HOMLEN=1;HOMSEQ=C;SVLEN=-66 GT:GQ 1/1:13.9 2 321682 . T 6 PASS IMPRECISE;SVTYPE=DEL;END=321887;SVLEN=-105;CIPOS=-56,20;CIEND=-10,62 GT:GQ 0/1:12 2 14477084 . C 12 PASS IMPRECISE;SVTYPE=DEL;END=14477381;SVLEN=-297;MEINFO=AluYa5,5,307,+;CIPOS=-22,18;CIEND=-12,32 GT:GQ 0/1:12 3 9425916 . C 23 PASS IMPRECISE;SVTYPE=INS;END=9425916;SVLEN=6027;CIPOS=-16,22;MEINFO=L1HS,1,6025,- GT:GQ 1/1:15 3 12665100 . A 14 PASS IMPRECISE;SVTYPE=DUP;END=12686200;SVLEN=21100;CIPOS=-500,500;CIEND=-500,500 GT:GQ:CN:CNQ ./.:0:3:16.2 4 18665128 . T 11 PASS IMPRECISE;SVTYPE=DUP;END=18665204;SVLEN=76;CIPOS=-10,10;CIEND=-10,10 GT:GQ:CN:CNQ ./.:0:5:8.3 VariantAnnotation/inst/unitTests/cases/multiple_INFO_fields.vcf0000644000175100017510000000066714614305321026012 0ustar00biocbuildbiocbuild##fileformat=VCFv4.1 ##fileDate=20141205 ##INFO= ##INFO= ##contig= #CHROM POS ID REF ALT QUAL FILTER INFO 17 10001541 rs12451372 C G,T 100 PASS AF=0.139776,0.0595048;NS=2504 17 10001547 17:10001547_G/A G A 100 PASS AF=0.000199681;NS=2504 VariantAnnotation/inst/unitTests/cases/negative_FORMAT_Number.vcf0000644000175100017510000000574414614305321026201 0ustar00biocbuildbiocbuild##fileformat=VCFv4.1 ##samtoolsVersion=0.1.17 (r973:277) ##INFO= ##INFO= ##INFO= ##INFO= ##INFO= ##INFO= ##INFO= ##INFO= ##INFO= ##INFO= ##INFO= ##INFO= ##INFO= ##INFO= ##INFO= ##INFO= ##INFO= ##FORMAT= ##FORMAT= ##FORMAT= ##FORMAT= ##FORMAT= ##FORMAT= #CHROM POS ID REF ALT QUAL FILTER INFO FORMAT test chr1 32386425 . T C 24 . DP=3;AF1=1;AC1=2;DP4=0,0,0,3;MQ=50;FQ=-36 GT:PL:GQ 1/1:56,9,0:15 chr1 32507666 . G T 6.2 . DP=5;AF1=0.4999;AC1=1;DP4=3,0,2,0;MQ=50;FQ=8.65;PV4=1,0.062,1,0.36 GT:PL:GQ 0/1:35,0,78:36 chr1 32524459 . A C 3.54 . DP=5;AF1=0.4998;AC1=1;DP4=1,2,0,2;MQ=50;FQ=5.47;PV4=1,0.0021,1,1 GT:PL:GQ 0/1:31,0,98:30 chr1 32622505 . G A 101 . DP=18;AF1=0.5;AC1=1;DP4=10,0,5,2;MQ=50;FQ=104;PV4=0.15,0.0055,1,0.0075 GT:PL:GQ 0/1:131,0,162:99 chr12 25357574 . CAA C 109 . INDEL;DP=5;AF1=1;AC1=2;DP4=0,0,4,0;MQ=50;FQ=-46.5 GT:PL:GQ 1/1:149,12,0:21 VariantAnnotation/inst/unitTests/cases/no_FORMAT_column.vcf0000644000175100017510000000043714614305321025052 0ustar00biocbuildbiocbuild##fileformat=VCFv4.1 ##fileDate=20111001 ##INFO= #CHROM POS ID REF ALT QUAL FILTER INFO 1 2020003 . G A . . DP=10 1 2158344 . C T . . DP=10 1 2179799 . C T . . DP=20 1 2229439 . C G . . DP=20 1 2229827 . G A . . DP=10 VariantAnnotation/inst/unitTests/cases/no_GENO_row.vcf0000644000175100017510000000107614614305321024124 0ustar00biocbuildbiocbuild##fileformat=VCFv4.1 ##source=VariantTools 1.3.2 ##phasing=unphased ##project=example ##fileDate=20130419 ##FORMAT= ##FORMAT= ##FORMAT= #CHROM POS ID REF ALT QUAL FILTER INFO FORMAT H1993 H1994 TP53 1011392 . C G . . . AD:DP:AP 41,540:582:1,1 41,540:583:1,1 TP53 1012027 . T C . . . TP53 1011392 . C G . . . AD:DP:AP 41,540:584:1,1 41,540:585:1,1 VariantAnnotation/inst/unitTests/cases/no_INFO_header.vcf0000644000175100017510000000045414614305321024547 0ustar00biocbuildbiocbuild##fileformat=VCFv4.1 ##fileDate=20111001 ##FORMAT= #CHROM POS ID REF ALT QUAL FILTER INFO FORMAT 587338 1 2020003 . G A . . . GT ./. 1 2158344 . C T . . . GT ./. 1 2179799 . C T . . . GT ./. 1 2229439 . C G . . . GT ./. 1 2229827 . G A . . . GT ./. VariantAnnotation/inst/unitTests/cases/unspecified_INFO_FORMAT_fields.vcf0000644000175100017510000000321714614305321027517 0ustar00biocbuildbiocbuild##fileformat=VCFv4.1 ##fileDate=20090805 ##source=myImputationProgramV3.1 ##reference=file:///seq/references/1000GenomesPilot-NCBI36.fasta ##contig= ##phasing=partial ##INFO= ##INFO= ##INFO= ##INFO= ##INFO= ##INFO= ##FILTER= ##FILTER= ##FORMAT= ##FORMAT= ##FORMAT= ##FORMAT= #CHROM POS ID REF ALT QUAL FILTER INFO FORMAT NA00001 NA00002 NA00003 20 14370 rs6054257 G A 29 PASS NS=3;DP=14;AF=0.5;DB;H2 GT:GQ:DP:HQ 0|0:48:1:51,51 1|0:48:8:51,51 1/1:43:5:.,. 20 17330 . T A 3 q10 NS=3;XX;DP=11;AF=0.017 GT:GQ:DP:HQ 0|0:49:3:58,50 0|1:3:5:65,3 0/0:41:3 20 1110696 rs6040355 A G,T 67 PASS NS=2;DP=10;XX=1,2;AF=0.333,0.667;AA=T;DB GT:GQ:DP:HQ 1|2:21:6:23,27 2|1:2:0:18,2 2/2:35:4 20 1230237 . T . 47 PASS NS=3;DP=13;AA=T GT:GQ:DP:YY:HQ 0|0:54:7:0:56,60 0|0:48:4:0:51,51 0/0:61:2:0 20 1234567 microsat1 GTC G,GTCT 50 PASS NS=3;DP=9;AA=G YY:GT:GQ:DP 0/0:0/1:35:4 0/0:0/2:17:2 0/0:1/1:40:3 VariantAnnotation/inst/unitTests/cases/VarScan_header.vcf0000644000175100017510000000600214614305321024650 0ustar00biocbuildbiocbuild##fileformat=VCFv4.1 ##source=VarScan2 ##INFO= ##INFO= ##INFO= ##INFO= ##INFO= ##INFO= ##FILTER= ##FILTER= ##FORMAT= ##FORMAT= ##FORMAT= ##FORMAT= ##FORMAT= ##FORMAT= ##SnpEffVersion="2.0.5 (build 2011-12-24), by Pablo Cingolani" ##SnpEffCmd="SnpEff eff GRCh37.64 -c /home/adminrig/src/snpeff/snpEff_2_0_5/snpEff.config -i vcf -o vcf -v 48vcf.snp.somatic.vcf " ##INFO= #CHROM POS ID REF ALT QUAL FILTER INFO FORMAT NORMAL TUMOR chr1 1574019 . A G 0.0 PASS DP=27;SOMATIC;SS=2;SSC=14;GPV=1E0;SPV=3.7681E-2;EFF=DOWNSTREAM(MODIFIER||||MMP23B|protein_coding|CODING|ENST00000356026|),TRANSCRIPT(MODIFIER||||AL691432.2|unprocessed_pseudogene|NON_CODING|ENST00000317673|),SPLICE_SITE_ACCEPTOR(HIGH||||WASH2P|unprocessed_pseudogene|NON_CODING|ENST00000542901|),UTR_5_PRIME(MODIFIER||||ARHGEF16|protein_coding|CODING|ENST00000378371|exon_1_3383535_3383901),NON_SYNONYMOUS_CODING(MODERATE|MISSENSE|Cgg/Tgg|R10W|CYP4B1|processed_transcript|CODING|ENST00000468637|exon_1_47279154_47279278) GT:GQ:DP:RD:AD:FREQ 0/0:.:12:11:1:8.33% 0/1:.:15:8:7:46.67% chr1 1580738 . T A 0.0 PASS DP=76;SOMATIC;SS=2;SSC=14;GPV=1E0;SPV=3.1816E-2;EFF=TRANSCRIPT(MODIFIER||||AL691432.2|unprocessed_pseudogene|NON_CODING|ENST00000317673|),TRANSCRIPT(MODIFIER||||AL691432.2|unprocessed_pseudogene|NON_CODING|ENST00000340677|),TRANSCRIPT(MODIFIER||||AL691432.2|unprocessed_pseudogene|NON_CODING|ENST00000341832|),TRANSCRIPT(MODIFIER||||AL691432.2|unprocessed_pseudogene|NON_CODING|ENST00000407249|),TRANSCRIPT(MODIFIER||||AL691432.2|unprocessed_pseudogene|NON_CODING|ENST00000513088|) GT:GQ:DP:RD:AD:FREQ 1/1:.:25:15:4:21.05% 0/1:.:51:25:24:48.98% VariantAnnotation/inst/unitTests/test_expand-methods.R0000644000175100017510000000552314614305321024316 0ustar00biocbuildbiocbuildexpand <- VariantAnnotation::expand test_expand_info_geno <- function() { fl <- system.file("unitTests", "cases", "expand.vcf", package="VariantAnnotation") vcf <- suppressWarnings(readVcf(fl, "hg19")) exp <- expand(vcf) names(mcols(rowRanges(exp))) cnms <- c("paramRangeID", "REF", "ALT", "QUAL", "FILTER") checkIdentical(cnms, names(mcols(rowRanges(exp)))) checkTrue(nrow(exp) == 22L) checkIdentical(info(exp)$DP[5:6], c(10L, 10L)) checkIdentical(info(exp)$AF[5:6], c(0.333, 0.667)) checkIdentical(geno(exp)$AAP[6:11], c(2L, rep(1L, 4L), 3L)) } test_expand_structural <- function() { fl <- system.file("extdata", "structural.vcf", package="VariantAnnotation") vcf <- readVcf(fl, "hg19") exp <- expand(vcf) checkTrue(nrow(vcf) == nrow(exp)) checkTrue(ncol(vcf) == ncol(exp)) checkTrue(is.character(alt(exp))) } test_expand_structural <- function() { fl <- system.file("unitTests", "cases", "mixedStructural.vcf", package="VariantAnnotation") vcf <- readVcf(fl, "hg19") exp <- expand(vcf) checkTrue(nrow(exp) == 8L) } test_expand_multiple_info <- function() { fl <- system.file("unitTests", "cases", "multiple_INFO_fields.vcf", package="VariantAnnotation") vcf <- readVcf(fl, "GRCh37") exp <- expand(vcf) checkTrue(nrow(exp) == 3L) ## single row, subset of INFO fields selected exp <- expand(vcf[1,]) checkTrue(nrow(exp) == 2L) ## single row, single (only) INFO field selected vcf <- readVcf(fl, "GRCh37", param=ScanVcfParam(info="AF")) exp <- expand(vcf[1,]) checkTrue(nrow(exp) == 2L) } test_expand_gvcf <- function() { fl <- system.file("unitTests", "cases", "banded_gvcf.vcf", package="VariantAnnotation") vcf <- suppressWarnings(readVcf(fl, "")) exp <- expand(vcf) checkIdentical(dim(exp), c(7L, 1L)) checkIdentical(dim(geno(exp)$AD), c(7L, 1L, 2L)) checkIdentical(geno(exp)$AD[,,1], as.integer(c(NA, 17, 17, NA, 20, 20, NA))) } test_expand_adr_adf <- function() { fl <- system.file("unitTests", "cases", "ex1-seq1-90.vcf", package = "VariantAnnotation") vcf <- readVcf(fl, genome = "") exp <- expand(vcf) seqName <- "seq1:90_N/G";sampleName <- colnames(vcf)[1] # for all/forward/revert strands tmp <- sapply(c("AD", "ADF", "ADR"), function(var){ # original counts countsVcf <- geno(vcf)[[var]][seqName, sampleName][[1]] # in VCF: counts reported first for reference, then alternative alleles # check counts for reference allele checkTrue(all(geno(exp)[[var]][, sampleName, 1] == countsVcf[1])) # check if counts for alternative alleles idxMatch <- match(alt(vcf)[[1]], alt(exp)) countsExpand <- geno(exp)[[var]][idxMatch, sampleName, 2] checkIdentical(countsExpand, countsVcf[-1]) }) } VariantAnnotation/inst/unitTests/test_filterVcf.R0000644000175100017510000001263514614305321023324 0ustar00biocbuildbiocbuildquiet <- suppressMessages test_filterVcf_TabixFile <- function() { fl <- system.file("extdata", "chr22.vcf.gz", package="VariantAnnotation") tbx <- TabixFile(fl, yieldSize=5000) dest <- tempfile() filt <- FilterRules(list(fun=function(...) TRUE)) ans <- quiet(filterVcf(tbx, "hg19", dest, filters=filt)) checkIdentical(dest, ans) vcf0 <- readVcf(fl, "hg19") vcf1 <- readVcf(dest, "hg19") checkIdentical(dim(vcf0), dim(vcf1)) ## with ranges param <- ScanVcfParam(which=GRanges("22", IRanges(50301340, width=10000))) ans1 <- quiet(filterVcf(tbx, "", tempfile(), filters=filt, param=param)) ans2 <- quiet(filterVcf(fl, "", tempfile(), filters=filt, param=param)) vcf1 <- readVcf(ans1, "") vcf2 <- readVcf(ans2, "") checkIdentical(rowRanges(vcf1), rowRanges(vcf2)) } test_filterVcf_filter <- function() { fl <- system.file("extdata", "chr22.vcf.gz", package="VariantAnnotation") tbx <- TabixFile(fl, yieldSize=5000) filt <- FilterRules(list(filt1=function(x) { rowSums(geno(x)$DS > 0.5) > 0 })) dest <- tempfile() ans <- filterVcf(tbx, "hg19", dest, filters=filt, verbose=FALSE) vcf0 <- subsetByFilter(readVcf(fl, "hg19"), filt) vcf1 <- readVcf(ans, "hg19") checkIdentical(dim(vcf0), dim(vcf1)) } test_filterVcf_prefilter_only <- function() { fl <- system.file("extdata", "chr22.vcf.gz", package="VariantAnnotation") tbx <- TabixFile(fl, yieldSize=5000) prefilt <- FilterRules(list(filt1=function(x) { grepl("LOWCOV", x, fixed=TRUE) })) dest <- tempfile() ans <- filterVcf(tbx, "hg19", dest, prefilters=prefilt, verbose=FALSE) vcf <- readVcf(fl, "hg19") idx <- any(info(vcf)$SNPSOURCE == "LOWCOV") vcf0 <- vcf[!is.na(idx) & idx,] vcf1 <- readVcf(ans, "hg19") checkIdentical(dim(vcf0), dim(vcf1)) } test_filterOnSomaticStatus <- function() { f <- system.file("extdata", "h1187-10k.vcf.gz", package="VariantAnnotation") vcf <- readVcf(f, "hg19") somaticStatus <- as.list(table(info(vcf)$SS)) checkEquals(somaticStatus, list(Germline=103, LOH=1, Somatic=4)) somaticStatusGermlineFilter <- function(x){ !is.na(info(x)$SS) & info(x)$SS=="Germline" } # filter the in-memory data structure filters <- FilterRules(list(filter.1=somaticStatusGermlineFilter)) checkEquals(dim(subsetByFilter(vcf, filters)), c(103, 2)) } test_prefilterOnSomaticStatus <- function() { f <- system.file("extdata", "h1187-10k.vcf.gz", package="VariantAnnotation") tabix.file <- TabixFile(f, yieldSize=1000) isGermline=function(x) { grepl("Germline", x, fixed=TRUE) } prefilteringFunctions <- FilterRules(list(isGermline=isGermline)) filtered.filename <- filterVcf(tabix.file, "hg19", tempfile(), prefilters=prefilteringFunctions, verbose=FALSE) checkEquals(nrow(readVcf(filtered.filename, "hg19")), 103) } test_filterOnSnps <- function() { f <- system.file("extdata", "h1187-10k.vcf.gz", package="VariantAnnotation") tabix.file <- TabixFile(f, yieldSize=1000) # filter only on snp isSnp=function(x) { refSnp <- nchar(ref(x)) == 1L a <- alt(x) altSnp <- elementNROWS(a) == 1L ai <- unlist(a[altSnp]) # all length 1, so unlisting is 1:1 map altSnp[altSnp] <- nchar(ai) == 1L & (ai %in% c("A", "C", "G", "T")) refSnp & altSnp } filteringFunctions <- FilterRules(list(isSnp=isSnp)) filtered.filename <- filterVcf(tabix.file, "hg19", tempfile(), filters=filteringFunctions, verbose=FALSE) # now check the results vcf.germline <- readVcf(filtered.filename, "hg19") # checkEquals(nrow(vcf.germline), 186) } test_prefilterOnSomaticStatusThenFilterOnSnps <- function() { f <- system.file("extdata", "h1187-10k.vcf.gz", package="VariantAnnotation") tabix.file <- TabixFile(f, yieldSize=1000) isGermline=function(x) { grepl("Germline", x, fixed=TRUE) } prefilteringFunctions <- FilterRules(list(isGermline=isGermline)) isSnp=function(x) { refSnp <- nchar(ref(x)) == 1L a <- alt(x) altSnp <- elementNROWS(a) == 1L ai <- unlist(a[altSnp]) # all length 1, so unlisting is 1:1 map altSnp[altSnp] <- nchar(ai) == 1L & (ai %in% c("A", "C", "G", "T")) refSnp & altSnp } filteringFunctions <- FilterRules(list(isSnp=isSnp)) # now filter on both. should be 98 rows filtered.filename <- filterVcf(tabix.file, "hg19", tempfile(), prefilters=prefilteringFunctions, filters=filteringFunctions, verbose=FALSE) # now check the results vcf.germline.snp <- readVcf(filtered.filename, "hg19") # checkEquals(nrow(vcf.germline.snp), 98) } test_index <- function() { vcf <- VcfFile(system.file("extdata", "chr22.vcf.gz", package="VariantAnnotation")) prefilter = FilterRules(function(x) TRUE) ans <- filterVcf(vcf, destination=tempfile(), index=TRUE, prefilters=prefilter) checkTrue(file.exists(ans)) filter <- FilterRules(function(x) TRUE) ans <- filterVcf(vcf, destination=tempfile(), index=TRUE, filters=filter) checkTrue(file.exists(ans)) } VariantAnnotation/inst/unitTests/test_genotypeToSnpMatrix.R0000644000175100017510000001707414614305321025405 0ustar00biocbuildbiocbuildlibrary(snpStats) ## SnpMatrix class quiet <- suppressWarnings test_gSM_array_GT <- function() { mat <- matrix(c(".|.", "0|0", "0|1", "1|0", "1|1", "./.", "0/0", "0/1", "1/0", "1/1"), ncol=2, dimnames=list(1:5,1:2)) sm <- new("SnpMatrix", matrix(as.raw(c(0, 1, 2, 2, 3, 0, 1, 2, 2, 3)), nrow=2, byrow=TRUE, dimnames=list(1:2,1:5))) ref <- DNAStringSet(rep("A",5)) alt <- DNAStringSetList("C", "G", "T", "C", "G") map <- DataFrame(snp.names=rownames(mat), allele.1=ref, allele.2=alt, ignore=rep(FALSE,5)) gtsm <- genotypeToSnpMatrix(mat, ref, alt) checkIdentical(sm, gtsm$genotypes) checkIdentical(map, gtsm$map) } test_gSM_array_GT_2alt <- function() { mat <- matrix(c("0|1", "1|0", "1|1", "1/2", "2/1", "2/2"), ncol=2, dimnames=list(1:3,1:2)) sm <- new("SnpMatrix", matrix(as.raw(rep(0,6)), nrow=2, byrow=TRUE, dimnames=list(1:2,1:3))) ref <- DNAStringSet(rep("A",3)) alt <- DNAStringSetList(c("C","G"), c("G","T"), c("T","C")) map <- DataFrame(snp.names=rownames(mat), allele.1=ref, allele.2=alt, ignore=rep(TRUE,3)) gtsm <- quiet(genotypeToSnpMatrix(mat, ref, alt)) checkIdentical(sm, gtsm$genotypes) checkIdentical(map, gtsm$map) } test_gSM_array_GT_nonsnv <- function() { mat <- matrix(c("0|0", "0|1", "1|0", "0/0", "0/1", "1/0"), ncol=2, dimnames=list(1:3,1:2)) sm <- new("SnpMatrix", matrix(as.raw(rep(0,6)), nrow=2, byrow=TRUE, dimnames=list(1:2,1:3))) ref <- DNAStringSet(c("A","ACG","ACG")) alt <- DNAStringSetList("CGT", "G", "GAC") map <- DataFrame(snp.names=rownames(mat), allele.1=ref, allele.2=alt, ignore=rep(TRUE,3)) gtsm <- quiet(genotypeToSnpMatrix(mat, ref, alt)) checkIdentical(sm, gtsm$genotypes) checkIdentical(map, gtsm$map) } test_gSM_VCF_GL <- function() { fl <- system.file("extdata", "gl_chr1.vcf", package="VariantAnnotation") vcf <- readVcf(fl, "hg19") gtsm <- quiet(genotypeToSnpMatrix(vcf, uncertain=TRUE)) checkIdentical(colnames(vcf), rownames(gtsm$genotypes)) checkIdentical(rownames(vcf), colnames(gtsm$genotypes)) checkIdentical(rownames(vcf), gtsm$map$snp.names) checkIdentical(ref(vcf), gtsm$map$allele.1) checkIdentical(alt(vcf), gtsm$map$allele.2) checkEquals(unlist(GLtoGP(geno(vcf)$GL)[1,4]), as.vector(g2post(gtsm$genotypes[4,1]))) } test_gSM_VCF_PL <- function() { fl <- system.file("extdata", "hapmap_exome_chr22.vcf.gz", package="VariantAnnotation") vcf <- readVcf(fl, "hg19") gtsm <- quiet(genotypeToSnpMatrix(vcf, uncertain=TRUE)) checkIdentical(colnames(vcf), rownames(gtsm$genotypes)) checkIdentical(rownames(vcf), colnames(gtsm$genotypes)) checkIdentical(rownames(vcf), gtsm$map$snp.names) checkIdentical(ref(vcf), gtsm$map$allele.1) checkIdentical(alt(vcf), gtsm$map$allele.2) checkEquals(unlist(PLtoGP(geno(vcf)$PL)[1,4]), as.vector(g2post(gtsm$genotypes[4,1])), tolerance=0.01) } test_gSM_VCF_structural <- function() { fl <- system.file("extdata", "structural.vcf", package="VariantAnnotation") vcf <- readVcf(fl, "hg19") gtsn <- quiet(genotypeToSnpMatrix(vcf)) checkTrue(nrow(gtsn$genotype) == 1L) checkTrue(rownames(gtsn$genotype) == "NA00001") } test_gSM_VCF_noSamples <- function() { fl <- system.file("unitTests", "cases", "FORMAT_header_no_SAMPLEs.vcf", package="VariantAnnotation") vcf <- readVcf(fl, "hg19") gtsm <- quiet(genotypeToSnpMatrix(vcf)) checkEquals(0, nrow(gtsm$genotypes)) } test_pSM_valid <- function() { probs <- matrix(c(1,0,0, 0,1,0, 0,0,1, NA,NA,NA), ncol=3, byrow=TRUE, dimnames=list(1:4,c("RR","RA","AA"))) sm <- new("SnpMatrix", matrix(as.raw(c(1,2,3,0)), nrow=1, dimnames=list(NULL,1:4))) checkIdentical(sm, probabilityToSnpMatrix(probs)) } test_pSM_invalid <- function() { # invalid matrix - probs do not sum to 1 probs <- matrix(c(1,1,0, 0,1,0, 0,0,1, NA,NA,NA), ncol=3, byrow=TRUE) checkException(probabilityToSnpMatrix(probs)) } test_pSM_onerow <- function() { probs <- matrix(c(1,0,0, NA,NA,NA), ncol=3, byrow=TRUE, dimnames=list(1:2,c("RR","RA","AA"))) sm <- new("SnpMatrix", matrix(as.raw(c(1,0)), nrow=1, dimnames=list(NULL,1:2))) checkIdentical(sm, probabilityToSnpMatrix(probs)) } test_GLtoGP_array <- function() { probs <- aperm(array(c(0.4,0.3,0.3, 0.5,0.1,0.4, 0.9,0.05,0.05, 0,1,0, 0,0,1, 1,NA,NA), dim=c(3,3,2)), c(2,3,1)) gl <- probs for (i in 1:nrow(probs)) { for (j in 1:ncol(probs)) { gl[i,j,] <- log10(probs[i,j,]) } } gp <- GLtoGP(gl) checkEquals(probs, gp) } test_PLtoGP_array <- function() { probs <- aperm(array(c(0.4,0.3,0.3, 0.5,0.1,0.4, 0.9,0.05,0.05, 0,1,0, 0,0,1, 1,NA,NA), dim=c(3,3,2)), c(2,3,1)) pl <- probs for (i in 1:nrow(probs)) { for (j in 1:ncol(probs)) { pl[i,j,] <- -10*log10(probs[i,j,]) } } gp <- PLtoGP(pl) checkEquals(probs, gp) } test_GLtoGP_matrix <- function() { probs <- matrix(c(list(c(0.4,0.3,0.3)), list(c(0.5,0.1,0.4)), list(c(0.9,0.05,0.05)), list(c(0,1,0)), list(c(0,0,1)), list(c(1))), ncol=2) gl <- probs for (i in 1:nrow(probs)) { for (j in 1:ncol(probs)) { gl[i,j] <- list(log10(unlist(probs[i,j]))) } } gp <- GLtoGP(gl) checkEquals(probs, gp) } test_PLtoGP_matrix <- function() { probs <- matrix(c(list(c(0.4,0.3,0.3)), list(c(0.5,0.1,0.4)), list(c(0.9,0.05,0.05)), list(c(0,1,0)), list(c(0,0,1)), list(c(1))), ncol=2) pl <- probs for (i in 1:nrow(probs)) { for (j in 1:ncol(probs)) { pl[i,j] <- list(-10*log10(unlist(probs[i,j]))) } } gp <- PLtoGP(pl) checkEquals(probs, gp) } test_matrixToArray <- function() { mat <- matrix(c(list(c(1,2,3)), list(c(4,5,6)), list(c(7,8,9)), list(c(10,11,12)), list(c(13,14)), list(c(15))), ncol=2) arr <- VariantAnnotation:::.matrixOfListsToArray(mat) for (i in 1:nrow(mat)) { for (j in 1:ncol(mat)) { n <- elementNROWS(mat[i,j]) checkEquals(unlist(mat[i,j]), arr[i,j,1:n]) } } TRUE } VariantAnnotation/inst/unitTests/test_isSNV.R0000644000175100017510000000737714614305321022411 0ustar00biocbuildbiocbuildvcf0 <- VCF(rowRanges=GRanges("chr1", IRanges(1:9, width=c(rep(1, 6), 2, 2, 3))), fixed=DataFrame( REF=DNAStringSet(c("A", "G", "C", "T", "T", "G", "GG", "TCT", "AC")), ALT=DNAStringSetList("G", "A", "T", "C", c("C", "TT"), "GG", "G", "GCG", "ACC"))) str <- system.file("extdata", "structural.vcf", package="VariantAnnotation") vcf <- readVcf(str, "") test_isSNV_CollapsedVCF <- function() { checkIdentical(isSNV(vcf), logical(nrow(vcf))) checkIdentical(isInsertion(vcf), logical(nrow(vcf))) target <- c(FALSE, TRUE, rep(FALSE, 5)) checkIdentical(isDeletion(vcf), target) checkIdentical(isIndel(vcf), target) checkIdentical(isDelins(vcf), logical(nrow(vcf))) checkIdentical(isTransition(vcf), logical(nrow(vcf))) checkIdentical(isSubstitution(vcf), logical(nrow(vcf))) res1 <- isSNV(vcf0, singleAltOnly=TRUE) checkIdentical(res1, c(rep(TRUE, 4), rep(FALSE, 5))) res2 <- isSNV(vcf0, singleAltOnly=FALSE) checkIdentical(res2, c(rep(TRUE, 5), rep(FALSE, 4))) res1 <- isInsertion(vcf0) checkIdentical(res1, c(rep(FALSE, 5), TRUE, FALSE, FALSE, FALSE)) res2 <- isDeletion(vcf0) checkIdentical(res2, c(rep(FALSE, 6), TRUE, FALSE, FALSE)) res3 <- isIndel(vcf0) checkIdentical(res3, res1 | res2) res4 <- isDelins(vcf0) checkIdentical(res4, c(rep(FALSE, 8), TRUE)) res1 <- isSubstitution(vcf0, singleAltOnly=TRUE) checkIdentical(res1, c(rep(TRUE, 4), rep(FALSE, 3), TRUE, FALSE)) res2 <- isSubstitution(vcf0, singleAltOnly=FALSE) checkIdentical(res2, c(rep(TRUE, 5), rep(FALSE, 2), TRUE, FALSE)) res1 <- isTransition(vcf0, singleAltOnly=TRUE) checkIdentical(res1, c(rep(TRUE, 4), rep(FALSE, 5))) res2 <- isTransition(vcf0, singleAltOnly=FALSE) checkIdentical(res2, c(rep(TRUE, 5), rep(FALSE, 4))) } expand <- VariantAnnotation::expand test_isSNV_ExpandedVCF <- function() { evcf <- expand(vcf) checkIdentical(isSNV(evcf), logical(nrow(evcf))) checkIdentical(isInsertion(evcf), logical(nrow(evcf))) target <- c(FALSE, TRUE, rep(FALSE, 5)) checkIdentical(isDeletion(evcf), target) checkIdentical(isIndel(evcf), target) checkIdentical(isDelins(evcf), logical(nrow(evcf))) checkIdentical(isTransition(evcf), logical(nrow(evcf))) checkIdentical(isSubstitution(evcf), logical(nrow(evcf))) evcf0 <- expand(vcf0) res <- isSNV(evcf0) checkIdentical(sum(res), 5L) res1 <- isInsertion(evcf0) checkIdentical(sum(res1), 2L) res2 <- isDeletion(evcf0) checkIdentical(sum(res2), 1L) res3 <- isIndel(evcf0) checkIdentical(sum(res3), sum(res1 + res2)) res4 <- isDelins(evcf0) checkIdentical(sum(res4), 1L) res <- isSubstitution(evcf0) checkIdentical(sum(res), 6L) res <- isTransition(evcf0) checkIdentical(sum(res), 5L) } test_isSNV_VRanges <- function() { vr <- as(vcf0, "VRanges") res <- isSNV(vr) checkIdentical(sum(res), 5L) res1 <- isInsertion(vr) checkIdentical(sum(res1), 2L) res2 <- isDeletion(vr) checkIdentical(sum(res2), 1L) res3 <- isIndel(vr) checkIdentical(sum(res3), sum(res1 + res2)) res4 <- isDelins(vr) checkIdentical(sum(res4), 1L) res <- isSubstitution(vr) checkIdentical(sum(res), 6L) res <- isTransition(vr) checkIdentical(sum(res), 5L) } test_isSNV_gvcf_format <- function() { ## ignore fl <- system.file("unitTests", "cases", "banded_gvcf.vcf", package="VariantAnnotation") vcf <- suppressWarnings(readVcf(fl, "")) checkIdentical(isSNV(vcf), c(TRUE, FALSE, TRUE, FALSE, TRUE)) checkIdentical(isSNV(vcf, singleAltOnly=FALSE), rep(TRUE, nrow(vcf))) vr <- as(vcf, "VRanges") checkIdentical(isSNV(expand(vcf)), isSNV(vr)) } VariantAnnotation/inst/unitTests/test_locateVariants-methods.R0000644000175100017510000001331714614305321026016 0ustar00biocbuildbiocbuildlibrary(TxDb.Hsapiens.UCSC.hg19.knownGene) txdb <- TxDb.Hsapiens.UCSC.hg19.knownGene cdsbytx <- cdsBy(txdb, use.names=TRUE) intbytx <- intronsByTranscript(txdb) txbygene <- transcriptsBy(txdb, "gene") gr <- GRanges("chr22", IRanges(c(16268137, 16287254, 16190792, 16164570, 18209442, 18121652, 24314750, 25508661), width=c(1,1,1,1,3,3,2,2)), strand=c("-", "-", "-", "+", "+", "+", "+", "+")) test_locateVariants_upstream_downstream <- function() { loc <- locateVariants(gr, txdb, IntergenicVariants(1, 1)) target <- CharacterList(character(), character()) checkIdentical(loc$FOLLOWID, target) loc <- locateVariants(gr, txbygene, IntergenicVariants(2, 2)) target <- CharacterList(character(), "100037417") checkIdentical(loc$FOLLOWID, target) loc <- locateVariants(gr, txbygene, IntergenicVariants(100000, 100000)) target <- CharacterList("23784", c("100037417","4282", "66035")) checkIdentical(loc$FOLLOWID, target) target <- CharacterList(character(), c("23523", "2953", "391322")) checkIdentical(loc$PRECEDEID, target) } test_locateVariants_queryAsVCF <- function() { fl <- system.file("extdata", "gl_chr1.vcf", package="VariantAnnotation") vcf <- readVcf(fl, "hg19") vcf <- renameSeqlevels(vcf, c("1" = "chr1")) loc1 <- locateVariants(vcf, txdb, IntergenicVariants()) loc2 <- locateVariants(rowRanges(vcf), txdb, IntergenicVariants()) checkIdentical(loc1, loc2) } test_locateVariants_ignore.strand <- function() { cdsbytx <- cdsbytx[1:5] gr <- GRanges("chr1", IRanges(c(12190, 12595, 13403), width=1), "-") loc1 <- locateVariants(gr, cdsbytx, CodingVariants(), ignore.strand=TRUE) checkIdentical(c(1L, 2L, 3L), mcols(loc1)$QUERYID) loc2 <- locateVariants(gr, cdsbytx, CodingVariants(), ignore.strand=FALSE) checkIdentical(integer(), mcols(loc2)$QUERYID) loc1 <- locateVariants(gr, cdsbytx, SpliceSiteVariants(), ignore.strand=TRUE) checkIdentical(c(1L, 2L, 3L), mcols(loc1)$QUERYID) loc2 <- locateVariants(gr, cdsbytx, SpliceSiteVariants(), ignore.strand=FALSE) checkIdentical(integer(), mcols(loc2)$QUERYID) } test_locateVariants_asHits <- function() { gr <- GRanges("chr1", IRanges(c(12190, 69091, 13403), width=1)) loc <- locateVariants(gr, cdsbytx, CodingVariants()) hit <- locateVariants(gr, cdsbytx, CodingVariants(), asHits=TRUE) ## annotation element loc_nms <- as.character(mcols(loc)$TXID) hit_nms <- names(cdsbytx[subjectHits(hit)]) checkIdentical(loc_nms, hit_nms) ## Hits lengths checkIdentical(length(gr), queryLength(hit)) checkIdentical(length(cdsbytx), subjectLength(hit)) } .extract <- function(x, col) as.vector(mcols(x)[[col]]) test_locateVariants_PromoterVariants <- function() { s <- GRangesList(GRanges("chr1", IRanges(10, width=11), "+"), GRanges("chr1", IRanges(30, width=11) , "+")) ## empty q <- GRanges("chr1", IRanges(15, width=1), "+") current <- locateVariants(q, s, PromoterVariants(5, 5)) checkTrue(length(current) == 0) ## endpoint q <- GRanges("chr1", IRanges(20, width=1), "+") current <- locateVariants(q, s, PromoterVariants(5, 5)) checkTrue(length(current) == 0) ## strand q <- GRanges(c("chr1", "chr1"), IRanges(c(8, 12), width=1), "+") current <- locateVariants(q, s, PromoterVariants(5, 5)) checkEquals(c(1L, 2L), .extract(current, "QUERYID")) strand(s) <- RleList(Rle(factor("*")), Rle(factor("*"))) strand(q) <- "*" current <- suppressWarnings(locateVariants(q, s, PromoterVariants(5, 5))) checkEquals(c(1L, 2L), .extract(current, "QUERYID")) q <- GRanges(c("chr1", "chr1"), IRanges(c(21, 41), width=1), "-") strand(s) <- RleList(Rle(factor("-")), Rle(factor("-"))) current <- locateVariants(q, s, PromoterVariants(5, 5)) checkEquals(c(1L, 2L), .extract(current, "QUERYID")) q <- GRanges(c("chr2", "chr2"), IRanges(c(9, 10), width=1), "+") s <- GRangesList(GRanges("chr2", IRanges(10, width=11), "+")) current <- locateVariants(q, s, PromoterVariants(5, 0)) checkEquals(1L, .extract(current, "QUERYID")) current <- locateVariants(q, s, PromoterVariants(5, 1)) checkEquals(c(1L, 2L), .extract(current, "QUERYID")) current <- locateVariants(q, s, PromoterVariants(0, 0)) checkTrue(length(current) == 0L) q <- GRanges("chr22", IRanges(50310410, 50310420)) current <- locateVariants(q, txdb, PromoterVariants()) checkIdentical(unique(current$GENEID), "79174") } test_locateVariants_match_predictCoding <- function() { library(BSgenome.Hsapiens.UCSC.hg19) gr <- GRanges("chr20", IRanges( start=c(77055, 77054, 77054, 77058, 77057, 77057, 77055), end=c(77055, 77055, 77055, 77058, 77058, 77058, 77054)), paramRangeID=rep(NA, 7)) fixed <- DataFrame( REF=DNAStringSet(c('T', 'AT', 'AT', 'A', 'AA', 'AA', 'T')), ALT=DNAStringSetList('G', 'A', 'ATT', 'G', 'A', 'AAT', 'G'), QUAL=70, FILTER="PASS") vcf <- VCF(rowRanges=gr, fixed=fixed) ## coding regions match, zero-width loc <- locateVariants(vcf, txdb, CodingVariants()) coding <- predictCoding(vcf, txdb, Hsapiens) checkIdentical(loc$QUERYID, as.integer(1:7)) checkIdentical(length(coding) , 6L) checkIdentical(unname(loc$CDSID[1:6]), unname(coding$CDSID)) checkIdentical(unname(as.character(coding$VARCODON[c(1,4)])), as.character(DNAStringSet(c("AAG", "TAG")))) } gr2 <- GRanges("chr20", IRanges( start=c(5, 77054, 77054, 77058, 77057, 77057, 77055), end=c(55, 77055, 77055, 77058, 77058, 77058, 77054)), paramRangeID=rep(NA, 7)) VariantAnnotation/inst/unitTests/test_predictCoding-methods.R0000644000175100017510000001045514614305321025615 0ustar00biocbuildbiocbuildquiet <- suppressWarnings library(BSgenome.Hsapiens.UCSC.hg19) fun <- VariantAnnotation:::.predictCodingGRangesList cdsbytx <- GRangesList(tx1=GRanges(seqnames="chr1", IRanges(c(10001, 10010), width=5), strand="+"), tx2=GRanges(seqnames="chr1", IRanges(c(10100, 10001), width=5), strand="-"), tx3=GRanges(seqnames="chr1", IRanges(c(10010, 10001), width=5), strand="-")) test_predictCoding_empty <- function() { query <- GRanges("chr1", IRanges(start=c(1, 10, 20), width=1)) current <- fun(query, cdsbytx, Hsapiens, DNAStringSet(c("G", "T", "A"))) checkIdentical(dim(mcols(current)), c(0L, 8L)) } test_predictCoding_varAllele <- function() { variant=DNAStringSet(c("G", "", "C", "AA", "GGA")) query <- GRanges(seqnames="chr1", ranges=IRanges(c(rep(10003, 3), 10011, 10101), width=c(1, 1, 1, 2, 3)), strand=c("+", "-", "*", "*", "*"), variant=variant) names(query) <- LETTERS[1:5] current <- quiet(fun(query, cdsbytx[1:2], Hsapiens, variant)) current_varaa <- values(current[names(current) == "B"])[["VARAA"]] checkTrue(as.character(current_varaa) == "") current_consequence <- values(current[names(current) == "B"])[["CONSEQUENCE"]] checkTrue(current_consequence == "not translated") variant=DNAStringSet(c("GGA", "GGA")) query <- GRanges("chr1", IRanges(rep(10101, 2), width=c(2,3)), variant=variant) current <- quiet(fun(query, cdsbytx[1:2], Hsapiens, variant)) checkIdentical(unname(as.character(mcols(current)$VARCODON)), c("TATCCGG", "TTCCGG")) } test_mapToTranscripts <- function() { ## both in 'first' cds query <- GRanges(seqnames="chr1", ranges=IRanges(rep(c(10002, 10005), 2), width=1), strand=c("+", "+", "-", "-")) current <- mapToTranscripts(query, cdsbytx[c(1,3)], ignore.strand=FALSE) expected <- IRanges(c(2, 5, 9, 6), width=1) checkIdentical(ranges(current), expected) ## one in each cds query <- GRanges(seqnames="chr1", ranges=IRanges(rep(c(10002, 10011), 2), width=1), strand=c("+", "+", "-", "-")) current <- mapToTranscripts(query, cdsbytx[c(1,3)], ignore.strand=FALSE) expected <- IRanges(c(2, 7, 9, 4), width=1) checkIdentical(ranges(current), expected) ## both in 'last' cds query <- GRanges(seqnames="chr1", ranges=IRanges(rep(c(10010, 10013), 2), width=1), strand=c("+", "+", "-", "-")) current <- mapToTranscripts(query, cdsbytx[c(1,3)], ignore.strand=FALSE) expected <- IRanges(c(6, 9, 5, 2), width=1) checkIdentical(ranges(current), expected) } test_predictCoding_strand <- function() { variant=DNAStringSet(c("G", "G", "C", "T", "G")) query <- GRanges(seqnames="chr1", ranges=IRanges(c(rep(10003, 3), 10011, 10101), width=1), strand=c("+", "-", "*", "*", "*"), variant=variant) names(query) <- LETTERS[1:5] current <- quiet(fun(query, cdsbytx, Hsapiens, variant)) expected <- c("G", "C", "C", "C", "G", "G", "T", "A", "C") checkIdentical(as.character(mcols(current)$varAllele), expected) ## query "+", subject "-" v <- variant[2] q <- query[2] strand(q) <- "+" s <- cdsbytx[3] current <- quiet(fun(q, s, Hsapiens, v, ignore.strand=FALSE)) checkIdentical(length(current), 0L) current <- quiet(fun(q, s, Hsapiens, v, ignore.strand=TRUE)) checkIdentical(as.character(mcols(current)$REFAA), "V") checkIdentical(as.character(mcols(current)$VARAA), "A") checkIdentical(mcols(current)$CDSLOC, IRanges(8, 8)) ## query "-", subject "+" strand(q) <- "-" s <- cdsbytx[1] current <- quiet(fun(q, s, Hsapiens, v, ignore.strand=FALSE)) checkIdentical(length(current), 0L) current <- quiet(fun(q, s, Hsapiens, v, ignore.strand=TRUE)) checkIdentical(as.character(mcols(current)$REFAA), "*") checkIdentical(as.character(mcols(current)$VARAA), "*") checkIdentical(mcols(current)$CDSLOC, IRanges(3, 3)) } VariantAnnotation/inst/unitTests/test_readVcf-methods.R0000644000175100017510000002245114614305321024410 0ustar00biocbuildbiocbuildtest_readVcf_format <- function() { ## arrays in geno fl <- system.file("extdata", "ex2.vcf", package="VariantAnnotation") vcf <- readVcf(fl, "hg19") checkTrue(class(geno(vcf)$HQ) == "array") ## missing QUAL, FILTER, INFO fl <- system.file(package="VariantAnnotation", "unitTests", "cases", "no_INFO_header.vcf") vcf <- suppressWarnings(readVcf(fl, "hg19")) checkTrue("DNAStringSetList" == class(alt(vcf))) checkTrue("numeric" == class(qual(vcf))) checkTrue("character" == class(filt(vcf))) ## structural fl <- system.file("extdata", "structural.vcf", package="VariantAnnotation") vcf <- readVcf(fl, "hg19") checkTrue(class(alt(vcf)) == "CompressedCharacterList") checkIdentical(qual(vcf), c(6, NA, 6, 12, 23, 14, 11)) } test_readVcf_unspecified_INFO_FORMAT <- function() { ## As of 1.7.32, warnings are no longer thrown for 'INFO' ## and 'FORMAT' fields in the data but with no header entry. ## Fields are silently skipped. fl <- system.file(package="VariantAnnotation", "unitTests", "cases", "unspecified_INFO_FORMAT_fields.vcf") vcf <- readVcf(fl, "hg19") ## columns immediately after XX entries exp <- c(14L, 11L, 10L, 13L, 9L) checkIdentical(exp, info(vcf)$DP) exp <- NumericList(0.5, 0.017, c(0.333, 0.667), NA, c(NA, NA)) checkIdentical(exp, info(vcf)$AF) ## columns immediately after FORMAT entries exp <- c("0|0", "0/1", "0|0", "0/2", "0/0", "1/1") checkIdentical(exp, as.vector(geno(vcf)$GT[4:5,])) exp <- c(56L, NA, 51L, NA, NA, NA, 60L, NA, 51L, NA, NA, NA) checkIdentical(exp, as.vector(geno(vcf)$HQ[4:5,,])) } test_readVcf_fewer_FORMAT_than_GENO <- function() { fl <- system.file(package="VariantAnnotation", "unitTests", "cases", "fewer-FORMAT-than-GENO.vcf") exp <- "record 2 sample H1993: fewer FORMAT fields than GENO fields" obs <- tryCatch(readVcf(fl, "hg19"), warning=conditionMessage) checkIdentical(exp, obs) } test_readVcf_missing_FORMAT_metadata_elt <- function() { fl <- system.file(package="VariantAnnotation", "unitTests", "cases", "missing-FORMAT-metadata-elt.vcf") vcf <- readVcf(fl, "hg19") exp <- c(1L, NA_integer_, 1L) obs <- unlist(geno(vcf)$AP, use.names=FALSE) checkIdentical(exp, obs) } test_readVcf_no_GENO_row <- function() { fl <- system.file(package="VariantAnnotation", "unitTests", "cases", "no_GENO_row.vcf") exp <- c(582L, NA, 584L, 583L, NA, 585L) vcf <- readVcf(fl, "hg19") checkIdentical(exp, as.vector(geno(vcf)[["DP"]])) } test_readVcf_ranges <- function() { fl <- system.file("extdata", "ex2.vcf", package="VariantAnnotation") vcf <- readVcf(fl, "hg19") checkEquals(width(rowRanges(vcf)), width(ref(vcf))) compressVcf <- bgzip(fl, tempfile()) idx <- indexTabix(compressVcf, "vcf") tab <- TabixFile(compressVcf, idx) rd <- rowRanges(vcf) param <- ScanVcfParam(which=rd) vcf_rd <- readVcf(tab, "hg19", param) checkIdentical(values(info(vcf))[c("AA", "AF", "DB", "DP", "H2")], values(info(vcf_rd))[c("AA", "AF", "DB", "DP", "H2")]) param <- ScanVcfParam(which=rd[c(3,5)]) vcf_rd <- readVcf(tab, "hg19", param) checkEquals(2L, dim(vcf_rd)[1]) } test_readVcf_param <- function() { fl <- system.file("extdata", "ex2.vcf", package="VariantAnnotation") gnms <- c("GT", "GQ", "DP", "HQ") inms <- c("NS", "DP", "AF", "DB") snms <- c("NA00001", "NA00002", "NA00003") ## samples samp <- snms[3:2] vcf <- readVcf(fl, "hg19", param=ScanVcfParam(samples=samp)) checkTrue(ncol(vcf) == 2L) checkIdentical(colnames(vcf), snms[3:2]) param <- ScanVcfParam(geno=c("GT", "HQ"), samples=snms) vcf1 <- readVcf(fl, "hg19") vcf2 <- readVcf(fl, "hg19", param=param) checkIdentical(geno(vcf1)$GT, geno(vcf2)$GT) checkIdentical(geno(vcf1)$HQ, geno(vcf2)$HQ) samp <- snms[3] param <- ScanVcfParam(geno=c("GT", "HQ"), samples=samp) vcf3 <- readVcf(fl, "hg19", param=param) current <- c(geno(vcf3)$GT) checkIdentical(unname(geno(vcf1)$GT[,samp]), current) current <- c(geno(vcf3)$HQ) checkIdentical(c(geno(vcf1)$HQ[,samp,]), current) ## geno g <- gnms[3:2] param <- ScanVcfParam(geno=g) vcf <- readVcf(fl, "hg19", param) checkTrue(length(names(geno(vcf))) == length(g)) checkIdentical(names(geno(vcf)), gnms[3:2]) fl <- system.file("extdata", "chr22.vcf.gz", package="VariantAnnotation") param1 <- ScanVcfParam(which=GRanges("22", IRanges(5e7, 50302629))) vcf1 <- readVcf(fl, "hg19", param=param1) param2 <- ScanVcfParam(which=rowRanges(vcf1)[1:10]) vcf2 <- readVcf(fl, "hg19", param=param2) checkIdentical(geno(vcf1)$GT[1:10, ], geno(vcf2)$GT) ## info fl <- system.file("extdata", "ex2.vcf", package="VariantAnnotation") i <- inms[c(4,1)] param <- ScanVcfParam(info=i) vcf <- readVcf(fl, "hg19", param) checkTrue(ncol(info(vcf)) == length(i)) checkIdentical(colnames(info(vcf)), inms[c(4,1)]) ## geno, info param <- ScanVcfParam() vcf1 <- readVcf(fl, "hg19", param) vcf2 <- readVcf(fl, "hg19") checkIdentical(names(geno(vcf1)), names(geno(vcf2))) checkIdentical(rowRanges(vcf1), rowRanges(vcf2)) ## info, geno, ranges, samples g <- gnms[1] i <- inms[2:3] s <- snms[3] rngs <- GRanges("20", IRanges(1110000, 1234600)) param <- ScanVcfParam(geno=g, info=i, which=rngs, samples=s) compressVcf <- bgzip(fl, tempfile()) idx <- indexTabix(compressVcf, "vcf") tab <- TabixFile(compressVcf, idx) vcf <- readVcf(tab, "hg19", param) checkTrue(all(i %in% colnames(info(vcf)))) checkTrue(all(names(geno(vcf)) %in% g)) checkTrue(length(rowRanges(vcf)) == 3) checkTrue(ncol(vcf) == 1L) checkTrue(colnames(vcf) == s) ## no info, geno, samples checkTrue(validObject(readVcf(fl, "hg19", ScanVcfParam(geno=NA)))) checkTrue(validObject(readVcf(fl, "hg19", ScanVcfParam(info=NA)))) obs <- # no warnings on samples=NA tryCatch(readVcf(fl, "hg19", ScanVcfParam(samples=NA)), warning=conditionMessage) checkTrue(is(obs, "VCF") && validObject(obs)) } test_readVcf_genome <- function() { fl <- system.file("extdata", "structural.vcf", package="VariantAnnotation") checkException(readVcf(fl, genome=2), silent=TRUE) vcf0 <- readVcf(fl, "test") si0 <- seqinfo(vcf0) ## no param si1 <- si0 isCircular(si1) <- rep(TRUE, 4) vcf1 <- readVcf(fl, si1) checkIdentical(si1, seqinfo(vcf1)) si2 <- si0[c("1", "2")] vcf2 <- readVcf(fl, si2) checkIdentical(unname(genome(vcf2)), c("test", "test", NA, NA)) si3 <- Seqinfo(as.character(1:5), NA, NA, "test") vcf3 <- readVcf(fl, si3) checkIdentical(merge(si3, si0), seqinfo(vcf3)) ## param bgz <- bgzip(fl, tempfile()) idx <- indexTabix(bgz, "vcf") tab <- TabixFile(bgz, idx) param <- GRanges(c("2", "3"), IRanges(c(321682, 12665100), width=1)) si4 <- Seqinfo(c("2", "3"), NA, NA) vcf4 <- readVcf(tab, si4, param=param) checkIdentical(si4, seqinfo(rowRanges(vcf4))) si5 <- Seqinfo("2", NA, NA) checkException(readVcf(tab, si5, param=param), silent=TRUE) si6 <- Seqinfo(c("1", "2", "3"), NA, NA) vcf6 <- readVcf(tab, si6, param=param) checkIdentical(merge(si6, si4), seqinfo(vcf6)) } test_readVcf_tabix <- function() { fl <- system.file("extdata", "ex2.vcf", package="VariantAnnotation") param1 <- GRanges(seqnames="20", ranges=IRanges(start=17320, end=17330)) param2 <- GRanges(seqnames="20", ranges=IRanges(start=17330, end=17330)) param3 <- GRanges(seqnames="20", ranges=IRanges(start=17330, end=17340)) cmp <- bgzip(fl, tempfile()) idx <- indexTabix(cmp, "vcf") tbx <- TabixFile(cmp, idx) scn1 <- scanTabix(tbx, param=param1) scn2 <- scanTabix(tbx, param=param2) scn3 <- scanTabix(tbx, param=param3) names(scn1) <- names(scn2) <- names(scn3) <- NULL checkIdentical(scn1, scn2) checkIdentical(scn2, scn3) checkIdentical(scn1, scn3) } test_readGT <- function() { fl <- system.file("extdata", "ex2.vcf", package="VariantAnnotation") GT <- readGT(fl, nucleotides=TRUE) checkIdentical(colnames(GT), c("NA00001", "NA00002", "NA00003")) checkIdentical(unname(GT[1,]), c("G|G", "A|G", "A/A")) checkIdentical(unname(GT[2,]), c("T|T", "T|A", "T/T")) checkIdentical(unname(GT[5,]), c("GTC/G", "GTC/GTCT", "G/G")) ## genotypeCodesToNucleotides vcf0 <- readVcf(fl, "") vcf1 <- genotypeCodesToNucleotides(vcf0) checkIdentical(GT, geno(vcf1)$GT) fl <- system.file("unitTests", "cases", "no_INFO_header.vcf", package="VariantAnnotation") GT <- readGT(fl, nucleotides=TRUE) checkIdentical(unname(GT[,1]), rep("./.", 5)) ## genotypeCodesToNucleotides vcf0 <- readVcf(fl, "") vcf1 <- genotypeCodesToNucleotides(vcf0) checkIdentical(GT, geno(vcf1)$GT) } test_buffer_realloc <- function() { fl <- system.file("unitTests", "cases", "buffer_realloc.vcf", package="VariantAnnotation") vcf <- readVcf(fl) target <- c("gridss9_272646b", "gridss9_31852o") checkIdentical(target, names(vcf)) target <- ".GGGGGGGGG" checkIdentical(target, alt(vcf)[[1]]) } VariantAnnotation/inst/unitTests/test_scanVcf.R0000644000175100017510000001246514614305321022764 0ustar00biocbuildbiocbuildfl <- system.file("extdata", "ex2.vcf", package="VariantAnnotation") scn <- scanVcf(fl) test_FixedTypes <- function() { .vcf_map_fixed <- VariantAnnotation:::.vcf_map_fixed exp <- exp0 <- list(rowRanges=NULL, REF=NULL, ALT=list("A", character()), QUAL=list("1", numeric()), FILTER=list("1", character())) named <- names(exp)[-(1:2)] checkIdentical(exp, .vcf_map_fixed(character(), FALSE)) checkIdentical(exp[1:2], .vcf_map_fixed(NA, FALSE)) exp <- exp0 exp[1] <- list(NULL) checkIdentical(exp, .vcf_map_fixed(named, FALSE)) warn <- FALSE obs <- withCallingHandlers({ .vcf_map_fixed("FOO", FALSE) }, warning=function(w) { warn <<- TRUE invokeRestart("muffleWarning") }) checkTrue(warn) checkIdentical(exp[1:2], obs) } test_InfoTypes <- function() { fmt <- info(scanVcfHeader(fl)) info <- scn[[1]]$INFO checkIdentical(as.integer(c(3, 3, 2, 3, 3)), info$NS) checkIdentical(as.integer(c(14, 11, 10, 13, 9)), info$DP) checkTrue(is(info$AF,"matrix")) checkIdentical(c(TRUE, FALSE, TRUE, FALSE, FALSE), info$DB) checkIdentical(c(TRUE, rep(FALSE, 4)), info$H2) } test_GenoTypes <- function() { fmt <- geno(scanVcfHeader(fl)) geno <- scn[[1]]$GENO checkEquals(typeof(unlist(geno$GT)), "character") checkIdentical(lapply(geno, function(x) class(x)[1]), list(GT="matrix", GQ="matrix", DP="matrix", HQ="array")) mat <- matrix(c(1, 3, 6, 7, 4, 8, 5, 0, 4, 2, 5, 3, 4, 2, 3), nrow=5, dimnames=list(NULL, c("NA00001", "NA00002", "NA00003"))) checkEquals(mat, geno$DP) } test_scanVcf_no_FORMAT_column <- function() { ## no FORMAT -- don't parse GENO fl <- system.file(package="VariantAnnotation", "unitTests", "cases", "no_FORMAT_column.vcf") geno <- scanVcf(fl)[[1]]$GENO checkIdentical(setNames(list(), character()), geno) } test_scanVcf_FORMAT_header_no_SAMPLEs <- function() { ## GENO tags, but no SAMPLE or actual samples fl <- system.file(package="VariantAnnotation", "unitTests", "cases", "FORMAT_header_no_SAMPLEs.vcf") geno <- scanVcf(fl)[[1]]$GENO checkIdentical(c("GT", "DS", "GL"), names(geno)) checkTrue(all(sapply(geno, nrow) == 5L)) checkTrue(all(sapply(geno, ncol) == 0L)) } test_scanVcf_no_INFO_header <- function() { fl <- system.file(package="VariantAnnotation", "unitTests", "cases", "no_INFO_header.vcf") info <- suppressWarnings(scanVcf(fl)[[1]]$INFO$INFO) checkIdentical(rep(".", 5L), info) } test_scanVcf_negative_Number <- function() { fl <- system.file(package="VariantAnnotation", "unitTests", "cases", "negative_FORMAT_Number.vcf") pl <- scanVcf(fl)[[1]]$GENO$PL checkIdentical(3L, unique(sapply(pl, length))) checkIdentical(761L, sum(sapply(pl, sum))) } test_scanVcf_crlf <- function() { writeLines(readLines(fl), xx <- tempfile(), sep="\r\n") checkIdentical(scanVcfHeader(fl), scanVcfHeader(xx)) } test_scanVcf_connection_n <- function() { fl <- system.file("extdata", "ex2.vcf", package="VariantAnnotation") res <- scanVcf(file(fl)) checkIdentical(5L, length(res[[1]]$REF)) res <- scanVcf(file(fl), n = 1000) checkIdentical(5L, length(res[[1]]$REF)) res <- scanVcf(file(fl), n = 1) checkIdentical(1L, length(res[[1]]$REF)) vcf <- file(fl) open(vcf) for (i in 1:10) { res <- scanVcf(vcf, n = 1) if (!length(res[[1]]$REF)) break } close(vcf) checkIdentical(6L, i) } test_scanVcfHeader <- function() { checkIdentical(VCFHeader(), scanVcfHeader()) checkIdentical(VCFHeader(), scanVcfHeader(character())) } test_scanVcfHeader_VarScan <- function() { fl <- system.file("unitTests", "cases", "VarScan_header.vcf", package="VariantAnnotation") hd <- scanVcfHeader(fl) checkIdentical(dim(info(hd)), c(7L, 3L)) checkIdentical(names(info(hd)), c("Number", "Type", "Description")) expected <- paste0("Somatic status of variant ", "(0=Reference,1=Germline,2=Somatic,3=LOH, or 5=Unknown)") checkIdentical(info(hd)["SS", "Description"], expected) fl <- system.file("extdata", "ex2.vcf", package="VariantAnnotation") hd <- scanVcfHeader(fl) checkIdentical(length(names(header(hd))), 11L) nms <- c("fileDate", "fileformat", "phasing", "reference", "source", "contig", "SAMPLE", "PEDIGREE") checkIdentical(names(meta(hd)), nms) checkIdentical(header(hd)$contig[["assembly"]], "B36") } test_scanVcfHeader_META <- function() { fl <- system.file("unitTests", "cases", "meta_header.vcf", package="VariantAnnotation") hd <- scanVcfHeader(fl) nms <- c("fileformat", "Tassel", "META", "SAMPLE") checkIdentical(names(meta(hd)), nms) } test_scan_row.names <- function() { fl <- system.file("extdata", "chr7-sub.vcf.gz", package="VariantAnnotation") scn <- scanVcf(fl)[[1]] checkTrue(!is.null(names(scn$rowRanges))) scn <- scanVcf(fl, row.names=FALSE) checkTrue(is.null(names(scn$rowRanges))) param <- ScanVcfParam(which=GRanges("7", IRanges(55000723, 55000789))) scn <- scanVcf(fl, param=param, row.names=FALSE) checkTrue(is.null(names(scn$rowRanges))) } VariantAnnotation/inst/unitTests/test_ScanVcfParam-class.R0000644000175100017510000000550014614305321025000 0ustar00biocbuildbiocbuildtest_ScanVcfParam_which <- function() { which <- IRangesList(seq1=IRanges(1000, 2000), seq2=IRanges(c(100, 1000), c(1000, 2000))) svp <- ScanVcfParam(which=which) checkIdentical(which, vcfWhich(svp)) checkException(vcfWhich(svp) <- DataFrame(), silent=TRUE) checkException(vcfWhich(svp) <- SimpleList(), silent=TRUE) } test_ScanVcfParam_fixed <- function() { fx <- c("GT", "ALT") svp <- ScanVcfParam(fixed=fx) checkIdentical(fx, vcfFixed(svp)) checkException(vcfFixed(svp) <- DataFrame(), silent=TRUE) checkException(vcfFixed(svp) <- 1:5, silent=TRUE) fx <- NA_character_ svp <- ScanVcfParam(fixed=fx) checkIdentical(fx, vcfFixed(svp)) } test_ScanVcfParam_info <- function() { info <- c("NS", "DP") svp <- ScanVcfParam(info=info) checkIdentical(info, vcfInfo(svp)) checkException(vcfInfo(svp) <- DataFrame(), silent=TRUE) checkException(vcfInfo(svp) <- 1:5, silent=TRUE) info <- NA_character_ svp <- ScanVcfParam(info=info) checkIdentical(info, vcfInfo(svp)) } test_ScanVcfParam_geno <- function() { geno <- c("GT", "GQ") svp <- ScanVcfParam(geno=geno) checkIdentical(geno, vcfGeno(svp)) checkException(vcfGeno(svp) <- DataFrame(), silent=TRUE) checkException(vcfGeno(svp) <- 1:5, silent=TRUE) geno <- NA_character_ svp <- ScanVcfParam(geno=geno) checkIdentical(geno, vcfGeno(svp)) } test_ScanVcfParam_samples <- function() { samp <- c("GT", "GQ") svp <- ScanVcfParam(samples=samp) checkIdentical(samp, vcfSamples(svp)) checkException(vcfSamples(svp) <- DataFrame(), silent=TRUE) checkException(vcfSamples(svp) <- 1:5, silent=TRUE) checkException(ScanVcfParam(geno="DP", samples=NA), silent=TRUE) checkException(ScanVcfParam(geno=NA, samples="foo"), silent=TRUE) } test_ScanVcfParam_trimEmpty <- function() { svp <- ScanVcfParam() checkIdentical(TRUE, vcfTrimEmpty(svp)) vcfTrimEmpty(svp) <- FALSE checkIdentical(FALSE, vcfTrimEmpty(svp)) checkException(vcfTrimEmpty(svp) <- "a", silent=TRUE) checkException(vcfTrimEmpty(svp) <- 1:5, silent=TRUE) ## FIXME : should this work? #checkException(vcfTrimEmpty(svp) <- NA, silent=TRUE) } test_ScanVcfParam_GRangesList <- function() { fl <- system.file("extdata", "structural.vcf", package="VariantAnnotation") compressVcf <- bgzip(fl, tempfile()) idx <- indexTabix(compressVcf, "vcf") tab <- TabixFile(compressVcf, idx) gr1 <- GRanges("1", IRanges(13219, 2827695, name="regionA")) gr2 <- GRanges(rep("2", 2), IRanges(c(321680, 14477080), c(321689, 14477090), name=c("regionB", "regionC"))) grl <- GRangesList("1"=gr1, "2"=gr2) vcf_grl <- readVcf(tab, "hg19", grl) gr <- unlist(grl, use.names=FALSE) vcf_gr <- readVcf(tab, "hg19", gr) checkIdentical(rowRanges(vcf_grl), rowRanges(vcf_gr)) } VariantAnnotation/inst/unitTests/test_SIFTandPolyPhen.R0000644000175100017510000000201714614305321024300 0ustar00biocbuildbiocbuildlibrary(SIFT.Hsapiens.dbSNP132) library(PolyPhen.Hsapiens.dbSNP131) quiet <- suppressWarnings test_SIFT_132 <- function() { db <- SIFT.Hsapiens.dbSNP132 scol <- columns(db) checkIdentical(length(scol), 10L) res <- select(db, "rs2142947") checkIdentical(nrow(res), 4L) res <- select(db, "rs2142947", columns="AACHANGE") checkIdentical(nrow(res), 1L) res <- quiet(select(db, keys=c("rs17970171", "INVALID", "rs17970171"))) checkIdentical(nrow(res), 9L) checkTrue(all(res$RSID %in% c("rs17970171", "INVALID"))) } test_PolyPhen <- function() { db <- PolyPhen.Hsapiens.dbSNP131 pcol <- columns(db) checkIdentical(length(pcol), 58L) res <- select(db, "rs3026284") checkIdentical(nrow(res), 2L) res <- select(db, "rs3026284", columns="POS") checkIdentical(nrow(res), 1L) res <- suppressWarnings(select(db, keys=c("rs3026284", "INVALID", "rs3026284"))) checkIdentical(nrow(res), 5L) checkTrue(all(res$RSID %in% c("rs3026284", "INVALID"))) } VariantAnnotation/inst/unitTests/test_snpSummary.R0000644000175100017510000000251314614305321023550 0ustar00biocbuildbiocbuildtest_snpSummary_empty <- function() { cnames <- c("g00", "g01", "g11", "a0Freq", "a1Freq", "HWEzscore", "HWEpvalue") ## no genotype fl <- system.file("unitTests", "cases", "FORMAT_header_no_SAMPLEs.vcf", package="VariantAnnotation") res <- suppressWarnings(snpSummary(readVcf(fl, "hg19"))) checkTrue(all(dim(res) == c(0L, 7L))) checkEquals(names(res), cnames) ## structural fl <- system.file("extdata", "structural.vcf", package="VariantAnnotation") res <- suppressWarnings(snpSummary(readVcf(fl, "hg19"))) checkTrue(all(dim(res) == c(0L, 7L))) checkEquals(names(res), cnames) } test_snpSummary_output <- function() { fl <- system.file("extdata", "ex2.vcf", package="VariantAnnotation") vcf <- readVcf(fl, "hg19") res <- snpSummary(vcf) checkTrue(is.integer(res$g00)) checkTrue(is.integer(res$g01)) checkTrue(is.integer(res$g11)) checkEquals(nrow(res), nrow(vcf)) checkEquals(rownames(res), rownames(vcf)) checkEquals(as.integer(res[1,c("g00", "g01", "g11")]), c(1L, 1L, 1L)) checkEquals(as.integer(res[2,c("g00", "g01", "g11")]), c(2L, 1L, 0L)) checkEquals(round(res[["a0Freq"]], 2), c(0.50, 0.83, NA, NA, NA)) checkEquals(round(res[["a1Freq"]], 2), c(0.50, 0.17, NA, NA, NA)) } VariantAnnotation/inst/unitTests/test_summarizeVariants-methods.R0000644000175100017510000000117314614305321026560 0ustar00biocbuildbiocbuildgrl <- GRangesList( A=GRanges("20", IRanges(14370, 17331)), B=GRanges("20", IRanges(17330, width=1)), C=GRanges("20", IRanges(1110696, width=1)), D=GRanges("20", IRanges(1110696, width=2))) test_summarizeVariants <- function() { fl <- system.file("extdata", "ex2.vcf", package="VariantAnnotation") vcf <- readVcf(fl, "hg19") sv <- summarizeVariants(grl, vcf, findOverlaps) checkIdentical(ncol(vcf), ncol(sv)) checkIdentical(length(grl), nrow(sv)) target <- matrix(c(0, 0, 1, 1, 2, 1, 1, 1, 1, 0, 1, 1), ncol=3) current <- unname(assays(sv)$counts) checkIdentical(current, target) } VariantAnnotation/inst/unitTests/test_VCF-class.R0000644000175100017510000002333414614305321023117 0ustar00biocbuildbiocbuild test_VCF_construction <- function() { ## empty m1 <- matrix(0, 0, 0) checkTrue(validObject(VCF())) checkTrue(validObject(VCF(collapsed=TRUE))) checkTrue(validObject(VCF(collapsed=FALSE))) checkTrue(validObject(VCF(geno=SimpleList(m1)))) ## substance fl <- system.file("extdata", "ex2.vcf", package="VariantAnnotation") target <- readVcf(fl, genome="hg19") current <- VCF(rowRanges=rowRanges(target), colData=colData(target), geno=geno(target), info=info(target), fixed=fixed(target)) checkTrue(validObject(current)) checkIdentical("hg19", unique(genome(rowRanges(target)))) } test_VCF_fixed <- function() { fl <- system.file("extdata", "ex2.vcf", package="VariantAnnotation") vcf <- readVcf(fl, genome="hg19") vcf1 <- vcf ## ref checkTrue(class(ref(vcf)) == "DNAStringSet") checkException(ref(vcf) <- NULL, silent=TRUE) checkException(ref(vcf) <- as.character(ref(vcf)), silent=TRUE) ref(vcf1) <- ref(vcf)[1] checkIdentical(rep(ref(vcf)[1], nrow(vcf)), ref(vcf1)) ## alt checkTrue(class(alt(vcf)) == "DNAStringSetList") checkException(alt(vcf) <- NULL, silent=TRUE) v1 <- vcf alt(v1) <- CharacterList(list("A", "A", c("G", "T"), "", c("G", "GTCT"))) checkTrue(validObject(v1)) alt(vcf1) <- alt(vcf)[1] checkIdentical(rep(alt(vcf)[1], nrow(vcf)), alt(vcf1)) ## qual checkTrue(class(qual(vcf)) == "numeric") checkException(qual(vcf) <- NULL, silent=TRUE) checkException(qual(vcf) <- as.character(qual(vcf)), silent=TRUE) qual(vcf1) <- qual(vcf)[1] # recylcing checkIdentical(rep(qual(vcf)[1], nrow(vcf)), qual(vcf1)) ## filt checkTrue(class(filt(vcf)) == "character") checkException(filt(vcf) <- NULL, silent=TRUE) checkException(filt(vcf) <- as.list(filt(vcf)), silent=TRUE) filt(vcf1) <- filt(vcf)[1] # recylcing checkIdentical(rep(filt(vcf)[1], nrow(vcf)), filt(vcf1)) ## fixed checkTrue(is(fixed(vcf), "DataFrame")) checkException(fixed(vcf) <- NULL, silent=TRUE) checkException(fixed(vcf) <- as.matrix(fixed(vcf)), silent=TRUE) DF <- DataFrame(REF=DNAStringSet(c("A", "C", "T", "T", "C")), ALT=CharacterList(list("C", "G", "A", "G", "G")), QUAL=c(10, 20, NA, 10, 10), FILTER=c("pass", "pass", "q10", "q10", "pass")) df <- DF names(df) <- c("reference", "ALT", "QUAL", "FILTER") checkException(fixed(vcf) <- df, silent=TRUE) df <- DF names(df) <- c("REF", "ALT", "qual", "FILTER") checkException(fixed(vcf) <- df, silent=TRUE) df <- DF df$QUAL <- as.character(df$QUAL) checkException(fixed(vcf) <- df, silent=TRUE) df <- DF df$ALT <- unlist(df$ALT, use.names=FALSE) checkException(fixed(vcf) <- df, silent=TRUE) df <- DF df$REF <- as.character(df$REF) checkException(fixed(vcf) <- df, silent=TRUE) } test_VCF_rowRanges_info_geno <- function() { fl <- system.file("extdata", "ex2.vcf", package="VariantAnnotation") vcf <- readVcf(fl, genome="hg19") ## rowRanges v1 <- vcf rowRanges(v1) <- rowRanges(v1, fixed=FALSE)[5:1] checkIdentical(rownames(vcf), rev(rownames(v1))) ## info checkTrue(is(info(vcf), "DataFrame")) checkException(info(vcf) <- NULL, silent=TRUE) v1 <- vcf checkException(info(v1) <- DataFrame(), silent=TRUE) checkTrue(class(info(vcf)$AF) == "CompressedNumericList") AF <- NumericList(0.5, 0.017, c(0.333,0.667), NA, c(NA, NA)) checkIdentical(info(vcf)$AF, AF) ## geno checkTrue(class(geno(vcf)) == "SimpleList") checkException(geno(vcf) <- NULL, silent=TRUE) checkIdentical(geno(vcf)$CNQ, geno(vcf)[["CNQ"]]) checkIdentical(geno(vcf)[["CN"]], assays(vcf)[["CN"]]) checkTrue(is(geno(vcf, "GT"), "matrix")) v1 <- vcf geno(v1, "GT") <- matrix(NA, nrow=5, ncol=3) checkIdentical(unique(as.vector(geno(v1, "GT"))), NA) } test_VCF_subset <- function() { fl <- system.file("extdata", "ex2.vcf", package="VariantAnnotation") vcf <- readVcf(fl, genome="hg19") ## numeric ss1 <- vcf[2:3,] checkIdentical(c(2L, ncol(vcf)), dim(ss1)) ss1 <- vcf[,2] checkIdentical(c(nrow(vcf), 1L), dim(ss1)) checkIdentical(rownames(ss1), rownames(info(ss1))) ss1 <- vcf[2:3, 2] checkIdentical(c(2L, 1L), dim(ss1)) checkIdentical(rownames(ss1), rownames(info(ss1))) ## character ss1 <- vcf dimnames(ss1) <- list(LETTERS[seq_len(nrow(ss1))], letters[seq_len(ncol(ss1))]) ridx <- c("B", "C") checkIdentical(rowRanges(ss1[ridx,]), rowRanges(ss1)[ridx,]) checkIdentical(rowRanges(ss1["C",]), rowRanges(ss1)["C",,drop=FALSE]) checkException(ss1[LETTERS,], "i-index out of bounds", TRUE) cidx <- "b" checkIdentical(colData(ss1[,cidx]), colData(ss1)[cidx,,drop=FALSE]) checkException(ss1[,letters], "j-index out of bounds", TRUE) ## logical ss1 <- vcf dimnames(ss1) <- list(LETTERS[seq_len(nrow(ss1))], letters[seq_len(ncol(ss1))]) checkIdentical(fixed(ss1), fixed(ss1[TRUE,])) checkIdentical(info(ss1), info(ss1[TRUE,])) checkIdentical(c(0L, ncol(ss1)), dim(ss1[FALSE,])) checkIdentical(fixed(ss1), fixed(ss1[,TRUE])) checkIdentical(info(ss1), info(ss1[,TRUE])) checkIdentical(c(nrow(ss1), 0L), dim(ss1[,FALSE])) idx <- c(TRUE, FALSE) # recycling ss2 <- ss1[idx,] checkIdentical(rowRanges(ss1)[idx,,drop=FALSE], rowRanges(ss2)) ss2 <- ss1[,idx] checkIdentical(colData(ss1)[idx,,drop=FALSE], colData(ss2)) ## 0 columns vcf <- VCF(rowRanges=GRanges("chr1", IRanges(1:10, width=1))) checkIdentical(dim(vcf[1:5, ]), c(5L, 0L)) ## 0 rows vcf <- VCF(colData=DataFrame(samples=1:10)) checkIdentical(dim(vcf[ ,1:5]), c(0L, 5L)) } test_VCF_subset_empty_slots <- function() { fl <- system.file("extdata", "ex2.vcf", package="VariantAnnotation") vcf <- readVcf(fl, "hg19", param=ScanVcfParam(info=NA, fixed=NA)) checkTrue(all(names(mcols(vcf)) %in% c("paramRangeID", "REF"))) checkTrue(nrow(vcf[2:4]) == 3L) checkTrue(ncol(vcf[,1:2]) == 2L) vcf <- readVcf(fl, "hg19", param=ScanVcfParam(geno=NA)) checkTrue(length(names(geno(vcf))) == 0L) checkTrue(ncol(vcf) == 0L) checkException(vcf[,1:5], silent=TRUE) checkTrue(nrow(vcf[2:4]) == 3L) } test_VCF_subsetassign <- function() { fl <- system.file("extdata", "ex2.vcf", package="VariantAnnotation") vcf <- readVcf(fl, genome="hg19") ss1 <- vcf ss1[1:2,] <- ss1[2:1,] checkIdentical(rowRanges(vcf)[2:1,], rowRanges(ss1)[1:2,]) checkIdentical(rowRanges(vcf[-(1:2),]), rowRanges(ss1)[-(1:2),]) checkIdentical(colData(vcf), colData(ss1)) checkIdentical(metadata(vcf), metadata(ss1)) ss1 <- vcf ss1[,1:2] <- ss1[,2:1,drop=FALSE] checkIdentical(colData(vcf)[2:1,,drop=FALSE], colData(ss1)[1:2,,drop=FALSE]) checkIdentical(colData(vcf)[-(1:2),,drop=FALSE], colData(ss1)[-(1:2),,drop=FALSE]) checkIdentical(rowRanges(vcf), rowRanges(ss1)) checkIdentical(metadata(vcf), metadata(ss1)) } test_VCF_seqlevels <- function() { fl <- system.file("extdata", "structural.vcf", package="VariantAnnotation") vcf <- readVcf(fl, genome="hg19") seqlev <- seqlevels(vcf) checkIdentical(seqlev, c("1", "2", "3", "4")) seqlevels(vcf)[seqlevels(vcf) == "1"] <- "chr1" checkIdentical(seqlevels(vcf), c("chr1", "2", "3", "4")) seqlevels(vcf, pruning.mode="coarse") <- "3" checkIdentical(seqlevels(vcf), "3") } quiet <- suppressWarnings test_VCF_cbind <- function() ## requires matching ranges { ## empty vcf <- VCF() empty <- cbind(vcf, vcf) checkEquals(dim(empty), c(0, 0)) ## different ranges fl <- system.file("extdata", "ex2.vcf", package="VariantAnnotation") vcf1 <- readVcf(fl, genome="hg19") vcf2 <- vcf1[2:4] rownames(vcf2) <- month.name[seq_len(nrow(vcf2))] checkException(quiet(cbind(vcf1, vcf2)), silent=TRUE) ## same ranges vcf2 <- vcf1[,1] #colnames(vcf2) <- month.name[seq_len(ncol(vcf2))] res <- cbind(vcf1, vcf2) checkTrue(nrow(res) == 5) checkTrue(ncol(res) == 4) ## info checkTrue(ncol(info(res)) == 6) info2 <- info(vcf2) info2$H2 <- !info2$H2 info(vcf2) <- info2 checkException(cbind(vcf1, vcf2), silent=TRUE) info(vcf2) <- info(vcf2)[,1:2] res <- cbind(vcf1, vcf2) checkTrue(ncol(info(res)) == 6) info(vcf2) <- DataFrame("noMatch"=1:5) res <- cbind(vcf1, vcf2) checkTrue(ncol(info(res)) == 7) ## fixed vcf2 <- vcf1[,1] res <- cbind(vcf1, vcf2) checkTrue(ncol(fixed(res)) == 4) fixed2 <- fixed(vcf2) fixed2$QUAL <- c(rep(1, 5)) fixed(vcf2) <- fixed2 checkException(cbind(vcf1, vcf2), silent=TRUE) } test_VCF_rbind <- function() ## requires matching samples { ## empty vcf0 <- VCF() empty <- rbind(vcf0, vcf0) checkEquals(dim(empty), c(0, 0)) ## different sample fl <- system.file("extdata", "ex2.vcf", package="VariantAnnotation") vcf1 <- readVcf(fl, genome="hg19") vcf2 <- vcf1[,2] colnames(vcf2) <- month.name[seq_len(ncol(vcf2))] checkException(quiet(rbind(vcf1, vcf2)), silent=TRUE) ## same samples vcf2 <- vcf1[1:3,] res <- rbind(vcf1, vcf2) checkTrue(nrow(res) == 8) checkTrue(ncol(res) == 3) ## info checkTrue(ncol(info(res)) == 6) info2 <- info(vcf2) info2$H2 <- !info2$H2 info(vcf2) <- info2 res <- rbind(vcf1, vcf2) checkTrue(ncol(info(res)) == 6) ## fixed vcf2 <- vcf1[1:3,] res <- rbind(vcf1, vcf2) checkTrue(ncol(fixed(res)) == 4) fixed2 <- fixed(vcf2) fixed2$QUAL <- c(rep(1, nrow(vcf2))) fixed(vcf2) <- fixed2 res <- rbind(vcf1, vcf2) checkTrue(ncol(fixed(res)) == 4) } VariantAnnotation/inst/unitTests/test_vcfFields.R0000644000175100017510000000206014614305321023274 0ustar00biocbuildbiocbuildtest_vcfFields <- function(){ ## invalid checkException(vcfFields(NA_character_), silent = TRUE) checkException(vcfFields(tempfile()), silent = TRUE) ## empty target <- CharacterList( fixed = character(), info = character(), geno = character(), samples = character() ) checkIdentical(target, vcfFields()) ## structure fl <- system.file("extdata", "ex2.vcf", package="VariantAnnotation") flds <- vcfFields(fl) checkTrue(validObject(flds)) checkTrue(is(flds, "CharacterList")) target <- c(fixed = 4L, info = 6L, geno = 4L, samples = 3L) checkIdentical(target, lengths(flds)) ## signatures hdr <- scanVcfHeader(fl) flds.hdr <- vcfFields(hdr) vf <- VcfFile(fl) flds.vf <- vcfFields(vf) vcf <- readVcf(fl, genome = "hg19") flds.vcf <- vcfFields(vcf) checkIdentical(flds, flds.hdr) checkIdentical(flds, flds.vf) checkIdentical(flds, flds.vcf) ## VCFFileList vfl <- VcfFileList(c(fl, fl)) checkIdentical(vcfFields(vfl[[1]]), vcfFields(vfl)) } VariantAnnotation/inst/unitTests/test_VRanges-class.R0000644000175100017510000002531714614305321024051 0ustar00biocbuildbiocbuild.TARGET_seqnames <- Rle(factor(c("chr1", "chr2"))) .TARGET_ranges <- IRanges(c(1, 10), c(5, 20)) .TARGET_strand <- Rle(strand("*"), 2) .TARGET_gr <- GRanges(.TARGET_seqnames, .TARGET_ranges, .TARGET_strand) .TARGET_ref <- c("T", "A") .TARGET_alt <- c("C", "T") .TARGET_refDepth <- as.integer(c(5, 10)) .TARGET_altDepth <- as.integer(c(7, 6)) .TARGET_totalDepth <- as.integer(c(12, 17)) .TARGET_sampleNames <- factor(letters[1:2]) .TARGET_hardFilters <- FilterRules(list(a = function(x) softFilterMatrix(x)[,1])) .TARGET_softFilterMatrix <- FilterMatrix(matrix = cbind(a = c(TRUE, FALSE)), filterRules = .TARGET_hardFilters) .TARGET_tumorSpecific <- c(FALSE, TRUE) integerRle <- function(...) as(Rle(...), "integerRle") characterRle <- function(...) as(Rle(...), "characterRle") factorRle <- function(...) as(Rle(...), "factorRle") make_TARGET_VRanges_simple <- function() { new("VRanges", .TARGET_gr, ref = .TARGET_ref, alt = .TARGET_alt, totalDepth = integerRle(NA_integer_, 2), refDepth = integerRle(NA_integer_, 2), altDepth = integerRle(NA_integer_, 2), sampleNames = factorRle(NA_character_, 2), softFilterMatrix = FilterMatrix(matrix(nrow = length(.TARGET_gr), ncol = 0L), filterRules = FilterRules()), hardFilters = FilterRules()) } make_TARGET_VRanges <- function(i = seq_len(length(.TARGET_seqnames))) { new("VRanges", GRanges(.TARGET_seqnames, .TARGET_ranges, .TARGET_strand, tumorSpecific = .TARGET_tumorSpecific)[i], ref = .TARGET_ref[i], alt = .TARGET_alt[i], totalDepth = .TARGET_totalDepth[i], refDepth = .TARGET_refDepth[i], altDepth = .TARGET_altDepth[i], sampleNames = .TARGET_sampleNames[i], softFilterMatrix = .TARGET_softFilterMatrix[i,,drop=FALSE], hardFilters = .TARGET_hardFilters) } make_TARGET_VRanges_empty <- function() { new("VRanges", GRanges(), ref = character(), alt = characterRle(character()), totalDepth = integerRle(integer()), refDepth = integerRle(integer()), altDepth = integerRle(integer()), sampleNames = factorRle(factor()), softFilterMatrix = FilterMatrix(matrix(nrow = 0L, ncol = 0L), filterRules = FilterRules()), hardFilters = FilterRules()) } test_VRanges_constructor <- function() { vr <- make_TARGET_VRanges_simple() ## CHECK: 'ref' NA ranges <- IRanges(c(1, 5), c(10, 20)) checkException(VRanges(.TARGET_seqnames, .TARGET_ranges, NA_character_)) ## CHECK: depth < 0 checkException(VRanges(.TARGET_seqnames, .TARGET_ranges, .TARGET_ref, refDepth = -1)) checkException(VRanges(.TARGET_seqnames, .TARGET_ranges, .TARGET_ref, altDepth = -1)) ## CHECK: invalid totalDepth sum options(warn=2) checkException(VRanges(.TARGET_seqnames, .TARGET_ranges, .TARGET_ref, .TARGET_alt, 0, 1)) options(warn=0) ## CHECK: DNAStringSet handling (ref and alt); also, coercion depth=>integer test.vr <- VRanges(.TARGET_seqnames, .TARGET_ranges, DNAStringSet(.TARGET_ref), DNAStringSet(.TARGET_alt), totalDepth = .TARGET_totalDepth) vr@totalDepth <- .TARGET_totalDepth checkIdentical(test.vr, vr) ## CHECK: recycling (every column) test.vr <- VRanges(.TARGET_seqnames[1], .TARGET_ranges, DNAStringSet(.TARGET_ref[1]), .TARGET_alt[1], tumorSpecific = .TARGET_tumorSpecific, totalDepth = .TARGET_totalDepth) seqnames(vr) <- .TARGET_seqnames[1] vr$tumorSpecific <- .TARGET_tumorSpecific vr@ref <- rep(.TARGET_ref[1], 2) alt(vr) <- .TARGET_alt[1] checkIdentical(test.vr, vr) ## CHECK: coercion of 0/1 matrix to logical test.vr <- VRanges(.TARGET_seqnames, .TARGET_ranges, .TARGET_ref, .TARGET_alt, softFilterMatrix = cbind(a = c(1, 0))) vr <- make_TARGET_VRanges_simple() vr@softFilterMatrix <- as(.TARGET_softFilterMatrix, "matrix") checkIdentical(test.vr, vr) ## CHECK: all arguments, with unnamed metadata test.vr <- VRanges(.TARGET_seqnames, .TARGET_ranges, .TARGET_ref, .TARGET_alt, .TARGET_totalDepth, .TARGET_refDepth, .TARGET_altDepth, tumorSpecific = .TARGET_tumorSpecific, sampleNames = .TARGET_sampleNames, softFilterMatrix = .TARGET_softFilterMatrix, hardFilters = .TARGET_hardFilters) vr <- make_TARGET_VRanges() checkIdentical(test.vr, vr) ## CHECK: zero-length input test.vr <- VRanges() empty.vr <- make_TARGET_VRanges_empty() checkIdentical(test.vr, empty.vr) } test_VRanges_extract <- function() { vr <- make_TARGET_VRanges() checkIdentical(vr[1], make_TARGET_VRanges(1)) checkIdentical(vr[0], make_TARGET_VRanges(0)) checkIdentical(vr[rep(1, 3)], make_TARGET_VRanges(rep(1, 3))) checkIdentical(vr[c(2, 1)], make_TARGET_VRanges(c(2, 1))) checkIdentical(vr[c(TRUE,FALSE)], make_TARGET_VRanges(c(TRUE,FALSE))) checkIdentical(vr[TRUE], make_TARGET_VRanges(TRUE)) checkIdentical(vr[], make_TARGET_VRanges()) checkIdentical(vr[logical()], make_TARGET_VRanges(logical())) vr$tumorSpecific <- NULL checkIdentical(vr, make_TARGET_VRanges()[,integer()]) } test_VRanges_accessors <- function() { vr <- make_TARGET_VRanges() checkIdentical(seqnames(vr), .TARGET_seqnames) checkIdentical(ranges(vr), .TARGET_ranges) checkIdentical(strand(vr), .TARGET_strand) checkIdentical(ref(vr), .TARGET_ref) checkIdentical(alt(vr), .TARGET_alt) checkIdentical(refDepth(vr), .TARGET_refDepth) checkIdentical(altDepth(vr), .TARGET_altDepth) checkIdentical(totalDepth(vr), .TARGET_totalDepth) checkIdentical(Biobase::sampleNames(vr), .TARGET_sampleNames) checkIdentical(softFilterMatrix(vr), .TARGET_softFilterMatrix) checkIdentical(hardFilters(vr), .TARGET_hardFilters) checkIdentical(vr$tumorSpecific, .TARGET_tumorSpecific) alt(vr) <- NA checkIdentical(alt(vr), characterRle(NA, 2)) twoFilters <- rep(.TARGET_hardFilters, 2) hardFilters(vr) <- twoFilters checkIdentical(hardFilters(vr), twoFilters) twoFilters <- .TARGET_softFilterMatrix[,c(1,1)] softFilterMatrix(vr) <- twoFilters checkIdentical(softFilterMatrix(vr), twoFilters) } test_VRanges_subassign <- function() { vr <- make_TARGET_VRanges() vr[2:1] <- vr checkIdentical(vr, make_TARGET_VRanges(2:1)) vr <- make_TARGET_VRanges() sampleNames(vr) <- Rle(sampleNames(vr)) vr2 <- vr vr2[2:1] <- vr checkIdentical(vr2, vr[2:1]) } test_VRanges_combine <- function() { vr <- make_TARGET_VRanges() vr.rep <- vr[rep(seq_len(length(vr)), 2)] vr.combined <- c(vr, vr) checkIdentical(vr.combined, vr.rep) } test_VRanges_coerce <- function() { df <- data.frame(as.data.frame(.TARGET_gr), ref = as.vector(.TARGET_ref), alt = as.vector(.TARGET_alt), totalDepth = as.vector(.TARGET_totalDepth), refDepth = as.vector(.TARGET_refDepth), altDepth = as.vector(.TARGET_altDepth), sampleNames = as.factor(.TARGET_sampleNames), softFilterMatrix = .TARGET_softFilterMatrix, tumorSpecific = .TARGET_tumorSpecific, stringsAsFactors = FALSE) vr <- make_TARGET_VRanges() checkIdentical(as.data.frame(vr), df) } make_TARGET_VRanges_vcf <- function() { gr <- GRanges(c("1", "1", "3", "2", "1", "2"), IRanges(c(1, 20, 15, 5, 20, 5), width = 1L), "*", TS = c(TRUE, FALSE, FALSE, TRUE, TRUE, NA)) seqinfo(gr) <- Seqinfo(c("1", "3", "2"), c(NA, 22, 25), genome = c(NA, "foo", NA)) new("VRanges", gr, ref = c("A", "C", "G", "T", "C", "T"), alt = c("T", "G", NA, "A", "G", "C"), totalDepth = c(NA, 10L, 15L, 10L, 5L, 3L), refDepth = c(0L, 5L, 15L, 10L, NA, 2L), altDepth = c(5L, NA, NA, 0L, 3L, 1L), sampleNames = factorRle(factor(c("A", "B", "B", "A", "A", "B"))), softFilterMatrix = FilterMatrix(matrix = cbind(a = c(FALSE, TRUE, FALSE, TRUE, FALSE, TRUE), b = c(FALSE, FALSE, TRUE, TRUE, NA, NA)), filterRules = FilterRules(a = x > 1, b = c != "foo")), hardFilters = FilterRules()) } checkIdenticalVCF <- function(orig, vcf) { vcf$QUAL <- NULL if (!is.null(orig$TS)) orig$TS <- as.integer(orig$TS) softFilterMatrix(orig) <- as(softFilterMatrix(orig), "matrix") softFilterMatrix(vcf) <- as(softFilterMatrix(vcf), "matrix") names(orig) <- names(vcf) ## Information loss due to binary nature of VCF filters if (!identical(softFilterMatrix(orig), softFilterMatrix(vcf))) { origFilt <- softFilterMatrix(orig) origFilt[is.na(origFilt)] <- TRUE origFilt[rowSums(origFilt) == 2 & rowSums(softFilterMatrix(orig),na.rm=TRUE) != 2,] <- NA softFilterMatrix(orig) <- origFilt } checkIdentical(orig, vcf) } test_VRanges_vcf <- function() { dest <- tempfile() vr <- make_TARGET_VRanges_vcf() writeVcf(vr, dest) vcf <- readVcf(dest, genome = "hg19") perm <- c(1, 7, 8, 4, 2, 10) vcf.vr <- as(vcf, "VRanges")[perm] genome(vr) <- "hg19" checkIdenticalVCF(vr, vcf.vr) test.vcf <- asVCF(vr, info = "TS", filter = "a") writeVcf(test.vcf, dest) vcf <- readVcf(dest, genome = "hg19") vcf.vr <- as(vcf, "VRanges")[perm] ## adjustments necessary, due to folding at INFO and FILTER vcf.vr$TS[5:6] <- c(1L, NA) softFilterMatrix(vcf.vr)[5,] <- cbind(FALSE, NA) checkIdenticalVCF(vr, vcf.vr) vrA <- vr[sampleNames(vr) == "A"] runValue(sampleNames(vrA)) <- factor(runValue(sampleNames(vrA))) vrA <- keepSeqlevels(vrA, unique(as.character(seqnames(vrA)))) writeVcf(vrA, dest) vcfA <- readVcf(dest, genome = "hg19") vcfA.vr <- as(vcfA, "VRanges") checkIdenticalVCF(vrA, vcfA.vr) geno(vcfA) <- SimpleList() vrA.vcf <- as(vcfA, "VRanges") vrA.stripped <- VRanges(seqnames(vrA), ranges(vrA), ref(vrA), alt(vrA), sampleNames = sampleNames(vrA)) seqinfo(vrA.stripped) <- seqinfo(vrA) checkIdenticalVCF(vrA.stripped, vrA.vcf) vr <- VRanges(seqnames = c("chr1", "chr2"), ranges = IRanges(c(1, 10), c(5, 20)), ref = c("T", "A"), alt = c("C", "T"), refDepth = c(5, 10), altDepth = c(7, 6), totalDepth = c(12, 17), sampleNames = Rle(letters[1:2]), softFilterMatrix = cbind(depth = c(TRUE, FALSE)), tumorSpecific = c(FALSE, TRUE)) vr_small <- vr width(vr_small) <- 1 vcf_small <- as(vr_small, "VCF") vr_back <- as(vcf_small, "VRanges") vr_back$QUAL <- NULL vr_back$tumorSpecific <- as.logical(vr_back$tumorSpecific) checkIdentical(vr_small, vr_back[-(2:3)]) } ### GOT TIRED OF TESTING. I mean, seriously, that VCF stuff was insane. VariantAnnotation/inst/unitTests/test_writeVcf-methods.R0000644000175100017510000001117314614305321024626 0ustar00biocbuildbiocbuildtest_writeVcf_connection_increment <- function() { fl <- system.file("extdata", "ex2.vcf", package="VariantAnnotation") vcf1 <- readVcf(fl, "hg19") outfl <- tempfile() con <- file(outfl, open="a") writeVcf(vcf1[1:2,], con) writeVcf(vcf1[-(1:2),], con) close(con) vcf2 <- readVcf(outfl, "hg19") checkIdentical(dim(vcf1), dim(vcf2)) } test_writeVcf_tags <- function() { fl <- system.file("extdata", "chr22.vcf.gz", package="VariantAnnotation") dest <- tempfile() vcf1 <- readVcf(fl, "hg19") hd1 <- metadata(vcf1)$header writeVcf(vcf1, dest) vcf2 <- readVcf(dest, "hg19") hd2 <- metadata(vcf2)$header checkTrue(names(meta(hd1)) %in% names(meta(hd2))) checkIdentical(names(geno(vcf1)), names(geno(vcf2))) checkIdentical(colnames(mcols(info(vcf1))), colnames(mcols(info(vcf2)))) } test_writeVcf_flatgeno <- function() { fl <- system.file("extdata", "structural.vcf", package="VariantAnnotation") dest <- tempfile() vcf1 <- readVcf(fl, "hg19") writeVcf(vcf1, dest) vcf2 <- readVcf(dest, "hg19") } test_writeVcf_geno <- function() { fl <- system.file("extdata", "ex2.vcf", package="VariantAnnotation") dest <- tempfile() ## empty vcf1 <- readVcf(fl, "hg19", param=ScanVcfParam(geno=NA)) writeVcf(vcf1, dest) vcf2 <- readVcf(dest, "hg19") checkTrue(length(geno(vcf2)$GT) == 0L) ## Rle vcf1 <- readVcf(fl, "hg19", param=ScanVcfParam(geno="GT")) writeVcf(vcf1, dest) vcf2 <- readVcf(dest, "hg19") checkIdentical(geno(vcf1)$GT, geno(vcf2)$GT) checkIdentical(geno(header(vcf1)), geno(header(vcf2))) ## matrix vcf1 <- readVcf(fl, "hg19", param=ScanVcfParam(geno="GT")) writeVcf(vcf1, dest) vcf2 <- readVcf(dest, "hg19") checkIdentical(geno(vcf1)$GT, geno(vcf2)$GT) checkIdentical(geno(header(vcf1)), geno(header(vcf2))) param=ScanVcfParam(geno="GT", samples="NA00003") vcf1 <- readVcf(fl, "hg19", param=param) writeVcf(vcf1, dest) vcf2 <- readVcf(dest, "hg19") checkIdentical(geno(vcf1)$GT, geno(vcf2)$GT) checkIdentical(geno(header(vcf1)), geno(header(vcf2))) checkTrue(samples(header(vcf2)) == "NA00003") ## array vcf1 <- readVcf(fl, "hg19", param=ScanVcfParam(geno="HQ")) writeVcf(vcf1, dest) vcf2 <- # FORMAT descriptors for GENO fields tryCatch(readVcf(dest, "hg19"), error=conditionMessage, warning=conditionMessage) checkIdentical(geno(vcf1)$HQ, geno(vcf2)$HQ) checkIdentical(geno(header(vcf1)), geno(header(vcf2))) param=ScanVcfParam(geno="HQ", sample="NA00003") vcf1 <- readVcf(fl, "hg19", param=param) suppressWarnings(writeVcf(vcf1, dest)) vcf2 <- readVcf(dest, "hg19") checkIdentical(geno(vcf1)$HQ, geno(vcf2)$HQ) checkIdentical(geno(header(vcf1)), geno(header(vcf2))) checkTrue(samples(header(vcf2)) == "NA00003") ## matrix and array param=ScanVcfParam(geno=c("GT", "HQ"), samples="NA00002") vcf1 <- readVcf(fl, "hg19", param=param) writeVcf(vcf1, dest) vcf2 <- readVcf(dest, "hg19") checkIdentical(geno(vcf1)$GT, geno(vcf2)$GT) checkIdentical(geno(vcf1)$HQ, geno(vcf2)$HQ) checkTrue(samples(header(vcf2)) == "NA00002") vcf3 <- vcf1 geno(vcf3) <- geno(vcf3)[c("HQ", "GT")] writeVcf(vcf3, dest) vcf2 <- readVcf(dest, "hg19", param=param) checkIdentical(geno(vcf1), geno(vcf2)) ## list fl <- system.file("extdata", "gl_chr1.vcf", package="VariantAnnotation") hdr <- scanVcfHeader(fl) param <- ScanVcfParam(samples=samples(hdr)[1:2]) vcf1 <- readVcf(fl, "", param=param) writeVcf(vcf1, dest) vcf2 <- readVcf(dest, "") checkIdentical(geno(vcf1)$GL, geno(vcf2)$GL) } test_alt_description_quoting = function() # https://github.com/Bioconductor/VariantAnnotation/issues/52 { fl <- system.file("extdata", "structural.vcf", package="VariantAnnotation") vcf <- readVcf(fl, genome="hg19") tmp <- tempfile() writeVcf(vcf, filename=tmp) #lines = readLines(tmp) # missing from chunk above #lines[grepl("ALT=", lines)] require("S4Vectors") good = new("DFrame", rownames = c("DEL", "DEL:ME:ALU", "DEL:ME:L1", "DUP", "DUP:TANDEM", "INS", "INS:ME:ALU", "INS:ME:L1", "INV", "CNV"), nrows = 10L, elementType = "ANY", elementMetadata = NULL, metadata = list(), listData = list(Description = c("Deletion", "Deletion of ALU element", "Deletion of L1 element", "Duplication", "Tandem Duplication", "Insertion of novel sequence", "Insertion of ALU element", "Insertion of L1 element", "Inversion", "Copy number variable region" ))) chkr = readVcf(tmp) chk = fixed(header(readVcf(tmp)))$ALT checkIdentical(chk, good) } VariantAnnotation/man/0000755000175100017510000000000014614305321016003 5ustar00biocbuildbiocbuildVariantAnnotation/man/defunct.Rd0000644000175100017510000000461314614305321017726 0ustar00biocbuildbiocbuild\name{VariantAnnotation-defunct} \alias{VariantAnnotation-defunct} \alias{refLocsToLocalLocs} \alias{refLocsToLocalLocs,GRanges,TxDb,missing-method} \alias{refLocsToLocalLocs,GRanges,missing,GRangesList-method} \alias{readVcfLongForm} \alias{readVcfLongForm,TabixFile,character,GRanges-method} \alias{readVcfLongForm,TabixFile,character,missing-method} \alias{readVcfLongForm,TabixFile,character,IntegerRangesList-method} \alias{readVcfLongForm,TabixFile,character,ScanVcfParam-method} \alias{readVcfLongForm,character,character,ScanVcfParam-method} \alias{readVcfLongForm,character,character,missing-method} \alias{readVcfLongForm,character,missing,missing-method} \alias{dbSNPFilter} \alias{regionFilter} \alias{MatrixToSnpMatrix} \alias{VRangesScanVcfParam} \alias{restrictToSNV} \title{Defunct Functions in Package \code{VariantAnnotation}} \description{ The functions or variables listed here are no longer part of \code{VariantAnnotation}. } \section{usage}{ ## Removed \itemize{ \item refLocsToLocalLocs() \item readVcfLongForm() \item dbSNPFilter() \item regionFilter() \item MatrixToSnpMatrix() \item getTranscriptSeqs() \item VRangesScanVcfParam() \item restrictToSNV() } } \details{ ## Removed \itemize{ \item{ \code{refLocsToLocalLocs} has been replaced by \code{mapToTranscripts} and \code{pmapToTranscripts}. } \item{ \code{readVcfLongForm} has been replaced by \code{expand}. } \item{ \code{dbSNPFilter} and \code{regionFilter} have been replaced by \code{filterVcf}. } \item{ \code{regionFilter} has been replaced by \code{filterVcf}. } \item{ \code{MatrixToSnpMatrix} has been replaced by \code{genotypeToSnpMatrix}. } \item{ \code{getTranscriptSeqs} has been replaced by \code{extractTranscriptSeqs} in the GenomicFeatures package. } \item{ \code{VRangesScanVcfParam} has been replaced by \code{ScanVcfParam}. } \item{ \code{restrictToSVN} has been replaced by \code{isSNV}. } } } \author{ Valerie Obenchain } \seealso{ \itemize{ \item \code{\link{expand}} \item \code{\link{filterVcf}} \item \code{\link{genotypeToSnpMatrix}} \item \code{\link[GenomicFeatures]{mapToTranscripts}} \item \code{\link[GenomicFeatures]{extractTranscriptSeqs}} \item \code{\link{isSNV}} \item \code{\link{ScanVcfParam}} } } VariantAnnotation/man/filterVcf-methods.Rd0000644000175100017510000001135614614305321021665 0ustar00biocbuildbiocbuild\name{filterVcf} \alias{filterVcf} \alias{filterVcf,character-method} \alias{filterVcf,TabixFile-method} \title{Filter VCF files} \description{ Filter Variant Call Format (VCF) files from one file to another } \usage{ \S4method{filterVcf}{character}(file, genome, destination, ..., verbose = TRUE, index = FALSE, prefilters = FilterRules(), filters = FilterRules(), param = ScanVcfParam()) \S4method{filterVcf}{TabixFile}(file, genome, destination, ..., verbose = TRUE, index = FALSE, prefilters = FilterRules(), filters = FilterRules(), param = ScanVcfParam()) } \arguments{ \item{file}{A \code{character(1)} file path or \code{\link{TabixFile}} specifying the VCF file to be filtered.} \item{genome}{A \code{character(1)} identifier} \item{destination}{A \code{character(1)} path to the location where the filtered VCF file will be written.} \item{...}{Additional arguments, possibly used by future methods.} \item{verbose}{A \code{logical(1)} indicating whether progress messages should be printed.} \item{index}{A \code{logical(1)} indicating whether the filtered file should be compressed and indexed (using \code{\link{bgzip}} and \code{indexTabix}).} \item{prefilters}{A \code{\link{FilterRules}} instance contains rules for filtering un-parsed lines of the VCF file.} \item{filters}{A \code{\link{FilterRules}} instance contains rules for filtering fully parsed VCF objects.} \item{param}{A \code{\link{ScanVcfParam}} instance restricting input of particular \code{info} or \code{geno} fields, or genomic locations. Applicable when applying a \code{filter} only. Prefiltering involves a grep of unparsed lines in the file; indexing is not used.} } \details{ This function transfers content of one VCF file to another, removing records that fail to satisfy \code{prefilters} and \code{filters}. Filtering is done in a memory efficient manner, iterating over the input VCF file in chunks of default size 100,000 (when invoked with \code{character(1)} for \code{file}) or as specified by the \code{yieldSize} argument of \code{TabixFile} (when invoked with \code{TabixFile}). There are up to two passes. In the first pass, unparsed lines are passed to \code{prefilters} for filtering, e.g., searching for a fixed character string. In the second pass lines successfully passing \code{prefilters} are parsed into \code{VCF} instances and made available for further filtering. One or both of \code{prefilter} and \code{filter} can be present. Filtering works by removing the rows (variants) that do not meet a criteria. Because this is a row-based approach and samples are column-based most genotype filters are only meaningful for single-sample files. If a single samples fails the criteria the entire row (all samples) are removed. The case where genotype filtering is effective for multiple samples is when the criteria is applied across samples and not to the individual (e.g., keep rows where all samples have DP > 10). } \value{The destination file path as a \code{character(1)}.} \author{ Martin Morgan and Paul Shannon } \seealso{ \code{\link{readVcf}}, \code{\link{writeVcf}}. } \examples{ fl <- system.file("extdata", "chr22.vcf.gz", package="VariantAnnotation") ## ----------------------------------------------------------------------- ## Filter for SNVs in a defined set of ranges: ## ----------------------------------------------------------------------- if (require(TxDb.Hsapiens.UCSC.hg19.knownGene)) { txdb <- TxDb.Hsapiens.UCSC.hg19.knownGene exons <- exons(txdb) exons22 <- exons[seqnames(exons) == "chr22"] seqlevelsStyle(exons22) <- "NCBI" ## match chrom names in VCF file ## Range-based filter: withinRange <- function(rng) function(x) x %within% rng ## The first filter identifies SNVs and the second applies the ## range restriction. filters <- FilterRules(list( isSNV = isSNV, withinRange = withinRange(exons22))) ## Apply \dontrun{ filt1 <- filterVcf(fl, "hg19", tempfile(), filters=filters, verbose=TRUE) } } ## ----------------------------------------------------------------------- ## Using a pre-filter and filter: ## ----------------------------------------------------------------------- ## Low coverage exome snp filter: lowCoverageExomeSNP = function(x) grepl("LOWCOV,EXOME", x, fixed=TRUE) ## The pre-filter identifies low coverage exome snps and the filter ## identifies variants with INFO variable VT = SNP. pre <- FilterRules(list(lowCoverageExomeSNP = lowCoverageExomeSNP)) filt <- FilterRules(list(VTisSNP = function(x) info(x)$VT == "SNP")) ## Apply filt2 <- filterVcf(fl, "hg19", tempfile(), prefilters=pre, filters=filt) ## Filtered results vcf <- readVcf(filt2, "hg19") } \keyword{manip} VariantAnnotation/man/genotypeToSnpMatrix-methods.Rd0000644000175100017510000001544114614305321023743 0ustar00biocbuildbiocbuild\name{genotypeToSnpMatrix} \alias{genotypeToSnpMatrix} \alias{genotypeToSnpMatrix,CollapsedVCF-method} \alias{genotypeToSnpMatrix,array-method} \title{Convert genotype calls from a VCF file to a SnpMatrix object} \description{ Convert an array of genotype calls from the "GT", "GP", "GL" or "PL" FORMAT field of a VCF file to a \link[snpStats:SnpMatrix-class]{SnpMatrix}. } \usage{ \S4method{genotypeToSnpMatrix}{CollapsedVCF}(x, uncertain=FALSE, ...) \S4method{genotypeToSnpMatrix}{array}(x, ref, alt, ...) } \arguments{ \item{x}{ A \code{CollapsedVCF} object or a \code{array} of genotype data from the "GT", "GP", "GL" or "PL" FORMAT field of a VCF file. This \code{array} is created with a call to \code{readVcf} and can be accessed with \code{geno()}. } \item{uncertain}{ A logical indicating whether the genotypes to convert should come from the "GT" field (\code{uncertain=FALSE}) or the "GP", "GL" or "PL" field (\code{uncertain=TRUE}). } \item{ref}{ A \code{DNAStringSet} of reference alleles. } \item{alt}{ A \code{DNAStringSetList} of alternate alleles. } \item{\dots}{ Additional arguments, passed to methods. } } \details{ \code{genotypeToSnpMatrix} converts an array of genotype calls from the "GT", "GP", "GL" or "PL" FORMAT field of a VCF file into a \link[snpStats:SnpMatrix-class]{SnpMatrix}. The following caveats apply, \itemize{ \item{no distinction is made between phased and unphased genotypes} \item{variants with >1 ALT allele are set to NA} \item{only single nucleotide variants are included; others are set to NA} \item{only diploid calls are included; others are set to NA} } In VCF files, 0 represents the reference allele and integers greater than 0 represent the alternate alleles (i.e., 2, 3, 4 would indicate the 2nd, 3rd or 4th allele in the ALT field for a particular variant). This function only supports variants with a single alternate allele and therefore the alternate values will always be 1. Genotypes are stored in the SnpMatrix as 0, 1, 2 or 3 where 0 = missing, 1 = "0/0", 2 = "0/1" or "1/0" and 3 = "1/1". In SnpMatrix terminology, "A" is the reference allele and "B" is the risk allele. Equivalent statements to those made with 0 and 1 allele values would be 0 = missing, 1 = "A/A", 2 = "A/B" or "B/A" and 3 = "B/B". The genotype fields are defined as follows: \itemize{ \item{GT : genotype, encoded as allele values separated by either of "/" or "|". The allele values are 0 for the reference allele and 1 for the alternate allele.} \item{GL : genotype likelihoods comprised of comma separated floating point log10-scaled likelihoods for all possible genotypes. In the case of a reference allele A and a single alternate allele B, the likelihoods will be ordered "A/A", "A/B", "B/B".} \item{PL : the phred-scaled genotype likelihoods rounded to the closest integer. The ordering of values is the same as for the GL field.} \item{GP : the phred-scaled genotype posterior probabilities for all possible genotypes; intended to store imputed genotype probabilities. The ordering of values is the same as for the GL field.} } If \code{uncertain=TRUE}, the posterior probabilities of the three genotypes ("A/A", "A/B", "B/B") are encoded (approximately) as byte values. This encoding allows uncertain genotypes to be used in \link[snpStats]{snpStats} functions, which in some cases may be more appropriate than using only the called genotypes. The byte encoding conserves memory by allowing the uncertain genotypes to be stored in a two-dimensional raw matrix. See the \link[snpStats]{snpStats} documentation for more details. } \value{ A list with the following elements, \item{genotypes}{ The output genotype data as an object of class \code{"SnpMatrix"}. The columns are snps and the rows are the samples. See ?\code{SnpMatrix} details of the class structure. } \item{map}{ A \code{DataFrame} giving the snp names and alleles at each locus. The \code{ignore} column indicates which variants were set to \code{NA} (see \code{NA} criteria in 'details' section). } } \references{ \url{http://www.1000genomes.org/wiki/Analysis/Variant\%20Call\%20Format/vcf-variant-call-format-version-41} } \author{ Stephanie Gogarten and Valerie Obenchain } \seealso{ \link{readVcf}, \linkS4class{VCF}, \link[snpStats:SnpMatrix-class]{SnpMatrix} } \examples{ ## ---------------------------------------------------------------- ## Non-probability based snp encoding using "GT" ## ---------------------------------------------------------------- fl <- system.file("extdata", "ex2.vcf", package="VariantAnnotation") vcf <- readVcf(fl, "hg19") ## This file has no "GL" or "GP" field so we use "GT". geno(vcf) ## Convert the "GT" FORMAT field to a SnpMatrix. mat <- genotypeToSnpMatrix(vcf) ## The result is a list of length 2. names(mat) ## Compare coding in the VCF file to the SnpMatrix. geno(vcf)$GT t(as(mat$genotype, "character")) ## The 'ignore' column in 'map' indicates which variants ## were set to NA. Variant rs6040355 was ignored because ## it has multiple alternate alleles, microsat1 is not a ## snp, and chr20:1230237 has no alternate allele. mat$map ## ---------------------------------------------------------------- ## Probability-based encoding using "GL", "PL" or "GP" ## ---------------------------------------------------------------- ## Read a vcf file with a "GL" field. fl <- system.file("extdata", "gl_chr1.vcf", package="VariantAnnotation") vcf <- readVcf(fl, "hg19") geno(vcf) ## Convert the "GL" FORMAT field to a SnpMatrix mat <- genotypeToSnpMatrix(vcf, uncertain=TRUE) ## Only 3 of the 9 variants passed the filters. The ## other 6 variants had no alternate alleles. mat$map ## Compare genotype representations for a subset of ## samples in variant rs180734498. ## Original called genotype geno(vcf)$GT["rs180734498", 14:16] ## Original genotype likelihoods geno(vcf)$GL["rs180734498", 14:16] ## Posterior probability (computed inside genotypeToSnpMatrix) GLtoGP(geno(vcf)$GL["rs180734498", 14:16, drop=FALSE])[1,] ## SnpMatrix coding. t(as(mat$genotype, "character"))["rs180734498", 14:16] t(as(mat$genotype, "numeric"))["rs180734498", 14:16] ## For samples NA11829 and NA11830, one probability is significantly ## higher than the others, so SnpMatrix calls the genotype. These ## calls match the original coding: "0|1" -> "A/B", "0|0" -> "A/A". ## Sample NA11831 was originally called as "0|1" but the probability ## of "0|0" is only a factor of 3 lower, so SnpMatrix calls it as ## "Uncertain" with an appropriate byte-level encoding. } \keyword{manip} \keyword{methods} VariantAnnotation/man/getTranscriptSeqs-methods.Rd0000644000175100017510000000337014614305321023423 0ustar00biocbuildbiocbuild\name{getTranscriptSeqs} \alias{getTranscriptSeqs} \alias{getTranscriptSeqs,GRangesList,ANY-method} \alias{getTranscriptSeqs,GRangesList,BSgenome-method} \alias{getTranscriptSeqs,GRangesList,FaFile-method} \alias{getTranscriptSeqs,GRanges,FaFile-method} \title{Get transcript sequences} \description{ This function is defunct. Use GenomicFeatures::extractTranscriptSeqs() instead. Extract transcript sequences from a \link[BSgenome]{BSgenome} object or an \link[Rsamtools]{FaFile}. } \usage{ \S4method{getTranscriptSeqs}{GRangesList,BSgenome}(query, subject, ...) \S4method{getTranscriptSeqs}{GRangesList,FaFile}(query, subject, ...) \S4method{getTranscriptSeqs}{GRanges,FaFile}(query, subject, ...) } \arguments{ \item{query}{A \link[GenomicRanges]{GRangesList} object containing exons or cds grouped by transcript. } \item{subject}{A \link[BSgenome]{BSgenome} object or a \link[Rsamtools]{FaFile} from which the sequences will be taken. } \item{\dots}{Additional arguments } } \details{ \code{getTranscriptSeqs} is a wrapper for the \code{extractTranscriptSeqs} and \code{getSeq} functions. The purpose is to allow sequence extraction from either a \link[BSgenome]{BSgenome} or \link[Rsamtools]{FaFile}. Transcript sequences are extracted based on the boundaries of the feature provided in the \code{query} (i.e., either exons or cds regions). } \value{ A \link[Biostrings]{DNAStringSet} instance containing the sequences for all transcripts specified in \code{query}. } \author{Valerie Obenchain} \seealso{ \link{predictCoding} \link[GenomicFeatures]{extractTranscriptSeqs} \link[Biostrings]{getSeq} } \examples{ ## See ?extractTranscriptSeqs in the GenomicFeatures package. } \keyword{methods} \keyword{manip} VariantAnnotation/man/GLtoGP.Rd0000644000175100017510000000354114614305321017371 0ustar00biocbuildbiocbuild\name{GLtoGP} \alias{GLtoGP} \alias{PLtoGP} \title{Convert genotype likelihoods to genotype probabilities} \description{ Convert an array of genotype likelihoods to posterior genotype probabilities. } \usage{ GLtoGP(gl) PLtoGP(pl) } \arguments{ \item{gl}{Array of genotype likelihoods (log10-scaled). The format can be a matrix of lists, or a three-dimensional array in which the third dimension corresponds to the probabilities for each genotype.} \item{pl}{Array of genotype likelihoods (phred-scaled, i.e. -10*log10). The format can be a matrix of lists, or a three-dimensional array in which the third dimension corresponds to the probabilities for each genotype.} } \details{ \code{GLtoGP} computes the probability of each genotype as \code{10^x / sum(10^x)}. \code{PLtoGP} first divides by -10 and then proceeds as in \code{GLtoGP}. } \value{ An array of posterior genotype probabilities, in the same format as the input (matrix of lists or 3D array). } \author{ Stephanie Gogarten } \seealso{ \link{readVcf}, \link{genotypeToSnpMatrix} } \examples{ ## Read a vcf file with a "GL" field. vcfFile <- system.file("extdata", "gl_chr1.vcf", package="VariantAnnotation") vcf <- readVcf(vcfFile, "hg19") ## extract genotype likelihoods as a matrix of lists gl <- geno(vcf)$GL class(gl) mode(gl) # convert to posterior probabilities gp <- GLtoGP(gl) ## Read a vcf file with a "PL" field. vcfFile <- system.file("extdata", "hapmap_exome_chr22.vcf.gz", package="VariantAnnotation") vcf <- readVcf(vcfFile, "hg19") ## extract genotype likelihoods as a matrix of lists pl <- geno(vcf)$PL class(pl) mode(pl) # convert to posterior probabilities gp <- PLtoGP(pl) } \keyword{manip} VariantAnnotation/man/indexVcf-method.Rd0000644000175100017510000000266514614305321021327 0ustar00biocbuildbiocbuild\name{indexVcf} \alias{indexVcf} \alias{indexVcf,character-method} \alias{indexVcf,VcfFile-method} \alias{indexVcf,VcfFileList-method} \title{Create index files for VCF files} \description{ \code{indexVcf()} creates an index file for compressed VCF files, if the index file does not exist. } \usage{ \S4method{indexVcf}{character}(x, ...) \S4method{indexVcf}{VcfFile}(x, ...) \S4method{indexVcf}{VcfFileList}(x, ...) } \arguments{ \item{x}{ \code{character()}, \code{VcfFile}, or \code{VcfFileList} pointing to bgzf-compressed VCF files. } \item{...}{ Additional arguments to \code{\link[Rsamtools]{indexTabix}} } } \details{ If \code{x} is a character vector, assumes they are the path(s) to bgzf-compressed VCF file(s). If an index does not exist, one is created. VCF files can be compreseed using \code{\link{bgzip}}. A \code{VcfFile} or \code{VcfFileList} is returned. If a \code{VcfFile} or \code{VcfFileList} is given, the index file is checked, if it does not exist it will crete one. If the index file was NA or missing, the path of the associated VCF file is used as the index file path. An updated \code{VcfFile} or \code{VcfFileList} is returned. } \value{VcfFile or VcfFileList} \author{Lori Shepherd} \seealso{ \code{\link{VcfFile}} } \examples{ fl <- system.file( "extdata", "chr7-sub.vcf.gz", package="VariantAnnotation", mustWork=TRUE ) vcf1 <- indexVcf(fl) vcf1 } \keyword{manip} VariantAnnotation/man/isSNV-methods.Rd0000644000175100017510000001341014614305321020734 0ustar00biocbuildbiocbuild\name{isSNV} \alias{isSNV} \alias{isInsertion} \alias{isDeletion} \alias{isIndel} \alias{isDelins} \alias{isTransition} \alias{isSubstitution} % VCF \alias{isSNV,ExpandedVCF-method} \alias{isSNV,CollapsedVCF-method} \alias{isInsertion,ExpandedVCF-method} \alias{isInsertion,CollapsedVCF-method} \alias{isDeletion,ExpandedVCF-method} \alias{isDeletion,CollapsedVCF-method} \alias{isIndel,ExpandedVCF-method} \alias{isIndel,CollapsedVCF-method} \alias{isDelins,ExpandedVCF-method} \alias{isDelins,CollapsedVCF-method} \alias{isSubstitution,ExpandedVCF-method} \alias{isSubstitution,CollapsedVCF-method} \alias{isTransition,ExpandedVCF-method} \alias{isTransition,CollapsedVCF-method} % VRanges \alias{isSNV,VRanges-method} \alias{isInsertion,VRanges-method} \alias{isDeletion,VRanges-method} \alias{isIndel,VRanges-method} \alias{isDelins,VRanges-method} \alias{isSubstitution,VRanges-method} \alias{isTransition,VRanges-method} \title{ Identification of genomic variant types. } \description{ Functions for identifying variant types such as SNVs, insertions, deletions, transitions, and structural rearrangements. } \usage{ \S4method{isSNV}{VRanges}(x, ...) \S4method{isSNV}{ExpandedVCF}(x, ...) \S4method{isSNV}{CollapsedVCF}(x, ..., singleAltOnly = TRUE) \S4method{isInsertion}{VRanges}(x, ...) \S4method{isInsertion}{ExpandedVCF}(x, ...) \S4method{isInsertion}{CollapsedVCF}(x, ..., singleAltOnly = TRUE) \S4method{isDeletion}{VRanges}(x, ...) \S4method{isDeletion}{ExpandedVCF}(x, ...) \S4method{isDeletion}{CollapsedVCF}(x, ..., singleAltOnly = TRUE) \S4method{isIndel}{VRanges}(x, ...) \S4method{isIndel}{ExpandedVCF}(x, ...) \S4method{isIndel}{CollapsedVCF}(x, ..., singleAltOnly = TRUE) \S4method{isDelins}{VRanges}(x, ...) \S4method{isDelins}{ExpandedVCF}(x, ...) \S4method{isDelins}{CollapsedVCF}(x, ..., singleAltOnly = TRUE) \S4method{isTransition}{VRanges}(x, ...) \S4method{isTransition}{ExpandedVCF}(x, ...) \S4method{isTransition}{CollapsedVCF}(x, ..., singleAltOnly = TRUE) \S4method{isSubstitution}{VRanges}(x, ...) \S4method{isSubstitution}{ExpandedVCF}(x, ...) \S4method{isSubstitution}{CollapsedVCF}(x, ..., singleAltOnly = TRUE) } \arguments{ \item{x}{A \linkS4class{VCF} or \linkS4class{VRanges} object. } \item{singleAltOnly}{A \code{logical} only applicable when \code{x} is a \link{CollapsedVCF} class. When \code{TRUE} (default) only variants with a single alternate allele are evaluated; all multi-alt variants evaluate to \code{FALSE}. When \code{singleAltOnly=FALSE} all ref / alt pairs for each variant are evaluated. If any ref / alt pairs meet the test criteria a value of TRUE is returned for the variant; this may result in a value of TRUE for a variant with a mixture of alternate alleles, some that pass the criteria and some that do not. To retain single ref / alt pairs that pass the critera use \code{expand} on the \code{CollapsedVCF} and then apply the test. } \item{\dots}{Arguments passed to other methods. } } \details{ All functions return a logical vector the length of \code{x}. Variants in gvcf files with NON_REF alt alleles return TRUE; structural variants return FALSE. \itemize{ \item{isSNV: }{ Reference and alternate alleles are both a single nucleotide long. } \item{isInsertion: }{ Reference allele is a single nucleotide and the alternate allele is greater (longer) than a single nucleotide and the first nucleotide of the alternate allele matches the reference. } \item{isDeletion: }{ Alternate allele is a single nucleotide and the reference allele is greater (longer) than a single nucleotide and the first nucleotide of the reference allele matches the alternate. } \item{isIndel: }{ The variant is either a deletion or insertion as determined by \code{isDeletion} and \code{isInsertion}. } \item{isDelins: }{ The variant is a deletion followed by an insertion, either of them involving two or more nucleotides. } \item{isSubstition: }{ Reference and alternate alleles are the same length (1 or more nucleotides long). } \item{isTransition: }{ Reference and alternate alleles are both a single nucleotide long. The reference-alternate pair interchange is of either two-ring purines (A <-> G) or one-ring pyrimidines (C <-> T). } } } \value{ A \code{logical} vector the same length as \code{x}. } \author{Michael Lawrence, Valerie Obenchain and Robert Castelo} \examples{ fl <- system.file("extdata", "ex2.vcf", package="VariantAnnotation") ## --------------------------------------------------------------------- ## VCF objects ## --------------------------------------------------------------------- vcf <- readVcf(fl, "hg19") DataFrame(ref(vcf), alt(vcf)) ## This vcf has transitions in row 2 and 3. When 'singleAltOnly=TRUE' ## only the row 2 variant is identified: isTransition(vcf) ## Both row 2 and 3 are identified when 'singleAltOnly=FALSE': isTransition(vcf, singleAltOnly=FALSE) ## Expand the CollapsedVCF to ExpandedVCF evcf <- expand(vcf) ## All ref / alt pairs are now expanded and there is no need to ## use 'singleAltOnly'. The return length is now 7 instead of 5: transition <- isTransition(evcf) transition DataFrame(ref(evcf)[transition], alt(evcf)[transition]) ## --------------------------------------------------------------------- ## VRanges objects ## --------------------------------------------------------------------- ## A VRanges object holds data from a VCF class in a completely ## 'flat' fashion. INFO and FORMAT variables for all subjects are ## 'repped' out such that each row is a unique combination of data. vr <- as(vcf, "VRanges") isSNV(vr, singleAltOnly=FALSE) } \keyword{methods} VariantAnnotation/man/locateVariants-methods.Rd0000644000175100017510000004152314614305321022717 0ustar00biocbuildbiocbuild\name{locateVariants} \alias{locateVariants} \alias{locateVariants,IntegerRanges,TxDb,VariantType-method} \alias{locateVariants,IntegerRanges,GRangesList,VariantType-method} \alias{locateVariants,GRanges,TxDb,VariantType-method} \alias{locateVariants,GRanges,GRangesList,VariantType-method} \alias{locateVariants,VCF,TxDb,VariantType-method} \alias{locateVariants,VCF,GRangesList,VariantType-method} \alias{locateVariants,GRanges,TxDb,CodingVariants-method} \alias{locateVariants,GRanges,GRangesList,CodingVariants-method} \alias{locateVariants,GRanges,TxDb,IntronVariants-method} \alias{locateVariants,GRanges,GRangesList,IntronVariants-method} \alias{locateVariants,GRanges,TxDb,FiveUTRVariants-method} \alias{locateVariants,GRanges,GRangesList,FiveUTRVariants-method} \alias{locateVariants,GRanges,TxDb,ThreeUTRVariants-method} \alias{locateVariants,GRanges,GRangesList,ThreeUTRVariants-method} \alias{locateVariants,GRanges,TxDb,IntergenicVariants-method} \alias{locateVariants,GRanges,GRangesList,IntergenicVariants-method} \alias{locateVariants,GRanges,TxDb,SpliceSiteVariants-method} \alias{locateVariants,GRanges,GRangesList,SpliceSiteVariants-method} \alias{locateVariants,GRanges,TxDb,PromoterVariants-method} \alias{locateVariants,GRanges,GRangesList,PromoterVariants-method} \alias{locateVariants,GRanges,TxDb,AllVariants-method} \alias{locateVariants,GRanges,GRangesList,AllVariants-method} \title{Locate variants} \description{Variant location with respect to gene function} \usage{ locateVariants(query, subject, region, ...) \S4method{locateVariants}{VCF,TxDb,VariantType}(query, subject, region, ..., cache=new.env(parent=emptyenv()), ignore.strand=FALSE, asHits=FALSE) \S4method{locateVariants}{GRanges,TxDb,VariantType}(query, subject, region, ..., cache=new.env(parent=emptyenv()), ignore.strand=FALSE, asHits=FALSE) } \arguments{ \item{query}{A \link[IRanges]{IntegerRanges}, \link[GenomicRanges]{GRanges} or \linkS4class{VCF} object containing the variants. Metadata columns are allowed but ignored. NOTE: Zero-width ranges are treated as width-1 ranges; start values are decremented to equal the end value. } \item{subject}{A \link[GenomicFeatures]{TxDb} or \code{GRangesList} object that serves as the annotation. GFF files can be converted to \link[GenomicFeatures]{TxDb} objects with \code{makeTxDbFromGFF()} in the \code{txdbmaker} package. } \item{region}{An instance of one of the 8 VariantType classes: \code{CodingVariants}, \code{IntronVariants}, \code{FiveUTRVariants}, \code{ThreeUTRVariants}, \code{IntergenicVariants}, \code{SpliceSiteVariants}, \code{PromoterVariants}, \code{AllVariants}. All objects can be instantiated with no arguments, e.g., CodingVariants() will create an object of \code{CodingVariants}. \code{AllVariants}, \code{PromoterVariants} and \code{IntergenicVariants} have \code{upstream} and \code{downstream} arguments. For \code{PromoterVariants} and \code{IntergenicVariants} these are single integer values >= 0. For \code{AllVariants} these are integer vectors of length 2 named \sQuote{promoter} and \sQuote{intergenic}. See ?\code{upstream} for more details. When using \code{AllVariants}, a range in \code{query} may fall in multiple regions (e.g., 'intergenic' and 'promoter'). In this case the result will have a row for each match. All data in the row will be equivalent except the LOCATION column. } \item{\dots}{Additional arguments passed to methods } \item{cache}{An \code{environment} into which required components of \code{subject} are loaded. Provide, and re-use, a cache to speed repeated queries to the same \code{subject} across different \code{query} instances. } \item{ignore.strand}{A \code{logical} indicating if strand should be ignored when performing overlaps. } \item{asHits}{A \code{logical} indicating if the results should be returned as a \link[S4Vectors]{Hits} object. Not applicable when \code{region} is AllVariants or IntergenicVariants. } } \details{ \describe{ \item{Range representation:}{ The ranges in \code{query} should reflect the position(s) of the reference allele. For snps the range will be of width 1. For range insertions or deletions the reference allele could be a sequence such as GGTG in which case the width of the range should be 4. } \item{Location:}{ Possible locations are \sQuote{coding}, \sQuote{intron}, \sQuote{threeUTR}, \sQuote{fiveUTR}, \sQuote{intergenic}, \sQuote{spliceSite}, or \sQuote{promoter}. Overlap operations for \sQuote{coding}, \sQuote{intron}, \sQuote{threeUTR}, and \sQuote{fiveUTR} require variants to fall completely within the defined region to be classified as such. To be classified as a \sQuote{spliceSite} the variant must overlap with any portion of the first 2 or last 2 nucleotides in an intron. \sQuote{intergenic} variants are ranges that do not fall within a defined gene region. \sQuote{transcripts by gene} are extracted from the annotation and overlapped with the variant positions. Variants with no overlaps are classified as \code{intergenic}. When available, gene IDs for the flanking genes are provided as \code{PRECEDEID} and \code{FOLLOWID}. \code{upstream} and \code{downstream} arguments define the acceptable distance from the query for the flanking genes. \code{PRECEDEID} and \code{FOLLOWID} results are lists and contain all genes that fall within the defined distance. See the examples for how to compute the distance from ranges to PRECEDEID and FOLLOWID. \sQuote{promoter} variants fall within a specified range upstream and downstream of the transcription start site. Ranges values can be set with the \code{upstream} and \code{downstream} arguments when creating the \code{PromoterVariants()} or \code{AllVariants()} classes. } \item{Subject as GRangesList:}{ The \code{subject} can be a \code{TxDb} or \code{GRangesList} object. When using a \code{GRangesList} the type of data required is driven by the \code{VariantType} class. Below is a description of the appropriate \code{GRangesList} for each \code{VariantType}. \describe{ \item{CodingVariants:}{coding (CDS) by transcript} \item{IntronVariants:}{introns by transcript} \item{FiveUTRVariants:}{five prime UTR by transcript} \item{ThreeUTRVariants:}{three prime UTR by transcript} \item{IntergenicVariants:}{transcripts by gene} \item{SpliceSiteVariants:}{introns by transcript} \item{PromoterVariants:}{list of transcripts} \item{AllVariants:}{no GRangeList method available} } } \item{Using the cache:}{ When processing multiple VCF files performance is enhanced by specifying an environment as the \code{cache} argument. This cache is used to store and reuse extracted components of the subject (TxDb) required by the function. The first call to the function (i.e., processing the first VCF file in a list of many) populates the cache; repeated calls to \code{locateVariants} will access these objects from the cache vs re-extracting the same information. } } } \value{ A \code{GRanges} object with a row for each variant-transcript match. Strand of the output is from the \code{subject} hit except in the case of IntergenicVariants. For intergenic, multiple precede and follow gene ids are returned for each variant. When \code{ignore.strand=TRUE} the return strand is \code{*} because genes on both strands are considered and it is possible to have a mixture. When \code{ignore.strand=FALSE} the strand will match the \code{query} because only genes on the same strand are considered. Metadata columns are \code{LOCATION}, \code{QUERYID}, \code{TXID}, \code{GENEID}, \code{PRECEDEID}, \code{FOLLOWID} and \code{CDSID}. Results are ordered by \code{QUERYID}, \code{TXID} and \code{GENEID}. Columns are described in detail below. \describe{ \item{\code{LOCATION}}{ Possible locations are \sQuote{coding}, \sQuote{intron}, \sQuote{threeUTR}, \sQuote{fiveUTR}, \sQuote{intergenic}, \sQuote{spliceSite} and \sQuote{promoter}. To be classified as \sQuote{coding}, \sQuote{intron}, \sQuote{threeUTR} or \sQuote{fiveUTR} the variant must fall completely within the region. \sQuote{intergenic} variants do not fall within a transcript. The \sQuote{GENEID} for these positions are \code{NA}. Lists of flanking genes that fall within the distance defined by \code{upstream} and \code{downstream} are given as \sQuote{PRECEDEID} and \sQuote{FOLLOWID}. By default, the gene ID is returned in the \sQuote{PRECEDEID} and \sQuote{FOLLOWID} columns. To return the transcript ids instead set \code{idType = "tx"} in the \code{IntergenicVariants()} constructor. A \sQuote{spliceSite} variant overlaps any portion of the first 2 or last 2 nucleotides of an intron. } \item{\code{LOCSTART, LOCEND}}{ Genomic position in LOCATION-centric coordinates. If LOCATION is `intron`, these are intron-centric coordinates, if LOCATION is `coding` then cds-centric. All coordinates are relative to the start of the transcript. SpliceSiteVariants, IntergenicVariants and PromoterVariants have no formal extraction `by transcript` so for these variants LOCSTART and LOCEND are NA. Coordinates are computed with \code{mapToTranscripts}; see ?\code{mapToTranscripts} in the GenomicFeatures package for details. } \item{\code{QUERYID}}{ The \code{QUERYID} column provides a map back to the row in the original \code{query}. If the \code{query} was a \code{VCF} object this index corresponds to the row in the \code{GRanges} object returned by the \code{rowRanges} accessor. } \item{\code{TXID}}{ The transcript id taken from the \code{TxDb} object. } \item{\code{CDSID}}{ The coding sequence id(s) taken from the \code{TxDb} object. } \item{\code{GENEID}}{ The gene id taken from the \code{TxDb} object. } \item{\code{PRECEDEID}}{ IDs for all genes the query precedes within the defined \code{upstream} and \code{downstream} distance. Only applicable for \sQuote{intergenic} variants. By default this column contains gene ids; to return transcript ids set \code{idType = "tx"} in the \code{IntergenicVariants} constructor. } \item{\code{FOLLOWID}}{ IDs for all genes the query follows within the defined \code{upstream} and \code{downstream} distance. Only applicable for \sQuote{intergenic} variants. By default this column contains gene ids; to return transcript ids set \code{idType = "tx"} in the \code{IntergenicVariants} constructor. } All ID values will be \sQuote{NA} for variants with a location of \code{transcript_region} or \code{NA}. } } \author{Valerie Obenchain} \seealso{ \itemize{ \item The \link{readVcf} function. \item The \link{predictCoding} function. \item The promoters function on the \link[GenomicRanges]{intra-range-methods} man page in the \pkg{GenomicRanges} package. } } \examples{ library(TxDb.Hsapiens.UCSC.hg19.knownGene) txdb <- TxDb.Hsapiens.UCSC.hg19.knownGene ## --------------------------------------------------------------------- ## Variants in all gene regions ## --------------------------------------------------------------------- ## Read variants from a VCF file. fl <- system.file("extdata", "gl_chr1.vcf", package="VariantAnnotation") vcf <- readVcf(fl, "hg19") ## Often the seqlevels in the VCF file do not match those in the TxDb. head(seqlevels(vcf)) head(seqlevels(txdb)) intersect(seqlevels(vcf), seqlevels(txdb)) ## Rename seqlevels with renameSeqlevesl(). vcf <- renameSeqlevels(vcf, paste0("chr", seqlevels(vcf))) ## Confirm. intersect(seqlevels(vcf), seqlevels(txdb)) ## Overlaps for all possible variant locations. loc_all <- locateVariants(vcf, txdb, AllVariants()) table(loc_all$LOCATION) ## --------------------------------------------------------------------- ## Variants in intergenic regions ## --------------------------------------------------------------------- ## Intergenic variants do not overlap a gene range in the ## annotation and therefore 'GENEID' is always NA. Flanking genes ## that fall within the 'upstream' and 'downstream' distances are ## reported as PRECEDEID and FOLLOWID. region <- IntergenicVariants(upstream=70000, downstream=70000) loc_int <- locateVariants(vcf, txdb, region) mcols(loc_int)[c("LOCATION", "PRECEDEID", "FOLLOWID")] ## Distance to the flanking genes can be computed for variants that ## have PRECEDEID(s) or FOLLOWID(s). Each variant can have multiple ## flanking id's so we first expand PRECEDEID and the corresponding ## variant ranges. p_ids <- unlist(loc_int$PRECEDEID, use.names=FALSE) exp_ranges <- rep(loc_int, elementNROWS(loc_int$PRECEDEID)) ## Compute distances with the distance method defined in GenomicFeatures. ## Help page can be found at ?`distance,GenomicRanges,TxDb-method`. ## The method returns NA for ids that cannot be collapsed into a single ## range (e.g., genes with ranges on multiple chromosomes). distance(exp_ranges, txdb, id=p_ids, type="gene") ## To search for distance by transcript id set idType='tx' in the ## IntergenicVariants() constructor, e.g., ## locateVariants(vcf, txdb, region=IntergenicVariants(idType="tx")) ## Unlist ids and expand ranges as before to get p_ids and exp_ranges. ## Then call distance() with type = "tx": ## distance(exp_ranges, txdb, id=p_ids, type="tx") ## --------------------------------------------------------------------- ## GRangesList as subject ## --------------------------------------------------------------------- ## When 'subject' is a GRangesList the GENEID is unavailable and ## will always be reported as NA. This is because the GRangesList ## objects are extractions of region-by-transcript, not region-by-gene. \dontrun{ cdsbytx <- cdsBy(txdb) locateVariants(vcf, cdsbytx, CodingVariants()) intbytx <- intronsByTranscript(txdb) locateVariants(vcf, intbytx, IntronVariants()) } ## --------------------------------------------------------------------- ## Using the cache ## --------------------------------------------------------------------- ## When processing multiple VCF files, the 'cache' can be used ## to store the extracted components of the TxDb ## (i.e., cds by tx, introns by tx etc.). This avoids having to ## re-extract these GRangesLists during each loop. \dontrun{ myenv <- new.env() files <- list(vcf1, vcf2, vcf3) lapply(files, function(fl) { vcf <- readVcf(fl, "hg19") ## modify seqlevels to match TxDb seqlevels(vcf_mod) <- paste0("chr", seqlevels(vcf)) locateVariants(vcf_mod, txdb, AllVariants(), cache=myenv) }) } ## --------------------------------------------------------------------- ## Parallel implmentation ## --------------------------------------------------------------------- \dontrun{ library(BiocParallel) ## A connection to a TxDb object is established when ## the package is loaded. Because each process reading from an ## sqlite db must have a unique connection the TxDb ## object cannot be passed as an argument when running in ## parallel. Instead the package must be loaded on each worker. ## The overhead of the multiple loading may defeat the ## purpose of running the job in parallel. An alternative is ## to instead pass the appropriate GRangesList as an argument. ## The details section on this man page under the heading ## 'Subject as GRangesList' explains what GRangesList is ## appropriate for each variant type. ## A. Passing a GRangesList: fun <- function(x, subject, ...) locateVariants(x, subject, IntronVariants()) library(TxDb.Hsapiens.UCSC.hg19.knownGene) grl <- intronsByTranscript(TxDb.Hsapiens.UCSC.hg19.knownGene) mclapply(c(vcf, vcf), fun, subject=grl) ## B. Passing a TxDb: ## Forking: ## In the case of forking, the TxDb cannot be loaded ## in the current workspace. ## To detach the NAMESPACE: ## unloadNamespace("TxDb.Hsapiens.UCSC.hg19.knownGene") fun <- function(x) { library(TxDb.Hsapiens.UCSC.hg19.knownGene) locateVariants(x, TxDb.Hsapiens.UCSC.hg19.knownGene, IntronVariants()) } mclapply(c(vcf, vcf), fun) ## Clusters: cl <- makeCluster(2, type = "SOCK") fun <- function(query, subject, region) { library(VariantAnnotation) library(TxDb.Hsapiens.UCSC.hg19.knownGene) locateVariants(query, TxDb.Hsapiens.UCSC.hg19.knownGene, region) } parLapply(cl, c(vcf, vcf), fun, region=IntronVariants()) stopCluster(cl) } } \keyword{methods} VariantAnnotation/man/PolyPhenDb-class.Rd0000644000175100017510000001065114614305321021404 0ustar00biocbuildbiocbuild\name{PolyPhenDb-class} \docType{class} \alias{PolyPhen} \alias{PolyPhenDb} \alias{class:PolyPhenDb} \alias{PolyPhenDb-class} \alias{duplicateRSID} \alias{metadata,PolyPhenDb-method} \alias{columns,PolyPhenDb-method} \alias{keys,PolyPhenDb-method} \alias{select,PolyPhenDb-method} \title{PolyPhenDb objects} \description{ The PolyPhenDb class is a container for storing a connection to a PolyPhen sqlite database. } \section{Methods}{ In the code below, \code{x} is a \code{PolyPhenDb} object. \describe{ \item{}{ \code{metadata(x)}: Returns \code{x}'s metadata in a data frame. } \item{}{ \code{columns(x)}: Returns the names of the \code{columns} that can be used to subset the data columns. For column descriptions see \code{?PolyPhenDbColumns}. } \item{}{ \code{keys(x)}: Returns the names of the \code{keys} that can be used to subset the data rows. The \code{keys} values are the rsid's. } \item{}{ \code{select(x, keys = NULL, columns = NULL, ...)}: Returns a subset of data defined by the character vectors \code{keys} and \code{columns}. If no \code{keys} are supplied, all rows are returned. If no \code{columns} are supplied, all columns are returned. See \code{?PolyPhenDbColumns} for column descriptions. } \item{}{ \code{duplicateRSID(x)}: Returns a named list of duplicate rsid groups. The names are the \code{keys}, the list elements are the rsid's that have been reported as having identical chromosome position and alleles and therefore translating into the same amino acid residue substitution. } } } \details{ PolyPhen (Polymorphism Phenotyping) is a tool which predicts the possible impact of an amino acid substitution on the structure and function of a human protein by applying empirical rules to the sequence, phylogenetic and structural information characterizing the substitution. PolyPhen makes its predictions using UniProt features, PSIC profiles scores derived from multiple alignment and matches to PDP or PQS structural databases. The procedure can be roughly outlined in the following steps, see the references for complete details, \itemize{ \item sequence-based characterization of substitution site \item calculation of PSIC profile scores for two amino acid variants \item calculation of structural parameters and contacts \item prediction } PolyPhen uses empirically derived rules to predict that a non-synonymous SNP is \itemize{ \item probably damaging : it is with high confidence supposed to affect protein function or structure \item possibly damaging : it is supposed to affect protein function or structure \item benign : most likely lacking any phenotypic effect \item unknown : when in some rare cases, the lack of data do not allow PolyPhen to make a prediction } } \seealso{ \code{?PolyPhenDbColumns} } \references{ PolyPhen Home: \url{http://genetics.bwh.harvard.edu/pph2/dokuwiki/} Adzhubei IA, Schmidt S, Peshkin L, Ramensky VE, Gerasimova A, Bork P, Kondrashov AS, Sunyaev SR. Nat Methods 7(4):248-249 (2010). Ramensky V, Bork P, Sunyaev S. Human non-synonymous SNPs: server and survey. Nucleic Acids Res 30(17):3894-3900 (2002). Sunyaev SR, Eisenhaber F, Rodchenkov IV, Eisenhaber B, Tumanyan VG, Kuznetsov EN. PSIC: profile extraction from sequence alignments with position-specific counts of independent observations. Protein Eng 12(5):387-394 (1999). } \author{Valerie Obenchain} \examples{ library(PolyPhen.Hsapiens.dbSNP131) ## metadata metadata(PolyPhen.Hsapiens.dbSNP131) ## available rsid's head(keys(PolyPhen.Hsapiens.dbSNP131)) ## column descriptions found at ?PolyPhenDbColumns columns(PolyPhen.Hsapiens.dbSNP131) ## subset on keys and columns subst <- c("AA1", "AA2", "PREDICTION") rsids <- c("rs2142947", "rs4995127", "rs3026284") select(PolyPhen.Hsapiens.dbSNP131, keys=rsids, columns=subst) ## retrieve substitution scores subst <- c("IDPMAX", "IDPSNP", "IDQMIN") select(PolyPhen.Hsapiens.dbSNP131, keys=rsids, columns=subst) ## retrieve the PolyPhen-2 classifiers subst <- c("PPH2CLASS", "PPH2PROB", "PPH2FPR", "PPH2TPR", "PPH2FDR") select(PolyPhen.Hsapiens.dbSNP131, keys=rsids, columns=subst) ## duplicate groups of rsid's duplicateRSID(PolyPhen.Hsapiens.dbSNP131, c("rs71225486", "rs1063796")) } \keyword{classes} \keyword{methods} VariantAnnotation/man/PolyPhenDbColumns.Rd0000644000175100017510000001263214614305321021643 0ustar00biocbuildbiocbuild\name{PolyPhenDbColumns} \alias{PolyPhenDbColumns} \title{PolyPhenDb Columns} \description{ Description of the PolyPhen Sqlite Database Columns } \section{Column descriptions}{ These column names are displayed when \code{columns} is called on a \code{PolyPhenDb} object. \itemize{ \item rsid : rsid } Original query : \itemize{ \item OSNPID : original SNP identifier from user input \item OSNPACC : original protein identifier from user input \item OPOS : original substitution position in the protein sequence from user input \item OAA1 : original wild type (reference) aa residue from user input \item OAA2 : original mutant (reference) aa residue from user input } Mapped query : \itemize{ \item SNPID : SNP identifier mapped to dbSNP rsID if available, otherwise same as o_snp_id. This value was used as the rsid column \item ACC : protein UniProtKB accession if known protein, otherwise same as o_acc \item POS : substitution position mapped to UniProtKB protein sequence if known, otherwise same as o_pos \item AA1 : wild type aa residue \item AA2 : mutant aa residue \item NT1 : wild type allele nucleotide \item NT2 : mutant allele nucleotide } PolyPhen-2 prediction : \itemize{ \item PREDICTION : qualitative ternary classification FPR thresholds } PolyPhen-1 prediction : \itemize{ \item BASEDON : prediction basis \item EFFECT : predicted substitution effect on the protein structure or function } PolyPhen-2 classifiers : \itemize{ \item PPH2CLASS : binary classifier outcome ("damaging" or "neutral") \item PPH2PROB : probability of the variation being dammaging \item PPH2FPR : false positive rate at the pph2_prob level \item PPH2TPR : true positive rate at the pph2_prob level \item PPH2FDR : false discovery rate at the pph2_prob level } UniProtKB-SwissProt derived protein sequence annotations : \itemize{ \item SITE : substitution SITE annotation \item REGION : substitution REGION annotation \item PHAT : PHAT matrix element for substitution in the TRANSMEM region } Multiple sequence alignment scores : \itemize{ \item DSCORE : difference of PSIC scores for two aa variants (Score1 - Score2) \item SCORE1 : PSIC score for wild type aa residue (aa1) \item SCORE2 : PSIC score for mutant aa residue (aa2) \item NOBS : number of residues observed at the substitution position in the multiple alignment (sans gaps) } Protein 3D structure features : \itemize{ \item NSTRUCT : initial number of BLAST hits to similar proteins with 3D structures in PDB \item NFILT : number of 3D BLAST hits after identity threshold filtering \item PDBID : protein structure identifier from PDB \item PDBPOS : position of substitution in PDB protein sequence \item PDBCH : PDB polypeptide chain identifier \item IDENT : sequence identity between query and aligned PDB sequences \item LENGTH : PDB sequence alignment length \item NORMACC : normalized accessible surface \item SECSTR : DSSP secondary structure assignment \item MAPREG : region of the phi-psi (Ramachandran) map derived from the residue dihedral angles \item DVOL : change in residue side chain volume \item DPROP : change in solvent accessible surface propensity resulting from the substitution \item BFACT : normalized B-factor (temperature factor) for the residue \item HBONDS : number of hydrogen sidechain-sidechain and sidechain-mainchain bonds formed by the residue \item AVENHET : average number of contacts with heteroatoms per residue \item MINDHET : closest contact with heteroatom \item AVENINT : average number of contacts with other chains per residue \item MINDINT : closest contact with other chain \item AVENSIT : average number of contacts with critical sites per residue \item MINDSIT : closest contact with a critical site } Nucleotide sequence features (CpG/codon/exon junction) : \itemize{ \item TRANSV : whether substitution is a transversion \item CODPOS : position of the substitution within the codon \item CPG : whether or not the substitution changes CpG context \item MINDJNC : substitution distance from exon/intron junction } Pfam protein family : \itemize{ \item PFAMHIT : Pfam identifier of the query protein } Substitution scores : \itemize{ \item IDPMAX : maximum congruency of the mutant aa residue to all sequences in multiple alignment \item IDPSNP : maximum congruency of the mutant aa residue to the sequence in alignment with the mutant residue \item IDQMIN : query sequence identity with the closest homologue deviating from the wild type aa residue } Comments : \itemize{ \item COMMENTS : Optional user comments } } \seealso{ \code{?PolyPhenDb} } \author{Valerie Obenchain} \keyword{classes} \keyword{methods} VariantAnnotation/man/post_Hs_region.Rd0000644000175100017510000000160314614305321021254 0ustar00biocbuildbiocbuild% Generated by roxygen2: do not edit by hand % Please edit documentation in R/use_vep_api.R \name{post_Hs_region} \alias{post_Hs_region} \title{elementary vep/homo_sapiens/region call to ensembl VEP REST API} \usage{ post_Hs_region(chr, pos, id, ref, alt) } \arguments{ \item{chr}{character(1) ensembl chromosome identifier (e.g., "7")} \item{pos}{numeric(1) 1-based chromosome position} \item{id}{character(1) arbitrary identifier} \item{ref}{character(1) reference allele} \item{alt}{character(1) alternative allele} } \value{ Instance of 'response' defined in httr package. } \description{ elementary vep/homo_sapiens/region call to ensembl VEP REST API } \note{ This function prepares a POST to rest.ensembl.org/vep/homo_sapiens/region endpoint. } \examples{ chk = post_Hs_region("7", 155800001, "chk", "A", "T") chk res = jsonlite::fromJSON(jsonlite::toJSON(httr::content(chk))) dim(chk) } VariantAnnotation/man/predictCoding-methods.Rd0000644000175100017510000002344014614305321022514 0ustar00biocbuildbiocbuild\name{predictCoding} \alias{predictCoding} \alias{predictCoding,IntegerRanges,TxDb,ANY,DNAStringSet-method} \alias{predictCoding,GRanges,TxDb,ANY,DNAStringSet-method} \alias{predictCoding,CollapsedVCF,TxDb,ANY,missing-method} \alias{predictCoding,ExpandedVCF,TxDb,ANY,missing-method} \alias{predictCoding,VRanges,TxDb,ANY,missing-method} \title{Predict amino acid coding changes for variants} \description{ Predict amino acid coding changes for variants a coding regions } \usage{ \S4method{predictCoding}{CollapsedVCF,TxDb,ANY,missing}(query, subject, seqSource, varAllele, ..., ignore.strand=FALSE) \S4method{predictCoding}{ExpandedVCF,TxDb,ANY,missing}(query, subject, seqSource, varAllele, ..., ignore.strand=FALSE) \S4method{predictCoding}{IntegerRanges,TxDb,ANY,DNAStringSet}(query, subject, seqSource, varAllele, ..., ignore.strand=FALSE) \S4method{predictCoding}{GRanges,TxDb,ANY,DNAStringSet}(query, subject, seqSource, varAllele, ..., ignore.strand=FALSE) \S4method{predictCoding}{VRanges,TxDb,ANY,missing}(query, subject, seqSource, varAllele, ..., ignore.strand=FALSE) } \arguments{ \item{query}{A \linkS4class{VCF}, \link{IntegerRanges}, \link{GRanges} or \code{VRanges} instance containing the variants to be annotated. If \code{query} is a \link[IRanges]{IntegerRanges} or \code{VRanges} it is coerced to a \link{GRanges}. If a \linkS4class{VCF} is provided the \code{GRanges} returned by the \code{rowRanges()} accessor will be used. All metadata columns are ignored. When \code{query} is not a \code{VCF} object a \code{varAllele} must be provided. The \code{varAllele} must be a \code{DNAStringSet} the same length as the \code{query}. If there are multiple alternate alleles per variant the \code{query} must be expanded to one row per each alternate allele. See examples. NOTE: Variants are expected to conform to the VCF specs as described on the 1000 Genomes page (see references). Indels must include the reference allele; zero-width ranges are not valid and return no matches. } \item{subject}{A \link[GenomicFeatures]{TxDb} object that serves as the annotation. GFF files can be converted to \link[GenomicFeatures]{TxDb} objects with \code{makeTxDbFromGFF()} in the \code{txdbmaker} package. } \item{seqSource}{A \code{\link[BSgenome]{BSgenome}} instance or an \link{FaFile} to be used for sequence extraction. } \item{varAllele}{A \link[Biostrings]{DNAStringSet} containing the variant (alternate) alleles. The length of \code{varAllele} must equal the length of \code{query}. Missing values are represented by a zero width empty element. Ranges with missing \code{varAllele} values are ignored; those with an \sQuote{N} character are not translated. When the \code{query} is a \code{VCF} object the \code{varAllele} argument will be missing. This value is taken internally from the \code{VCF} with \code{alt()}. } \item{\dots}{Additional arguments passed to methods. Arguments \code{genetic.code} and \code{if.fuzzy.codon} are supported for the \code{translate} function. } \item{ignore.strand}{A \code{logical} indicating if strand should be ignored when performing overlaps. When \code{ignore.strand == TRUE} the \code{query} strand is set to '*' and can overlap with any strand of the \code{subject}. The return \code{GRanges} reflects the strand of the \code{subject} hit, however, the positions and alleles reported are computed as if both were from the '+' strand. \code{ignore.strand == FALSE} requires the \code{query} and \code{subject} to have compatible strand. Again the return \code{GRanges} reports the strand of the \code{subject} hit but in this case positions and alleles are computed according to the strand of the \code{subject}. } } \details{ This function returns the amino acid coding for variants that fall completely `within' a coding region. The reference sequences are taken from a fasta file or \link[BSgenome]{BSgenome}. The width of the reference is determined from the start position and width of the range in the \code{query}. For guidance on how to represent an insertion, deletion or substitution see the 1000 Genomes VCF format (references). Variant alleles are taken from the \code{varAllele} when supplied. When the \code{query} is a \code{VCF} object the \code{varAllele} will be missing. This value is taken internally from the \code{VCF} with \code{alt()}. The variant allele is substituted into the reference sequences and transcribed. Transcription only occurs if the substitution, insertion or deletion results in a new sequence length divisible by 3. When the \code{query} is an unstranded (*) \code{GRanges} \code{predictCoding} will attempt to find overlaps on both the positive and negative strands of the \code{subject}. When the subject hit is on the negative strand the return \code{varAllele} is reverse complemented. The strand of the returned \code{GRanges} represents the strand of the subject hit. } \value{ A \link[GenomicRanges]{GRanges} with a row for each variant-transcript match. The result includes only variants that fell within coding regions. The strand of the output \code{GRanges} represents the strand of the \code{subject} hit. At a minimum, the metadata columns (accessible with \code{mcols}) include, \describe{ \item{\code{varAllele}}{ Variant allele. This value is reverse complemented for an unstranded \code{query} that overlaps a \code{subject} on the negative strand. } \item{\code{QUERYID}}{ Map back to the row in the original query } \item{\code{TXID}}{ Internal transcript id from the annotation } \item{\code{CDSID}}{ Internal coding region id from the annotation } \item{\code{GENEID}}{ Internal gene id from the annotation } \item{\code{CDSLOC}}{ Variant location in coding region-based coordinates. This position is relative to the start of the coding (cds) region defined in the \code{subject} annotation. } \item{\code{PROTEINLOC}}{ Variant codon triplet location in coding region-based coordinates. This position is relative to the start of the coding (cds) region defined in the \code{subject} annotation. } \item{\code{CONSEQUENCE}}{ Possible values are `synonymous', `nonsynonymous', `frameshift', `nonsense' and `not translated'. Variant sequences are translated only when the codon sequence is a multiple of 3. The value will be `frameshift' when a sequence is of incompatible length. `not translated' is used when \code{varAllele} is missing or there is an \sQuote{N} in the sequence. `nonsense' is used for premature stop codons. } \item{\code{REFCODON}}{ The reference codon sequence. This range is typically greater than the width of the range in the \code{GRanges} because it includes all codons involved in the sequence modification. If the reference sequence is of width 2 but the alternate allele is of width 4 then at least two codons must be included in the \code{REFSEQ}. } \item{\code{VARCODON}}{ This sequence is the result of inserting, deleting or replacing the position(s) in the reference sequence alternate allele. If the result of this substitution is not a multiple of 3 it will not be translated. } \item{\code{REFAA}}{ The reference amino acid column contains the translated \code{REFSEQ}. When translation is not possible this value is missing. } \item{\code{VARAA}}{ The variant amino acid column contains the translated \code{VARSEQ}. When translation is not possible this value is missing. } } } \references{ \url{http://www.1000genomes.org/wiki/analysis/variant-call-format/} \url{http://vcftools.sourceforge.net/specs.html} } \author{Michael Lawrence and Valerie Obenchain} \seealso{ \link{readVcf}, \link{locateVariants}, \link{refLocsToLocalLocs} \link{getTranscriptSeqs} } \examples{ library(BSgenome.Hsapiens.UCSC.hg19) library(TxDb.Hsapiens.UCSC.hg19.knownGene) txdb <- TxDb.Hsapiens.UCSC.hg19.knownGene ## ---------------------------- ## VCF object as query ## ---------------------------- ## Read variants from a VCF file fl <- system.file("extdata", "chr22.vcf.gz", package="VariantAnnotation") vcf <- readVcf(fl, "hg19") ## Rename seqlevels in the VCF object to match those in the TxDb. vcf <- renameSeqlevels(vcf, "chr22") ## Confirm common seqlevels intersect(seqlevels(vcf), seqlevels(txdb)) ## When 'query' is a VCF object the varAllele argument is missing. coding1 <- predictCoding(vcf, txdb, Hsapiens) head(coding1, 3) ## Exon-centric or cDNA locations: exonsbytx <- exonsBy(txdb, "tx") cDNA <- mapToTranscripts(coding1, exonsbytx) mcols(cDNA)$TXID <- names(exonsbytx)[mcols(cDNA)$transcriptsHits] cDNA <- cDNA[mcols(cDNA)$TXID == mcols(coding1)$TXID[mcols(cDNA)$xHits]] ## Make sure cDNA is parallel to coding1 stopifnot(identical(mcols(cDNA)$xHits, seq_along(coding1))) coding1$cDNA <- ranges(cDNA) ## ---------------------------- ## GRanges object as query ## ---------------------------- ## A GRanges can also be used as the 'query'. The seqlevels in the VCF ## were adjusted in previous example so the GRanges extracted with ## has the correct seqlevels. rd <- rowRanges(vcf) ## The GRanges must be expanded to have one row per alternate allele. ## Variants 1, 2 and 10 have two alternate alleles. altallele <- alt(vcf) eltROWS <- elementNROWS(altallele) rd_exp <- rep(rd, eltROWS) ## Call predictCoding() with the expanded GRanges and the unlisted ## alternate allele as the 'varAllele'. coding2 <- predictCoding(rd_exp, txdb, Hsapiens, unlist(altallele)) } \keyword{methods} VariantAnnotation/man/probabilityToSnpMatrix.Rd0000644000175100017510000000253414614305321022767 0ustar00biocbuildbiocbuild\name{probabilityToSnpMatrix} \alias{probabilityToSnpMatrix} \title{Convert posterior genotype probability to a SnpMatrix object} \description{ Convert a matrix of posterior genotype probabilites P(AA), P(AB), P(BB) to a \link[snpStats:SnpMatrix-class]{SnpMatrix}. } \usage{ probabilityToSnpMatrix(probs) } \arguments{ \item{probs}{Matrix with three columns for the posterior probabilities of the three genotypes: "P(A/A)", "P(A/B)", "P(B/B)". Each row must sum to 1.} } \details{ \code{probabilityToSnpMatrix} converts a matrix of posterior probabilites of genotype calls into a \link[snpStats:SnpMatrix-class]{SnpMatrix}. } \value{ An object of class \code{"SnpMatrix"} with one row (one sample). Posterior probabilities are encoded (approximately) as byte values, one per SNP. See the help page for \link[snpStats:SnpMatrix-class]{SnpMatrix} for complete details of the class structure. } \author{ Stephanie Gogarten } \seealso{ \link{genotypeToSnpMatrix}, \link[snpStats:SnpMatrix-class]{SnpMatrix} } \examples{ probs <- matrix(c(1,0,0, 0,1,0, 0,0,1, NA,NA,NA), ncol=3, byrow=TRUE, dimnames=list(1:4,c("A/A","A/B","B/B"))) sm <- probabilityToSnpMatrix(probs) as(sm, "character") } \keyword{manip} VariantAnnotation/man/PROVEANDb-class.Rd0000644000175100017510000001006214614305321020754 0ustar00biocbuildbiocbuild\name{PROVEANDb-class} \docType{class} \alias{PROVEAN} \alias{PROVEANDb} \alias{class:PROVEANDb} \alias{PROVEANDb-class} \alias{columns,PROVEANDb-method} \alias{keys,PROVEANDb-method} \alias{keytypes,PROVEANDb-method} \alias{select,PROVEANDb-method} \title{PROVEANDb objects} \description{ The PROVEANDb class is a container for storing a connection to a PROVEAN sqlite database. } \section{Methods}{ In the code below, \code{x} is a \code{PROVEANDb} object. \describe{ \item{}{ \code{metadata(x)}: Returns \code{x}'s metadata in a data frame. } \item{}{ \code{columns(x)}: Returns the names of the \code{columns} that can be used to subset the data columns. } \item{}{ \code{keys(x, keytype="DBSNPID", ...)}: Returns the names of the \code{keys} that can be used to subset the data rows. For SIFT.Hsapiens.dbSNP137 the \code{keys} are NCBI dbSNP ids. } \item{}{ \code{keytypes(x)}: Returns the names of the \code{columns} that can be used as \code{keys}. For SIFT.Hsapiens.dbSNP137 the NCBI dbSNP ids are the only keytype. } \item{}{ \code{select(x, keys = NULL, columns = NULL, keytype = "DBSNPID", ...)}: Returns a subset of data defined by the character vectors \code{keys} and \code{columns}. If no \code{keys} are supplied, all rows are returned. If no \code{columns} are supplied, all columns are returned. } } } \details{ The SIFT tool is no longer actively maintained. A few of the orginal authors have started the PROVEAN (Protein Variation Effect Analyzer) project. PROVEAN is a software tool which predicts whether an amino acid substitution or indel has an impact on the biological function of a protein. PROVEAN is useful for filtering sequence variants to identify nonsynonymous or indel variants that are predicted to be functionally important. See the web pages for a complete description of the methods. \itemize{ \item PROVEAN Home: \url{http://provean.jcvi.org/index.php/} \item SIFT Home: \url{http://sift.jcvi.org/} } Though SIFT is not under active development, the PROVEAN team still provids the SIFT scores in the pre-computed downloads. This package, \code{SIFT.Hsapiens.dbSNP137}, contains both SIFT and PROVEAN scores. One notable difference between this and the previous SIFT database package is that \code{keys} in \code{SIFT.Hsapiens.dbSNP132} are rs IDs whereas in \code{SIFT.Hsapiens.dbSNP137} they are NCBI dbSNP IDs. } \references{ The PROVEAN tool has replaced SIFT: \url{http://provean.jcvi.org/about.php} Choi Y, Sims GE, Murphy S, Miller JR, Chan AP (2012) Predicting the Functional Effect of Amino Acid Substitutions and Indels. PLoS ONE 7(10): e46688. Choi Y (2012) A Fast Computation of Pairwise Sequence Alignment Scores Between a Protein and a Set of Single-Locus Variants of Another Protein. In Proceedings of the ACM Conference on Bioinformatics, Computational Biology and Biomedicine (BCB '12). ACM, New York, NY, USA, 414-417. Kumar P, Henikoff S, Ng PC. Predicting the effects of coding non-synonymous variants on protein function using the SIFT algorithm. Nat Protoc. 2009;4(7):1073-81 Ng PC, Henikoff S. Predicting the Effects of Amino Acid Substitutions on Protein Function Annu Rev Genomics Hum Genet. 2006;7:61-80. Ng PC, Henikoff S. SIFT: predicting amino acid changes that affect protein function. Nucleic Acids Res. 2003 Jul 1;31(13):3812-4. } \author{Valerie Obenchain} \examples{ if (require(SIFT.Hsapiens.dbSNP137)) { ## metadata metadata(SIFT.Hsapiens.dbSNP137) ## keys are the DBSNPID (NCBI dbSNP ID) dbsnp <- keys(SIFT.Hsapiens.dbSNP137) head(dbsnp) columns(SIFT.Hsapiens.dbSNP137) ## Return all columns. Note that the key, DBSNPID, ## is always returned. select(SIFT.Hsapiens.dbSNP137, dbsnp[10]) ## subset on keys and cols cols <- c("VARIANT", "PROVEANPRED", "SIFTPRED") select(SIFT.Hsapiens.dbSNP137, dbsnp[20:23], cols) } } \keyword{classes} \keyword{methods} VariantAnnotation/man/readVcf-methods.Rd0000644000175100017510000003760614614305321021321 0ustar00biocbuildbiocbuild\name{readVcf} \alias{readVcf} \alias{readVcf,character,ANY-method} \alias{readVcf,character,missing-method} \alias{readVcf,character,missing-method} \alias{readVcf,TabixFile,ScanVcfParam-method} \alias{readVcf,TabixFile,IntegerRangesList-method} \alias{readVcf,TabixFile,GRanges-method} \alias{readVcf,TabixFile,GRangesList-method} \alias{readVcf,TabixFile,missing-method} %% lightweight read* functions \alias{readInfo} \alias{readGeno} \alias{readGT} %% import wrapper \alias{import,VcfFile,ANY,ANY-method} \title{Read VCF files} \description{Read Variant Call Format (VCF) files} \usage{ \S4method{readVcf}{TabixFile,ScanVcfParam}(file, genome, param, ..., row.names=TRUE) \S4method{readVcf}{TabixFile,IntegerRangesList}(file, genome, param, ..., row.names=TRUE) \S4method{readVcf}{TabixFile,GRanges}(file, genome, param, ..., row.names=TRUE) \S4method{readVcf}{TabixFile,GRangesList}(file, genome, param, ..., row.names=TRUE) \S4method{readVcf}{TabixFile,missing}(file, genome, param, ..., row.names=TRUE) \S4method{readVcf}{character,ANY}(file, genome, param, ..., row.names=TRUE) \S4method{readVcf}{character,missing}(file, genome, param, ..., row.names=TRUE) \S4method{readVcf}{character,missing}(file, genome, param, ..., row.names=TRUE) ## Lightweight functions to read a single variable readInfo(file, x, param=ScanVcfParam(), ..., row.names=TRUE) readGeno(file, x, param=ScanVcfParam(), ..., row.names=TRUE) readGT(file, nucleotides=FALSE, param=ScanVcfParam(), ..., row.names=TRUE) ## Import wrapper \S4method{import}{VcfFile,ANY,ANY}(con, format, text, ...) } \arguments{ \item{file}{A \code{\link{VcfFile}} (synonymous with \code{\link{TabixFile}}) instance or character() name of the VCF file to be processed. When ranges are specified in \code{param}, \code{file} must be a \code{\link{VcfFile}}. Use of the \code{\link{VcfFile}} methods are encouraged as they are more efficient than the character() methods. See ?\code{VcfFile}, and ?\code{indexVcf} for help creating a \code{\link{VcfFile}}. } \item{genome}{A \code{character} or \code{Seqinfo} object. \itemize{ \item{\code{character}:}{ Genome identifier as a single string or named character vector. Names of the character vector correspond to chromosome names in the file. This identifier replaces the genome information in the VCF \code{Seqinfo} (i.e., \code{seqinfo(vcf)}). When not provided, \code{genome} is taken from the VCF file header. } \item{\code{Seqinfo}:}{ When \code{genome} is provided as a \code{Seqinfo} it is propagated to the VCF output. If seqinfo information can be obtained from the file, (i.e., seqinfo(scanVcfHeader(fl)) is not empty), the output \code{Seqinfo} is a product of merging the two. If a param (i.e., ScanVcfParam) is used in the call to \code{readVcf}, the seqlevels of the param ranges must be present in \code{genome}. } } } \item{param}{An instance of \code{\linkS4class{ScanVcfParam}}, \code{GRanges}, \code{GRangesList} or \code{IntegerRangesList}. VCF files can be subset on genomic coordinates (ranges) or elements in the VCF fields. Both genomic coordinates and VCF elements can be specified in a \code{\linkS4class{ScanVcfParam}}. See ?\code{ScanVcfParam} for details. } \item{x}{\code{character} name of single \code{info} or \code{geno} field to import. Applicable to \code{readInfo} and \code{readGeno} only. } \item{row.names}{A \code{logical} specifying if rownames should be returned. In the case of \code{readVcf}, rownames appear on the \code{GRanges} returned by the \code{rowRanges} accessor. } \item{nucleotides}{A \code{logical} indicating if genotypes should be returned as nucleotides instead of the numeric representation. Applicable to \code{readGT} only. } \item{con}{The \code{VcfFile} object to import.} \item{format, text}{Ignored.} \item{\dots}{Additional arguments, passed to methods. For \code{import}, the arguments are passed to \code{readVcf}. } } \details{ \describe{ \item{Data Import: }{ \describe{ \item{VCF object: }{ \code{readVcf} imports records from bzip compressed or uncompressed VCF files. Data are parsed into a \code{\linkS4class{VCF}} object using the file header information if available. To import a subset of ranges the VCF must have an index file. An index file can be created with \code{bzip} and \code{indexVcf} functions. The \code{readInfo}, \code{readGeno} and \code{readGT} functions are lightweight versions of \code{readVcf} and import a single variable. The return object is a vector, matrix or CompressedList instead of a VCF class. } } \code{readVcf} calls \code{\link{scanVcf}}, the details of which can be found with \code{?scanVcf}. } \item{Header lines (aka Meta-information): }{ readVcf() reads and parses fields according to the multiplicity and data type specified in the header lines. Fields without header lines are skipped (not read or parsed). To see what fields are present in the header use \code{scanVcfHeader()}. See ?\code{VCFHeader} for more details. Passing \code{verbose = TRUE} to \code{readVcf()} prints the fields with header lines that will be parsed by \code{readVcf}. } \item{Data type: }{ CHROM, POS, ID and REF fields are used to create the \code{GRanges} stored in the \code{VCF} object and accessible with the \code{rowRanges} accessor. REF, ALT, QUAL and FILTER are parsed into the \code{DataFrame} in the \code{fixed} slot. Because ALT can have more than one value per variant it is represented as a \code{DNAStringSetList}. REF is a \code{DNAStringSet}, QUAL is \code{numeric} and FILTER is a \code{character}. Accessors include \code{fixed}, \code{ref}, \code{alt}, \code{qual}, and \code{filt}. Data from the INFO field can be accessed with the \code{info} accessor. Genotype data (i.e., data immediately following the FORMAT field in the VCF) can be accessed with the \code{geno} accessor. INFO and genotype data types are determined according to the \sQuote{Number} and \sQuote{Type} information in the file header as follows: \sQuote{Number} should only be 0 when \sQuote{Type} is 'flag'. These fields are parsed as logical vectors. If \sQuote{Number} is 1, \sQuote{info} data are parsed into a \code{vector} and \sQuote{geno} into a \code{matrix}. If \sQuote{Number} is >1, \sQuote{info} data are parsed into a \code{DataFrame} with the same number of columns. \sQuote{geno} are parsed into an \code{array} with the same dimensions as \sQuote{Number}. Columns of the \sQuote{geno} matrices are the samples. If \sQuote{Number} is \sQuote{.}, \sQuote{A} or \sQuote{G}, both \sQuote{info} and \sQuote{geno} data are parsed into a \code{matrix}. When the header does not contain any \sQuote{INFO} lines, the data are returned as a single, unparsed column. } \item{Missing data: }{ Missing data in VCF files on disk are represented by a dot ("."). \code{readVcf} retains the dot as a character string for data type character and converts it to \code{NA} for data types numeric or double. Because the data are stored in rectangular data structures there is a value for each \code{info} and \code{geno} field element in the \code{VCF} class. If the element was missing or was not collected for a particular variant the value will be \code{NA}. In the case of the ALT field we have the following treatment of special characters / missing values: \itemize{ \item '.' true missings become empty characters \item '*' are treated as missing and become empty characters \item 'I' are treated as undefined and become '.' } } \item{Efficient Usage: }{ Subsets of data (i.e., specific variables, positions or samples) can be read from a VCF file by providing a \code{ScanVcfParam} object in the call to \code{readVcf}. Other lightweight options are the \code{readGT}, \code{readInfo} and \code{readGeno} functions which return data as a matrix instead of the \code{VCF} class. Another option for handling large files is to iterate through the data in chunks by setting the \code{yieldSize} parameter in a \code{VcfFile} object. Iteration can be through all data fields or a subset defined by a \code{ScanVcfParam}. See example below, `Iterating through VCF with yieldSize`. } } } \value{ \code{readVcf} returns a \code{\linkS4class{VCF}} object. See ?\code{VCF} for complete details of the class structure. \code{readGT}, \code{readInfo} and \code{readGeno} return a \code{matrix}. \describe{ \item{rowRanges: }{ The CHROM, POS, ID and REF fields are used to create a \code{GRanges} object. Ranges are created using POS as the start value and width of the reference allele (REF). By default, the IDs become the rownames ('row.names = FALSE' to turn this off). If IDs are missing (i.e., \sQuote{.}) a string of CHROM:POS_REF/ALT is used instead. The \code{genome} argument is stored in the seqinfo of the \code{GRanges} and can be accessed with \code{genome()}. One metadata column, \code{paramRangeID}, is included with the \code{rowRanges}. This ID is meaningful when multiple ranges are specified in the \code{ScanVcfParam} and distinguishes which records match each range. } \item{fixed: }{ REF, ALT, QUAL and FILTER fields of the VCF are parsed into a \code{DataFrame}. REF is returned as a DNAStringSet. ALT is a CharacterList when it contains structural variants and a DNAStringSetList otherwise. See also the 'Details' section for 'Missing data'. } \item{info: }{ Data from the INFO field of the VCF is parsed into a \code{DataFrame}. } \item{geno: }{ If present, the genotype data are parsed into a list of \code{matrices} or \code{arrays}. Each list element represents a field in the FORMAT column of the VCF file. Rows are the variants, columns are the samples. } \item{colData: }{ This slot contains a \code{DataFrame} describing the samples. If present, the sample names following FORMAT in the VCF file become the row names. } \item{metadata: }{ Header information present in the file is put into a \code{list} in \code{metadata}. } } See references for complete details of the VCF file format. } \references{ \url{http://vcftools.sourceforge.net/specs.html} outlines the VCF specification. \url{http://samtools.sourceforge.net/mpileup.shtml} contains information on the portion of the specification implemented by \code{bcftools}. \url{http://samtools.sourceforge.net/} provides information on \code{samtools}. } \author{ Valerie Obenchain> } \seealso{ \code{\link{indexVcf}}, \code{\link{VcfFile}}, \code{\link{indexTabix}}, \code{\link{TabixFile}}, \code{\link{scanTabix}}, \code{\link{scanBcf}}, \code{\link{expand,CollapsedVCF-method}} } \examples{ fl <- system.file("extdata", "ex2.vcf", package="VariantAnnotation") vcf <- readVcf(fl, "hg19") ## vcf <- readVcf(fl, c("20"="hg19")) ## 'genome' as named vector ## --------------------------------------------------------------------- ## Header and genome information ## --------------------------------------------------------------------- vcf ## all header information hdr <- header(vcf) ## header information for 'info' and 'fixed' fields info(hdr) fixed(hdr) ## --------------------------------------------------------------------- ## Accessors ## --------------------------------------------------------------------- ## fixed fields together head(fixed(vcf), 5) ## fixed fields separately filt(vcf) ref(vcf) ## info data info(hdr) info(vcf) info(vcf)$DP ## geno data geno(hdr) geno(vcf) head(geno(vcf)$GT) ## genome unique(genome(rowRanges(vcf))) ## --------------------------------------------------------------------- ## Data subsets with lightweight read* functions ## --------------------------------------------------------------------- ## Import a single 'info' or 'geno' variable DP <- readInfo(fl, "DP") HQ <- readGeno(fl, "HQ") ## Import GT as numeric representation GT <- readGT(fl) ## Import GT as nucleotides GT <- readGT(fl, nucleotides=TRUE) ## --------------------------------------------------------------------- ## Data subsets with ScanVcfParam ## --------------------------------------------------------------------- ## Subset on genome coordinates: ## 'file' must have an index rngs <- GRanges("20", IRanges(c(14370, 1110000), c(17330, 1234600))) names(rngs) <- c("geneA", "geneB") param <- ScanVcfParam(which=rngs) compressVcf <- bgzip(fl, tempfile()) tab <- indexVcf(compressVcf) vcf <- readVcf(tab, "hg19", param) ## When data are subset by range ('which' argument in ScanVcfParam), ## the 'paramRangeID' column provides a map back to the original ## range in 'param'. rowRanges(vcf)[,"paramRangeID"] vcfWhich(param) ## Subset on samples: ## Consult the header for the sample names. samples(hdr) ## Specify one or more names in 'samples' in a ScanVcfParam. param <- ScanVcfParam(samples="NA00002") vcf <- readVcf(tab, "hg19", param) geno(vcf)$GT ## Subset on 'fixed', 'info' or 'geno' fields: param <- ScanVcfParam(fixed="ALT", geno=c("GT", "HQ"), info=c("NS", "AF")) vcf_tab <- readVcf(tab, "hg19", param) info(vcf_tab) geno(vcf_tab) ## No ranges are specified in the 'param' so tabix file is not ## required. Instead, the uncompressed VCF can be used as 'file'. vcf_fname <- readVcf(fl, "hg19", param) ## The header will always contain information for all variables ## in the original file reguardless of how the data were subset. ## For example, all 'geno' fields are listed in the header geno(header(vcf_fname)) ## but only 'GT' and 'HQ' are present in the VCF object. geno(vcf_fname) ## Subset on both genome coordinates and 'info', 'geno' fields: param <- ScanVcfParam(geno="HQ", info="AF", which=rngs) vcf <- readVcf(tab, "hg19", param) ## When any of 'fixed', 'info' or 'geno' are omitted (i.e., no ## elements specified) all records are retrieved. Use NA to indicate ## that no records should be retrieved. This param specifies ## all 'fixed fields, the "GT" 'geno' field and none of 'info'. ScanVcfParam(geno="GT", info=NA) ## --------------------------------------------------------------------- ## Iterate through VCF with 'yieldSize' ## --------------------------------------------------------------------- fl <- system.file("extdata", "chr22.vcf.gz", package="VariantAnnotation") param <- ScanVcfParam(fixed="ALT", geno=c("GT", "GL"), info=c("LDAF")) tab <- VcfFile(fl, yieldSize=4000) open(tab) while (nrow(vcf_yield <- readVcf(tab, "hg19", param=param))) cat("vcf dim:", dim(vcf_yield), "\n") close(tab) ## --------------------------------------------------------------------- ## Debugging with 'verbose' ## --------------------------------------------------------------------- ## readVcf() uses information in the header lines to parse the data to ## the correct number and type. Fields without header lines are skipped. ## If a call to readVcf() results in no info(VCF) or geno(VCF) data the ## file may be missing header lines. Set 'verbose = TRUE' to get ## a listing of fields found in the header. ## readVcf(myfile, "mygenome", verbose=TRUE) ## Header fields can also be discovered with scanVcfHeader(). hdr <- scanVcfHeader(fl) geno(hdr) } \keyword{manip} VariantAnnotation/man/scanVcf-methods.Rd0000644000175100017510000001214714614305321021323 0ustar00biocbuildbiocbuild\name{scanVcf} \Rdversion{1.1} \alias{scanVcfHeader} \alias{scanVcfHeader,missing-method} \alias{scanVcfHeader,character-method} \alias{scanVcf} \alias{scanVcf,character,ScanVcfParam-method} \alias{scanVcf,character,missing-method} \alias{scanVcf,connection,missing-method} \alias{scanVcfHeader,TabixFile-method} \alias{scanVcf,TabixFile,GRanges-method} \alias{scanVcf,TabixFile,IntegerRangesList-method} \alias{scanVcf,TabixFile,ScanVcfParam-method} \alias{scanVcf,TabixFile,missing-method} \title{ Import VCF files } \description{ Import Variant Call Format (VCF) files in text or binary format } \usage{ scanVcfHeader(file, ...) \S4method{scanVcfHeader}{character}(file, ...) scanVcf(file, ..., param) \S4method{scanVcf}{character,ScanVcfParam}(file, ..., param) \S4method{scanVcf}{character,missing}(file, ..., param) \S4method{scanVcf}{connection,missing}(file, ..., param) \S4method{scanVcfHeader}{TabixFile}(file, ...) \S4method{scanVcf}{TabixFile,missing}(file, ..., param) \S4method{scanVcf}{TabixFile,ScanVcfParam}(file, ..., param) \S4method{scanVcf}{TabixFile,GRanges}(file, ..., param) \S4method{scanVcf}{TabixFile,IntegerRangesList}(file, ..., param) } \arguments{ \item{file}{For \code{scanVcf} and \code{scanVcfHeader}, the character() file name, \code{\link{TabixFile}}, or class \code{connection} (\code{file()} or \code{bgzip()}) of the \sQuote{VCF} file to be processed. } \item{param}{A instance of \code{\linkS4class{ScanVcfParam}} influencing which records are parsed and the \sQuote{INFO} and \sQuote{GENO} information returned. } \item{...}{Additional arguments for methods } } \details{ The argument \code{param} allows portions of the file to be input, but requires that the file be bgzip'd and indexed as a \code{\linkS4class{TabixFile}}. \code{scanVcf} with \code{param="missing"} and \code{file="character"} or \code{file="connection"} scan the entire file. With \code{file="connection"}, an argument \code{n} indicates the number of lines of the VCF file to input; a connection open at the beginning of the call is open and incremented by \code{n} lines at the end of the call, providing a convenient way to stream through large VCF files. The INFO field of the scanned VCF file is returned as a single \sQuote{packed} vector, as in the VCF file. The GENO field is a list of matrices, each matrix corresponds to a field as defined in the FORMAT field of the VCF header. Each matrix has as many rows as scanned in the VCF file, and as many columns as there are samples. As with the INFO field, the elements of the matrix are \sQuote{packed}. The reason that INFO and GENO are returned packed is to facilitate manipulation, e.g., selecting particular rows or samples in a consistent manner across elements. } \value{ \code{scanVcfHeader} returns a \code{VCFHeader} object with header information parsed into five categories, \code{samples}, \code{meta}, \code{fixed}, \code{info} and \code{geno}. Each can be accessed with a `getter' of the same name (e.g., info()). If the file header has multiple rows with the same name (e.g., 'source') the row names of the DataFrame are made unique in the usual way, 'source', 'source.1' etc. \code{scanVcf} returns a list, with one element per range. Each list has 7 elements, obtained from the columns of the VCF specification: \describe{ \item{rowRanges}{ \code{GRanges} instance derived from \code{CHROM}, \code{POS}, \code{ID}, and the width of \code{REF} } \item{REF}{ reference allele } \item{ALT}{ alternate allele } \item{QUAL}{ phred-scaled quality score for the assertion made in ALT } \item{FILTER}{ indicator of whether or not the position passed all filters applied } \item{INFO}{ additional information } \item{GENO}{ genotype information immediately following the FORMAT field in the VCF } } The \code{GENO} element is itself a list, with elements corresponding to those defined in the VCF file header. For \code{scanVcf}, elements of GENO are returned as a matrix of records x samples; if the description of the element in the file header indicated multiplicity other than 1 (e.g., variable number for \dQuote{A}, \dQuote{G}, or \dQuote{.}), then each entry in the matrix is a character string with sub-entries comma-delimited. } \references{ \url{http://vcftools.sourceforge.net/specs.html} outlines the VCF specification. \url{http://samtools.sourceforge.net/mpileup.shtml} contains information on the portion of the specification implemented by \code{bcftools}. \url{http://samtools.sourceforge.net/} provides information on \code{samtools}. } \seealso{ \code{\link{readVcf}} \code{\link{BcfFile}} \code{\link{TabixFile}} } \author{ Martin Morgan and Valerie Obenchain> } \examples{ fl <- system.file("extdata", "ex2.vcf", package="VariantAnnotation") scanVcfHeader(fl) vcf <- scanVcf(fl) ## value: list-of-lists str(vcf) names(vcf[[1]][["GENO"]]) vcf[[1]][["GENO"]][["GT"]] } \keyword{ manip } VariantAnnotation/man/ScanVcfParam-class.Rd0000644000175100017510000001511114614305321021700 0ustar00biocbuildbiocbuild\name{ScanVcfParam-class} \docType{class} \alias{ScanVcfParam} \alias{ScanVcfParam-class} \alias{ScanVcfParam,missing-method} \alias{ScanVcfParam,ANY-method} \alias{vcfFixed} \alias{vcfFixed<-} \alias{vcfInfo} \alias{vcfInfo<-} \alias{vcfGeno} \alias{vcfGeno<-} \alias{vcfSamples} \alias{vcfSamples<-} \alias{vcfTrimEmpty} \alias{vcfTrimEmpty<-} \alias{vcfWhich} \alias{vcfWhich<-} \title{Parameters for scanning VCF files} \description{ Use \code{ScanVcfParam()} to create a parameter object influencing which records and fields are imported from a VCF file. Record parsing is based on genomic coordinates and requires a Tabix index file. Individual VCF elements can be specified in the \sQuote{fixed}, \sQuote{info}, \sQuote{geno} and \sQuote{samples} arguments. } \usage{ ScanVcfParam(fixed=character(), info=character(), geno=character(), samples=character(), trimEmpty=TRUE, which, ...) ## Getters and Setters vcfFixed(object) vcfFixed(object) <- value vcfInfo(object) vcfInfo(object) <- value vcfGeno(object) vcfGeno(object) <- value vcfSamples(object) vcfSamples(object) <- value vcfTrimEmpty(object) vcfTrimEmpty(object) <- value vcfWhich(object) vcfWhich(object) <- value } \arguments{ \item{fixed}{A character() vector of fixed fields to be returned. Possible values are ALT, QUAL and FILTER. The CHROM, POS, ID and REF fields are needed to create the \code{GRanges} of variant locations. Because these are essential fields there is no option to request or omit them. If not specified, all fields are returned; if \code{fixed=NA} only REF is returned. } \item{info}{A character() vector naming the \sQuote{INFO} fields to return. \code{scanVcfHeader()} returns a vector of available fields. If not specified, all fields are returned; if \code{info=NA} no fields are returned. } \item{geno}{A character() vector naming the \sQuote{GENO} fields to return. \code{scanVcfHeader()} returns a vector of available fields. If not specified, all fields are returned; if \code{geno=NA} no fields are returned and requests for specific samples are ignored. } \item{samples}{A character() vector of sample names to return. \code{samples(scanVcfHeader())} returns all possible names. If not specified, data for all samples are returned; if either \code{samples=NA} or \code{geno=NA} no fields are returned. Requests for specific samples when \code{geno=NA} are ignored. } \item{trimEmpty}{A logical(1) indicating whether \sQuote{GENO} fields with no values should be returned. } \item{which}{A \code{\linkS4class{GRanges}} describing the sequences and ranges to be queried. Variants whose \code{POS} lies in the interval(s) \code{[start, end]} are returned. If \code{which} is not specified all ranges are returned. } \item{object}{An instance of class \code{ScanVcfParam}. } \item{value}{An instance of the corresponding slot, to be assigned to \code{object}. } \item{\dots}{Arguments passed to methods. } } \section{Objects from the Class}{ Objects can be created by calls of the form \code{ScanVcfParam()}. } \section{Slots}{ \describe{ \item{\code{which}:}{Object of class \code{"IntegerRangesList"} indicating which reference sequence and coordinate variants must overlap. } \item{\code{fixed}:}{Object of class \code{"character"} indicating fixed fields to be returned. } \item{\code{info}:}{Object of class \code{"character"} indicating portions of \sQuote{INFO} to be returned. } \item{\code{geno}:}{Object of class \code{"character"} indicating portions of \sQuote{GENO} to be returned. } \item{\code{samples}:}{Object of class \code{"character"} indicating the samples to be returned. } \item{\code{trimEmpty}:}{Object of class \code{"logical"} indicating whether empty \sQuote{GENO} fields are to be returned. } } } \section{Functions and methods}{ See 'Usage' for details on invocation. Constructor: \describe{ \item{ScanVcfParam:}{Returns a \code{ScanVcfParam} object. The \code{which} argument to the constructor can be one of several types, as documented above.} } Accessors: \describe{ \item{vcfFixed, vcfInfo, vcfGeno, vcfSamples, vcfTrimEmpty, vcfWhich:}{ Return the corresponding field from \code{object}. } } Methods: \describe{ \item{show}{Compactly display the object. } } } \author{ Martin Morgan and Valerie Obenchain } \seealso{ \code{\link{readVcf}} } \examples{ ScanVcfParam() fl <- system.file("extdata", "structural.vcf", package="VariantAnnotation") compressVcf <- bgzip(fl, tempfile()) idx <- indexTabix(compressVcf, "vcf") tab <- TabixFile(compressVcf, idx) ## --------------------------------------------------------------------- ## 'which' argument ## --------------------------------------------------------------------- ## To subset on genomic coordinates, supply an object ## containing the ranges of interest. These ranges can ## be given directly to the 'param' argument or wrapped ## inside ScanVcfParam() as the 'which' argument. ## When using a list, the outer list names must correspond to valid ## chromosome names in the vcf file. In this example they are "1" ## and "2". gr1 <- GRanges("1", IRanges(13219, 2827695, name="regionA")) gr2 <- GRanges(rep("2", 2), IRanges(c(321680, 14477080), c(321689, 14477090), name=c("regionB", "regionC"))) grl <- GRangesList("1"=gr1, "2"=gr2) vcf <- readVcf(tab, "hg19", grl) ## Names of the ranges are in the 'paramRangeID' metadata column of the ## GRanges object returned by the rowRanges() accessor. rowRanges(vcf) ## which can be used for subsetting the VCF object vcf[rowRanges(vcf)$paramRangeID == "regionA"] ## When using ranges, the seqnames must correspond to valid ## chromosome names in the vcf file. gr <- unlist(grl, use.names=FALSE) vcf <- readVcf(tab, "hg19", gr) ## --------------------------------------------------------------------- ## 'fixed', 'info', 'geno' and 'samples' arguments ## --------------------------------------------------------------------- ## This param specifies the "GT" 'geno' field for a single sample ## and the subset of ranges in 'which'. All 'fixed' and 'info' fields ## will be returned. ScanVcfParam(geno="GT", samples="NA00002", which=gr) ## Here two 'fixed' and one 'geno' field are specified ScanVcfParam(fixed=c("ALT", "QUAL"), geno="GT", info=NA) ## Return only the 'fixed' fields ScanVcfParam(geno=NA, info=NA) } \keyword{classes} VariantAnnotation/man/seqinfo-method.Rd0000644000175100017510000000152014614305321021212 0ustar00biocbuildbiocbuild\name{seqinfo} \alias{seqinfo} \alias{seqinfo,VcfFile-method} \alias{seqinfo,VcfFileList-method} \title{Get seqinfo for VCF file} \description{Get seqinfo for VCF file} \usage{ \S4method{seqinfo}{VcfFile}(x) \S4method{seqinfo}{VcfFileList}(x) } \arguments{ \item{x}{Either character(), \code{VcfFile}, or \code{VcfFileList}} } \details{ If a \code{VcfFile}The file header is scanned an appropriate seqinfo object in given. If a \code{VcfFileList} is given, all file headers are scanned, and appropriate combined seqinfo object is given. } \value{Seqinfo object} \author{Lori Shepherd} \seealso{ \code{\link{VcfFile}}, \code{\link[GenomeInfoDb]{Seqinfo}} } \examples{ fl <- system.file("extdata", "chr7-sub.vcf.gz", package="VariantAnnotation", mustWork=TRUE) vcf <- VcfFile(fl) seqinfo(vcf) } \keyword{manip} VariantAnnotation/man/SIFTDb-class.Rd0000644000175100017510000000665114614305321020420 0ustar00biocbuildbiocbuild\name{SIFTDb-class} \docType{class} \alias{SIFT} \alias{SIFTDb} \alias{class:SIFTDb} \alias{SIFTDb-class} \alias{metadata,SIFTDb-method} \alias{columns,SIFTDb-method} \alias{keys,SIFTDb-method} \alias{select,SIFTDb-method} \title{SIFTDb objects} \description{ The SIFTDb class is a container for storing a connection to a SIFT sqlite database. } \section{Methods}{ In the code below, \code{x} is a \code{SIFTDb} object. \describe{ \item{}{ \code{metadata(x)}: Returns \code{x}'s metadata in a data frame. } \item{}{ \code{columns(x)}: Returns the names of the \code{columns} that can be used to subset the data columns. } \item{}{ \code{keys(x)}: Returns the names of the \code{keys} that can be used to subset the data rows. The \code{keys} values are the rsid's. } \item{}{ \code{select(x, keys = NULL, columns = NULL, ...)}: Returns a subset of data defined by the character vectors \code{keys} and \code{columns}. If no \code{keys} are supplied, all rows are returned. If no \code{columns} are supplied, all columns are returned. For column descriptions see \code{?SIFTDbColumns}. } } } \details{ SIFT is a sequence homology-based tool that sorts intolerant from tolerant amino acid substitutions and predicts whether an amino acid substitution in a protein will have a phenotypic effect. SIFT is based on the premise that protein evolution is correlated with protein function. Positions important for function should be conserved in an alignment of the protein family, whereas unimportant positions should appear diverse in an alignment. SIFT uses multiple alignment information to predict tolerated and deleterious substitutions for every position of the query sequence. The procedure can be outlined in the following steps, \itemize{ \item search for similar sequences \item choose closely related sequences that may share similar function to the query sequence \item obtain the alignment of the chosen sequences \item calculate normalized probabilities for all possible substitutions from the alignment. } Positions with normalized probabilities less than 0.05 are predicted to be deleterious, those greater than or equal to 0.05 are predicted to be tolerated. } \references{ SIFT Home: \url{http://sift.jcvi.org/} Kumar P, Henikoff S, Ng PC. Predicting the effects of coding non-synonymous variants on protein function using the SIFT algorithm. Nat Protoc. 2009;4(7):1073-81 Ng PC, Henikoff S. Predicting the Effects of Amino Acid Substitutions on Protein Function Annu Rev Genomics Hum Genet. 2006;7:61-80. Ng PC, Henikoff S. SIFT: predicting amino acid changes that affect protein function. Nucleic Acids Res. 2003 Jul 1;31(13):3812-4. } \author{Valerie Obenchain} \examples{ if (interactive()) { library(SIFT.Hsapiens.dbSNP132) ## metadata metadata(SIFT.Hsapiens.dbSNP132) ## available rsid's head(keys(SIFT.Hsapiens.dbSNP132)) ## for column descriptions see ?SIFTDbColumns columns(SIFT.Hsapiens.dbSNP132) ## subset on keys and columns rsids <- c("rs2142947", "rs17970171", "rs8692231", "rs3026284") subst <- c("RSID", "PREDICTION", "SCORE") select(SIFT.Hsapiens.dbSNP132, keys=rsids, columns=subst) select(SIFT.Hsapiens.dbSNP132, keys=rsids[1:2]) } } \keyword{classes} \keyword{methods} VariantAnnotation/man/SIFTDbColumns.Rd0000644000175100017510000000303614614305321020650 0ustar00biocbuildbiocbuild\name{SIFTDbColumns} \alias{SIFTDbColumns} \title{SIFTDb Columns} \description{ Description of the SIFT Sqlite Database Columns } \section{Column descriptions}{ These column names are displayed when \code{columns} is called on a \code{SIFTDb} object. \itemize{ \item RSID : rsid \item PROTEINID : NCBI RefSeq protein ID \item AACHANGE : amino acid substitution; reference aa is preceeding, followed by the position and finally the snp aa \item METHOD : method of obtaining related sequences using PSI-BLAST \item AA : either the reference or snp residue amino acid \item PREDICTION : SIFT prediction \item SCORE : SIFT score (range 0 to 1) \itemize{ \item TOLERATED : score is greater than 0.05 \item DAMAGING : score is less than or equal to 0.05 \item NOT SCORED : no prediction is made if there are less than 2 homologous sequences that have an amino acid at the position of the given SNP or if the SIFT prediction is not available } \item MEDIAN : diversity measurement of the sequences used for prediction (range 0 to 4.32) \item POSITIONSEQS : number of sequences with an amino acide at the position of prediction \item TOTALSEQS : total number of sequences in alignment } } \seealso{ \code{?SIFTDb} } \author{Valerie Obenchain} \keyword{classes} \keyword{methods} VariantAnnotation/man/snpSummary.Rd0000644000175100017510000000475114614305321020457 0ustar00biocbuildbiocbuild\name{snpSummary} \alias{snpSummary} \alias{snpSummary,CollapsedVCF-method} \title{Counts and distribution statistics for SNPs in a VCF object} \description{ Counts and distribution statistics for SNPs in a VCF object } \usage{ \S4method{snpSummary}{CollapsedVCF}(x, ...) } \arguments{ \item{x}{ A \link{CollapsedVCF} object. } \item{\dots}{ Additional arguments to methods. } } \details{ Genotype counts, allele counts and Hardy Weinberg equilibrium (HWE) statistics are calculated for single nucleotide variants in a \link{CollapsedVCF} object. HWE has been established as a useful quality filter on genotype data. This equilibrium should be attained in a single generation of random mating. Departures from HWE are indicated by small p values and are almost invariably indicative of a problem with genotype calls. The following caveats apply: \itemize{ \item No distinction is made between phased and unphased genotypes. \item Only diploid calls are included. \item Only `valid' SNPs are included. A `valid' SNP is defined as having a reference allele of length 1 and a single alternate allele of length 1. } Variants that do not meet these criteria are set to NA. } \value{ The object returned is a \code{data.frame} with seven columns. \describe{ \item{g00}{ Counts for genotype 00 (homozygous reference). } \item{g01}{ Counts for genotype 01 or 10 (heterozygous). } \item{g11}{ Counts for genotype 11 (homozygous alternate). } \item{a0Freq}{ Frequency of the reference allele. } \item{a1Freq}{ Frequency of the alternate allele. } \item{HWEzscore}{ Z-score for departure from a null hypothesis of Hardy Weinberg equilibrium. } \item{HWEpvalue}{ p-value for departure from a null hypothesis of Hardy Weinberg equilibrium. } } } \author{ Chris Wallace } \seealso{ \link{genotypeToSnpMatrix}, \link{probabilityToSnpMatrix} } \examples{ fl <- system.file("extdata", "ex2.vcf", package="VariantAnnotation") vcf <- readVcf(fl, "hg19") ## The return value is a data.frame with genotype counts ## and allele frequencies. df <- snpSummary(vcf) df ## Compare to ranges in the VCF object: rowRanges(vcf) ## No statistics were computed for the variants in rows 3, 4 ## and 5. They were omitted because row 3 has two alternate ## alleles, row 4 has none and row 5 is not a SNP. } \keyword{manip} VariantAnnotation/man/summarizeVariants-methods.Rd0000644000175100017510000001430614614305321023463 0ustar00biocbuildbiocbuild\name{summarizeVariants} \alias{summarizeVariants} \alias{summarizeVariants,TxDb,VCF,CodingVariants-method} \alias{summarizeVariants,TxDb,VCF,FiveUTRVariants-method} \alias{summarizeVariants,TxDb,VCF,ThreeUTRVariants-method} \alias{summarizeVariants,TxDb,VCF,SpliceSiteVariants-method} \alias{summarizeVariants,TxDb,VCF,IntronVariants-method} \alias{summarizeVariants,TxDb,VCF,PromoterVariants-method} \alias{summarizeVariants,GRangesList,VCF,VariantType-method} \alias{summarizeVariants,GRangesList,VCF,function-method} \title{Summarize variants by sample} \description{ Variants in a VCF file are overlapped with an annotation region and summarized by sample. Genotype information in the VCF is used to determine which samples express each variant. } \usage{ \S4method{summarizeVariants}{TxDb,VCF,CodingVariants}(query, subject, mode, ...) \S4method{summarizeVariants}{TxDb,VCF,FiveUTRVariants}(query, subject, mode, ...) \S4method{summarizeVariants}{TxDb,VCF,ThreeUTRVariants}(query, subject, mode, ...) \S4method{summarizeVariants}{TxDb,VCF,SpliceSiteVariants}(query, subject, mode, ...) \S4method{summarizeVariants}{TxDb,VCF,IntronVariants}(query, subject, mode, ...) \S4method{summarizeVariants}{TxDb,VCF,PromoterVariants}(query, subject, mode, ...) \S4method{summarizeVariants}{GRangesList,VCF,VariantType}(query, subject, mode, ...) \S4method{summarizeVariants}{GRangesList,VCF,function}(query, subject, mode, ...) } \arguments{ \item{query}{A \link[GenomicFeatures]{TxDb} or \code{GRangesList} object that serves as the annotation. GFF files can be converted to \link[GenomicFeatures]{TxDb} objects with \code{makeTxDbFromGFF()} in the \code{txdbmaker} package. } \item{subject}{A \linkS4class{VCF} object containing the variants. } \item{mode}{\code{mode} can be a \code{VariantType} class or the name of a function. When \code{mode} is a \code{VariantType} class, counting is done with \code{locateVariants} and counts are summarized transcript-by-sample. Supported \code{VariantType} classes include \code{CodingVariants}, \code{IntronVariants}, \code{FiveUTRVariants}, \code{ThreeUTRVariants}, \code{SpliceSiteVariants} or \code{PromoterVariants}. \code{AllVariants()} and \code{IntergenicVariants} are not supported. See ?\code{locateVariants} for more detail on the variant classes. \code{mode} can also be the name of any counting function that outputs a \code{Hits} object. Variants will be summarized by the length of the \code{GRangesList} annotation (i.e., 'length-of-GRangesList'-by-sample). } \item{\dots}{Additional arguments passed to methods such as \describe{ \item{ignore.strand}{A \code{logical} indicating if strand should be igored when performing overlaps. } } } } \details{ \code{summarizeVariants} uses the genotype information in a VCF file to determine which samples are positive for each variant. Variants are overlapped with the annotation and the counts are summarized annotation-by-sample. If the annotation is a \code{GRangesList} of transcripts, the count matrix will be transcripts-by-sample. If the \code{GRangesList} is genes, the count matrix will be gene-by-sample. \itemize{ \item{Counting with locateVariants() :}{ Variant counts are always summarized transcript-by-sample. When \code{query} is a \code{GRangesList}, it must be compatible with the \code{VariantType}-class given as the \code{mode} argument. The list below specifies the appropriate \code{GRangesList} for each \code{mode}. \describe{ \item{CodingVariants :}{coding (CDS) by transcript} \item{IntronVariants :}{introns by transcript} \item{FiveUTRVariants :}{five prime UTR by transcript} \item{ThreeUTRVariants :}{three prime UTR by transcript} \item{SpliceSiteVariants :}{introns by transcript} \item{PromoterVariants :}{list of transcripts} } When \code{query} is a \code{TxDb}, the appropriate region-by-transcript \code{GRangesList} listed above is extracted internally and used as the annotation. } \item{Counting with a user-supplied function :}{ \code{subject} must be a \code{GRangesList} and \code{mode} must be the name of a function. The count function must take 'query' and 'subject' arguments and return a \code{Hits} object. Counts are summarized by the outer list elements of the \code{GRangesList}. } } } \value{ A \code{RangedSummarizedExperiment} object with count summaries in the \code{assays} slot. The \code{rowRanges} contains the annotation used for counting. Information in \code{colData} and \code{metadata} are taken from the VCF file. } \author{Valerie Obenchain} \seealso{ \code{\link{readVcf}}, \code{\link{predictCoding}}, \code{\link{locateVariants}} } \examples{ library(TxDb.Hsapiens.UCSC.hg19.knownGene) txdb <- TxDb.Hsapiens.UCSC.hg19.knownGene ## Read variants from VCF. fl <- system.file("extdata", "chr22.vcf.gz", package="VariantAnnotation") vcf <- readVcf(fl, "hg19") ## Rename seqlevels to match TxDb; confirm the match. seqlevels(vcf) <- paste0("chr", seqlevels(vcf)) intersect(seqlevels(vcf), seqlevels(txdb)) ## ---------------------------------------- ## Counting with locateVariants() ## ---------------------------------------- ## TxDb as the 'query' coding1 <- summarizeVariants(txdb, vcf, CodingVariants()) colSums(assays(coding1)$counts) ## GRangesList as the 'query' cdsbytx <- cdsBy(txdb, "tx") coding2 <- summarizeVariants(cdsbytx, vcf, CodingVariants()) stopifnot(identical(assays(coding1)$counts, assays(coding2)$counts)) ## Promoter region variants summarized by transcript tx <- transcripts(txdb) txlst <- splitAsList(tx, seq_len(length(tx))) promoter <- summarizeVariants(txlst, vcf, PromoterVariants(upstream=100, downstream=10)) colSums(assays(promoter)$counts) ## ---------------------------------------- ## Counting with findOverlaps() ## ---------------------------------------- ## Summarize all variants by transcript allvariants <- summarizeVariants(txlst, vcf, findOverlaps) colSums(assays(allvariants)$counts) } \keyword{methods} VariantAnnotation/man/variant_body.Rd0000644000175100017510000000101214614305321020745 0ustar00biocbuildbiocbuild% Generated by roxygen2: do not edit by hand % Please edit documentation in R/use_vep_api.R \name{variant_body} \alias{variant_body} \title{helper function to construct inputs for VEP REST API} \usage{ variant_body(chr, pos, id, ref, alt) } \arguments{ \item{chr}{character(1)} \item{pos}{numeric(1)} \item{id}{character(1)} \item{ref}{character(1)} \item{alt}{character(1)} } \description{ helper function to construct inputs for VEP REST API } \note{ Produces a string used as an example in VEP API documentation. } VariantAnnotation/man/VariantType-class.Rd0000644000175100017510000001357214614305321021653 0ustar00biocbuildbiocbuild\name{VariantType-class} \docType{class} % Class \alias{VariantType-class} % Subclasses \alias{CodingVariants-class} \alias{IntronVariants-class} \alias{FiveUTRVariants-class} \alias{ThreeUTRVariants-class} \alias{SpliceSiteVariants-class} \alias{IntergenicVariants-class} \alias{PromoterVariants-class} \alias{AllVariants-class} % Constructors for subclasses : \alias{CodingVariants} \alias{IntronVariants} \alias{FiveUTRVariants} \alias{ThreeUTRVariants} \alias{SpliceSiteVariants} \alias{IntergenicVariants} \alias{PromoterVariants} \alias{AllVariants} % accessors : \alias{upstream} \alias{upstream,AllVariants-method} \alias{upstream,PromoterVariants-method} \alias{upstream,IntergenicVariants-method} \alias{upstream<-} \alias{upstream<-,AllVariants-method} \alias{upstream<-,PromoterVariants-method} \alias{upstream<-,IntergenicVariants-method} \alias{idType} \alias{idType<-} \alias{idType,IntergenicVariants-method} \alias{idType<-,IntergenicVariants-method} \alias{downstream} \alias{downstream,AllVariants-method} \alias{downstream,PromoterVariants-method} \alias{downstream,IntergenicVariants-method} \alias{downstream<-} \alias{downstream<-,AllVariants-method} \alias{downstream<-,PromoterVariants-method} \alias{downstream<-,IntergenicVariants-method} \alias{promoter} \alias{promoter,AllVariants-method} \alias{promoter<-} \alias{promoter<-,AllVariants-method} \alias{intergenic} \alias{intergenic,AllVariants-method} \alias{intergenic<-} \alias{intergenic<-,AllVariants-method} % show method: \alias{show,VariantType-method} \alias{show,AllVariants-method} \alias{show,PromoterVariants-method} \title{VariantType subclasses} \description{ \code{VariantType} subclasses specify the type of variant to be located with \code{locateVariants}. } \usage{ CodingVariants() IntronVariants() FiveUTRVariants() ThreeUTRVariants() SpliceSiteVariants() IntergenicVariants(upstream = 1e+06L, downstream = 1e+06L, idType=c("gene", "tx")) PromoterVariants(upstream = 2000L, downstream = 200L) AllVariants(promoter = PromoterVariants(), intergenic = IntergenicVariants()) } \details{ \code{VariantType} is a virtual class inherited by the \code{CodingVariants}, \code{IntronVariants}, \code{FiveUTRVariants}, \code{ThreeUTRVariants}, \code{SpliceSiteVariants}, \code{IntergenicVariants} and \code{AllVariants} subclasses. The subclasses are used as the \code{region} argument to \code{locateVariants}. They designate the type of variant (i.e., region of the annotation to match) when calling \code{locateVariants}. The majority of subclasses have no slots and require no arguments for an instance to be created. \code{PromoterVariants} and \code{IntergenicVariants} and accept \code{upstream} and \code{downstream} arguments that define the number of base pairs upstream from the 5'-end and downstream from the 3'-end of the transcript region. See the ?\code{locateVariants} man page for details. \code{IntergenicVariants} also accepts a \code{idType} that controls what IDs are returned in the PRECEDEID and FOLLOWID metadata columns. \code{AllVariants} accepts \code{promoter} and \code{intergenic} arguments which are \code{PromoterVariants()} and \code{IntergenicVariants()} objects with the appropriate \code{upstream} and \code{downstream} values. } \arguments{ \item{upstream, downstream}{ Single \code{integer} values representing the number of base pairs upstream of the 5'-end and downstream of the 3'-end. Used in contructing \code{PromoterVariants()} and \code{IntergenicVariants()} objects only. } \item{idType}{ \code{character} indicating if the ids in the PRECEDEID and FOLLOWID metadata columns should be gene ids ("gene") or transcript ids ("tx"). Applicable to \code{IntergenicVariants()} objects only. } \item{promoter}{ \code{PromoterVariants} object with appropriate \code{upstream} and \code{downstream} values. Used when constructing \code{AllVariants} objects only. } \item{intergenic}{ \code{IntergenicVariants} object with appropriate \code{upstream} and \code{downstream} values. Used when constructing \code{AllVariants} objects only. } } \section{Accessors}{ In the following code, \code{x} is a \code{PromoterVariants} or a \code{AllVariants} object. \describe{ \item{}{ \code{upstream(x)}, \code{upstream(x) <- value}: Gets or sets the number of base pairs defining a range upstream of the 5' end (excludes 5' start value). } \item{}{ \code{downstream(x)}, \code{downstream(x) <- value}: Gets or sets the number of base pairs defining a range downstream of the 3' end (excludes 3' end value). } \item{}{ \code{idType(x)}, \code{idType(x) <- value}: Gets or sets the \code{character()} which controls the id returned in the PRECEDEID and FOLLOWID output columns. Possible values are "gene" and "tx". } \item{}{ \code{promoters(x)}, \code{promoters(x) <- value}: Gets or sets the \code{PromoterVariants} in the \code{AllVariants} object. } \item{}{ \code{intergenic(x)}, \code{intergenic(x) <- value}: Gets or sets the \code{IntergenicVariants} in the \code{AllVariants} object. } } } \author{Valerie Obenchain} \seealso{ \itemize{ \item The promoters function on the \link[GenomicRanges]{intra-range-methods} man page in the GenomicRanges package. } } \examples{ CodingVariants() SpliceSiteVariants() PromoterVariants(upstream=1000, downstream=10000) ## Default values for PromoterVariants and IntergenicVariants AllVariants() ## Modify 'upstream' and 'downstream' for IntergenicVariants AllVariants(intergenic=IntergenicVariants(500, 100)) ## Reset PromoterVariants on existing AllVariants object av <- AllVariants() av promoter(av) <- PromoterVariants(100, 50) av } VariantAnnotation/man/VCF-class.Rd0000644000175100017510000004661114614305321020023 0ustar00biocbuildbiocbuild\name{VCF-class} \docType{class} % Class \alias{class:VCF} \alias{VCF-class} \alias{CollapsedVCF} \alias{class:CollapsedVCF} \alias{CollapsedVCF-class} \alias{ExpandedVCF} \alias{class:ExpandedVCF} \alias{ExpandedVCF-class} % Constructors: \alias{VCF} % Coercion methods: \alias{SnpMatrixToVCF} % Accessor methods: \alias{updateObject,VCF-method} \alias{fixed} \alias{fixed,VCF-method} \alias{fixed<-} \alias{fixed<-,VCF,DataFrame-method} \alias{ref} \alias{ref,VCF-method} \alias{ref<-} \alias{ref<-,VCF,DNAStringSet-method} \alias{alt} \alias{alt,VCF-method} \alias{alt<-} \alias{alt<-,CollapsedVCF,CharacterList-method} \alias{alt<-,ExpandedVCF,character-method} \alias{alt<-,CollapsedVCF,DNAStringSetList-method} \alias{alt<-,ExpandedVCF,DNAStringSet-method} \alias{qual} \alias{qual,VCF-method} \alias{qual<-} \alias{qual<-,VCF,numeric-method} \alias{filt} \alias{filt,VCF-method} \alias{filt<-} \alias{filt<-,VCF,character-method} \alias{info} \alias{info,VCF-method} \alias{info<-} \alias{info<-,VCF,DataFrame-method} \alias{rowRanges,VCF-method} \alias{rowRanges<-,VCF,GRanges-method} \alias{mcols<-,VCF-method} \alias{mcols<-,VCF,ANY-method} \alias{dimnames<-,VCF,list-method} \alias{geno} \alias{geno,VCF-method} \alias{geno,VCF,ANY-method} \alias{geno,VCF,character-method} \alias{geno,VCF,numeric-method} \alias{geno,VCFHeader,ANY-method} \alias{geno<-} \alias{geno<-,VCF,character,matrix-method} \alias{geno<-,VCF,numeric,matrix-method} \alias{geno<-,VCF,missing,matrix-method} \alias{geno<-,VCF,missing,SimpleList-method} \alias{strand,VCF-method} \alias{strand<-,VCF,ANY-method} \alias{header,VCF-method} \alias{header<-} \alias{header<-,VCF,VCFHeader-method} \alias{vcfFields,VCF-method} % Subset methods \alias{[,VCF-method} \alias{[,VCF,ANY,ANY-method} \alias{[,VCF,ANY,ANY,ANY-method} \alias{subset,VCF-method} \alias{[<-,VCF,ANY,ANY,VCF-method} % Combine methods \alias{cbind,VCF-method} \alias{rbind,VCF-method} % Other methods / functions \alias{genome,VCF-method} \alias{seqlevels,VCF-method} \alias{expand,CollapsedVCF-method} \alias{expand,ExpandedVCF-method} \alias{genotypeCodesToNucleotides} % show method: \alias{show,VCF-method} \alias{show,CollapsedVCF-method} \alias{show,ExpandedVCF-method} \title{VCF class objects} \description{ The VCF class is a virtual class extended from \code{RangedSummarizedExperiment}. The subclasses, \code{CompressedVCF} and \code{ExtendedVCF}, are containers for holding data from Variant Call Format files. } \section{Constructors}{ \describe{ \item{}{ \code{readVcf(file, genome, param, ..., row.names=TRUE)} } \item{}{ \code{VCF(rowRanges = GRanges(), colData = DataFrame(), exptData = list(header = VCFHeader()), fixed = DataFrame(), info = DataFrame(), geno = SimpleList(), ..., collapsed=TRUE, verbose = FALSE)} Creates CollapsedVCF when \code{collapsed = TRUE} and an ExpandedVCF when \code{collapsed = FALSE}. This is a low-level constructor used internally. Most instances of the \code{VCF} class are created with \code{readVCF}. } } } \section{Accessors}{ In the following code snippets \code{x} is a CollapsedVCF or ExpandedVCF object. \describe{ \item{}{ \code{rowRanges(x, ..., fixed = TRUE)}, \code{rowRanges(x) <- value}: Gets or sets the rowRanges. The CHROM, POS, ID, POS and REF fields are used to create a \code{GRanges} object. The start of the ranges are defined by POS and the width is equal to the width of the reference allele REF. The IDs become the rownames. If they are missing (i.e., \sQuote{.}) a string of CHROM:POS_REF/ALT is used instead. The \code{genome} argument is stored in the seqinfo of the \code{GRanges} and can be accessed with \code{genome()}. When \code{fixed = TRUE}, REF, ALT, QUAL and FILTER metadata columns are displayed as metadata columns. To modify the \code{fixed} fields, use the \code{fixed<-} setter. One metadata column, \code{paramRangeID}, is included with the \code{rowRanges}. This ID is meaningful when multiple ranges are specified in the \code{ScanVcfParam} and distinguishes which records match each range. The metadata columns of a VCF object are accessed with the following: \itemize{ \item{\code{ref(x)}, \code{ref(x) <- value}: Gets or sets the reference allele (REF). \code{value} must be a \code{DNAStringSet}. } \item{\code{alt(x)}, \code{alt(x) <- value}: Gets or sets the alternate allele data (ALT). When \code{x} is a CollapsedVCF, \code{value} must be a \code{DNAStringSetList} or \code{CompressedCharacterList}. For ExpandedVCF, \code{value} must be a \code{DNAStringSet} or \code{character}. } \item{\code{qual(x)}, \code{qual(x) <- value}: Returns or sets the quality scores (QUAL). \code{value} must be an \code{numeric(1L)}. } \item{\code{filt(x)}, \code{filt(x) <- value}: Returns or sets the filter data. \code{value} must be a \code{character(1L)}. Names must be one of 'REF', 'ALT', 'QUAL' or 'FILTER'. } } } \item{}{ \code{mcols(x)}, \code{mcols(x) <- value}: These methods behave the same as \code{mcols(rowRanges(x))} and \code{mcols(rowRanges(x)) <- value}. This method does not manage the fixed fields, 'REF', 'ALT', 'QUAL' or 'FILTER'. To modify those columns use \code{fixed<-}. } \item{}{ \code{fixed(x)}, \code{fixed(x) <- value}: Gets or sets a DataFrame of REF, ALT, QUAL and FILTER only. Note these fields are displayed as metadata columns with the rowRanges() data (set to fixed = FALSE to suppress). } \item{}{ \code{info(x, ..., row.names = TRUE)}, \code{info(x) <- value}: Gets or sets a DataFrame of INFO variables. Row names are added if unique and \code{row.names=TRUE}. } \item{}{ \code{geno(x, withDimnames=TRUE)}, \code{geno(x) <- value}: oets a SimpleList of genotype data. \code{value} is a SimpleList. To replace a single variable in the SimpleList use \code{geno(x)$variable <- value}; in this case \code{value} must be a matrix or array. By default row names are returned; to override specify \code{geno(vcf, withDimnames=FALSE)}. } \item{}{ \code{metadata(x)}: Gets a \code{list} of experiment-related data. By default this list includes the \sQuote{header} information from the VCF file. See the use of \code{header()} for details in extracting header information. } \item{}{ \code{colData(x)}, \code{colData(x) <- value}: Gets or sets a \code{DataFrame} of sample-specific information. Each row represents a sample in the VCF file. \code{value} must be a \code{DataFrame} with rownames representing the samples in the VCF file. } \item{}{ \code{genome(x)}: Extract the \code{genome} information from the \code{GRanges} object returned by the \code{rowRanges} accessor. } \item{}{ \code{seqlevels(x)}: Extract the \code{seqlevels} from the \code{GRanges} object returned by the \code{rowRanges} accessor. } \item{}{ \code{strand(x)}: Extract the \code{strand} from the \code{GRanges} object returned by the \code{rowRanges} accessor. } \item{}{ \code{header(x)}, \code{header(x)<- value}: Get or set the VCF header information. Replacement value must be a \code{VCFHeader} object. To modify individual elements use \code{info<-}, \code{geno<-} or \code{meta<-} on a \sQuote{VCFHeader} object. See ?\code{VCFHeader} man page for details. \itemize{ \item{\code{info(header(x))}} \item{\code{geno(header(x))}} \item{\code{meta(header(x))}} \item{\code{samples(header(x))}} } } \item{}{\code{vcfFields(x)} Returns a \code{\link[IRanges]{CharacterList}} of all available VCF fields, with names of \code{fixed}, \code{info}, \code{geno} and \code{samples} indicating the four categories. Each element is a character() vector of available VCF field names within each category. } } } \section{Subsetting and combining}{ In the following code \code{x} is a VCF object, and \dots is a list of VCF objects. \describe{ \item{}{ \code{x[i, j]}, \code{x[i, j] <- value}: Gets or sets rows and columns. \code{i} and \code{j} can be integer or logical vectors. \code{value} is a replacement \code{VCF} object. } \item{}{ \code{subset(x, subset, select, ...)}: Restricts \code{x} by evaluating the \code{subset} argument in the scope of \code{rowData(x)} and \code{info(x)}, and \code{select} in the context of \code{colData(x)}. The \code{subset} argument restricts by rows, while the \code{select} argument restricts by column. The \code{\dots} are passed to the underlying \code{subset()} calls. } \item{}{ \code{cbind(...)}, \code{rbind(...)}: \code{cbind} combines objects with identical ranges (\code{rowRanges}) but different samples (columns in \code{assays}). The colnames in \code{colData} must match or an error is thrown. Columns with duplicate names in \code{fixed}, \code{info} and \code{mcols(rowRanges(VCF))} must contain the same data. \code{rbind} combines objects with different ranges (\code{rowRanges}) and the same subjects (columns in \code{assays}). Columns with duplicate names in \code{colData} must contain the same data. The \sQuote{Samples} columns in \code{colData} (created by \code{readVcf}) are renamed with a numeric extension ordered as they were input to \code{rbind} e.g., \dQuote{Samples.1, Samples.2, ...} etc. \code{metadata} from all objects are combined into a \code{list} with no name checking. } } } \section{expand}{ In the following code snippets \code{x} is a CollapsedVCF object. \describe{ \item{}{ \code{expand(x, ..., row.names = FALSE)}: Expand (unlist) the ALT column of a CollapsedVCF object to one row per ALT value. Variables with Number='A' have one value per alternate allele and are expanded accordingly. The 'AD' genotype field (and any variables with 'Number' set to 'R') is expanded into REF/ALT pairs. For all other fields, the rows are replicated to match the elementNROWS of ALT. The output is an ExpandedVCF with ALT as a \code{DNAStringSet} or \code{character} (structural variants). By default rownames are NULL. When \code{row.names=TRUE} the expanded output has duplicated rownames corresponding to the original \code{x}. } } } \section{genotypeCodesToNucleotides(vcf, ...)}{ This function converts the `GT` genotype codes in a \code{VCF} object to nucleotides. See also ?\code{readGT} to read in only `GT` data as codes or nucleotides. } \section{SnpMatrixToVCF(from, seqSource)}{ This function converts the output from the \link[snpStats]{read.plink} function to a \code{VCF} class. \code{from} must be a list of length 3 with named elements "map", "fam" and "genotypes". \code{seqSource} can be a \code{\link[BSgenome]{BSgenome}} or an \link{FaFile} used for reference sequence extraction. } \section{Variant Type}{ Functions to identify variant type include \link{isSNV}, \link{isInsertion}, \link{isDeletion}, \link{isIndel}, \link{isSubstitution} and \link{isTransition}. See the ?\code{isSNV} man page for details. } \section{Arguments}{ \describe{ \item{geno}{A \code{list} or \code{SimpleList} of matrix elements, or a \code{matrix} containing the genotype information from a VCF file. If present, these data immediately follow the FORMAT field in the VCF. Each element of the list must have the same dimensions, and dimension names (if present) must be consistent across elements and with the row names of \code{rowRanges}, \code{colData}. } \item{info}{A \code{DataFrame} of data from the INFO field of a VCF file. The number of rows must match that in the \code{rowRanges} object. } \item{fixed}{A \code{DataFrame} of REF, ALT, QUAL and FILTER fields from a VCF file. The number of rows must match that of the \code{rowRanges} object. } \item{rowRanges}{A \code{GRanges} instance describing the ranges of interest. Row names, if present, become the row names of the \code{VCF}. The length of the \code{GRanges} must equal the number of rows of the matrices in \code{geno}. } \item{colData}{A \code{DataFrame} describing the samples. Row names, if present, become the column names of the \code{VCF}. } \item{metadata}{A \code{list} describing the header of the VCF file or additional information for the overall experiment. } \item{...}{For \code{cbind} and \code{rbind} a list of VCF objects. For all other methods \dots are additional arguments passed to methods. } \item{collapsed}{A \code{logical(1)} indicating whether a CollapsedVCF or ExpandedVCF should be created. The ALT in a CollapsedVCF is a \code{DNAStringSetList} while in a ExpandedVCF it is a \code{DNAStringSet}. } \item{verbose}{A \code{logical(1)} indicating whether messages about data coercion during construction should be printed. } } } \details{ The \code{VCF} class is a virtual class with two concrete subclasses, \code{CollapsedVCF} and \code{ExtendedVCF}. Slots unique to \code{VCF} and subclasses, \itemize{ \item \code{fixed}: A \link{DataFrame} containing the REF, ALT, QUAL and FILTER fields from a VCF file. \item \code{info}: A \link{DataFrame} containing the INFO fields from a VCF file. } Slots inherited from \code{RangedSummarizedExperiment}, \itemize{ \item \code{metadata}: A \code{list} containing the file header or other information about the overall experiment. \item \code{rowRanges}: A \link{GRanges}-class instance defining the variant ranges and associated metadata columns of REF, ALT, QUAL and FILTER. While the REF, ALT, QUAL and FILTER fields can be displayed as metadata columns they cannot be modified with \code{rowRanges<-}. To modify these fields use \code{fixed<-}. \item \code{colData}: A \link{DataFrame}-class instance describing the samples and associated metadata. \item \code{geno}: The \code{assays} slot from \code{RangedSummarizedExperiment} has been renamed as \code{geno} for the VCF class. This slot contains the genotype information immediately following the FORMAT field in a VCF file. Each element of the \code{list} or \code{SimpleList} is a matrix or array. } It is expected that users will not create instances of the VCF class but instead one of the concrete subclasses, CollapsedVCF or ExpandVCF. CollapsedVCF contains the ALT data as a \code{DNAStringSetList} allowing for multiple alleles per variant. The ExpandedVCF ALT data is a \code{DNAStringSet} where the ALT column has been expanded to create a flat form of the data with one row per variant-allele combination. In the case of strucutral variants, ALT will be a \code{CompressedCharacterList} or \code{character} in the collapsed or expanded forms. } \author{Valerie Obenchain} \seealso{ \link{GRanges}, \link[S4Vectors]{DataFrame}, \link[S4Vectors]{SimpleList}, \link[SummarizedExperiment]{RangedSummarizedExperiment}, \code{\link{readVcf}}, \code{\link{writeVcf}} \code{\link{isSNV}} } \examples{ ## readVcf() parses data into a VCF object: fl <- system.file("extdata", "structural.vcf", package="VariantAnnotation") vcf <- readVcf(fl, genome="hg19") ## ---------------------------------------------------------------- ## Accessors ## ---------------------------------------------------------------- ## Variant locations are stored in the GRanges object returned by ## the rowRanges() accessor. rowRanges(vcf) ## Suppress fixed fields: rowRanges(vcf, fixed=FALSE) ## Individual fields can be extracted with ref(), alt(), qual(), filt() etc. qual(vcf) ref(vcf) head(info(vcf)) ## All available VCF field names can be contracted with vcfFields(). vcfFields(vcf) ## Extract genotype fields with geno(). Access specific fields with ## '$' or '[['. geno(vcf) identical(geno(vcf)$GQ, geno(vcf)[[2]]) ## ---------------------------------------------------------------- ## Renaming seqlevels and subsetting ## ---------------------------------------------------------------- ## Overlap and matching operations require that the objects ## being compared have the same seqlevels (chromosome names). ## It is often the case that the seqlevesls in on of the objects ## needs to be modified to match the other. In this VCF, the ## seqlevels are numbers instead of preceded by "chr" or "ch". seqlevels(vcf) ## Rename the seqlevels to start with 'chr'. vcf2 <- renameSeqlevels(vcf, paste0("chr", seqlevels(vcf))) seqlevels(vcf2) ## The VCF can also be subset by seqlevel using 'keepSeqlevels' ## or 'dropSeqlevels'. See ?keepSeqlevels for details. vcf3 <- keepSeqlevels(vcf2, "chr2", pruning.mode="coarse") seqlevels(vcf3) ## ---------------------------------------------------------------- ## Header information ## ---------------------------------------------------------------- ## Header data can be modified in the 'meta', 'info' and 'geno' ## slots of the VCFHeader object. See ?VCFHeader for details. ## Current 'info' fields. rownames(info(header(vcf))) ## Add a new field to the header. newInfo <- DataFrame(Number=1, Type="Integer", Description="Number of Samples With Data", row.names="NS") info(header(vcf)) <- rbind(info(header(vcf)), newInfo) rownames(info(header(vcf))) ## ---------------------------------------------------------------- ## Collapsed and Expanded VCF ## ---------------------------------------------------------------- ## readVCF() produces a CollapsedVCF object. fl <- system.file("extdata", "ex2.vcf", package="VariantAnnotation") vcf <- readVcf(fl, genome="hg19") vcf ## The ALT column is a DNAStringSetList to allow for more ## than one alternate allele per variant. alt(vcf) ## For structural variants ALT is a CharacterList. fl <- system.file("extdata", "structural.vcf", package="VariantAnnotation") vcf <- readVcf(fl, genome="hg19") alt(vcf) ## ExpandedVCF is the 'flattened' counterpart of CollapsedVCF. ## The ALT and all variables with Number='A' in the header are ## expanded to one row per alternate allele. vcfLong <- expand(vcf) alt(vcfLong) ## Also see the ?VRanges class for an alternative form of ## 'flattened' VCF data. ## ---------------------------------------------------------------- ## isSNV() ## ---------------------------------------------------------------- ## isSNV() returns a subset VCF containing SNVs only. vcf <- VCF(rowRanges = GRanges("chr1", IRanges(1:4*3, width=c(1, 2, 1, 1)))) alt(vcf) <- DNAStringSetList("A", c("TT"), c("G", "A"), c("TT", "C")) ref(vcf) <- DNAStringSet(c("G", c("AA"), "T", "G")) fixed(vcf)[c("REF", "ALT")] ## SNVs are present in rows 1 (single ALT value), 3 (both ALT values) ## and 4 (1 of the 2 ALT values). vcf[isSNV(vcf, singleAltOnly=TRUE)] vcf[isSNV(vcf, singleAltOnly=FALSE)] ## all 3 SNVs } VariantAnnotation/man/VcfFile-class.Rd0000644000175100017510000001022614614305321020714 0ustar00biocbuildbiocbuild\name{VcfFile} \Rdversion{1.1} \docType{class} \alias{VcfFile-class} \alias{VcfFileList-class} % con/destructors \alias{VcfFile} \alias{VcfFileList} \alias{vcfFields,missing-method} \alias{vcfFields,character-method} \alias{vcfFields,VcfFile-method} \alias{vcfFields,VcfFileList-method} \title{Manipulate Variant Call Format (Vcf) files.} \description{ Use \code{VcfFile()} to create a reference to a Vcf file (and its index). Once opened, the reference remains open across calls to methods, avoiding costly index re-loading. \code{VcfFileList()} provides a convenient way of managing a list of \code{VcfFile} instances. } \section{usage}{ ## Constructors \describe{ \item{}{ VcfFile(file, index = paste(file, "tbi", sep="."), ..., yieldSize=NA_integer_) } \item{}{ VcfFileList(..., yieldSize=NA_integer_) } } ## Accessors \describe{ \item{}{ index(object)} \item{}{ path(object, ...)} \item{}{ isOpen(con, rw="")} \item{}{ yieldSize(object, ...)} \item{}{ yieldSize(object, ...) <- value} \item{}{ show(object) } } ## Opening / closing \describe{ \item{}{ open(con, ...)} \item{}{ close(con, ...)} } ## method \describe{ \item{}{ vcfFields(object) } } } \section{arguments}{ \itemize{ \item{con}{An instance of \code{VcfFile}.} \item{file}{A character(1) vector to the Vcf file path; can be remote (http://, ftp://).} \item{index}{A character(1) vector of the Vcf file index (.tbi file).} \item{yieldSize}{Number of records to yield each time the file is read from using \code{scanVcf} or \code{readVcf}.} \item{...}{Additional arguments. For \code{VcfFileList}, this can either be a single character vector of paths to Vcf files, or several instances of \code{VcfFile} objects.} \item{rw}{character() indicating mode of file.} } } \section{Objects from the Class}{ Objects are created by calls of the form \code{VcfFile()}. } \section{Fields}{ \code{VcfFile} and \code{VcfFileList} classes inherit fields from the \code{\linkS4class{TabixFile}} and \code{\linkS4class{TabixFileList}} classes. } \section{Functions and methods}{ \code{VcfFile} and \code{VcfFileList} classes inherit methods from the \code{\linkS4class{TabixFile}} and \code{\linkS4class{TabixFileList}} classes. ## Opening / closing: \describe{ \item{open}{Opens the (local or remote) \code{path} and \code{index}. Returns a \code{VcfFile} instance. \code{yieldSize} determines the number of records parsed during each call to \code{scanVcf} or \code{readVcf}; \code{NA} indicates that all records are to be parsed.} \item{close}{Closes the \code{VcfFile} \code{con}; returning (invisibly) the updated \code{VcfFile}. The instance may be re-opened with \code{open.VcfFile}.} } ## Accessors: \describe{ \item{path}{Returns a character(1) vector of the Vcf path name.} \item{index}{Returns a character(1) vector of Vcf index (tabix file) name.} \item{yieldSize, yieldSize<-}{Return or set an integer(1) vector indicating yield size.} } ## Methods: \describe{ \item{vcfFields}{ Returns a \code{\link[IRanges]{CharacterList}} of all available VCF fields, with names of \code{fixed}, \code{info}, \code{geno} and \code{samples} indicating the four categories. Each element is a character() vector of available VCF field names within each category. It works for both local and remote vcf file. } } } \author{Valerie Obenchain} \examples{ fl <- system.file("extdata", "chr7-sub.vcf.gz", package="VariantAnnotation", mustWork=TRUE) vcffile <- VcfFile(fl) vcffile vcfFields(fl) vcfFields(vcffile) param <- GRanges("7", IRanges(c(55000000, 55900000), width=10000)) vcf <- readVcf(vcffile, "hg19", param=param) dim(vcf) ## `vcfFields` also works for remote vcf filepath. \dontrun{ chr22url <- "ftp://ftp.1000genomes.ebi.ac.uk/vol1/ftp/release/20130502/ALL.chr22.phase3_shapeit2_mvncall_integrated_v5a.20130502.genotypes.vcf.gz" vcfFields(chr22url) } } \keyword{classes} VariantAnnotation/man/VCFHeader-class.Rd0000644000175100017510000001153614614305321021132 0ustar00biocbuildbiocbuild\name{VCFHeader-class} \docType{class} % Class \alias{VCFHeader-class} % Constructor-like function: \alias{VCFHeader} % Accessor methods: \alias{reference} \alias{reference,VCFHeader-method} \alias{samples} \alias{samples,VCFHeader-method} \alias{header} \alias{header,VCFHeader-method} \alias{contig} \alias{contig,VCFHeader-method} \alias{meta} \alias{meta,VCFHeader-method} \alias{meta<-} \alias{meta<-,VCFHeader,DataFrame-method} \alias{meta<-,VCFHeader,DataFrameList-method} \alias{fixed,VCFHeader-method} \alias{fixed<-,VCFHeader,DataFrameList-method} \alias{info,VCFHeader-method} \alias{info<-,VCFHeader,DataFrame-method} \alias{geno,VCFHeader-method} \alias{geno<-,VCFHeader,missing,DataFrame-method} \alias{seqinfo,VCFHeader-method} \alias{vcfFields} \alias{vcfFields,VCFHeader-method} % show method: \alias{show,VCFHeader-method} \title{VCFHeader instances} \description{ The \code{VCFHeader} class holds Variant Call Format (VCF) file header information and is produced from a call to \code{scanVcfHeader}. } \section{Constructor}{ \describe{ \item{}{ \code{VCFHeader(reference = character(), samples = character(), header = DataFrameList(), ...) } } } } \section{Accessors}{ In the following code snippets \code{x} is a VCFHeader object. \describe{ \item{}{ \code{samples(x)}: Returns a character() vector of names of samples. } \item{}{ \code{header(x)}: Returns all information in the header slot which includes \code{meta}, \code{info} and \code{geno} if present. } \item{}{ \code{meta(x)}, \code{meta(x)<- value}: The getter returns a \code{DataFrameList}. Each \code{DataFrame} represents a unique "key" name in the header file. Multiple header lines with the same "key" are parsed into the same \code{DataFrame} with the "ID" field as the row names. Simple header lines have no "ID" field in which case the "key" is used as the row name. NOTE: In VariantAnnotation <= 1.27.5, the \code{meta()} extractor returned a \code{DataFrame} called "META" which held all simple key-value header lines. The VCF 4.3 specs allowed headers lines with key name "META" which caused a name clash with the pre-existing "META" \code{DataFrame}. In \code{VariantAnnotation} >=1.27.6 the "META" \code{DataFrame} was split and each row became its own separate \code{DataFrame}. Calling \code{meta()} on a \code{VCFHeader} object now returns a list of \code{DataFrames}, one for each unique key name in the header. } \item{}{ \code{fixed(x), fixed(x)<- value}: Returns a \code{DataFrameList} of information pertaining to any of \sQuote{REF}, \sQuote{ALT}, \sQuote{FILTER} and \sQuote{QUAL}. Replacement value must be a \code{DataFrameList} with one or more of the following names, \sQuote{QUAL}, \sQuote{FILTER}, \sQuote{REF} and \sQuote{ALT}. } \item{}{ \code{info(x)}, \code{info(x)<- value}: Gets or sets a \code{DataFrame} of \sQuote{INFO} information. Replacement value must be a \code{DataFrame} with 3 columns named \sQuote{Number}, \sQuote{Type} and \sQuote{Description}. } \item{}{ \code{geno(x)}, \code{geno(x)<- value}: Returns a \code{DataFrame} of \sQuote{FORMAT} information. Replacement value must be a \code{DataFrame} with 3 columns named \sQuote{Number}, \sQuote{Type} and \sQuote{Description}. } \item{}{ \code{reference(x)}: Returns a character() vector with names of reference sequences. Not relevant for \code{scanVcfHeader}. } \item{}{ \code{vcfFields(x)}: Returns a \code{\link[IRanges]{CharacterList}} of all available VCF fields, with names of \code{fixed}, \code{info}, \code{geno} and \code{samples} indicating the four categories. Each element is a character() vector of available VCF field names within each category. } } } \section{Arguments}{ \describe{ \item{reference}{A character() vector of sequences. } \item{sample}{A character() vector of sample names. } \item{header}{A \code{DataFrameList} of parsed header lines (preceeded by \dQuote{##}) present in the VCF file. } \item{...}{Additional arguments passed to methods. } } } \details{ The \code{VCFHeader} class holds header information from a VCF file. Slots : \describe{ \item{\code{reference}}{character() vector } \item{\code{sample}}{character() vector } \item{\code{header}}{\link{DataFrameList}-class } } } \author{Valerie Obenchain} \seealso{ \code{\link{scanVcfHeader}}, \code{\link{DataFrameList}} } \examples{ fl <- system.file("extdata", "structural.vcf", package="VariantAnnotation") hdr <- scanVcfHeader(fl) fixed(hdr) info(hdr) geno(hdr) vcfFields(hdr) } VariantAnnotation/man/vep_by_region.Rd0000644000175100017510000000220314614305321021116 0ustar00biocbuildbiocbuild% Generated by roxygen2: do not edit by hand % Please edit documentation in R/use_vep_api.R \name{vep_by_region} \alias{vep_by_region} \title{Use the VEP region API on variant information in a VCF object as defined in VariantAnnotation.} \usage{ vep_by_region(vcfobj, snv_only = TRUE, chk_max = TRUE) } \arguments{ \item{vcfobj}{instance of VCF class; note the difference between the CollapsedVCF and ExpandedVCF instances.} \item{snv_only}{logical(1) if TRUE filter the VCF to information about single nucleotide addresses} \item{chk_max}{logical(1) requests to ensembl VEP API are limited to 200 positions; if TRUE and the request involves more than 200 positions, an error is thrown by this function.} } \value{ instance of 'response' from httr package } \description{ Use the VEP region API on variant information in a VCF object as defined in VariantAnnotation. } \examples{ fl <- system.file("extdata", "chr22.vcf.gz", package="VariantAnnotation") r22 = readVcf(fl) dr = which(width(rowRanges(r22))!=1) r22s = r22[-dr] res = vep_by_region(r22[1:100], snv_only=FALSE, chk_max=FALSE) ans = jsonlite::fromJSON(jsonlite::toJSON(httr::content(res))) } VariantAnnotation/man/VRanges-class.Rd0000644000175100017510000003673214614305321020755 0ustar00biocbuildbiocbuild\name{VRanges-class} \docType{class} % Class: \alias{class:VRanges} \alias{VRanges-class} \alias{VRanges} % Constructors: \alias{VRanges} \alias{makeVRangesFromGRanges} % Coercion: \alias{asVCF} \alias{asVCF,VRanges-method} \alias{coerce,VRanges,VCF-method} \alias{coerce,VCF,VRanges-method} \alias{coerce,GRanges,VRanges-method} % Accessors: \alias{alt,VRanges-method} \alias{alt<-,VRanges,ANY-method} \alias{ref,VRanges-method} \alias{ref<-,VRanges,ANY-method} \alias{altDepth} \alias{altDepth,VRanges-method} \alias{altDepth<-} \alias{altDepth<-,VRanges-method} \alias{refDepth} \alias{refDepth,VRanges-method} \alias{refDepth<-} \alias{refDepth<-,VRanges-method} \alias{totalDepth} \alias{totalDepth,VRanges-method} \alias{totalDepth<-} \alias{totalDepth<-,VRanges-method} \alias{altFraction} \alias{altFraction,VRanges-method} \alias{called} \alias{called,VRanges-method} \alias{hardFilters<-} \alias{hardFilters<-,VRanges-method} \alias{hardFilters} \alias{hardFilters,VRanges-method} \alias{sampleNames,VRanges-method} \alias{sampleNames<-,VRanges,ANY-method} \alias{softFilterMatrix} \alias{softFilterMatrix,VRanges-method} \alias{softFilterMatrix<-} \alias{softFilterMatrix<-,VRanges-method} \alias{resetFilter} % Aggregation: \alias{tabulate} \alias{tabulate,VRanges-method} % VCF reading/writing: \alias{writeVcf,VRanges,ANY-method} \alias{readVcfAsVRanges} % Utilities: \alias{match,VRanges,VRanges-method} \alias{softFilter} % Typed Rle classes (at least for now) \alias{characterRle-class} \alias{characterOrRle-class} \alias{complexRle-class} \alias{factorRle-class} \alias{factorOrRle-class} \alias{integerRle-class} \alias{integerOrRle-class} \alias{logicalRle-class} \alias{numericRle-class} \alias{rawRle-class} \title{VRanges objects} \description{ The VRanges class is a container for variant calls, including SNVs and indels. It extends \code{\link[GenomicRanges]{GRanges}} to provide special semantics on top of a simple vector of genomic locations. While it is not as expressive as the \code{\linkS4class{VCF}} object, it is a simpler alternative that may be convenient for variant calling/filtering and similar exercises. } \details{ VRanges extends GRanges to store the following components. Except where noted, the components are considered columns in the dataset, i.e., their lengths match the number of variants. Many columns can be stored as either an atomic vector or an Rle. \describe{ \item{\code{ref}}{(\code{character}), the reference allele. The range (start/end/width) should always correspond to this sequence.} \item{\code{alt}}{(\code{character/Rle}), the alternative allele (NA allowed). By convention there is only a single alt allele per element (row) of the VRanges. Many methods, like \code{match}, make this assumption. } \item{\code{refCount}}{(\code{integer/Rle}), read count for the reference allele (NA allowed)} \item{\code{altCount}}{(\code{integer/Rle}), read count for the alternative allele (NA allowed)} \item{\code{totalCount}}{(\code{integer/Rle}), total read count at the position, must be at least \code{refCount+altCount} (NA allowed)} \item{\code{sampleNames}}{(\code{factor/Rle}), name of the sample - results from multiple samplse can be combined into the same object (NA allowed)} \item{\code{softFilterMatrix}}{(\code{matrix/FilterMatrix}), variant by filter matrix, \code{TRUE} where variant passed the filter; use a \code{\link[S4Vectors]{FilterMatrix}} to store the actual \code{FilterRules} object that was applied} \item{\code{hardFilters}}{(\code{FilterRules}) record of hard filters applied, i.e., only the variants that passed the filters are present in this object; this is the only component that is not a column, i.e., its length does not match the number of variants} } Except in the special circumstances described here, a \code{VRanges} may be treated like a \code{GRanges}. The range should span the sequence in \code{ref}. Indels are typically represented by the VCF convention, i.e., the start position is one upstream of the event. The strand is always constrained to be positive (+). Indels, by convention, should be encoded VCF-style, with the upstream reference base prepended to the indel sequence. The ref/alt for a deletion of GCGT before A might be AGCGT/A and for an insertion might be A/AGCGT. Since the range always matches the \code{ref} sequence, this means a deletion will be the width of the deletion + 1, and an insertion is always of width 1. VRanges and the VCF class: The VRanges and VCF classes encode different types of information and are semantically incompatible. While methods exist for converting a VCF object to a VRanges and vice versa, information is lost in the transformation. There is no way to collapse multiple rows of a VRanges at the same genomic position and accurately represent missing data. For this reason, it is not reasonable to assume that an object resulting from multiple conversions (VRanges -> VCF -> VRanges) will be equivalent to the original. } \section{Constructors}{ \describe{ \item{}{ \code{VRanges(seqnames = Rle(), ranges = IRanges(), ref = character(), alt = NA_character_, totalDepth = NA_integer_, refDepth = NA_integer_, altDepth = NA_integer_, ..., sampleNames = NA_character_, softFilterMatrix = FilterMatrix(matrix(nrow = length(gr), ncol = 0L), FilterRules()), hardFilters = FilterRules())}: Creates a VRanges object. \describe{ \item{\code{seqnames}}{Rle object, character vector, or factor containing the sequence names.} \item{\code{ranges}}{IRanges object containing the ranges.} \item{\code{ref}}{character vector, containing the reference allele.} \item{\code{alt}}{character vector/Rle, containing the alternative allele (NA allowed).} \item{\code{totalDepth}}{integer vector/Rle, containing the total read depth (NA allowed).} \item{\code{refDepth}}{integer vector/Rle, containing the reference read depth (NA allowed).} \item{\code{altDepth}}{integer vector/Rle, containing the reference read depth (NA allowed).} \item{\code{\ldots}}{Arguments passed to the \code{GRanges} constructor.} \item{\code{sampleNames}}{character/factor vector/Rle, containing the sample names (NA allowed).} \item{\code{softFilterMatrix}}{a matrix (typically a \code{\link[S4Vectors]{FilterMatrix}}) of dimension variant by filter, with logical values indicating whether a variant passed the filter.} \item{\code{hardFilters}}{a \code{\link[S4Vectors]{FilterRules}}, containing the filters that have already been applied to subset the object to its current state.} } } \item{}{ \code{makeVRangesFromGRanges(gr, ref.field="ref", alt.field="alt", totalDepth.field="totalDepth", refDepth.field="refDepth", altDepth.field="altDepth", sampleNames.field="sampleNames", keep.extra.columns=TRUE)}: Creates a VRanges object from a GRanges. \describe{ \item{\code{gr}}{ A \link[GenomicRanges]{GenomicRanges} object. } \item{\code{ref.field}}{ The \code{character(1)} name of the GRanges metadata column to be used as the VRanges \code{ref} field. } \item{\code{alt.field}}{ The \code{character(1)} name of the GRanges metadata column to be used as the VRanges \code{alt} field. } \item{\code{totalDepth.field}}{ The \code{character(1)} name of the GRanges metadata column to be used as the VRanges \code{totalDepth} field. } \item{\code{refDepth.field}}{ The \code{character(1)} name of the GRanges metadata column to be used as the VRanges \code{refDepth} field. } \item{\code{altDepth.field}}{ The \code{character(1)} name of the GRanges metadata column to be used as the VRanges \code{altDepth} field. } \item{\code{sampleNames.field}}{ The \code{character(1)} name of the GRanges metadata column to be used as the VRanges \code{sampleNames} field. } \item{\code{keep.extra.columns}}{ \code{TRUE} (the default) or \code{FALSE}. If \code{TRUE}, then the columns in \code{gr} that are not used to form the VRanges are retained as metadata columns. Otherwise, they will be ignored. } } } } } \section{Coercion}{ These functions/methods coerce objects to and from \code{VRanges}: \describe{ \item{}{ \code{asVCF(x, info = character(), filter = character(), meta = character())}: Creates a VCF object from a VRanges object. The following gives the mapping from VRanges components to VCF: \describe{ \item{seqnames(x)}{CHROM column} \item{start(x)}{POS column} \item{names(x)}{ID column} \item{ref(x)}{REF column} \item{alt(x)}{ALT column} \item{totalDepth(x)}{DP in FORMAT column} \item{altDepth(x), refDepth(x)}{AD in FORMAT column} \item{sampleNames(x)}{Names the sample columns} \item{softFilterMatrix(x)}{FT in FORMAT column, except filters named in \code{filter} argument, which are considered per-position and placed in the FILTER column} \item{hardFilters(x)}{Not yet exported} \item{mcols(x)}{Become fields in the FORMAT column; unless they are named in the \code{info} argument, in which case they are considered per-position and placed in the INFO column} \item{metadata(x)}{If named in the \code{meta} argument, output in the VCF header; a component is required to be coercible to a character vector of length one.} } Note that \code{identical(x, as(as(x, "VCF"), "VRanges"))} generally return \code{FALSE}. During coercion to VCF, the "geno" components are reshaped into matrix form, with NAs filling the empty cells. The reverse coercion will not drop the NA values, so rows are added to the new VRanges. All logical values will become integers in VCF, and there is no automatic way of regenerating the logical column with the reverse coercion. There are many other cases of irreversibility. } \item{}{ \code{as(from, "VCF")}: Like calling \code{asVCF(from)}. } \item{}{ \code{as(from, "VRanges")}: When \code{from} is a \code{VCF} this coercion is essentially the inverse of \code{asVCF}. Information missing in the VCF is imputed as NA. When \code{from} is a \code{GRanges}, metadata columns of \code{ref}, \code{alt}, \code{refDepth}, \code{altDepth}, \code{totalDepth} and \code{sampleNames} are transfered to the \code{VRanges} object. Additional metadata columns in the \code{GRanges} can be retained or dropped with \code{keep.extra.columns}. See also \code{makeVRangesFromGRanges}. } } } \section{Accessors}{ In addition to all of the \code{GRanges} accessors, \code{VRanges} provides the following, where \code{x} is a VRanges object. \describe{ \item{}{ \code{alt(x), alt(x) <- value}: Get or set the alt allele (character). } \item{}{ \code{ref(x), ref(x) <- value}: Get or set the ref allele (character). } \item{}{ \code{altDepth(x), altDepth(x) <- value}: Get or set the alt allele read depth (integer). } \item{}{ \code{refDepth(x), refDepth(x) <- value}: Get or set the ref allele read depth (integer). } \item{}{ \code{totalDepth(x), totalDepth(x) <- value}: Get or set the total read depth (integer). } \item{}{ \code{altFraction(x)}: Returns \code{altDepth(x)/totalDepth(x)} (numeric). } \item{}{ \code{sampleNames(x), sampleNames(x) <- value}: Get or set the sample names (character/factor). } \item{}{ \code{softFilterMatrix(x), softFilterMatrix(x) <- value}: Gets or sets the soft filter matrix (any matrix, but ideally a \code{FilterMatrix}). } \item{}{ \code{resetFilter(x)}: Removes all columns from \code{softFilterMatrix}. } \item{}{ \code{called(x)}: Returns whether all filter results in \code{softFilterMatrix(x)} are \code{TRUE} for each variant. } \item{}{ \code{hardFilters(x), hardFilters(x) <- value}: Gets or sets the hard filters (those applied to yield the current subset). } } } \section{Utilities and Conveniences}{ \describe{ \item{}{ \code{match(x)}: Like GRanges \code{match}, except matches on the combination of chromosome, start, width, and \strong{alt}. } \item{}{ \code{tabulate(bin)}: Finds \code{unique(bin)} and counts how many times each unique element occurs in \code{bin}. The result is stored in \code{mcols(bin)$sample.count}. } \item{}{ \code{softFilter(x, filters, ...)}: applies the \code{FilterRules} in \code{filters} to \code{x}, storing the results in \code{softFilterMatrix}. } } } \section{Input/Output to/from VCF}{ \describe{ \item{}{ \code{writeVcf(obj, filename, ...)}: coerces to a VCF object and writes it to a file; see \code{\link{writeVcf}}. } \item{}{ \code{readVcfAsVRanges(x, genome, param = ScanVcfParam(), ...)}: Reads a VCF \code{x} directly into a \code{VRanges}; see \code{\link{readVcf}} for details on the arguments. \code{readVcfAsVRanges} is an alternative syntax to \preformatted{ as(readVcf(), "VRanges") } NOTE: By default all INFO and FORMAT fields are read in with \code{ScanVcfParam()}. The minimal information needed to create the \code{VRanges} can be specified as follows: \preformatted{ ScanVcfParam(fixed = "ALT", info = NA, geno = "AD")) } } } } \section{Variant Type}{ Functions to identify variant type include \link{isSNV}, \link{isInsertion}, \link{isDeletion}, \link{isIndel}, \link{isSubstitution} and \link{isTransition}. See the ?\code{isSNV} man page for details. } \author{Michael Lawrence. \code{makeVRangesFromGRanges} was contributed by Thomas Sandmann.} \seealso{ \link{VRangesList}, a list of \code{VRanges}; \code{bam_tally} in the gmapR package, which generates a \code{VRanges}. } \examples{ ## construction vr <- VRanges(seqnames = c("chr1", "chr2"), ranges = IRanges(c(1, 10), c(5, 20)), ref = c("T", "A"), alt = c("C", "T"), refDepth = c(5, 10), altDepth = c(7, 6), totalDepth = c(12, 17), sampleNames = letters[1:2], hardFilters = FilterRules(list(coverage = function(x) totalDepth > 10)), softFilterMatrix = FilterMatrix(matrix = cbind(depth = c(TRUE, FALSE)), FilterRules(depth = function(x) altDepth(x) > 6)), tumorSpecific = c(FALSE, TRUE)) ## simple accessors ref(vr) alt(vr) altDepth(vr) vr$tumorSpecific called(vr) ## coerce to VCF and write vcf <- as(vr, "VCF") ## writeVcf(vcf, "example.vcf") ## or just ## writeVcf(vr, "example.vcf") ## other utilities match(vr, vr[2:1]) } VariantAnnotation/man/VRangesList-class.Rd0000644000175100017510000000315314614305321021600 0ustar00biocbuildbiocbuild\name{VRangesList-class} \docType{class} % Class: \alias{class:VRangesList} \alias{VRangesList-class} \alias{CompressedVRangesList-class} \alias{class:CompressedVRangesList} \alias{SimpleVRangesList-class} \alias{class:SimpleVRangesList} % Constructors: \alias{VRangesList} % Accessors: \alias{alt,VRangesList-method} \alias{ref,VRangesList-method} % Aggregation: \alias{stackSamples} \alias{stackSamples,VRangesList-method} \title{VRangesList objects} \description{ VRangesList is a virtual class representing a list of \code{\linkS4class{VRanges}} objects and should behave much like any other derivative of \code{List}. It has both a simple and compressed implementation. VRangesList provides conveniences for manipulating sets of \code{VRanges} objects. } \section{Constructor}{ \describe{ \item{}{ \code{VRangesList(...)}: Creates a VRangesList object from \code{VRanges} objects in \dots. } } } \section{Accessors}{ \describe{ \item{}{ \code{alt(x)}: Returns a CharacterList or RleList, effectively by calling \code{alt(x[[i]])} on each element of \code{x}. } \item{}{ \code{ref(x)}: Returns a CharacterList, effectively by calling \code{ref(x[[i]])} on each element of \code{x}. } } } \section{Utilities}{ \describe{ \item{}{ \code{stackSamples(x)}: Concentrates the elements in \code{x}, using \code{names(x)} to appropriately fill \code{sampleNames} in the result. } } } \author{Michael Lawrence} \examples{ ## construction example(VRanges) vrl <- VRangesList(sampleA = vr, sampleB = vr) stackSamples(vrl) } VariantAnnotation/man/writeVcf-methods.Rd0000644000175100017510000000762214614305321021533 0ustar00biocbuildbiocbuild\name{writeVcf} \alias{writeVcf} \alias{writeVcf,VCF,character-method} \alias{writeVcf,VCF,connection-method} \title{Write VCF files} \description{Write Variant Call Format (VCF) files to disk} \usage{ \S4method{writeVcf}{VCF,character}(obj, filename, index = FALSE, ...) \S4method{writeVcf}{VCF,connection}(obj, filename, index = FALSE, ...) } \arguments{ \item{obj}{Object containing data to be written out. At present only accepts \linkS4class{VCF}. } \item{filename}{The character() name of the VCF file, or a connection (e.g., \code{\link{file}()}), to be written out. A connection opened with \code{open = "a"} will have header information written only if the file does not already exist.} \item{index}{Whether to bgzip the output file and generate a tabix index. } \item{\dots}{Additional arguments, passed to methods. \itemize{ \item{nchunk:} Integer or NA. When provided this argument overrides the default chunking behavior of \code{writeVcf}, see Details section. An integer value specifies the number of records in each chunk; NA disables chunking. } } } \note{ NOTE: \code{VariantAnnotation} >= 1.27.6 supports VCFv4.3. See the NOTE on the \code{?VCFHeader} man page under the \code{meta()} extractor for a description of how header parsing has changed to accommodate the new header lines with key name of 'META'. } \details{ A VCF file can be written out from data in a \code{VCF} object. More general methods to write out from other objects may be added in the future. \code{writeVcf} writes out the header fields in a \code{VCF} object 'as-is' with the exception of these key-value pairs: \itemize{ \item fileformat: When missing, a line is added at the top of the file with the current supported version. \code{VariantAnnotation} >=1.27.6 supports VCFv4.3. \item fileDate: When missing, a line is added with today's date. If the key-value pair exists, the date is overwritten with today's date. \item contig: When missing, \code{VariantAnnotation} attempts to use the \code{Seqinfo} of the \code{VCF} object to determine the contig information. } Large VCF files (i.e., > 1e5 records) are written out in chunks; VCF files with < 1e5 records are not chunked. The optimal number of records per chunk depends on both the number of records and complexity of the data. Currently \code{writeVcf} determines records per chunk based on the total number of records only. To override this behavior or experiment with other values use \code{nchunk} as an integer or NA. An integer value represents the number of records per chunk regardless of the size of the VCF; NA disables all chunking. \itemize{ \item writeVcf(vcf, tempfile()) ## default chunking \item writeVcf(vcf, tempfile(), nchunk = 1e6) ## chunk by 1e6 \item writeVcf(vcf, tempfile(), nchunk = NA) ## no chunking } } \value{VCF file } \references{ \url{http://vcftools.sourceforge.net/specs.html} outlines the VCF specification. \url{http://samtools.sourceforge.net/mpileup.shtml} contains information on the portion of the specification implemented by \code{bcftools}. \url{http://samtools.sourceforge.net/} provides information on \code{samtools}. } \author{Valerie Obenchain and Michael Lawrence} \seealso{ \code{\link{readVcf}} } \examples{ fl <- system.file("extdata", "ex2.vcf", package="VariantAnnotation") out1.vcf <- tempfile() out2.vcf <- tempfile() in1 <- readVcf(fl, "hg19") writeVcf(in1, out1.vcf) in2 <- readVcf(out1.vcf, "hg19") writeVcf(in2, out2.vcf) in3 <- readVcf(out2.vcf, "hg19") stopifnot(all(in2 == in3)) ## write incrementally out3.vcf <- tempfile() con <- file(out3.vcf, open="a") writeVcf(in1[1:2,], con) writeVcf(in1[-(1:2),], con) close(con) readVcf(out3.vcf, "hg19") } \keyword{manip} VariantAnnotation/NAMESPACE0000644000175100017510000000653314614305321016456 0ustar00biocbuildbiocbuilduseDynLib(VariantAnnotation, .registration=TRUE) import(methods) importFrom(utils, packageVersion) importFrom(stats, pchisq, setNames) import(BiocGenerics) importFrom(MatrixGenerics, rowRanges) import(S4Vectors) import(IRanges) import(GenomeInfoDb) import(GenomicRanges) import(SummarizedExperiment) import(Rsamtools) import(zlibbioc) importClassesFrom(Biobase, AssayData ) importFrom(Biobase, sampleNames, "sampleNames<-", samples ) importClassesFrom(AnnotationDbi, AnnotationDb ) importMethodsFrom(AnnotationDbi, colnames, exists, ncol, nrow, columns, keys, keytypes, select ) importFrom(GenomicFeatures, extractTranscriptSeqs, mapToTranscripts, transcriptsBy, intronsByTranscript ) importClassesFrom(GenomicFeatures, TxDb ) importMethodsFrom(GenomicFeatures, cdsBy, exons, transcripts, fiveUTRsByTranscript, threeUTRsByTranscript, distance, mapToTranscripts ) importFrom(XVector, "subseq", "subseq<-") importFrom(Biostrings, AAStringSet, DNAStringSet, DNAStringSetList, DNA_BASES, getSeq, GENETIC_CODE ) importMethodsFrom(Biostrings, nchar, reverseComplement, substr, translate ) importClassesFrom(Biostrings, DNAStringSet, DNAStringSetList ) importMethodsFrom(DBI, dbCommit, dbConnect, dbDisconnect, dbExistsTable, dbGetQuery, dbReadTable, dbWriteTable, dbListTables, dbListFields ) importClassesFrom(BSgenome, BSgenome ) importFrom(rtracklayer, liftOver, import ) importFrom(utils, txtProgressBar, setTxtProgressBar ) exportClassPattern("^.*$") export( VCF, VCFHeader, reference, header, "header<-", meta, "meta<-", vcfWhich, "vcfWhich<-", vcfFixed, "vcfFixed<-", vcfInfo, "vcfInfo<-", vcfGeno, "vcfGeno<-", vcfSamples, "vcfSamples<-", vcfTrimEmpty, "vcfTrimEmpty<-", duplicateRSID, CodingVariants, IntronVariants, FiveUTRVariants, ThreeUTRVariants, IntergenicVariants, SpliceSiteVariants, PromoterVariants, AllVariants, upstream, "upstream<-", downstream, "downstream<-", idType, "idType<-", promoter, "promoter<-", intergenic, "intergenic<-", probabilityToSnpMatrix, GLtoGP, PLtoGP, VRanges, VRangesList, asVCF, softFilter, resetFilter, totalDepth, altDepth, refDepth, sampleNames, "sampleNames<-", altFraction, softFilterMatrix, "softFilterMatrix<-", hardFilters, "hardFilters<-", called, stackSamples, "altDepth<-", "refDepth<-", "totalDepth<-", probabilityToSnpMatrix, GLtoGP, readInfo, readGeno, readGT, genotypeCodesToNucleotides, tabulate, VRangesScanVcfParam, readVcfAsVRanges, isSNV, isInsertion, isDeletion, isIndel, isDelins, isTransition, isSubstitution, VcfFile, VcfFileList, makeVRangesFromGRanges, post_Hs_region, vep_by_region ) exportMethods( filterVcf, scanVcf, scanVcfHeader, ScanVcfParam, readVcf, writeVcf, expand, predictCoding, getTranscriptSeqs, getSeq, genotypeToSnpMatrix, snpSummary, locateVariants, summarizeVariants, isSNV, isInsertion, isDeletion, isIndel, isDelins, isTransition, isSubstitution, updateObject, fixed, "fixed<-", ref, "ref<-", alt, "alt<-", qual, "qual<-", filt, "filt<-", info, "info<-", geno, "geno<-", strand, "strand<-", "[", "[<-", cbind, rbind, "mcols<-", indexVcf, vcfFields, subset, genome, seqlevels, seqinfo, reference, samples, header, meta, keys, keytypes, columns, select, import ) VariantAnnotation/NEWS0000644000175100017510000004501414614305321015733 0ustar00biocbuildbiocbuildCHANGES IN VERSION 1.36.0 ------------------------- NEW FEATURES o ref<-, alt<-, qual<- and filt<- allow replacement value length recycling CHANGES IN VERSION 1.28.0 ------------------------ NEW FEATURES o Update package to support VCF format version 4.3 - SAMPLE field lines can now have key 'SAMPLE' or 'META'. To avoid a name clash, the existing 'META' DataFrame has been split by row into separate DataFrames. The 'meta(VCFHeader)' getter now returns one DataFrame per unique key in the header. - PEDIGREE header line now begins with 'ID' o Add vcfFields method for character, VCFHeader, VcfFile and VCF to return all available vcf fields in CharacterList(). o Add support for single breakend notation (thanks d-cameron) BUG FIXES o .formatInfo() now return a column with all 'NA' for a missing value instead of dropping the column. CHANGES IN VERSION 1.26.0 ------------------------ MODIFICATIONS o Clarify fixed fields must be o Following renaming of RangesList class -> IntegerRangesList o Updates to accommodate change to '[<-' method for SummarizedExperiment o DP4 assumed to come from INFO field o Extract altDepth and totalDepth from DP4 GENO field when present o predictCoding() now respects 'alt_init_codons' at the start of CDS region only o Remove error message in 'rowRanges<-' and 'mcol<-' methods that check for fixed column name. CHANGES IN VERSION 1.24.0 ------------------------ NEW FEATURES o Add subset,VCF-method that knows about info() o Add alt,ref accessors for VRangesList o More efficient show,VCF-method o 'rowRanges<-' and 'mcols<-' on VCF class behave as they do on RangedSummarizedExperiment o info,VCFHeader() and geno,VCFHeader() return a DataFrame with the correct columns in the case of empty BUG FIXES o Fix "ref<-" recycling on VRanges o Fix bug in locateVariants(); code to fetch IntronVariants() was incorrectly fetching IntergenicVariants() o Fix bug in rbind,VCF,VCF-method CHANGES IN VERSION 1.22.0 ------------------------ NEW FEATURES o add import() wrapper for VCF files o add support for Number='R' in vcf parsing o add indexVcf() and methods for character,VcfFile,VcfFileList MODIFICATIONS o throw message() instead of warning() when non-nucleotide variations are set to NA o replace 'force=TRUE' with 'pruning.mode="coarse"' in seqlevels() setter o add 'pruning.mode' argument to keepSeqlevels() in man page example o idempotent VcfFile() o add 'idType' arg to IntergenicVariants() constructor o modify locateVariants man page example to work around issue that distance,GRanges,TxDb does not support gene ranges on multiple chromosomes o modify VcfFile() constructor to detect index file if not specified o order vignettes from intro to advanced; use BiocStyle::latex2() o remove unused SNPlocs.Hsapiens.dbSNP.20110815 from the Suggests field o follow rename change in S4Vectors from vector_OR_factor to vector_or_factor o pass classDef to .RsamtoolsFileList; VariantAnnotation may not be on the search path BUG FIXES o fix expansion of 'A' fields when there are multiple columns CHANGES IN VERSION 1.20.0 ------------------------ NEW FEATURES o add import() wrapper for VCF files MODIFICATIONS o use now-public R_GetConnection o remove defunct readVcfLongForm() generic o remove 'genome' argument from readVcf() o improvements to VCF to VRanges coercion o support Varscan2 AD/RD convention when coercing VCF to VRanges o use [["FT"]] to avoid picking up FTZ field o summarizeVariants() recognize '.' as missing GT field o document scanVcfheader() behavior for duplicate row names BUG FIXES o ensure only 1 matching hub resource selected in filterVcf vignette o fix check for FILT == "PASS" o correct column alignment in makeVRangesFromGRanges() o fix check for AD conformance CHANGES IN VERSION 1.19.0 ------------------------ NEW FEATURES o add SnpMatrixToVCF() o add patch from Stephanie Gogarten to support 'PL' in genotypeToSnpMatrix() MODIFICATIONS o move getSeq,XStringSet-method from VariantAnnotation to BSgenome o update filterVcf vignette o remove 'pivot' export o work on readVcf(): - 5X speedup for readVcf (at least in one case) by not using "==" to compare a list to a character (the list gets coerced to character, which is expensive for huge VCFs) - avoiding relist.list() o update summarizeVariants() to comply with new SummarizedExperiment rownames requirement o defunct VRangesScanVcfParam() and restrictToSNV() o use elementNROWS() instead of elementLengths() o togroup(x) now only works on a ManyToOneGrouping object so replace togroup(x, ...) calls with togroup(PartitioningByWidth(x), ...) when 'x' is a list-like object that is not a ManyToOneGrouping object. o drop validity assertion that altDepth must be NA when alt is NA there are VCFs in the wild that use e.g. "*" for alt, but include depth o export PLtoGP() o VariantAnnotation 100% RangedData-free BUG FIXES o use short path names in src/Makevars.win CHANGES IN VERSION 1.18.0 ------------------------ MODIFICATIONS o defunct VRangesScanVcfParam() o defunct restrictToSNV() BUG FIXES o scanVcf,character,missing-method ignores blank data lines. o Build path for C code made robust on Windows. CHANGES IN VERSION 1.16.0 ------------------------ NEW FEATURES o support REF and ALT values ".", "+" and "-" in predictCoding() o return non-translated characters in VARCODON in predictCoding() output o add 'verbose' option to readVcf() and friends o writeVcf() writes 'fileformat' header line always o readVcf() converts REF and ALT values "*" and "I" to '' and '.' MODIFICATIONS o VRanges uses '*' strand by default o coerce 'alt' to DNStringSet for predictCoding,VRanges-method o add detail to documentation for 'ignore.strand' in predictCoding() o be robust to single requrested INFO column not present in vcf file o replace old SummarizedExperiment class from GenomicRanges with the new new RangedSummarizedExperiment from SummarizedExperiment package o return strand of 'subject' for intronic variants in locateVariants() BUG FIXES o writeVcf() does not duplicate header lines when chunking o remove extra tab after INFO when no FORMAT data are present o filteVcf() supports 'param' with ranges CHANGES IN VERSION 1.14.0 ------------------------ NEW FEATURES o gVCF support: - missing END header written out with writeVcf() - expand() handles 'REF' value o support 'Type=Character' in INFO header fields o add 'row.names' argument to expand() o add 'Efficient Usage' section to readVcf() man page o efficiency improvements to info(..., row.names=) - anyDuplicated() less expensive than any(duplicated()) - use row.names=FALSE when not needed, e.g., show() o add genotypeCodesToNucleotides() o add support for gvcf in isSNP family of functions o add VcfFile and VcfFileList classes o support 'Type=Character' of unspecified length (.) o add isDelins() from Robert Castelo o add makeVRangesFromGRanges() from Thomas Sandmann MODIFICATIONS o VCFHeader support: - SAMPLE and PEDIGREE header fields are now parsed - meta(VCFHeader) returns DataFrameList instead of DataFrame - show(VCFHeader) displays the outer list names in meta - fixed(VCFHeader) returns 'ALT' and 'REF' if present o 'ALT' in expandedVCF output is DNAStringSet, not *List o remove .listCumsum() and .listCumsumShifted() helpers o add multiple INFO field unit test from Julian Gehring o add additional expand() unit tests o modify readVccfAsVRanges() to use ScanVcfParam() as the 'param'; deprecate VRangesScanVcfParam o replace mapCoords() with mapToTranscripts() o change 'CDSID' output from integer to IntegerList in locateVariants() and predictCoding() o add readVcf,character,ANY,ANY; remove readVcf,character,ANY,ScanVcfParam o replace rowData() accessor with rowRanges() o replace 'rowData' argument with 'rowRanges' (construct SE, VCF classes) o replace getTranscriptSeqs() with extractTranscripts() BUG FIXES o readVcf() properly handles Seqinfo class as 'genome' o allow 'ignore.strand' to pass through mapCoords() o writeVcf() no longer ignores rows with no genotype field o expand() properly handles - less than all INFO fields are selected - VCF has only one row - only one INFO column o don't call path() on non-*File objects o split (relist) of VRanges now yields a CompressedVRangesList o predictCoding() now ignores zero-width ranges CHANGES IN VERSION 1.12.0 ------------------------ NEW FEATURES o allow GRanges in 'rowData' to hold user-defined metadata cols (i.e., cols other than paramRangeID, REF, ALT, etc.) o add isSNV() family of functions o add faster method for converting a list matrix to an array o add 'c' method for typed Rle classes so class is preserved o add CITATION file o rework writeVCF(): - FORMAT and genotype fields are parsed in C - output file is written from C - chunking added for large VCFs MODIFICATIONS o add 'row.names' to readVcf() o deprecate restrictToSNV(); replaced by isSNV() family o remove use of seqapply() o show info / geno headers without splitting across blocks o use mapCoords() in predictCoding() and locateVariants() o deprecate refLocsToLocalLocs() o propagate strand in predictCoding() o replace deprecated seqsplit() with splitAsList() o ensure GT field, if present, comes first in VCF output o modify DESCRIPTION Author and Maintainer fileds with @R o add 'row.names' to info,VCF-method BUG FIXES o modify expand() to work with no 'info' fields are imported o remove duplicate rows from .splicesites() o fix handling of real-valued NAs in geno omatrix construction in writeVcf() CHANGES IN VERSION 1.10.0 ------------------------ NEW FEATURES o add support for ##contig in VCF header o add 'meta<-', 'info<-', 'geno<-' replacement methods for VCFHeader o add 'header<-' replacement method for VCF o add strand to output from locationVariants() o add support for writeVcf() to process Rle data in geno matrix o readVcf() now parses 'geno' fields with Number=G as ((#alleles + 1) * (#alleles + 2)) / 2 o writeVcf() now sorts the VCF when 'index=TRUE' o add 'fixed<-,VCFHeader,DataFrameList' method o add convenience functions for reading VCF into VRanges o add Rplinkseq test script o add 'isSNV', 'isInsertion', 'isDeletion', 'isIndel', 'isTransition', 'isPrecise', 'isSV' and 'isSubstitution' generics o add 'isSNV', 'isInsertion', 'isDeletion', 'isIndel' methods for VRanges and VCF classes o add match methods between ExpandedVCF and VRanges o add support for VRanges %in% TabixFile MODIFICATIONS o expand,VCF-method ignores 'AD' header of 'AD' geno is NULL o add support for SIFT.Hsapiens.dbSNP137 o remove locateVariants() dependence on chr7-sub.vcf.gz o modify expand() to handle 'AD' field where 'Number' is integer o rename readVRangesFromVCF() to readVcfAsVRanges() o remove check for circular chromosomes in locateVariants() and predictCoding() and refLocsToLocalLocs() o modify filterVcf() to handle ranges in ScanVcfParam o pass 'genetic.code' through predictCoding() o change default to 'row.names=TRUE' for readGT(), readGeno(), and readInfo() o fixed() on empty VCF now returns DataFrame with column names and data types vs an empty DataFrame o update biocViews o modify 'show,VCF' to represent empty values in XStringSet with '.' o replace rtracklayer:::pasteCollapse with unstrsplit() DEPRECATED and DEFUNCT o remove defunct dbSNPFilter(), regionfilter() and MatrixToSnpMatrix() o defunct readVcfLongForm() BUG FIXES o modify expand.geno() to handle case where header and geno don't match o modify writeVcf() to write out rownames with ":" character instead of treating as missing o fix how sample names were passed from 'ScanVcfParam' to scanVcf() o fix bug in 'show,VCF' method o fix bugs in VRanges -> VCF coercion methods o fix bug in lightweight read* functions that were ignoring samples in ScanVcfParam o fix bug in writeVcf() when no 'ALT' is present CHANGES IN VERSION 1.8.0 ------------------------ NEW FEATURES o Add 'upstream' and 'downstream' arguments to IntergenicVariants() constructor. o Add 'samples' argument to ScanVcfParam(). o Add readGT(), readGeno() and readInfo(). o Add VRanges, VRangesList, SimpleVRangesList, and CompressedVRangesList classes. o Add coercion VRanges -> VCF and VCF -> VRanges. o Add methods for VRanges family: altDepth(), refDepth(), totalDepth(), altFraction() called(), hardFilters(), sampleNames(), softFilterMatrix() isIndel(), resetFilter(). o Add stackedSamples,VRangesList method. MODIFICATIONS o VCF validity method now requires the number of rows in info() to match the length of rowData(). o PRECEDEID and FOLLOWID from locateVariants() are now CharacterLists with all genes in 'upstream' and 'downstream' range. o Modify rownames on rowData() GRanges to CHRAM:POS_REF/ALT for variants with no ID. o readVcf() returns info() and geno() in the order specified in the ScanVcfParam. o Work on scanVcf(): - free parse memory at first opportunity - define it_next in .c rather than .h - parse ALT "." in C - hash incoming strings - parse only param-requested 'fixed', 'info', 'geno' fields o Add dimnames<-,VCF method to prevent 'fixed' fields from being copied into 'rowData' when new rownames or colnames were assigned. o Support read/write for an emtpy VCF. o readVcf(file=character, ...) method attempts coercion to TabixFile. o Support for read/write an emtpy VCF. o Add performance section to vignette; convert to BiocStyle. o expand,CompressedVcf method expands geno() field 'AD' to length ALT + 1. The expanded field is a (n x y x 2) array. o 'genome' argument to readVcf() can be a character(1) or Seqinfo object. DEPRECATED and DEFUNCT o Defunct dbSNPFilter(), regionFilter() and MatrixToSnpMatrix(). o Deprecate readVcfLongForm(). BUG FIXES o Fix bug in compatibility of read/writeVcf() when no INFO are columns present. o Fix bug in locateVariants() when 'features' has no txid and cdsid. o Fix bug in asVCF() when writing header lines. o Fix bug in "expand" methods for VCF to handle multiple 'A' columns in info(). CHANGES IN VERSION 1.6.0 ------------------------ NEW FEATURES o VCF is now VIRTUAL. Concrete subclasses are CollapsedVCF and ExpandedVCF. o Add filterVcf() generic and methods for character and TabixFile. This method creates one VCF file from another, using FilterRules. o Enhance show,VCF method with header information. o Stephanie Gogarten added genotypeToSnpMatrix() generic and CollapsedVCF and matrix methods. o Chris Wallace added snpSummary() generic and CollapsedVCF method. o Add cbind and rbind for VCF objects. MODIFICATIONS o writeVcf,connection-method allows writing to console and appending. o writeVcf,connection-method accepts connections with open="a", only adding a header if the file does not already exist. o predictCoding and genotypeToSnpMatrix can now handle ALT as CharacterList. Structural variants are set to empty character (""). o When no INFO data are present in a vcf file, the info() slot is now an empty DataFrame. Previously an empty column named 'INFO' was returned. o Empty VCF class now has an empty VCFHeader o expand,CollapsedVCF-method expands 'geno' data with Number=A. o VCF class accessors "fixed", "info" now return DataFrame instead of GRanges. "rowData" returns fixed fileds as the mcols. o Updates to the vignette. DEPRECATED and DEFUNCT o Deprecate dbSNPFilter() and regionFilter(). o Deprecate MatrixToSnpMatrix(). BUG FIXES o Multiple bugs fixed in "locateVariants". o Multiple bugs fixed in "writeVcf". o Bug fixed in subsetting of VCF objects. o Bug fixed in "predictCoding" related to QUERYID column not mapping back to original indices (rows). CHANGES IN VERSION 1.4.0 ------------------------ NEW FEATURES o "summarizeVariants" for summarizing counts by sample o new VariantType 'PromoterVariants()' added to "locateVariants" MODIFICATIONS o "ref", "alt", "filt" and "qual" accessors for VCF-class now return a single variable instead of GRanges with variable as metadata CHANGES IN VERSION 1.2.0 ------------------------ NEW FEATURES o "readVcf" has genome argument, can be subset on ranges or VCF elements with "ScanVcfParam" o "scanVcfHeader" returns VCFHeader class with accessors fixed, info, geno, etc. o "writeVcf" writes out a VCF file from a VCF class o "locateVariants" methods - returns GRanges instead of DataFrame - 'region' argument allows specification of variants by region - output includes txID, geneID and cdsID - has cache argument for repeated calls over multiple vcf files o "predictCoding" methods - returns GRanges instead of DataFrame - output includes txID, geneID, cdsID, cds-based and protein-based coordinates CHANGES IN VERSION 1.0.0 ------------------------ NEW FEATURES o "readVcf" methods for reading and parsing VCF files into a SummarizedExperiment o "locateVariants" and "predictCoding" for identifying amino acid coding changes in nonsynonymous variants o "dbSNPFilter" and "regionFilter" for filtering variants on membership in dbSNP or on a particular location in the genome o access to PolyPhen and SIFT predictions through "keys" , "cols" and "select" methods. See ?SIFT or ?PolyPhen. BUG FIXES o No changes classified as 'bug fixes' (package under active development) VariantAnnotation/R/0000755000175100017510000000000014614305321015431 5ustar00biocbuildbiocbuildVariantAnnotation/R/AllClasses.R0000644000175100017510000003011214614305321017577 0ustar00biocbuildbiocbuild### ========================================================================= ### All classes ### ========================================================================= ### ------------------------------------------------------------------------- ### VCF (VIRTUAL) ### setClass("VCF", contains=c("VIRTUAL", "RangedSummarizedExperiment"), representation( fixed="DataFrame", info="DataFrame") ) .valid.VCF.fixed <- function(object) { xlen <- dim(object)[1] ffld <- fixed(object) nms <- colnames(ffld) if (nrow(ffld) != 0) { if (nrow(ffld) != xlen) return(paste0("'fixed(object)' and 'rowRanges(object) ", "must have the same number of rows", sep="")) if (!all(nms %in% c("REF", "ALT", "QUAL", "FILTER"))) return(paste("'fixed(object)' colnames must be ", "'REF', 'ALT', 'QUAL' and 'FILTER'", sep="")) if ("REF" %in% nms) if (!is(ffld$REF, "DNAStringSet")) return("'ref(object)' must be a DNAStringSet") if ("QUAL" %in% nms) if (!is(ffld$QUAL, "numeric")) return("'qual(object)' must be numeric") if ("FILTER" %in% nms) if (!is(ffld$FILTER, "character")) return("'filt(object)' must be a character") } NULL } .valid.VCF.info <- function(object) { xlen <- nrow(object) i <- info(object) if (nrow(i) != xlen) return("'info' must have the same number of rows as 'rowRanges'") NULL } .valid.VCF.alt <- function(object) { if (length(alt <- alt(object))) { if (is(object, "ExpandedVCF")) { if (!is(alt, "DNAStringSet") && !is.character(alt)) return(paste("'alt(object)' must be a DNAStringSet or a ", "character", sep="")) } else if (is(object, "CollapsedVCF")) { if (!is(alt, "DNAStringSetList") && !is(alt, "CharacterList")) return(paste("'alt(object)' must be a DNAStringSetList or a ", "CharacterList", sep="")) } } NULL } .valid.VCF <- function(object) { c(.valid.VCF.fixed(object), .valid.VCF.info(object), .valid.VCF.alt(object)) } ### ------------------------------------------------------------------------- ### CollapsedVCF ### setClass("CollapsedVCF", contains="VCF", prototype=prototype( fixed=DataFrame(REF=DNAStringSet(), ALT=DNAStringSetList(), QUAL=numeric(), FILTER=character())), validity=.valid.VCF) ### Automatically generated "coerce<-" method is broken so we fix it. ### See S4Vectors/R/S4-utils.R in the S4Vectors package for more information. S4Vectors:::setReplaceAs("CollapsedVCF", "RangedSummarizedExperiment", S4Vectors:::canonical_replace_as_2 ) ### ------------------------------------------------------------------------- ### ExpandedVCF ### setClass("ExpandedVCF", contains="VCF", prototype=prototype( fixed=DataFrame(REF=DNAStringSet(), ALT=DNAStringSet(), QUAL=numeric(), FILTER=character())), validity=.valid.VCF) ### Automatically generated "coerce<-" method is broken so we fix it. ### See S4Vectors/R/S4-utils.R in the S4Vectors package for more information. S4Vectors:::setReplaceAs("ExpandedVCF", "RangedSummarizedExperiment", S4Vectors:::canonical_replace_as_2 ) ### ------------------------------------------------------------------------- ### VCFHeader ### setClass("VCFHeader", representation( reference="character", samples="character", header="SimpleDataFrameList" ) ) .valid.VCFHeader.colnames <- function(value, slotname) { if (length(value)) { col <- c("Number", "Type", "Description") if (ncol(value) != 3 || !all(names(value) %in% col)) return(paste0("'", slotname, "(VCFHeader)' must be a ", "3 column DataFrame with names ", paste(col, collapse=", "))) else return(NULL) } NULL } .valid.VCFHeader.info <- function(object) .valid.VCFHeader.colnames(info(object), "info") .valid.VCFHeader.geno <- function(object) .valid.VCFHeader.colnames(geno(object), "geno") .valid.VCFHeader.meta <- function(object) { msg <- NULL len <- sapply(names(meta(object)), nchar) if (any(len == 0)) msg <- "all elements of 'meta(VCFHeader)' must be named" msg } ## Test VCFHeader only .valid.VCFHeader <- function(object) { return(c(.valid.VCFHeader.info(object), .valid.VCFHeader.geno(object), .valid.VCFHeader.meta(object))) } setValidity("VCFHeader", .valid.VCFHeader, where=asNamespace("VariantAnnotation")) ## These validity checks test the fields in the VCF against ## the VCFHeader. Called from header<-,VCF-method. .valid.VCFHeadervsVCF.fields <- function(object, slotname) { diff <- setdiff(names(slotname(object)), rownames(slotname(header(object)))) if (length(diff)) { warning(paste0(attributes(slotname)$generic[1], " fields with no header: ", paste(diff, collapse=",")), call.=FALSE) } NULL } .valid.VCFHeadervsVCF <- function(object) { validObject(header(object)) c(.valid.VCFHeadervsVCF.fields(object, info), .valid.VCFHeadervsVCF.fields(object, geno)) } ### ------------------------------------------------------------------------- ### VcfFile, VcfFileList ### .VcfFile = setRefClass("VcfFile", contains="TabixFile") setClass("VcfFileList", contains="TabixFileList", prototype=prototype(elementType="VcfFile")) ### ------------------------------------------------------------------------- ### ScanVcfParam ### setClass("ScanVcfParam", contains="ScanBVcfParam") ### ------------------------------------------------------------------------- ### SIFT, PROVEAN and PolyPhen ### .SIFTDb <- setRefClass("SIFTDb", contains = "AnnotationDb") .PROVEANDb <- setRefClass("PROVEANDb", contains = "AnnotationDb") .PolyPhenDb <- setRefClass("PolyPhenDb", contains = "AnnotationDb") ### ------------------------------------------------------------------------- ### VariantType class hierarchy ### setClass("VariantType", representation( "VIRTUAL" ) ) setMethod("show", "VariantType", function(object) { cat("class:", classNameForDisplay(object), "\n") } ) setClass("CodingVariants", contains="VariantType") setClass("IntronVariants", contains="VariantType") setClass("ThreeUTRVariants", contains="VariantType") setClass("FiveUTRVariants", contains="VariantType") setClass("SpliceSiteVariants", contains="VariantType") setClass("IntergenicVariants", contains="VariantType", representation(upstream="integer", downstream="integer", idType="character") ) setClass("PromoterVariants", contains="VariantType", representation(upstream="integer", downstream="integer") ) setClass("AllVariants", contains="VariantType", representation(promoter="PromoterVariants", intergenic="IntergenicVariants") ) ### ------------------------------------------------------------------------- ### VRanges ### ### This is useful when variants are the unit of analysis, especially ### when diagnosis the effects of filters. Thus, this corresponds to ### an expansion of VCF, where each variant only has one alt ### allele. If the alt is NA, it describes a reference/WT call. ### .valid.VRanges.ref <- function(object) { c(if (any(is.na(object@ref))) "'ref' must not contain 'NA' values", if (length(which(object@ref == object@alt)) > 0L) "'ref' must not match 'alt'") } .valid.VRanges.strand <- function(object) { if (any(object@strand == "-")) paste("'strand' must always be '+' or '*'") } naToZero <- function(x) { x[is.na(x)] <- 0L x } .valid.VRanges.depth <- function(object) { checkDepth <- function(name) { if (any(slot(object, name) < 0, na.rm = TRUE)) paste0("'", name, "' must be non-negative") } depth.slots <- c("refDepth", "altDepth", "totalDepth") do.call(c, lapply(depth.slots, checkDepth)) } ### FIXME: we are not yet checking for redundant observations, i.e., ### rows with the same seqname, start, end, alt, and sample. .valid.VRanges <- function(object) { c(.valid.VRanges.ref(object), .valid.VRanges.strand(object), .valid.VRanges.depth(object)) } setTypedRle <- function(type) { cname <- paste0(type, "Rle") setClass(cname, representation(values = type), contains = "Rle") setValidity(cname, function(object) { if (!is(runValue(object), type)) paste("run values in Rle must be", type) }) coercer <- get(paste0("as.", type)) # as(from, type) is NOT the same setAs("Rle", cname, function(from) { if (!is(runValue(from), type)) slot(from, "values", check = FALSE) <- coercer(runValue(from)) new(cname, from) }) setAs("vector_OR_factor", cname, function(from) { new(cname, Rle(coercer(from))) }) setMethod("[", cname, function(x, i, j, ..., drop = getOption("dropRle", FALSE)) { as(callNextMethod(), cname) }) setReplaceMethod("[", cname, function(x, i, j, ..., value) { if (missing(i)) ans <- callGeneric(x = as(x, "Rle"), value = value) else ans <- callGeneric(x = as(x, "Rle"), i = i, value = value) as(ans, paste0(class(runValue(ans)), "Rle")) }) setMethod("replaceROWS", cname, function(x, i, value) { x <- as(x, "Rle") ans <- callNextMethod() as(ans, paste0(class(runValue(ans)), "Rle")) }) setMethod("window", cname, function(x, ...) { as(callNextMethod(), cname) }) setMethod("c", cname, function(x, ...) { ### FIXME: cannot use callNextMethod, because broken for ### primitive generics where the actual primitive object ### is part of the call, e.g., do.call(generic, args) as(do.call(c, lapply(list(x, ...), as, "Rle")), cname) }) cname } ### TODO: These could land in IRanges at some point. They are useful ### for constraining class representations, and are convenient ### coercion targets. In theory, we could also use them more broadly, ### i.e., for Rle method dispatch, but that would require work in ### Rle() and runValue<- first, and would break serialized objects. setTypedRle("raw") setTypedRle("logical") setTypedRle("integer") setTypedRle("numeric") setTypedRle("complex") setTypedRle("character") setTypedRle("factor") setAtomicRleUnion <- function(type) { cname <- paste0(type, "OrRle") rlename <- paste0(type, "Rle") setClassUnion(cname, c(type, rlename)) coercer <- get(paste0("as.", type)) # as(from, type) is NOT the same setAs("ANY", cname, function(from) { coercer(from) }) setAs("Rle", cname, function(from) { as(from, rlename) }) cname } .integerOrRle <- setAtomicRleUnion("integer") .characterOrRle <- setAtomicRleUnion("character") .factorOrRle <- setAtomicRleUnion("factor") setClass("VRanges", representation(ref = "character", # or XStringSet? alt = .characterOrRle, totalDepth = .integerOrRle, refDepth = .integerOrRle, altDepth = .integerOrRle, sampleNames = .factorOrRle, softFilterMatrix = "matrix", hardFilters = "FilterRules"), contains = "GRanges", validity = .valid.VRanges) ### ------------------------------------------------------------------------- ### VRangesList ### setClass("VRangesList", representation("VIRTUAL"), prototype = prototype(elementType = "VRanges"), contains = "GRangesList") setClass("SimpleVRangesList", contains = c("VRangesList", "SimpleGRangesList")) setClass("CompressedVRangesList", representation(unlistData = "VRanges"), contains = c("VRangesList", "CompressedGRangesList")) VariantAnnotation/R/AllGenerics.R0000644000175100017510000001755314614305321017757 0ustar00biocbuildbiocbuild### ------------------------------------------------------------------------- ### predictCoding ### setGeneric("predictCoding", signature=c("query", "subject", "seqSource", "varAllele"), function(query, subject, seqSource, varAllele, ...) standardGeneric("predictCoding") ) setGeneric("getTranscriptSeqs", signature=c("query", "subject"), function(query, subject, ...) standardGeneric("getTranscriptSeqs") ) ### ------------------------------------------------------------------------- ### locateVariants ### setGeneric("locateVariants", signature=c("query", "subject", "region"), function(query, subject, region, ...) standardGeneric("locateVariants") ) ### ------------------------------------------------------------------------- ### summarizeVariants ### setGeneric("summarizeVariants", signature=c("query", "subject", "mode"), function(query, subject, mode, ...) standardGeneric("summarizeVariants") ) ### ------------------------------------------------------------------------- ### VariantRegion classes ### setGeneric("upstream", signature="x", function(x) standardGeneric("upstream") ) setGeneric("upstream<-", signature="x", function(x, value) standardGeneric("upstream<-") ) setGeneric("downstream", signature="x", function(x) standardGeneric("downstream") ) setGeneric("downstream<-", signature="x", function(x, value) standardGeneric("downstream<-") ) setGeneric("promoter", signature="x", function(x) standardGeneric("promoter") ) setGeneric("promoter<-", signature="x", function(x, value) standardGeneric("promoter<-") ) setGeneric("intergenic", signature="x", function(x) standardGeneric("intergenic") ) setGeneric("intergenic<-", signature="x", function(x, value) standardGeneric("intergenic<-") ) setGeneric("idType", signature="x", function(x) standardGeneric("idType") ) setGeneric("idType<-", signature="x", function(x, value) standardGeneric("idType<-") ) ### ------------------------------------------------------------------------- ### read/write Vcf ### setGeneric("readVcf", signature=c("file", "param"), function(file, genome, param, ...) standardGeneric("readVcf") ) setGeneric("writeVcf", signature=c("obj", "filename"), function(obj, filename, ...) standardGeneric("writeVcf") ) ### ------------------------------------------------------------------------- ### scanVcf ### setGeneric("ScanVcfParam", signature="which", function(fixed=character(), info=character(), geno=character(), samples=character(), trimEmpty=TRUE, which, ...) standardGeneric("ScanVcfParam") ) setGeneric("scanVcfHeader", signature="file", function(file, ...) standardGeneric("scanVcfHeader") ) setGeneric("scanVcf", signature=c("file", "param"), function(file, ..., param) standardGeneric("scanVcf") ) ### ------------------------------------------------------------------------- ### filterVcf ### setGeneric("filterVcf", signature="file", function(file, genome, destination, ..., verbose=FALSE, index=FALSE, prefilters=FilterRules(), filters=FilterRules(), param=ScanVcfParam()) standardGeneric("filterVcf") ) ### ------------------------------------------------------------------------- ### VCF class ### setGeneric("fixed", signature="x", function(x) standardGeneric("fixed") ) setGeneric("fixed<-", signature=c("x", "value"), function(x, value) standardGeneric("fixed<-") ) setGeneric("ref", signature="x", function(x) standardGeneric("ref") ) setGeneric("ref<-", signature=c("x", "value"), function(x, value) standardGeneric("ref<-") ) setGeneric("alt", signature="x", function(x) standardGeneric("alt") ) setGeneric("alt<-", signature=c("x", "value"), function(x, value) standardGeneric("alt<-") ) setGeneric("qual", signature="x", function(x) standardGeneric("qual") ) setGeneric("qual<-", signature=c("x", "value"), function(x, value) standardGeneric("qual<-") ) setGeneric("filt", signature="x", function(x) standardGeneric("filt") ) setGeneric("filt<-", signature=c("x", "value"), function(x, value) standardGeneric("filt<-") ) setGeneric("info", signature="x", function(x, ..., row.names = TRUE) standardGeneric("info") ) setGeneric("info<-", signature=c("x", "value"), function(x, value) standardGeneric("info<-") ) setGeneric("geno", signature=c("x", "i"), function(x, i, ..., withDimnames=TRUE) standardGeneric("geno"), ) setGeneric("geno<-", signature=c("x", "i", "value"), function(x, i, ..., value) standardGeneric("geno<-") ) ### ------------------------------------------------------------------------- ### VCFHeader class ### setGeneric("reference", signature="x", function(x) standardGeneric("reference"), ) setGeneric("header", signature="x", function(x) standardGeneric("header"), ) setGeneric("header<-", signature=c("x", "value"), function(x, value) standardGeneric("header<-"), ) setGeneric("contig", signature="x", function(x) standardGeneric("contig"), ) setGeneric("meta", signature="x", function(x) standardGeneric("meta"), ) setGeneric("meta<-", signature=c("x", "value"), function(x, value) standardGeneric("meta<-"), ) setGeneric("vcfFields", signature = "x", function(x, ...) standardGeneric("vcfFields")) ### ------------------------------------------------------------------------- ### snp encoding methods ### setGeneric("genotypeToSnpMatrix", signature="x", function(x, ...) standardGeneric("genotypeToSnpMatrix") ) setGeneric("snpSummary", function(x, ...) standardGeneric("snpSummary") ) ### ------------------------------------------------------------------------- ### isSNV helpers ### setGeneric("isSNV", signature="x", function(x, ...) standardGeneric("isSNV") ) setGeneric("isInsertion", signature="x", function(x, ...) standardGeneric("isInsertion") ) setGeneric("isDeletion", signature="x", function(x, ...) standardGeneric("isDeletion") ) setGeneric("isIndel", signature="x", function(x, ...) standardGeneric("isIndel") ) setGeneric("isDelins", signature="x", function(x, ...) standardGeneric("isDelins") ) setGeneric("isTransition", signature="x", function(x, ...) standardGeneric("isTransition") ) setGeneric("isSV", signature="x", function(x, ...) standardGeneric("isSV") ) setGeneric("isSVPrecise", signature="x", function(x, ...) standardGeneric("isSVPrecise") ) setGeneric("isSubstitution", signature="x", function(x, ...) standardGeneric("isSubstitution") ) ### ------------------------------------------------------------------------- ### VRanges class ### setGeneric("totalDepth", function(x, ...) standardGeneric("totalDepth")) setGeneric("altDepth", function(x, ...) standardGeneric("altDepth")) setGeneric("refDepth", function(x, ...) standardGeneric("refDepth")) setGeneric("softFilterMatrix", function(x, value) standardGeneric("softFilterMatrix")) setGeneric("softFilterMatrix<-", function(x, value) standardGeneric("softFilterMatrix<-")) setGeneric("hardFilters", function(x, value) standardGeneric("hardFilters")) setGeneric("hardFilters<-", function(x, value) standardGeneric("hardFilters<-")) setGeneric("called", function(x, ...) standardGeneric("called")) setGeneric("altFraction", function(x, ...) standardGeneric("altFraction")) setGeneric("refFraction", function(x, ...) standardGeneric("refFraction")) setGeneric("asVCF", function(x, ...) standardGeneric("asVCF")) setGeneric("tabulate", signature = "bin", # BiocGenerics? function(bin, nbins = max(1L, bin, na.rm = TRUE)) standardGeneric("tabulate")) ### ------------------------------------------------------------------------- ### VRangesList class ### setGeneric("stackSamples", function(x, ...) standardGeneric("stackSamples")) VariantAnnotation/R/AllUtilities.R0000644000175100017510000002435214614305321020166 0ustar00biocbuildbiocbuild### ========================================================================= ### Helper functions not exported ### ========================================================================= ### - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - ### readVcf() ### .collapseLists <- function(vcf, param) { idx <- sapply(vcf, function(elt) length(elt$rowRanges) > 0L) if (!sum(idx)) return(vcf[[1]]) if (length(vcf) > 1L) vcf <- vcf[idx] if (is(param, "ScanVcfParam")) paramRangeID <- names(unlist(vcfWhich(param), use.names=FALSE))[idx] else paramRangeID <- names(param)[idx] if (is.null(paramRangeID)) paramRangeID <- rep(NA_character_, length(vcf)) ## single range in 'which' if (1L == length(vcf)) { lst <- vcf[[1]] lst$paramRangeID <- as.factor(rep(paramRangeID, length(lst$rowRanges))) } else { ## multiple ranges in 'which' lst <- lapply(names(vcf[[1]]), function(elt) { suppressWarnings(do.call(c, unname(lapply(vcf, "[[", elt)))) }) names(lst) <- names(vcf[[1]]) len <- unlist(lapply(vcf, function(elt) length(elt$rowRanges)), use.names=FALSE) paramRangeID <- as.factor(rep(paramRangeID, len)) ## collapse info and geno info <- lst$INFO sp <- split(unname(info), unique(names(info))) sp <- sp[unique(names(info))] lst$INFO <- lapply(sp, function(elt) { d <- dim(elt[[1]]) if (is(elt[[1]], "list")) { as.matrix(elt) } else if (is(elt[[1]], "array") && !is.na(d[3])) { pc <- lapply(seq_len(d[2]), function(i) { do.call(rbind, lapply(elt, "[", ,i,)) }) array(do.call(c, pc), c(length(lst$rowRanges), d[2], d[3])) } else { do.call(c, elt) } }) geno <- lst$GENO sp <- split(geno, unique(names(geno))) lst$GENO <- lapply(sp, function(elt) { d <- dim(elt[[1]]) if (!is.na(d[3])) { pc <- lapply(seq_len(d[3]), function(i) { do.call(rbind, lapply(elt, "[", ,,i)) }) cmb <- array(do.call(c, pc), c(length(lst$rowRanges), d[2], d[3])) cmb } else { trans <- lapply(elt, t) cmb <- matrix(do.call(c, trans), length(lst$rowRanges), d[2], byrow=TRUE) cmb } }) lst$paramRangeID <- paramRangeID } lst } .formatList <- function(data, type) { switch(type, Integer = IntegerList(data), Float = NumericList(data), String = CharacterList(data), Character = CharacterList(data), Logical = LogicalList(data)) } ## Converts structural variants in a CharacterList to a '.' ## DNAStringSetList. Used in predictCoding(), genotypeToSnpMatrix() ## and snpSummary(). .toDNAStringSetList <- function(x) { pbw <- PartitioningByWidth(elementNROWS(x)) x <- unlist(x, use.names=FALSE) x[.isStructural(x)] <- "" xx <- sub(".", "", x, fixed=TRUE) relist(DNAStringSet(xx), pbw) } ## Returns structural variants in a CharacterList and all others in ## a DNAStringSetList. Used in .scanVcfToVCF(). .formatALT <- function(x) { if (is.null(x)) return(NULL) flat <- unlist(x, use.names=FALSE) if (any(.isStructural(flat))) { CharacterList(x) } else { flat[grepl("I", flat, fixed=TRUE)] <- "." flat[grepl("*", flat, fixed=TRUE)] <- "" relist(DNAStringSet(flat), x) } } ## The grep for '.' here is looking for '.' as *part* of the ALT field. ## If the ALT were '.' only, with no other characters, it would have been ## converted to an empty string in the C code before it reached this point. .isStructural <- function(x) { grepl("<", x, fixed=TRUE) | grepl("[", x, fixed=TRUE) | grepl("]", x, fixed=TRUE) | grepl(".", x, fixed=TRUE) } .formatInfo <- function(x, hdr, nrecords) { ## no data allNA <- 1L == length(x) && all(is.na(unlist(x[[1]]))) allMissing <- 1L == length(x) && all(unlist(x[[1]]) == ".", na.rm=TRUE) if (length(x) == 0L || (allMissing && !allNA)) return(DataFrame(row.names=seq_len(nrecords))) ## data in file but not in header if (!length(hdr) && length(x[[1]])) { DF <- DataFrame(x) names(DF) <- names(x) return(DF) } ## matrices and arrays type <- hdr$Type[match(names(x), rownames(hdr))] idx <- which(lapply(x, is.array) == TRUE) if (0L != length(idx)) { for (i in idx) { dat <- x[[i]] d <- dim(dat) ncol <- ifelse(!is.na(d[3]), d[3], d[2]) if (ncol > 1) dat <- split(unlist(dat, use.names=FALSE), rep(seq_len(d[1]), ncol)) x[[i]] <- .formatList(dat, type[i]) } } ## ragged lists lx <- which(lapply(x, is.list) == TRUE) if (0L != length(lx)) { for (i in lx) { x[[i]] <- .formatList(x[[i]], type[i]) } } DF <- DataFrame(x) names(DF) <- names(x) DF } .rleRecycleVector <- function(x, len) { if (is(x, "Rle")) rep(x, length.out = len) else if (length(x) == 1L) Rle(x, len) else S4Vectors:::recycleVector(x, len) } .pasteCollapseRows <- function(x, sep = ",") { if (!is.matrix(x) || storage.mode(x) != "character") { stop("'x' must be a matrix of mode character") } if (!isSingleString(sep) || nchar(sep) == 0L) { stop("'sep' must be a single, non-NA, non-empty string") } .Call(matrix_pasteCollapseRows, x, sep) } ### - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - ### PolyPhen, SIFT and PROVEAN ### .getKcol <- function(conn) { sql <- "SELECT value FROM metadata WHERE name='Key column'" as.character(dbGetQuery(conn, sql)) } .sqlIn <- function(vals) { if (0L == length(vals)) return("") sql <- lapply(seq_len(length(vals)), function(i) { v <- vals[[i]] if (!is.numeric(v)) v <- paste("'", v, "'", sep="") v }) paste(unlist(sql), collapse = ",") } .missingKeys <- function(x, keys, db) { if (missing(keys)) return(FALSE) if (any(mkeys <- !keys %in% keys(x))) { msg <- paste(S4Vectors:::selectSome(keys[mkeys]), collapse=" ") warning(sum(mkeys), " keys not found in ", db, " database: ", msg, call.=FALSE) } all(mkeys) } .missingCols <- function(x, cols, db) { if (missing(cols)) return(FALSE) if (any(mcols <- !cols %in% columns(x))) { msg <- paste(S4Vectors:::selectSome(cols[mcols], 5), collapse=" ") warning(sum(mcols), " columns not found in ", db, " database: ", msg, call.=FALSE) return(TRUE) } else { return(FALSE) } } ### - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - ### isSNV helpers ### .isSNV <- function(ref, alt) { nchar(ref) == 1L & nchar(alt) == 1L } .isDeletion <- function(ref, alt) { nchar(alt) == 1L & nchar(ref) > 1L & substring(ref, 1, 1) == alt } .isInsertion <- function(ref, alt) { nchar(ref) == 1L & nchar(alt) > 1L & substring(alt, 1, 1) == as.character(ref) } .isIndel <- function(ref, alt) { .isDeletion(ref, alt) | .isInsertion(ref, alt) } .isDelins <- function(ref, alt) { !.isIndel(ref, alt) & !.isSubstitution(ref, alt) } .isTransition <- function(ref, alt) { m1 <- match(as.character(ref), c("A", "G", "T", "C")) m2 <- match(as.character(alt), c("G", "A", "C", "T")) res <- (m1 - m2) == 0 res[is.na(res)] <- FALSE res } .isSubstitution <- function(ref, alt) { nchar(ref) == nchar(alt) } ### - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - ### predictCoding() ### .localCoordinates <- function(from, to, ignore.strand, ...) { ## 'to' is a GRangesList of cds by transcript map <- mapToTranscripts(unname(from), to, ignore.strand=ignore.strand, ...) if (length(map) == 0) { res <- GRanges() mcols(res) <- DataFrame(REF=DNAStringSet(), ALT=DNAStringSetList(), varAllele=DNAStringSet(), CDSLOC=IRanges(), PROTEINLOC=IntegerList(), QUERYID=integer(), TXID=character(), CDSID=IntegerList()) return(res) } xHits <- map$xHits txHits <- map$transcriptsHits flat_to <- unlist(to) ## names needed for mapping ## FIXME: cdsid is expensive cdsid <- IntegerList(integer(0)) map2 <- mapToTranscripts(unname(from)[xHits], flat_to, ignore.strand=ignore.strand) cds <- mcols(flat_to)$cds_id[map2$transcriptsHits] ## CodingVariants() must fall within a coding region. ## mapToTranscripts() will map ranges that span intron ## junctions and overlap multiple exons/cds regions. Because ## of this, it's possible for 'map' to return a hit for a ## query that 'map2' will not. (The subject in ## 'map' is a GRangesList and in 'map2' it's unlisted.) ## Only ranges identified by 'map' and 'map2' are kept. ## Ranges identified by 'map' only are discarded. if (length(cds)) { cdsid <- unique(splitAsList(cds, map2$xHits)) keep <- logical(length(xHits)) keep[unique(map2$xHits)] <- TRUE if (any(!keep)) { map <- map[keep] txHits <- map$transcriptsHits xHits <- map$xHits } } if (is.null(txid <- names(to)[txHits])) txid <- NA_integer_ ## protein coordinates pends <- c(ceiling(start(map)/3), ceiling(end(map)/3)) plocs <- unique(IntegerList(split(pends, rep(seq_len(length(pends)/2)), 2))) res <- from[xHits] strand(res) <- strand(map) mcols(res) <- append(values(res), DataFrame(CDSLOC=ranges(map), PROTEINLOC=plocs, QUERYID=xHits, TXID=txid, CDSID=cdsid)) res } VariantAnnotation/R/makeVRangesFromGRanges.R0000644000175100017510000000457314614305321022063 0ustar00biocbuildbiocbuild### ========================================================================= ### makeVRangesFromGRanges() ### ------------------------------------------------------------------------- makeVRangesFromGRanges <- function(gr, ref.field="ref", alt.field="alt", totalDepth.field="totalDepth", altDepth.field="altDepth", refDepth.field="refDepth", sampleNames.field="sampleNames", keep.extra.columns=TRUE) { ## check args if (!is(gr, "GenomicRanges")) stop("'gr' must be a GenomicRanges object") args <- list(ref.field=ref.field, alt.field=alt.field, totalDepth.field=totalDepth.field, altDepth.field=altDepth.field, refDepth.field=refDepth.field, sampleNames.field=sampleNames.field) if (!all(strings <- sapply(args, isSingleString))) stop(paste0("'",names(args[!strings]), "'", collapse=","), " must be character(1)") ## match fields matched.fields <- match(tolower(args), tolower(names(mcols(gr))), 0) found.fields <- matched.fields != 0L matched <- matched.fields[found.fields] ## error for 'ref' (required) if (!"ref.field" %in% names(args)[found.fields]) stop("No 'ref' column could be identified.") ## extract fields, coerce to type lst <- as.list(c(rep(NA_character_, 2), rep(NA_integer_, 3), NA_character_)) names(lst) <- names(args) type <- vapply(lst, class, character(1)) lst[found.fields] <- Map(as, mcols(gr)[matched], type[found.fields]) if (keep.extra.columns) extra <- mcols(gr)[, -matched, drop=FALSE] else extra <- DataFrame() ## construct VRanges VRanges(seqnames=seqnames(gr), seqinfo=seqinfo(gr), ranges=ranges(gr), ref=lst[["ref.field"]], alt=lst[["alt.field"]], totalDepth=lst[["totalDepth.field"]], altDepth=lst[["altDepth.field"]], refDepth=lst[["refDepth.field"]], sampleNames=lst[["sampleNames.field"]], extra) } setAs("GRanges", "VRanges", function(from) makeVRangesFromGRanges(from, keep.extra.columns=TRUE) ) VariantAnnotation/R/methods-CollapsedVCF-class.R0000644000175100017510000000503014614305321022563 0ustar00biocbuildbiocbuild### ========================================================================= ### CollapsedVCF class methods ### ========================================================================= ### - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - ### Constructor ### ### See VCF(..., collapsed=TRUE) in methods-VCF-class.R ### - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - ### Getters and Setters ### alt() setter setReplaceMethod("alt", c("CollapsedVCF", "DNAStringSetList"), function(x, value) { value <- .recycleVector(alt(x), value) slot(x, "fixed")$ALT <- value x }) ### - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - ### isSNV family ### .dispatchSNV_CollapsedVCF <- function(FUN, x, singleAltOnly) { alt <- alt(x) flat <- unlist(alt, use.names=FALSE) gvcf <- structural <- NULL if (is(flat, "character")) { gvcf <- grepl("NON_REF", flat, fixed=TRUE) structural <- .isStructural(flat) } res <- FUN(rep(ref(x), elementNROWS(alt(x))), flat) if (!is.null(structural)) res[structural | is.na(structural)] <- FALSE if (any(gvcf)) res[gvcf] <- TRUE ## relist lst <- relist(res, alt) if (singleAltOnly) all(lst) & elementNROWS(lst) == 1 else any(lst) } setMethod("isSNV", "CollapsedVCF", function(x, ..., singleAltOnly=TRUE) .dispatchSNV_CollapsedVCF(.isSNV, x, singleAltOnly) ) setMethod("isInsertion", "CollapsedVCF", function(x, ..., singleAltOnly=TRUE) .dispatchSNV_CollapsedVCF(.isInsertion, x, singleAltOnly) ) setMethod("isDeletion", "CollapsedVCF", function(x, ..., singleAltOnly=TRUE) .dispatchSNV_CollapsedVCF(.isDeletion, x, singleAltOnly) ) setMethod("isIndel", "CollapsedVCF", function(x, ..., singleAltOnly=TRUE) .dispatchSNV_CollapsedVCF(.isIndel, x, singleAltOnly) ) setMethod("isDelins", "CollapsedVCF", function(x, ..., singleAltOnly=TRUE) .dispatchSNV_CollapsedVCF(.isDelins, x, singleAltOnly) ) setMethod("isTransition", "CollapsedVCF", function(x, ..., singleAltOnly=TRUE) .dispatchSNV_CollapsedVCF(.isTransition, x, singleAltOnly) ) setMethod("isSubstitution", "CollapsedVCF", function(x, ..., singleAltOnly=TRUE) .dispatchSNV_CollapsedVCF(.isSubstitution, x, singleAltOnly) ) ### - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - ### show ### setMethod(show, "CollapsedVCF", function(object) { .showVCFSubclass(object) }) VariantAnnotation/R/methods-expand.R0000644000175100017510000001430414614305321020476 0ustar00biocbuildbiocbuild### ========================================================================= ### expand methods ### ========================================================================= setMethod("expand", "CollapsedVCF", function(x, ..., row.names = FALSE) { rd <- rowRanges(x, fixed=FALSE) if (!row.names) names(rd) <- NULL elt <- elementNROWS(alt(x)) if (all(elt == 1L)) { fxd <- fixed(x) fxd$ALT <- unlist(alt(x), use.names=FALSE) ghdr <- geno(header(x)) varsR <- rownames(ghdr)[rownames(ghdr) == "AD" | ghdr$Number == "R"] varsR <- varsR[varsR %in% names(geno(x))] if (length(varsR) > 0){ geno(x)[varsR] <- endoapply( geno(x)[varsR], function(i) .expandAD(i, nrow(x), ncol(x)) ) } return(VCF(rd, colData(x), metadata(x), fxd, .unlistAltInfo(x), geno(x), ..., collapsed=FALSE)) } ## info, fixed, geno hdr <- metadata(x)$header idx <- rep.int(seq_len(nrow(x)), elt) iexp <- .expandInfo(x, hdr, elt, idx) fexp <- fixed(x)[idx, ] fexp$ALT <- unlist(alt(x), use.names=FALSE) gexp <- .expandGeno(x, hdr, elt, idx) ## rowRanges if (is.null(rd$paramRangeID)) { rdexp <- rd[idx, ] mcols(rdexp) <- NULL } else rdexp <- rd[idx, "paramRangeID"] tmp = VCF(rdexp, colData(x), metadata(x), fexp, iexp, gexp, ..., collapsed=FALSE) # https://github.com/Bioconductor/VariantAnnotation/issues/79 says 'A' should not occur in info(header())$Number nhi = info(header(tmp)) num = nhi$Number inda = grep("A", num) if (length(inda)>0) num[inda] = "1" info(header(tmp))$Number = num tmp } ) .expandGeno <- function(x, hdr, elt, idx) { gvar <- geno(x) if (length(gvar) == 0L) return(gvar) ghdr <- geno(hdr)[rownames(geno(hdr)) %in% names(gvar),] ## 'Number=A': one value per ALT isA <- ghdr$Number == "A" if (any(isA)) { gnms <- rownames(ghdr)[isA] gelt <- do.call(cbind, lapply(gnms, function(i) elt - elementNROWS(gvar[[i]]))) ## elementNROWS same as ALT csums <- colSums(gelt) == 0L if (any(csums)) gvar[gnms[csums]] <- endoapply(gvar[gnms[csums]], function(i) matrix(unlist(i, use.names=FALSE), sum(elt), ncol(i))) ## elementNROWS shorter than ALT if (any(!csums)) { nms <- names(gvar) %in% names(csums)[!csums] reps <- lapply(list(gelt[!csums] + 1L), rep.int, x=seq_len(nrow(x))) gvar[nms] <- mendoapply(gvar[nms], function(d, r) unlist(d[r], use.names=FALSE), r=reps) } gvar } ## AD field: one value for REF and each ALT ## AD (for back-compatibility) and 'Number=A' (ADF/ADR): one value for REF and each ALT isR <- names(gvar) %in% c("AD", rownames(ghdr)[ghdr$Number == "R"]) for(i in which(isR)){ varR <- names(gvar)[i] gvarR <- gvar[[varR]] if (!is.list(gvarR)) { ## 'Number' is integer if (is(gvarR, "array") && length(dim(gvarR)) == 3L) { if ((length(unique(elt)) != 1L) || (dim(gvarR)[3] != unique(elt))) { warning("'", varR, "' was ignored: number of '", varR, "' values ", "do not match REF + ALT") isR[i] <- FALSE } } else { isR[i] <- FALSE } } else { ## 'Number' is '.' gvar[[varR]] <- .expandAD(gvarR, length(idx), ncol(x)) } } isA <- names(gvar) %in% rownames(ghdr)[isA] gvar[!isA & !isR] <- endoapply(gvar[!isA & !isR], function(i) { if (is(i, "matrix")) { matrix(i[idx, ], nrow=length(idx), ncol=ncol(x)) } else { idim <- c(length(idx), dim(i)[2], dim(i)[3]) array(i[idx, , ], dim=idim) }}) gvar } ## returns an array of REF,ALT and REF, pairs ## 'z' dimension of result is always 2 .expandAD <- function(AD, idxlen, xcols) { if (is.list(AD)) { adpart <- PartitioningByWidth(AD) if (any(zeros <- width(adpart) == 0L)) { AD[zeros] <- list(rep(NA_integer_, 2L)) adpart <- PartitioningByWidth(AD) } nalt <- width(adpart) - 1L AD <- unlist(AD, use.names=FALSE) ref <- logical(length(AD)) ref[start(adpart)] <- TRUE vec <- c(rep(AD[ref], nalt), AD[!ref]) array(vec, c(idxlen, xcols, 2L)) } else { AD } } .expandInfo <- function(x, hdr, elt, idx) { ivar <- info(x) ## no data if (ncol(ivar) == 0L) return(DataFrame(row.names=seq_along(idx))) hnms <- intersect(names(ivar), rownames(info(hdr))) inms <- hnms[info(hdr)[hnms, "Number"] == "A"] if (length(inms) > 0L) { ielt <- do.call(cbind, lapply(inms, function(i) elt - elementNROWS(ivar[[i]]))) ## elementNROWS same as ALT csums <- colSums(ielt) == 0L if (any(csums)) res <- S4Vectors:::expandByColumnSet(ivar, inms[csums], TRUE) else res <- ivar[idx, , drop=FALSE] ## elementNROWS shorter than ALT if (any(!csums)) { nms <- colnames(ivar) %in% names(csums)[!csums] reps <- lapply(list(ielt[!csums] + 1L), rep.int, x=seq_len(nrow(x))) res[nms] <- Map(function(d, r) unlist(d[r], use.names=FALSE), ivar[nms], reps) } res } else { ivar[idx, , drop=FALSE] } } .unlistAltInfo <- function(x) { ivar <- info(x) if (ncol(ivar) == 0L) return(ivar) hdr <- header(x) inms <- rownames(info(hdr))[info(hdr)$Number == "A"] inms <- inms[inms %in% names(ivar)] if (length(inms)) return(ivar) ivar[inms] <- lapply(ivar[inms], drop) ivar } setMethod("expand", "ExpandedVCF", function(x, ...) x ) VariantAnnotation/R/methods-ExpandedVCF-class.R0000644000175100017510000000611614614305321022413 0ustar00biocbuildbiocbuild### ========================================================================= ### ExpandedVCF class methods ### ========================================================================= ### - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - ### Constructor ### ### See VCF(..., collapsed=FALSE) in methods-VCF-class.R ### - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - ### Getters and Setters ### ### alt() setter setReplaceMethod("alt", c("ExpandedVCF", "DNAStringSet"), function(x, value) { if (length(value) != length(rowRanges(x))) stop("length(value) must equal length(rowRanges(x))") slot(x, "fixed")$ALT <- value x }) ### - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - ### Utilities ### VRangesForMatching <- function(x) { with(rowRanges(x), VRanges(seqnames, IRanges(start, end), ref=REF, alt=ALT)) } setMethod("match", c("ExpandedVCF", "ExpandedVCF"), function(x, table, nomatch = NA_integer_, incomparables = NULL, method = c("auto", "quick", "hash")) { x <- VRangesForMatching(x) table <- VRangesForMatching(table) callGeneric() }) setMethod("match", c("ExpandedVCF", "VRanges"), function(x, table, nomatch = NA_integer_, incomparables = NULL, method = c("auto", "quick", "hash")) { x <- VRangesForMatching(x) callGeneric() }) setMethod("match", c("VRanges", "ExpandedVCF"), function(x, table, nomatch = NA_integer_, incomparables = NULL, method = c("auto", "quick", "hash")) { table <- VRangesForMatching(table) callGeneric() }) ### - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - ### isSNV family ### .dispatchSNV_ExpandedVCF <- function(FUN, x) { alt <- alt(x) flat <- unlist(alt, use.names=FALSE) gvcf <- structural <- NULL if (is(flat, "character")) { gvcf <- grepl("NON_REF", flat, fixed=TRUE) structural <- .isStructural(flat) } res <- FUN(ref(x), alt) if (!is.null(structural)) res[structural | is.na(structural)] <- FALSE if (any(gvcf)) res[gvcf] <- TRUE res } setMethod("isSNV", "ExpandedVCF", function(x, ...) .dispatchSNV_ExpandedVCF(.isSNV, x) ) setMethod("isInsertion", "ExpandedVCF", function(x, ...) .dispatchSNV_ExpandedVCF(.isInsertion, x) ) setMethod("isDeletion", "ExpandedVCF", function(x, ...) .dispatchSNV_ExpandedVCF(.isDeletion, x) ) setMethod("isIndel", "ExpandedVCF", function(x, ...) .dispatchSNV_ExpandedVCF(.isIndel, x) ) setMethod("isDelins", "ExpandedVCF", function(x, ...) .dispatchSNV_ExpandedVCF(.isDelins, x) ) setMethod("isTransition", "ExpandedVCF", function(x, ...) .dispatchSNV_ExpandedVCF(.isTransition, x) ) setMethod("isSubstitution", "ExpandedVCF", function(x, ...) .dispatchSNV_ExpandedVCF(.isSubstitution, x) ) ### - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - ### show ### setMethod(show, "ExpandedVCF", function(object) { .showVCFSubclass(object) }) VariantAnnotation/R/methods-filterVcf.R0000644000175100017510000001132714614305321021145 0ustar00biocbuildbiocbuildsetMethod("filterVcf", "character", function(file, genome, destination, ..., verbose=TRUE, index=FALSE, prefilters=FilterRules(), filters=FilterRules(), param=ScanVcfParam()) { if (file.exists(destination)) stop(sprintf("file '%s' exists and will not be over-written", destination)) tbx <- open(TabixFile(file, yieldSize=100000)) on.exit(close(tbx)) filterVcf(tbx, genome=genome, destination=destination, ..., verbose=verbose, index=index, prefilters=prefilters, filters=filters, param=param) }) .unlistScan <- function(...) unlist(scanTabix(...), use.names=FALSE) .prefilter <- function(tbxFile, verbose, prefilters, param, ...) { if (verbose) message("starting prefilter") if (length(vcfWhich(param))) { warning("vcfWhich(param) ignored when using prefilter") vcfWhich(param) <- GRanges() } if (!isOpen(tbxFile)) { open(tbxFile) on.exit(close(tbxFile), add=TRUE) } prefilteredFilename <- tempfile() prefiltered <- file(prefilteredFilename, "w") needsClosing <- TRUE on.exit(if (needsClosing) close(prefiltered), add=TRUE) ## copy header writeLines(headerTabix(tbxFile)$header, prefiltered) ## prefilter param <- vcfWhich(param) nTotal <- 0L while (length(tbxChunk <- .unlistScan(tbxFile, ..., param=param))) { if (verbose) message("prefiltering ", nTotal <- nTotal + length(tbxChunk), " records") tbxChunk <- subsetByFilter(tbxChunk, prefilters) writeLines(tbxChunk, prefiltered) } close(prefiltered) needsClosing <- FALSE prefilteredFilename } .filter <- function(tbxFile, genome, destination, verbose, filters, param, ...) { if (verbose) message("starting filter") destfile <- file(destination, open="w") on.exit(close(destfile), add=TRUE) ## param with defined ranges -> read all if (length(vcfWhich(param))) { yieldSize(tbxFile) <- NA_integer_ vcfChunk <- readVcf(tbxFile, genome, ..., param=param) if (verbose) message("filtering ", nrow(vcfChunk), " records") vcfChunk <- subsetByFilter(vcfChunk, filters) writeVcf(vcfChunk, destfile) ## param with all ranges -> iterate by yieldSize } else { if (!isOpen(tbxFile)) { open(tbxFile) on.exit(close(tbxFile), add=TRUE) } nTotal <- 0L while (nrow(vcfChunk <- readVcf(tbxFile, genome, ..., param=param))) { if (verbose) message("filtering ", nTotal <- nTotal + nrow(vcfChunk), " records") vcfChunk <- subsetByFilter(vcfChunk, filters) writeVcf(vcfChunk, destfile) } } if (verbose) message("completed filtering") destination } setMethod("filterVcf", "TabixFile", function(file, genome, destination, ..., verbose = TRUE, index = FALSE, prefilters = FilterRules(), filters = FilterRules(), param = ScanVcfParam()) { if (!isSingleString(destination)) stop("'destination' must be character(1)") if (!isTRUEorFALSE(verbose)) stop("'verbose' must be TRUE or FALSE") if (!isTRUEorFALSE(index)) stop("'index' must be TRUE or FALSE") if (!length(prefilters) && !length(filters)) stop("no 'prefilters' or 'filters' specified") if (length(prefilters)) { yieldSize <- yieldSize(file) file <- .prefilter(file, verbose, prefilters, param, ...) if(verbose){ message(sprintf("prefiltered to %s", file)) } if (length(filters)) { ## TabixFile needs to be bgzipped and indexed ## FIXME: all records are read at next stage, so no need to index? if (verbose) message("prefilter compressing and indexing ", sQuote(file)) gzFilename <- bgzip(file, overwrite = TRUE) indexTabix(gzFilename, format = "vcf4") file <- TabixFile(gzFilename, yieldSize=yieldSize) } else { ## file.rename does not work across file systems, so be expensive file.copy(file, destination) } } if (length(filters)) { file <- .filter(file, genome, destination, verbose, filters, param, ...) } # if filters if (index) { if (verbose) message("compressing and indexing ", sQuote(file)) gzFilename <- sprintf("%s.bgz", destination) gzFilename <- bgzip(file, gzFilename, overwrite = TRUE) destination <- indexTabix(gzFilename, format = "vcf") unlink(file) } invisible(destination) }) VariantAnnotation/R/methods-genotypeToSnpMatrix.R0000644000175100017510000001632114614305321023223 0ustar00biocbuildbiocbuild### ========================================================================= ### genotypeToSnpMatrix methods ### ========================================================================= ## Coding for snpMatrix : ## 0 = missing OR multiallelic OR multi-ALT values ## 1 = homozygous reference (0|0 or 0/0) ## 2 = heterozygous (0|1 or 0/1 or 1|0 or 1/0) ## 3 = homozygous alternate (risk) allele (1|1 or 1/1) ## empty matrix to return if conditions not met .emptySnpMatrix <- function() { list(genotype=new("SnpMatrix"), map=DataFrame(snp.names=character(), allele.1=DNAStringSet(), allele.2=DNAStringSetList(), ignore=character())) } setMethod("genotypeToSnpMatrix", "CollapsedVCF", function(x, uncertain=FALSE, ...) { ok <- suppressWarnings(require("snpStats", quietly=TRUE, character.only=TRUE)) ok || stop("'snpStats' required; try BiocManager::install('snpStats')", call.=FALSE) alt <- alt(x) if (is(alt, "CompressedCharacterList")) { alt <- .toDNAStringSetList(alt) if (all(elementNROWS(alt) == 0L)) { warning("No nucleotide ALT values were detected.") return(.emptySnpMatrix()) } } ref <- ref(x) if (ncol(x) == 0) { warning("no samples in VCF") } if (!uncertain) { gt <- geno(x)$GT } else { geno.cols <- row.names(geno(metadata(x)[["header"]])) if ("GP" %in% geno.cols) { gt <- geno(x)$GP if (storage.mode(gt) == "list") { gt <- .matrixOfListsToArray(gt) } } else if ("GL" %in% geno.cols) { gt <- geno(x)$GL if (storage.mode(gt) == "list") { gt <- .matrixOfListsToArray(gt) } gt <- GLtoGP(gt) } else if ("PL" %in% geno.cols) { gt <- geno(x)$PL if (mode(gt) == "list") { gt <- .matrixOfListsToArray(gt) } gt <- PLtoGP(gt) } else { warning("uncertain=TRUE requires GP, GL or PL; returning NULL") return(.emptySnpMatrix()) } } callGeneric(gt, ref, alt) }) setMethod("genotypeToSnpMatrix", "array", function(x, ref, alt, ...) { if (!is(ref, "DNAStringSet")) stop("'ref' must be a DNAStringSet") if (!is(alt, "DNAStringSetList")) stop("'alt' must be a DNAStringSetList") # query ref and alt alleles for valid SNPs altelt <- elementNROWS(alt) == 1L snv <- .testForSNV(ref, alt) # if x is a matrix, we have GT with a single value for each snp if (is.matrix(x)) { if (!all(altelt)) { warning("variants with >1 ALT allele are set to NA") x[!altelt,] <- ".|." } if (!all(snv)) { message("non-single nucleotide variations are set to NA") x[!snv,] <- ".|." } map <- .genotypeToIntegerSNV(TRUE) diploid <- x %in% names(map) if (!all(diploid)) { warning("non-diploid variants are set to NA") x[!diploid] <- ".|." } mat <- matrix(map[x], nrow=ncol(x), ncol=nrow(x), byrow=TRUE, dimnames=rev(dimnames(x))) genotypes <- new("SnpMatrix", mat) } else { # if x is a 3D array, we have GP with multiple values for each snp if (!all(altelt)) { warning("variants with >1 ALT allele are set to NA") x[!altelt,,] <- NA } if (!all(snv)) { message("non-single nucleotide variations are set to NA") x[!snv,,] <- NA } # if there is more than one ALT allele for any variant, # the 3rd dimension of the array will be too big # any values here should already have been set to NA above if (dim(x)[3] > 3) { x <- x[,,1:3] } # for each sample, call probabilityToSnpMatrix smlist <- list() for (s in 1:ncol(x)) { sm <- probabilityToSnpMatrix(x[,s,]) rownames(sm) <- colnames(x)[s] smlist[[s]] <- sm } genotypes <- do.call(rbind, smlist) } flt <- !(snv & altelt) map <- .createMap(rownames(x), ref, alt, flt) list(genotypes = genotypes, map = map) }) .createMap <- function(nms, ref, alt, flt) { if (is.null(ref)) DataFrame(snp.names=character(0), allele.1=DNAStringSet(), allele.2=DNAStringSetList(), ignore=logical()) else DataFrame(snp.names=nms, allele.1=ref, allele.2=alt, ignore=flt) } probabilityToSnpMatrix <- function(probs) { requireNamespace("snpStats", quietly = TRUE) || stop("'snpStats' required; try BiocManager::install('snpStats')") if (ncol(probs) != 3) stop("input matrix should have 3 columns: P(A/A), P(A/B), P(B/B)") # skip missing values when checking for validity of probabilities missing <- rowSums(is.na(probs)) > 0 if (!isTRUE(all.equal(rowSums(probs[!missing,,drop=FALSE]), rep(1,sum(!missing)), check.attributes=FALSE, check.names=FALSE))) stop("sum of probabilities in each row of input matrix should = 1") # post2g can't handle missing data if (sum(missing) > 0) { probs[missing,] <- 0 g <- snpStats::post2g(probs) g[missing] <- as.raw(0) } else { g <- snpStats::post2g(probs) } g <- matrix(g, nrow=1, dimnames=list(NULL, rownames(probs))) new("SnpMatrix", g) } GLtoGP <- function(gl) { if (is.matrix(gl) && storage.mode(gl) == "list") { gp <- gl for (i in 1:length(gp)) { gp[[i]] <- 10^gl[[i]] / sum(10^gl[[i]], na.rm=TRUE) } gp } else if (is.array(gl) & length(dim(gl)) == 3) { aperm(apply(gl, c(1,2), function(x) 10^x / sum(10^x, na.rm=TRUE)), c(2,3,1)) } else { stop("gl must be a matrix of lists or a 3D array") } } ## PL is same as GL except for a factor of -10 ## GL = log10(L), PL = -10*log10(L) (phred-scaled) PLtoGP <- function(pl) { if (is.matrix(pl) && storage.mode(pl) == "list") { gl <- pl for (i in 1:length(gl)) { gl[[i]] <- pl[[i]]/(-10) } } else { gl <- pl/(-10) } GLtoGP(gl) } .listMatrixToArray <- function(x) { n <- elementNROWS(x) maxn <- max(n) v <- unlist(x, use.names=FALSE) a <- array(as(NA, class(v)), dim=c(maxn, nrow(x), ncol(x)), dimnames=list(NULL, rownames(x), colnames(x))) a[as.integer(IRanges(seq(1L, length(a), maxn), width=n))] <- v aperm(a, c(2,3,1)) } .matrixOfListsToArray <- function(x) { # find number of elements of each cell of x n <- elementNROWS(x) maxn <- max(n) # for cells with less than the max number of elements, add NAs idx <- n < maxn x[idx] <- lapply(x[idx], function(a){c(a, rep(NA, maxn-length(a)))}) # unlist and convert to array x <- array(unlist(x), dim=c(maxn, nrow(x), ncol(x)), dimnames=list(NULL, rownames(x), colnames(x))) x <- aperm(x, c(2,3,1)) x } VariantAnnotation/R/methods-import.R0000644000175100017510000000053014614305321020525 0ustar00biocbuildbiocbuild### ========================================================================= ### import methods ### ========================================================================= setMethod("import", "VcfFile", function(con, format, text, ...) { if (!missing(format)) rtracklayer:::checkArgFormat(con, format) readVcf(con, ...) }) VariantAnnotation/R/methods-locateVariants.R0000644000175100017510000006027614614305321022207 0ustar00biocbuildbiocbuild### ========================================================================= ### locateVariants methods ### ========================================================================= ### The 8 defined variant regions : ### CodingVariants, IntronVariants, ThreeUTRVariants, FiveUTRVariants, ### IntergenicVariants, SpliceSiteVariants, PromoterVariants, AllVariants ### ### Each region has the following methods : ### query %in% IntegerRanges, VCF, GRanges ### subject %in% TxDb, GRangesList ### ------------------------------------------------------------------------- ### methods applicable to all variant regions ### setMethod("locateVariants", c("IntegerRanges", "TxDb", "VariantType"), function(query, subject, region, ..., cache=new.env(parent=emptyenv()), ignore.strand=FALSE, asHits=FALSE) { callGeneric(as(query, "GRanges"), subject, region, ..., cache=cache, ignore.strand=ignore.strand, asHits=asHits) }) setMethod("locateVariants", c("IntegerRanges", "GRangesList", "VariantType"), function(query, subject, region, ..., cache=new.env(parent=emptyenv()), ignore.strand=FALSE, asHits=FALSE) { callGeneric(as(query, "GRanges"), subject, region, ..., cache=cache, ignore.strand=ignore.strand, asHits=asHits) }) setMethod("locateVariants", c("VCF", "TxDb", "VariantType"), function(query, subject, region, ..., cache=new.env(parent=emptyenv()), ignore.strand=FALSE, asHits=FALSE) { callGeneric(rowRanges(query), subject, region, ..., cache=cache, ignore.strand=ignore.strand, asHits=asHits) }) setMethod("locateVariants", c("VCF", "GRangesList", "VariantType"), function(query, subject, region, ..., cache=new.env(parent=emptyenv()), ignore.strand=FALSE, asHits=FALSE) { callGeneric(rowRanges(query), subject, region, ..., cache=cache, ignore.strand=ignore.strand, asHits=asHits) }) ### ------------------------------------------------------------------------- ### region = CodingVariants ### setMethod("locateVariants", c("GRanges", "TxDb", "CodingVariants"), function(query, subject, region, ..., cache=new.env(parent=emptyenv()), ignore.strand=FALSE, asHits=FALSE) { if (!any(seqlevels(query) %in% seqlevels(subject))) return(.returnEmpty()) ## for width(ranges) == 0 : decrement start to equal end value if (any(insertion <- width(query) == 0)) start(query)[insertion] <- start(query)[insertion] - 1 if (!exists("cdsbytx", cache, inherits=FALSE)) cache[["cdsbytx"]] <- cdsBy(subject) res <- callGeneric(query, cache[["cdsbytx"]], region, ..., ignore.strand=ignore.strand, asHits=asHits) if (is(res, "GenomicRanges") & length(res) > 0L) { res$GENEID <- select(subject, as.character(res$TXID), "GENEID", "TXID")$GENEID } res } ) setMethod("locateVariants", c("GRanges", "GRangesList", "CodingVariants"), function(query, subject, region, ..., ignore.strand=FALSE, asHits=FALSE) { .makeResult(query, subject, "coding", ignore.strand=ignore.strand, asHits=asHits) } ) ### ------------------------------------------------------------------------- ### region = IntronVariants ### setMethod("locateVariants", c("GRanges", "TxDb", "IntronVariants"), function(query, subject, region, ..., cache=new.env(parent=emptyenv()), ignore.strand=FALSE, asHits=FALSE) { if (!any(seqlevels(query) %in% seqlevels(subject))) return(.returnEmpty()) ## for width(ranges) == 0 : decrement start to equal end value if (any(insertion <- width(query) == 0)) start(query)[insertion] <- start(query)[insertion] - 1 if (!exists("intbytx", cache, inherits=FALSE)) cache[["intbytx"]] <- intronsByTranscript(subject) res <- callGeneric(query, cache[["intbytx"]], region, ..., ignore.strand=ignore.strand, asHits=asHits) if (is(res, "GenomicRanges") & length(res) > 0L) { res$GENEID <- select(subject, as.character(res$TXID), "GENEID", "TXID")$GENEID } res } ) setMethod("locateVariants", c("GRanges", "GRangesList", "IntronVariants"), function(query, subject, region, ..., ignore.strand=FALSE, asHits=FALSE) { .makeResult(query, subject, "intron", ignore.strand=ignore.strand, asHits=asHits) } ) ### ------------------------------------------------------------------------- ### region = ThreeUTRVariants ### setMethod("locateVariants", c("GRanges", "TxDb", "ThreeUTRVariants"), function(query, subject, region, ..., cache=new.env(parent=emptyenv()), ignore.strand=FALSE, asHits=FALSE) { if (!any(seqlevels(query) %in% seqlevels(subject))) return(.returnEmpty()) ## for width(ranges) == 0 : decrement start to equal end value if (any(insertion <- width(query) == 0)) start(query)[insertion] <- start(query)[insertion] - 1 if (!exists("threeUTRbytx", cache, inherits=FALSE)) cache[["threeUTRbytx"]] <- threeUTRsByTranscript(subject) res <- callGeneric(query, cache[["threeUTRbytx"]], region, ..., ignore.strand=ignore.strand, asHits=asHits) if (is(res, "GenomicRanges") & length(res) > 0L) { res$GENEID <- select(subject, as.character(res$TXID), "GENEID", "TXID")$GENEID } res } ) setMethod("locateVariants", c("GRanges", "GRangesList", "ThreeUTRVariants"), function(query, subject, region, ..., ignore.strand=FALSE, asHits=FALSE) { .makeResult(query, subject, "threeUTR", ignore.strand=ignore.strand, asHits=asHits) } ) ### ------------------------------------------------------------------------- ### region = FiveUTRVariants ### setMethod("locateVariants", c("GRanges", "TxDb", "FiveUTRVariants"), function(query, subject, region, ..., cache=new.env(parent=emptyenv()), ignore.strand=FALSE, asHits=FALSE) { if (!any(seqlevels(query) %in% seqlevels(subject))) return(.returnEmpty()) ## for width(ranges) == 0 : decrement start to equal end value if (any(insertion <- width(query) == 0)) start(query)[insertion] <- start(query)[insertion] - 1 if (!exists("fiveUTRbytx", cache, inherits=FALSE)) cache[["fiveUTRbytx"]] <- fiveUTRsByTranscript(subject) res <- callGeneric(query, cache[["fiveUTRbytx"]], region, ..., ignore.strand=ignore.strand, asHits=asHits) if (is(res, "GenomicRanges") & length(res) > 0L) { res$GENEID <- select(subject, as.character(res$TXID), "GENEID", "TXID")$GENEID } res } ) setMethod("locateVariants", c("GRanges", "GRangesList", "FiveUTRVariants"), function(query, subject, region, ..., ignore.strand=FALSE, asHits=FALSE) { .makeResult(query, subject, "fiveUTR", ignore.strand=ignore.strand, asHits=asHits) } ) ### ------------------------------------------------------------------------- ### region = IntergenicVariants ### setMethod("locateVariants", c("GRanges", "TxDb", "IntergenicVariants"), function(query, subject, region, ..., cache=new.env(parent=emptyenv()), ignore.strand=FALSE) { if (!any(seqlevels(query) %in% seqlevels(subject))) return(.returnEmpty()) ## for width(ranges) == 0 : decrement start to equal end value if (any(insertion <- width(query) == 0)) start(query)[insertion] <- start(query)[insertion] - 1 ## PRECEDEID and FOLLOWID as gene or transcript ids if (idType(region) == "gene") { if (!exists("txbygene", cache, inherits=FALSE)) cache[["txbygene"]] <- transcriptsBy(subject, "gene") callGeneric(query, cache[["txbygene"]], region, ..., ignore.strand=ignore.strand) } else if (idType(region) == "tx") { tx <- transcripts(subject) names(tx) <- mcols(tx)$tx_id callGeneric(query, as(tx, "GRangesList"), region, ..., ignore.strand=ignore.strand) } else { stop("'idType' must be one of 'gene' or 'tx'") } } ) setMethod("locateVariants", c("GRanges", "GRangesList", "IntergenicVariants"), function(query, subject, region, ..., ignore.strand=FALSE) { .intergenic(query, subject, region, ignore.strand=ignore.strand) } ) ### ------------------------------------------------------------------------- ## region = SpliceSiteVariants ## setMethod("locateVariants", c("GRanges", "TxDb", "SpliceSiteVariants"), function(query, subject, region, ..., cache=new.env(parent=emptyenv()), ignore.strand=FALSE, asHits=FALSE) { if (!any(seqlevels(query) %in% seqlevels(subject))) return(.returnEmpty()) ## for width(ranges) == 0 : decrement start to equal end value if (any(insertion <- width(query) == 0)) start(query)[insertion] <- start(query)[insertion] - 1 if (!exists("intbytx", cache, inherits=FALSE)) cache[["intbytx"]] <- intronsByTranscript(subject) res <- callGeneric(query, cache[["intbytx"]], region, ..., ignore.strand=ignore.strand, asHits=asHits) if (is(res, "GenomicRanges") & length(res) > 0L) { res$GENEID <- select(subject, as.character(res$TXID), "GENEID", "TXID")$GENEID } res } ) setMethod("locateVariants", c("GRanges", "GRangesList", "SpliceSiteVariants"), function(query, subject, region, ..., ignore.strand=FALSE, asHits=FALSE) { .spliceSites(query, subject, ignore.strand=ignore.strand, asHits=asHits) } ) ### ------------------------------------------------------------------------- ### region = PromoterVariants ### setMethod("locateVariants", c("GRanges", "TxDb", "PromoterVariants"), function(query, subject, region, ..., cache=new.env(parent=emptyenv()), ignore.strand=FALSE, asHits=FALSE) { if (!any(seqlevels(query) %in% seqlevels(subject))) return(.returnEmpty()) ## for width(ranges) == 0 : decrement start to equal end value if (any(insertion <- width(query) == 0)) start(query)[insertion] <- start(query)[insertion] - 1 if (!exists("tx", cache, inherits=FALSE)) { tx <- transcripts(subject) cache[["tx"]] <- splitAsList(tx, seq_len(length(tx))) names(cache[["tx"]]) <- tx$tx_id } res <- callGeneric(query, cache[["tx"]], region, ..., ignore.strand=ignore.strand, asHits=asHits) if (is(res, "GenomicRanges") & length(res) > 0L) { if (!is.null(res$TXID)) res$GENEID <- select(subject, as.character(res$TXID), "GENEID", "TXID")$GENEID } res } ) setMethod("locateVariants", c("GRanges", "GRangesList", "PromoterVariants"), function(query, subject, region, ..., ignore.strand=FALSE, asHits=FALSE) { if (is.null(txid <- names(subject))) txid <- NA_integer_ usub <- unlist(subject, use.names=FALSE) pm <- promoters(usub, upstream(region), downstream(region)) fo <- findOverlaps(query, pm, type="within", ignore.strand=ignore.strand) if (asHits) return(.consolidateHits(fo, length(query), length(subject), elementNROWS(subject))) if (length(fo) > 0) { queryid <- queryHits(fo) GRanges(seqnames=seqnames(query)[queryid], ranges=IRanges(ranges(query)[queryid]), strand=strand(pm)[subjectHits(fo)], LOCATION=.location(length(queryid), "promoter"), LOCSTART=NA_integer_, LOCEND=NA_integer_, QUERYID=queryid, TXID=as.integer(txid[subjectHits(fo)]), CDSID=IntegerList(integer(0)), GENEID=NA_character_, PRECEDEID=CharacterList(character(0)), FOLLOWID=CharacterList(character(0))) } else { res <- GRanges() values(res) <- DataFrame(LOCATION=.location(), LOCSTART=integer(), LOCEND=integer(), QUERYID=integer(), TXID=integer(), CDSID=IntegerList(), GENEID=character(), PRECEDEID=CharacterList(), FOLLOWID=CharacterList()) res } } ) ### ------------------------------------------------------------------------- ### region = AllVariants ### setMethod("locateVariants", c("GRanges", "TxDb", "AllVariants"), function(query, subject, region, ..., cache=new.env(parent=emptyenv()), ignore.strand=FALSE) { if (!any(seqlevels(query) %in% seqlevels(subject))) return(.returnEmpty()) cache[["mask"]] <- TRUE coding <- locateVariants(query, subject, CodingVariants(), cache=cache, ignore.strand=ignore.strand) intron <- locateVariants(query, subject, IntronVariants(), cache=cache, ignore.strand=ignore.strand) splice <- locateVariants(query, subject, SpliceSiteVariants(), cache=cache, ignore.strand=ignore.strand) promoter <- locateVariants(query, subject, PromoterVariants(upstream(promoter(region)), downstream(promoter(region))), cache=cache, ignore.strand=ignore.strand) ## Consolidate calls for UTR data: if (!exists("fiveUTRbytx", cache, inherits=FALSE)) { splicings <- GenomicFeatures:::.getSplicingsForTranscriptsWithCDSs(subject) cache[["fiveUTRbytx"]] <- GenomicFeatures:::.make5UTRsByTranscript(subject, splicings) } if (!exists("threeUTRbytx", cache, inherits=FALSE)) cache[["threeUTRbytx"]] <- GenomicFeatures:::.make3UTRsByTranscript(subject, splicings) fiveUTR <- locateVariants(query, subject, FiveUTRVariants(), cache=cache, ignore.strand=ignore.strand) threeUTR <- locateVariants(query, subject, ThreeUTRVariants(), cache=cache, ignore.strand=ignore.strand) intergenic <- locateVariants(query, subject, IntergenicVariants(upstream(intergenic(region)), downstream(intergenic(region)), idType(intergenic(region))), cache=cache, ignore.strand=ignore.strand) ans <- c(coding, intron, fiveUTR, threeUTR, splice, promoter, intergenic) meta <- values(ans) ans[order(meta$QUERYID, meta$TXID, meta$GENEID), ] } ) ### ------------------------------------------------------------------------- ### helpers ### .returnEmpty <- function() { warning("none of seqlevels(query) match seqlevels(subject)") res <- GRanges() values(res) <- DataFrame(LOCATION=.location(), LOCSTART=integer(), LOCEND=integer(), QUERYID=integer(), TXID=integer(), CDSID=IntegerList(), GENEID=character(), PRECEDEID=CharacterList(), FOLLOWID=CharacterList()) res } .location <- function(length=0, value=NA) { levels <- c("spliceSite", "intron", "fiveUTR", "threeUTR", "coding", "intergenic", "promoter") factor(rep(value, length), levels=levels) } .spliceSites <- function(query, subject, ignore.strand, asHits, ...) { ## Overlap any portion of first 2 and last 2 nucleotides of introns. usub <- unlist(subject, use.names=FALSE) int_start <- GRanges(seqnames(usub), IRanges(start(usub), start(usub) + 1), strand=strand(usub)) int_end <- GRanges(seqnames(usub), IRanges(end(usub) - 1, end(usub)), strand=strand(usub)) fo_start <- findOverlaps(query, int_start, type="any", ignore.strand=ignore.strand) fo_end <- findOverlaps(query, int_end, type="any", ignore.strand=ignore.strand) fo <- union(fo_start, fo_end) if (asHits) return(.consolidateHits(fo, length(query), length(subject), elementNROWS(subject))) if (length(fo) > 0) { df <- unique(data.frame( queryid=queryHits(fo), subjectid=togroup(PartitioningByWidth(subject))[subjectHits(fo)] )) GRanges(seqnames=seqnames(query)[df$queryid], ranges=IRanges(ranges(query)[df$queryid]), strand=unlist(strand(subject), use.names=FALSE)[df$subjectid], LOCATION=.location(length(df$queryid), "spliceSite"), LOCSTART=NA_integer_, LOCEND=NA_integer_, QUERYID=df$queryid, TXID=as.integer(names(subject)[df$subjectid]), CDSID=IntegerList(integer(0)), GENEID=NA_character_, PRECEDEID=CharacterList(character(0)), FOLLOWID=CharacterList(character(0))) } else { res <- GRanges() values(res) <- DataFrame(LOCATION=.location(), LOCSTART=integer(), LOCEND=integer(), QUERYID=integer(), TXID=integer(), CDSID=IntegerList(), GENEID=character(), PRECEDEID=CharacterList(), FOLLOWID=CharacterList()) res } } .consolidateHits <- function(hits, qlen, slen, elen, ...) { txid <- rep(seq_len(slen), elen) dat <- cbind(queryHits(hits), as.integer(txid[subjectHits(hits)])) unq <- dat[!duplicated(dat),] return(Hits(unq[,1], unq[,2], qlen, slen, sort.by.query=TRUE)) } .intergenic <- function(query, subject, region, ignore.strand, ...) { s_range <- range(subject) ## avoid duplicates co <- unname(countOverlaps(query, s_range, type="any", ignore.strand=ignore.strand)) ## variants that don't hit a gene feature AND ## zero-width ranges return '0' has_width <- width(query) != 0L intergenic <- co == 0 & has_width if (all(!intergenic | (length(subject) == 0L))) { res <- GRanges() values(res) <- DataFrame(LOCATION=.location(), LOCSTART=integer(), LOCEND=integer(), QUERYID=integer(), TXID=integer(), CDSID=IntegerList(), GENEID=character(), PRECEDEID=CharacterList(), FOLLOWID=CharacterList()) res } else { q_range <- query[intergenic] s_genes <- rep(names(subject), elementNROWS(s_range)) s_unlist <- unlist(s_range, use.names=FALSE) ## ID all genes that fall in upstream / downstream range. ## upstream == follow: f_range <- .shiftRangeUpDown(q_range, upstream(region), TRUE) f_fo <- findOverlaps(f_range, s_unlist, ignore.strand=ignore.strand) f_factor <- factor(queryHits(f_fo), seq_len(queryLength(f_fo))) f_genes <- unname(splitAsList(s_genes[subjectHits(f_fo)], f_factor)) ## downstream == precede: p_range <- .shiftRangeUpDown(q_range, downstream(region), FALSE) p_fo <- findOverlaps(p_range, s_unlist, ignore.strand=ignore.strand) p_factor <- factor(queryHits(p_fo), seq_len(queryLength(p_fo))) p_genes <- unname(splitAsList(s_genes[subjectHits(p_fo)], p_factor)) if (ignore.strand) strand(q_range) <- "*" values(q_range) <- DataFrame(LOCATION=.location(length(q_range), "intergenic"), LOCSTART=NA_integer_, LOCEND=NA_integer_, QUERYID=which(intergenic), TXID=NA_integer_, CDSID=IntegerList(integer(0)), GENEID=NA_character_, PRECEDEID=p_genes, FOLLOWID=f_genes) q_range } } ## Behaves like flank(); ranges do not include start/end values. .shiftRangeUpDown <- function(x, distance, upstream=TRUE) { on_plus <- which(strand(x) == "+" | strand(x) == "*") on_minus <- which(strand(x) == "-") if (upstream) { ## '+' strand on_plus_end <- start(x)[on_plus] - 1L on_plus_start <- start(x)[on_plus] - distance ## '-' strand on_minus_start <- end(x)[on_minus] + 1L on_minus_end <- end(x)[on_minus] + distance } else { ## '+' strand on_plus_start <- end(x)[on_plus] + 1L on_plus_end <- end(x)[on_plus] + distance ## '-' strand on_minus_start <- start(x)[on_minus] - distance on_minus_end <- start(x)[on_minus] - 1L } start(x)[on_plus] <- on_plus_start end(x)[on_plus] <- on_plus_end start(x)[on_minus] <- on_minus_start end(x)[on_minus] <- on_minus_end x } .makeResult <- function(query, subject, vtype, ignore.strand, asHits) { if (asHits) return(findOverlaps(query, subject, type="within", ignore.strand=ignore.strand)) map <- mapToTranscripts(unname(query), subject, ignore.strand=ignore.strand) if (length(map) > 0) { xHits <- map$xHits txHits <- map$transcriptsHits tx <- names(subject)[txHits] if (!is.null(tx)) txid <- tx else txid <- NA_integer_ ## FIXME: cdsid is expensive cdsid <- IntegerList(integer(0)) ## CodingVariants() must fall within a coding region. ## mapToTranscripts() will map ranges that span intron ## junctions and overlap multiple exons/cds regions. Because ## of this, it's possible for 'map' to return a hit for a ## query that 'map2' will not. (The subject in ## 'map' is a GRangesList and in 'map2' it's unlisted.) ## Only ranges identified by 'map' and 'map2' are kept. ## Ranges identified by 'map' only are discarded. if (vtype == "coding") { usub <- unlist(subject) ## names needed for mapping map2 <- mapToTranscripts(unname(query)[xHits], usub, ignore.strand=ignore.strand) cds <- mcols(usub)$cds_id[map2$transcriptsHits] if (length(cds)) { cdsid <- unique(splitAsList(cds, map2$xHits)) keep <- logical(length(xHits)) keep[unique(map2$xHits)] <- TRUE if (any(!keep)) { map <- map[keep] txHits <- map$transcriptsHits xHits <- map$xHits txid <- txid[keep] } } } ss <- runValue(strand(subject)[txHits]) if (any(elementNROWS(ss) > 1L)) { warning("'subject' has multiple strands per list element; ", "setting strand to '*'") sstrand <- Rle("*", length(txHits)) } sstrand <- unlist(ss, use.names=FALSE) GRanges(seqnames=seqnames(query)[xHits], ranges=IRanges(ranges(query)[xHits]), strand=sstrand, LOCATION=.location(length(xHits), vtype), LOCSTART=start(map), LOCEND=end(map), QUERYID=xHits, TXID=txid, CDSID=cdsid, GENEID=NA_character_, PRECEDEID=CharacterList(character(0)), FOLLOWID=CharacterList(character(0))) } else { res <- GRanges() mcols(res) <- DataFrame(LOCATION=.location(), LOCSTART=integer(), LOCEND=integer(), QUERYID=integer(), TXID=integer(), CDSID=IntegerList(), GENEID=character(), PRECEDEID=CharacterList(), FOLLOWID=CharacterList()) res } } VariantAnnotation/R/methods-PolyPhenDb-class.R0000644000175100017510000000653514614305321022335 0ustar00biocbuildbiocbuild### ========================================================================= ### PolyPhenDb methods ### ========================================================================= setMethod("keys", "PolyPhenDb", function(x) { sql <- paste("SELECT RSID FROM ppdata ", sep="") unique(dbGetQuery(x$conn, sql))[,1] } ) setMethod("columns", "PolyPhenDb", function(x) { dbListFields(conn=x$conn, "ppdata") } ) setMethod("select", "PolyPhenDb", function(x, keys, columns, keytype, ...) { sql <- .createPPDbQuery(x, keys, columns) if (length(sql)) { raw <- dbGetQuery(x$conn, sql) .formatPPDbSelect(raw, keys) } else { data.frame() } } ) .createPPDbQuery <- function(x, keys, cols) { if (missing(keys) && missing(cols)) { sql <- "SELECT * FROM ppdata" } if (!missing(keys)) { if(.missingKeys(x, keys, "PolyPhen")) return(character()) if (!missing(cols)) { if (.missingCols(x, cols, "PolyPhen")) return(character()) if (!"RSID" %in% cols) cols <- c("RSID", cols) fmtcols <- paste(cols, collapse=",") fmtkeys <- .sqlIn(keys) sql <- paste("SELECT ", fmtcols, " FROM ppdata WHERE RSID IN (", fmtkeys, ")", sep="") } else { fmtkeys <- .sqlIn(keys) sql <- paste("SELECT * FROM ppdata WHERE RSID IN (", fmtkeys, ")", sep="") } } else { if (.missingCols(x, cols, "PolyPhen")) return(character()) if (!"RSID" %in% cols) cols <- c("RSID", cols) fmtcols <- paste(cols, collapse=",") sql <- paste("SELECT ", fmtcols, " FROM ppdata", sep="") } sql } .formatPPDbSelect <- function(raw, keys) { ## no data if (!nrow(raw)) return(data.frame()) if (missing(keys)) { df <- data.frame(raw) rownames(df) <- NULL df } else { ## restore key order missing <- (!keys %in% as.character(raw$RSID)) lst <- as.list(rep(NA_character_, length(keys))) raw <- raw[!duplicated(raw), ] for (i in which(missing == FALSE)) lst[[i]] <- raw[raw$RSID %in% keys[i], ] df <- do.call(rbind, lst) df$RSID[is.na(df$RSID)] <- keys[missing] rownames(df) <- NULL df } } duplicateRSID <- function(db, keys, ...) { fmtrsid <- .sqlIn(keys) sql <- paste("SELECT * FROM duplicates WHERE RSID IN (", fmtrsid, ")", sep="") q1 <- dbGetQuery(db$conn, sql) fmtgp <- .sqlIn(unique(q1$DUPLICATEGROUP)) gpsql <- paste("SELECT * FROM duplicates WHERE DUPLICATEGROUP IN (", fmtgp, ")", sep="") q2 <- dbGetQuery(db$conn, gpsql) matched <- q2[!q2$RSID %in% keys, ] matchedlst <- split(matched$RSID, matched$DUPLICATEGROUP) names(matchedlst) <- q1$RSID[match(names(matchedlst), q1$DUPLICATEGROUP)] missing <- !keys %in% q2$RSID if (any(missing)) { warning(paste("keys not found in database : ", keys[missing], sep="")) missinglst <- list(rep(NA, sum(missing))) names(missinglst) <- keys[missing] matchedlst <- c(matchedlst, missinglst) } matchedlst[order(match(names(matchedlst), keys))] } VariantAnnotation/R/methods-predictCoding.R0000644000175100017510000001766014614305321022005 0ustar00biocbuildbiocbuild### ========================================================================= ### predictCoding methods ### ========================================================================= setMethod("predictCoding", c("IntegerRanges", "TxDb", "ANY", "DNAStringSet"), function(query, subject, seqSource, varAllele, ..., ignore.strand=FALSE) { callGeneric(as(query, "GRanges"), subject, seqSource, varAllele, ..., ignore.strand=ignore.strand) }) setMethod("predictCoding", c("CollapsedVCF", "TxDb", "ANY", "missing"), function(query, subject, seqSource, varAllele, ..., ignore.strand=FALSE) { rd <- rowRanges(query) alt <- alt(query) if (is(alt, "CharacterList")) { alt <- .toDNAStringSetList(alt) if (sum(elementNROWS(alt)) == 0L) { stop("No nucleotide ALT values were detected.") } } rd <- rep(rowRanges(query), elementNROWS(alt)) res <- callGeneric(rd, subject, seqSource, unlist(alt, use.names=FALSE), ..., ignore.strand=ignore.strand) ## adjust QUERYID for expansion of rowRanges res$QUERYID <- rep(seq_len(length(alt)), elementNROWS(alt))[res$QUERYID] res }) setMethod("predictCoding", c("ExpandedVCF", "TxDb", "ANY", "missing"), function(query, subject, seqSource, varAllele, ..., ignore.strand=FALSE) { if (is(alt(query), "CharacterList")) { stop("alt(query) must be a DNAStringSet (not a CharacterList)") } callGeneric(rowRanges(query), subject, seqSource, alt(query), ..., ignore.strand=ignore.strand) }) setMethod("predictCoding", c("GRanges", "TxDb", "ANY", "DNAStringSet"), function(query, subject, seqSource, varAllele, ..., ignore.strand=FALSE) { .predictCoding(query, subject, seqSource, varAllele, ..., ignore.strand=ignore.strand) }) setMethod("predictCoding", c("VRanges", "TxDb", "ANY", "missing"), function(query, subject, seqSource, varAllele, ..., ignore.strand=FALSE) { varAllele <- alt(query) query <- as(query, "GRanges") if (!is(varAllele, "DNAStringSet")) { tryCatch({ varAllele <- DNAStringSet(varAllele) }, error=function(e) { stop(paste0("attempt to coerce 'alt' to DNAStringSet failed with ", "error: ", conditionMessage(e))) }) } .predictCoding(query, subject, seqSource, varAllele, ..., ignore.strand=ignore.strand) }) .predictCoding <- function(query, subject, seqSource, varAllele, ..., cache=new.env(parent=emptyenv()), ignore.strand=FALSE) { stopifnot(length(varAllele) == length(query)) if (!any(seqlevels(query) %in% seqlevels(subject))) warning("none of seqlevels(query) match seqlevels(subject)") if (!exists(".__init__", cache, inherits=FALSE)) { cache[["cdsbytx"]] <- cdsBy(subject) cache[["txbygene"]] <- transcriptsBy(subject, "gene") cache[[".__init__"]] <- TRUE } map <- data.frame(geneid=rep(names(cache[["txbygene"]]), elementNROWS(cache[["txbygene"]])), txid=mcols(unlist(cache[["txbygene"]], use.names=FALSE))[["tx_id"]], stringsAsFactors=FALSE) txlocal <- .predictCodingGRangesList(query, cache[["cdsbytx"]], seqSource, varAllele, ..., ignore.strand=ignore.strand) txid <- mcols(txlocal)$TXID mcols(txlocal)$GENEID <- map$geneid[match(txid, map$txid)] txlocal } .predictCodingGRangesList <- function(query, cdsbytx, seqSource, varAllele, ..., genetic.code=GENETIC_CODE, if.fuzzy.codon="error", ignore.strand=FALSE) { if (ignore.strand) strand(query) <- "*" ## variant location in cds region mcols(query) <- append(mcols(query), DataFrame(varAllele=varAllele)) txlocal <- .localCoordinates(query, cdsbytx, ignore.strand=FALSE, ...) if (length(txlocal) == 0) return(txlocal) ## reverse complement "-" strand valid <- rep(TRUE, length(txlocal)) nstrand <- as.vector(strand(txlocal) == "-") if (any(nstrand)) { va <- mcols(txlocal)$varAllele va[nstrand] <- reverseComplement(va[valid & nstrand]) mcols(txlocal)$varAllele <- va } ## frameshift refwidth <- width(txlocal) altallele <- mcols(txlocal)$varAllele fmshift <- abs(width(altallele) - refwidth) %% 3 != 0 if (any(fmshift)) valid[fmshift] <- FALSE ## zero-width zwidth <- width(altallele) == 0 if (any(zwidth)) { warning("records with missing 'varAllele' were ignored") valid[zwidth] <- FALSE fmshift[zwidth] <- FALSE } ## reference codon sequences altpos <- (start(mcols(txlocal)$CDSLOC) - 1L) %% 3L + 1L refCodon <- varCodon <- .getRefCodons(txlocal, altpos, seqSource, cdsbytx) ## allowed characters that can't be translated ## "N", ".", "+" and "-" pattern <- "N|\\.|\\+|\\-" altCheck <- grepl(pattern, as.character(altallele, use.names=FALSE)) refCheck <- grepl(pattern, as.character(refCodon, use.names=FALSE)) noTrans <- rep(FALSE, length(txlocal)) noTrans[altCheck | refCheck] <- TRUE valid[noTrans] <- FALSE if (any(altCheck)) warning("'varAllele' values with 'N', '.', '+' or '-'", " were not translated") if (any(refCheck)) warning("reference codons with 'N', '.', '+' or '-'", " were not translated") ## substitute and translate refAA <- varAA <- AAStringSet(rep("", length(txlocal))) if (any(valid)) { ## 2 genetic.code versions alt.init.codons <- attr(genetic.code, "alt_init_codons") gc <- genetic.code gc.no.alt.init.codons <- genetic.code attr(gc.no.alt.init.codons, "alt_init_codons") <- character() ## ignore alt_init_codons subseq(varCodon, altpos, width=refwidth) <- altallele refAA[valid] <- translate(refCodon[valid], genetic.code=gc.no.alt.init.codons, if.fuzzy.codon=if.fuzzy.codon) varAA <- AAStringSet(rep("", length(txlocal))) varAA[valid] <- translate(varCodon[valid], genetic.code=gc.no.alt.init.codons, if.fuzzy.codon=if.fuzzy.codon) ## respect alt_init_codons at the start of the CDS cds.start <- start(txlocal$CDSLOC) == 1L varAA.to.modify <- varCodon %in% alt.init.codons & cds.start refAA.to.modify <- refCodon %in% alt.init.codons & cds.start if (any(cds.start)) { varAA[varCodon %in% alt.init.codons & cds.start] <- "M" refAA[refCodon %in% alt.init.codons & cds.start] <- "M" } } ## results nonsynonymous <- as.character(refAA) != as.character(varAA) consequence <- rep("synonymous", length(txlocal)) consequence[nonsynonymous] <- "nonsynonymous" consequence[fmshift] <- "frameshift" consequence[nonsynonymous & (as.character(varAA) %in% "*")] <- "nonsense" consequence[zwidth | noTrans] <- "not translated" consequence <- factor(consequence) mcols(txlocal) <- append(mcols(txlocal), DataFrame(GENEID=NA_character_, CONSEQUENCE=consequence, REFCODON=refCodon, VARCODON=varCodon, REFAA=refAA, VARAA=varAA)) txlocal } .getRefCodons <- function(txlocal, altpos, seqSource, cdsbytx) { ## adjust codon end for ## - width of the reference sequence ## - position of alt allele substitution in the codon cstart <- ((start(mcols(txlocal)$CDSLOC) - 1L) %/% 3L) * 3L + 1L cend <- cstart + (((altpos + width(txlocal) - 2L) %/% 3L) * 3L + 2L) txord <- match(mcols(txlocal)$TXID, names(cdsbytx)) txseqs <- extractTranscriptSeqs(seqSource, cdsbytx[txord]) DNAStringSet(substring(txseqs, cstart, cend)) } VariantAnnotation/R/methods-PROVEANDb.class.R0000644000175100017510000000576414614305321021715 0ustar00biocbuildbiocbuild### ======================================================================== ### PROVEANDb methods ### ========================================================================= setMethod("keys", "PROVEANDb", function(x, keytype, ...) { if (missing(keytype)) keytype <- "DBSNPID" sql <- paste("SELECT DISTINCT ", keytype, " FROM proveandata ", sep="") dbGetQuery(x$conn, sql)[,1] } ) setMethod("columns", "PROVEANDb", function(x) dbListFields(x$conn, "proveandata") ) setMethod("keytypes", "PROVEANDb", function(x) "DBSNPID" ) setMethod("select", "PROVEANDb", function(x, keys, columns, keytype, ...) { if (missing(keytype)) keytype <- "DBSNPID" sql <- .createPROVEANDbQuery(x, keys, columns, keytype) if (length(sql)) { raw <- dbGetQuery(x$conn, sql) .formatPROVEANDbSelect(raw, keys, columns, keytype) } else { data.frame() } } ) .createPROVEANDbQuery <- function(x, keys, columns, keytype) { if (missing(keys) && missing(columns)) { sql <- "SELECT * FROM proveandata" } if (!missing(keys)) { if(.missingKeys(x, keys, "PROVEAN")) return(character()) if (!missing(columns)) { if (.missingCols(x, columns, "PROVEAN")) return(character()) columns <- union(keytype, columns) fmtcols <- paste(columns, collapse=",") fmtkeys <- .sqlIn(keys) sql <- paste("SELECT ", fmtcols, " FROM proveandata WHERE ", keytype, " IN (", fmtkeys, ")", sep="") } else { fmtkeys <- .sqlIn(keys) sql <- paste("SELECT * FROM proveandata WHERE ", keytype, " IN (", fmtkeys, ")", sep="") } } else { if (.missingCols(x, columns, db="PROVEAN")) return(character()) columns <- union(keytype, columns) fmtcols <- paste(columns, collapse=",") sql <- paste("SELECT ", fmtcols, " FROM proveandata", sep="") } sql } .formatPROVEANDbSelect <- function(raw, keys, columns, keytype) { ## no data if (!nrow(raw)) return(data.frame()) ## remove duplicate rows if (any(dup <- duplicated(raw))) raw <- raw[!dup, ] ## reorder columns if (!missing(columns)) { if (!keytype %in% columns) columns <- c(keytype, columns) raw <- raw[,colnames(raw) %in% columns] } ## return keys not found index <- unique(raw[[keytype]]) missing <- rep(FALSE, length(index)) if (!missing(keys)) { missing <- (!keys %in% as.character(index)) lst <- as.list(rep(NA_character_, length(keys))) for (i in which(missing == FALSE)) lst[[i]] <- raw[raw[[keytype]] %in% keys[i], ] df <- do.call(rbind, lst) df[[keytype]][is.na(df[[keytype]])] <- keys[missing] rownames(df) <- NULL df } else { rownames(raw) <- NULL raw } } VariantAnnotation/R/methods-readVcf.R0000644000175100017510000001661414614305321020577 0ustar00biocbuildbiocbuild### ========================================================================= ### readVcf methods ### ========================================================================= ## TabixFile setMethod(readVcf, c(file="TabixFile", param="ScanVcfParam"), function(file, genome, param, ..., row.names=TRUE) { .readVcf(file, genome, param, row.names=row.names, ...) }) setMethod(readVcf, c(file="TabixFile", param="GRanges"), function(file, genome, param, ..., row.names=TRUE) { .readVcf(file, genome, param=ScanVcfParam(which=param), row.names=row.names, ...) }) setMethod(readVcf, c(file="TabixFile", param="GRangesList"), function(file, genome, param, ..., row.names=TRUE) { .readVcf(file, genome, param=ScanVcfParam(which=param), row.names=row.names, ...) }) setMethod(readVcf, c(file="TabixFile", param="IntegerRangesList"), function(file, genome, param, ..., row.names=TRUE) { .readVcf(file, genome, param=ScanVcfParam(which=param), row.names=row.names, ...) }) setMethod(readVcf, c(file="TabixFile", param="missing"), function(file, genome, param, ..., row.names=TRUE) { .readVcf(file, genome, param=ScanVcfParam(), row.names=row.names, ...) }) ## character setMethod(readVcf, c(file="character", param="ANY"), function(file, genome, param, ..., row.names=TRUE) { file <- .checkFile(file) .readVcf(file, genome, param, row.names=row.names, ...) }) setMethod(readVcf, c(file="character", param="missing"), function(file, genome, param, ..., row.names=TRUE) { file <- .checkFile(file) .readVcf(file, genome, param=ScanVcfParam(), row.names=row.names, ...) }) .checkFile <- function(x) { if (1L != length(x)) stop("'x' must be character(1)") ## Tabix index supplied as 'file' if (grepl("\\.tbi$", x)) return(TabixFile(sub("\\.tbi", "", x))) ## Attempt to create TabixFile tryCatch(x <- TabixFile(x), error=function(e) return(x)) x } .readVcf <- function(file, genome, param, row.names, ...) { if (missing(genome)) genome <- seqinfo(scanVcfHeader(file)) if (!is(genome, "character") & !is(genome, "Seqinfo")) stop("'genome' must be a 'character(1)' or 'Seqinfo' object") if (is(genome, "Seqinfo")) { if (is(param, "ScanVcfParam")) chr <- names(vcfWhich(param)) else chr <- seqlevels(param) ## confirm param seqlevels are in supplied Seqinfo if (any(!chr %in% seqnames(genome))) stop("'seqnames' in 'vcfWhich(param)' must be present in 'genome'") } .scanVcfToVCF(scanVcf(file, param=param, row.names=row.names, ...), file, genome, param) } .scanVcfToVCF <- function(vcf, file, genome, param, ...) { hdr <- scanVcfHeader(file) if (length(vcf[[1]]$GENO) > 0L) colnms <- colnames(vcf[[1]]$GENO[[1]]) else colnms <- NULL vcf <- .collapseLists(vcf, param) ## rowRanges rowRanges <- vcf$rowRanges if (length(rowRanges)) { if (is(genome, "character")) { if (length(seqinfo(hdr))) { merged <- merge(seqinfo(hdr), seqinfo(rowRanges)) map <- match(names(merged), names(seqinfo(rowRanges))) seqinfo(rowRanges, map) <- merged } genome(rowRanges) <- genome } else if (is(genome, "Seqinfo")) { if (length(seqinfo(hdr))) reference <- merge(seqinfo(hdr), genome) else reference <- genome merged <- merge(reference, seqinfo(rowRanges)) map <- match(names(merged), names(seqinfo(rowRanges))) seqinfo(rowRanges, map) <- merged } } values(rowRanges) <- DataFrame(vcf["paramRangeID"]) ## fixed fields fx <- vcf[c("REF", "ALT", "QUAL", "FILTER")] fx$ALT <- .formatALT(fx$ALT) fixed <- DataFrame(fx[!sapply(fx, is.null)]) ## info info <- .formatInfo(vcf$INFO, info(hdr), length(rowRanges)) ## colData colData <- DataFrame(Samples=seq_along(colnms), row.names=colnms) ## geno geno <- SimpleList(lapply(vcf$GENO, `dimnames<-`, NULL)) vcf <- NULL VCF(rowRanges=rowRanges, colData=colData, exptData=list(header=hdr), fixed=fixed, info=info, geno=geno) } ## lightweight read functions retrieve a single variable .geno2geno <- function(lst, ALT=NULL, REF=NULL, GT=NULL) { if (is.null(ALT) && is.null(REF) && is.null(GT)) { ALT <- lst$ALT REF <- as.character(lst$REF, use.names=FALSE) GT <- lst$GENO$GT } res <- GT ## ignore records with GT ".|." if (any(missing <- grepl(".", GT, fixed=TRUE))) GT[missing] <- ".|." phasing <- rep("|", length(GT)) phasing[grepl("/", GT, fixed=TRUE)] <- "/" ## replace GTstr <- strsplit(as.vector(GT), "[|,/]") if (any(elementNROWS(GTstr) !=2)) stop("only diploid variants are supported") GTmat <- matrix(unlist(GTstr), ncol=2, byrow=TRUE) GTA <- suppressWarnings(as.numeric(GTmat[,1])) GTB <- suppressWarnings(as.numeric(GTmat[,2])) REFcs <- cumsum(elementNROWS(REF)) ALTcs <- cumsum(elementNROWS(ALT)) cs <- REFcs + c(0, head(ALTcs, -1)) offset <- rep(cs, ncol(res)) alleles <- unlist(rbind(REF,ALT), use.names=FALSE) alleleA <- alleles[offset + GTA] alleleB <- alleles[offset + GTB] if (any(missing)) { res[!missing] <- paste0(alleleA[!missing], phasing[!missing], alleleB[!missing]) } else { res[] <- paste0(alleleA, phasing, alleleB) } res } .readLite <- function(file, var, param, type, ..., row.names) { msg <- NULL if (!is.character(var)) msg <- c(msg, paste0("'", var, "'", " must be a character string")) if (length(var) > 1L) msg <- c(msg, paste0("'", var, "'", " must be of length 1")) if (!is.null(msg)) stop(msg) if (is(param, "ScanVcfParam")) { which <- vcfWhich(param) samples <- vcfSamples(param) } else { which <- param samples <- character() } if (type == "info") param=ScanVcfParam(NA, var, NA, which=which) else if (type == "geno") param=ScanVcfParam(NA, NA, var, samples, which=which) else if (type == "GT") param=ScanVcfParam("ALT", NA, var, samples, which=which) scn <- scanVcf(file, param=param, row.names=row.names) .collapseLists(scn, param) } readInfo <- function(file, x, param=ScanVcfParam(), ..., row.names=TRUE) { lst <- .readLite(file, x, param, "info", row.names=row.names) rowRanges <- lst$rowRanges res <- .formatInfo(lst$INFO, info(scanVcfHeader(file)), length(rowRanges))[[1]] if (row.names) names(res) <- names(rowRanges) res } readGeno <- function(file, x, param=ScanVcfParam(), ..., row.names=TRUE) { lst <- .readLite(file, x, param, "geno", row.names=row.names) rowRanges <- lst$rowRanges res <- lst$GENO[[1]] if (row.names) dimnames(res)[[1]] <- names(rowRanges) res } readGT <- function(file, nucleotides=FALSE, param=ScanVcfParam(), ..., row.names=TRUE) { lst <- .readLite(file, "GT", param, "GT", row.names=row.names) if (nucleotides) res <- .geno2geno(lst) else res <- lst$GENO$GT rowRanges <- lst$rowRanges if (row.names) dimnames(res)[[1]] <- names(rowRanges) res } VariantAnnotation/R/methods-scanVcf.R0000644000175100017510000001727514614305321020614 0ustar00biocbuildbiocbuild### ========================================================================= ### scanVcf methods ### ========================================================================= selectSome <- S4Vectors:::selectSome .vcf_usertag <- function(map, tag, nm, verbose) { if (!identical(character(), tag)) { if (1L == length(tag) && is.na(tag)) { map[] <- NULL } else { ok <- tag %in% names(map) if (!all(ok)) { warning("ScanVcfParam ", sQuote(nm), " fields not found in ", " header: ", paste(sQuote(tag[!ok]), collapse=" ")) tag <- tag[ok] } map <- map[tag] # user-requested order } } if (verbose) { msg <- paste0("found header lines for ", length(map), " ", sQuote(nm), " fields: ", paste(selectSome(names(map)), collapse=", ")) cat(msg, "\n") } if (!length(map) && nm == "info" && !length(tag)) map <- list(list("1", character())) map } .vcf_map_fixed <- function(tag, verbose) { map <- list(ALT=list("A", character()), QUAL=list("1", numeric()), FILTER=list("1", character())) c(list(rowRanges=NULL, REF=NULL), .vcf_usertag(map, tag, "fixed", verbose)) } .vcf_map_samples <- function(fmt, tag, ...) { map <- setNames(seq_along(fmt), fmt) if (identical(character(), tag)) { map # keep everyone } else if (isTRUE(is.na(tag))) { map[0] # drop everyone } else { if (any(nx <- !tag %in% fmt)) { warning("samples not in file: ", paste(sQuote(tag[nx]), collapse=" ")) tag <- tag[!nx] } map[] <- 0L # drop everyone, except... map[match(tag, fmt)] <- seq_along(tag) # these guys, and... map[rev(cumsum(rev(map)) > 0)] # drop trailing unwanted samples } } ## used for both INFO and GENO .vcf_map <- function(fmt, tag, nm, verbose) { numberOk <- grepl("^[AGR\\.[:digit:]+]$", fmt$Number) fmt$Number[!numberOk] <- "." mapType <- function(n, t) { if (t == "Flag") n <- "1" t <- switch(t, String=character(), Character=character(), Integer=integer(), Float=numeric(), Flag=logical()) list(n, t) } map <- Map(mapType, fmt$Number, fmt$Type) names(map) <- rownames(fmt) ## user selected .vcf_usertag(map, tag, nm, verbose) } .vcf_scan_header_maps <- function(file, fixed, info, geno, samples, verbose=FALSE) { if (isTRUE(is.na(samples))) geno <- NA hdr <- suppressWarnings(scanVcfHeader(file)) fmap <- .vcf_map_fixed(fixed, verbose=verbose) imap <- .vcf_map(info(hdr), info, "info", verbose=verbose) if (!is.null(names(imap))) { mapply(function(field, fname) { if (field[[1]] == 0L && !is.logical(field[[2]])) stop(paste0('only "flag" INFO fields should ', 'have Number = 0 in the header; ', fname, ' is "', classNameForDisplay(field[[2]]), '"')) else field }, imap, names(imap) ) } gmap <- .vcf_map(geno(hdr), geno, "geno", verbose) smap <- .vcf_map_samples(samples(hdr), samples) list(hdr=hdr, samples=smap, fmap=fmap, imap=imap, gmap=gmap) } .vcf_scan <- function(file, ..., fixed=character(), info=character(), geno=character(), samples=character(), param, row.names=TRUE) { tryCatch({ if (!isOpen(file)) { open(file) on.exit(close(file)) } maps <- .vcf_scan_header_maps(file, fixed, info, geno, samples, ...) tbxstate <- maps[c("samples", "fmap", "imap", "gmap")] tbxsym <- getNativeSymbolInfo(".tabix_as_vcf", "VariantAnnotation") scanTabix(file, ..., param=param, tbxsym=tbxsym, tbxstate=tbxstate, row.names=row.names) }, scanTabix_io = function(err) { stop("scanVcf: ", conditionMessage(err), call. = FALSE) }, error = function(err) { stop("scanVcf: ", conditionMessage(err), "\n path: ", path(file), call. = FALSE) }) } .vcf_scan_character <- function(file, ..., fixed=character(), info=character(), geno=character(), samples=character(), yieldSize=100000L, row.names=TRUE) { tryCatch({ file <- normalizePath(path.expand(file)) if (!file.exists(file)) stop("file does not exist") maps <- .vcf_scan_header_maps(file, fixed, info, geno, samples, ...) result <- .Call(.scan_vcf_character, file, as.integer(yieldSize), maps$samples, maps$fmap, maps$imap, maps$gmap, row.names) setNames(result, "*:*-*") }, scanTabix_io = function(err) { stop("scanVcf: ", conditionMessage(err), call. = FALSE) }, error = function(err) { stop("scanVcf: ", conditionMessage(err), "\n path: ", file, call. = FALSE) }) } .vcf_scan_connection <- function(file, ..., n = -1, fixed=character(), info=character(), geno=character(), samples=character(), row.names=TRUE) { if (!isOpen(file)) { open(file, "r") on.exit(close(file)) } tryCatch({ path <- summary(file)$description maps <- .vcf_scan_header_maps(path, fixed, info, geno, samples) txt <- character(0) repeat { input <- readLines(file, n) if (length(input) == 0L) break input <- input[!grepl("^#", input)] n <- n - length(input) txt <- c(txt, input) } result <- .Call(.scan_vcf_connection, txt, maps$samples, maps$fmap, maps$imap, maps$gmap, row.names) setNames(result, "*:*-*") }, scanTabix_io = function(err) { stop("scanVcf: ", conditionMessage(err), call. = FALSE) }, error = function(err) { stop("scanVcf: ", conditionMessage(err), "\n path: ", summary(file)$description, call. = FALSE) }) } setMethod(scanVcf, c("TabixFile", "IntegerRangesList"), function(file, ..., param) { .vcf_scan(file, ..., param=param) }) setMethod(scanVcf, c("TabixFile", "GRanges"), function(file, ..., param) { .vcf_scan(file, ..., param=param) }) setMethod(scanVcf, c("TabixFile", "ScanVcfParam"), function(file, ..., param) { result <- scanVcf(file, ..., fixed=vcfFixed(param), info=vcfInfo(param), geno=vcfGeno(param), samples=vcfSamples(param), param=vcfWhich(param)) if (vcfTrimEmpty(param)) lapply(result, function(rng) { rng[["GENO"]] <- Filter(Negate(is.null), rng[["GENO"]]) rng }) else result }) setMethod(scanVcf, c("TabixFile", "missing"), function(file, ..., param) { callGeneric(file, ..., param=ScanVcfParam()) }) setMethod(scanVcf, c("character", "ScanVcfParam"), function(file, ..., param) { ## no ranges if (0L == length(vcfWhich(param))) { .vcf_scan_character(file, ..., fixed=vcfFixed(param), info=vcfInfo(param), geno=vcfGeno(param), samples=vcfSamples(param)) } else { ## ranges callGeneric(TabixFile(file), ..., param=param) } }) setMethod(scanVcf, c("character", "missing"), function(file, ..., param) { callGeneric(file, ..., param=ScanVcfParam()) }) setMethod(scanVcf, c("connection", "missing"), function(file, ..., param) { .vcf_scan_connection(file, ...) }) VariantAnnotation/R/methods-scanVcfHeader.R0000644000175100017510000000064714614305321021720 0ustar00biocbuildbiocbuildsetMethod(scanVcfHeader, "missing", function(file, ...) { VCFHeader() }) setMethod(scanVcfHeader, "character", function(file, ...) { if (length(file)) { hdr <- scanBcfHeader(file[[1]], ...)[[1]] VCFHeader(hdr$Reference, hdr$Sample, hdr$Header) } else { VCFHeader() } }) setMethod(scanVcfHeader, "TabixFile", function(file, ...) { scanVcfHeader(path(file), ...) }) VariantAnnotation/R/methods-ScanVcfParam-class.R0000644000175100017510000000457314614305321022635 0ustar00biocbuildbiocbuild### ========================================================================= ### ScanVcfParam class methods ### ========================================================================= ## - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - ## Constructor ## setMethod(ScanVcfParam, "ANY", function(fixed=character(), info=character(), geno=character(), samples=character(), trimEmpty=TRUE, which, ...) { ScanBcfParam(fixed, info, geno, samples, trimEmpty=trimEmpty, which=which, class="ScanVcfParam") }) setMethod(ScanVcfParam, "missing", function(fixed=character(), info=character(), geno=character(), samples=character(), trimEmpty=TRUE, which, ...) { ScanBcfParam(fixed, info, geno, samples, trimEmpty=trimEmpty, class="ScanVcfParam") }) ## - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - ## Validity ## .valid.ScanVcfParam <- function(object) { samples <- vcfSamples(object) geno <- vcfGeno(object) if (any(is.na(samples)) && length(geno) > 0L) return("ScanVcfParam: 'geno' cannot be specified if 'samples' is 'NA'") if (any(is.na(geno)) && length(samples) > 0L) return("ScanVcfParam: 'samples' cannot be specified if 'geno' is 'NA'") NULL } setValidity("ScanVcfParam", .valid.ScanVcfParam, where=asNamespace("VariantAnnotation")) ## - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - ## Getters and Setters ## vcfFixed <- function(object) slot(object, "fixed") "vcfFixed<-" <- function(object, value) { slot(object, "fixed") <- value object } vcfInfo <- function(object) slot(object, "info") "vcfInfo<-" <- function(object, value) { slot(object, "info") <- value object } vcfGeno <- function(object) slot(object, "geno") "vcfGeno<-" <- function(object, value) { slot(object, "geno") <- value object } vcfSamples <- function(object) slot(object, "samples") "vcfSamples<-" <- function(object, value) { slot(object, "samples") <- value object } vcfTrimEmpty <- function(object) slot(object, "trimEmpty") "vcfTrimEmpty<-" <- function(object, value) { slot(object, "trimEmpty") <- value object } vcfWhich <- function(object) { slot(object, "which") } "vcfWhich<-" <- function(object, value) { slot(object, "which") <- as(value, "IntegerRangesList") object } VariantAnnotation/R/methods-SIFTDb-class.R0000644000175100017510000000752514614305321021344 0ustar00biocbuildbiocbuild### ========================================================================= ### SIFTDb methods ### ========================================================================= setMethod("keys", "SIFTDb", function(x) { sql <- paste("SELECT rsid FROM siftdata ", sep="") unqsnp <- unique(dbGetQuery(x$conn, sql))[,1] paste("rs", unqsnp, sep="") } ) ## NOTE: The database table has 23 columns. There are two ## 'METHODS' (BEST HITS and ALL HITS) of obtaining related ## sequences using PSI-BLAST. Each variable outcome was listed ## once for each method which created a very wide non-coehesive ## table. We decided to consolidate the data by these methods ## to return a more compact result. (Not sure if this was wise ## or not.) This 'consolidating' of the data could have been ## done before creating the database or here before returning ## results to the user. We decided to keep the database table ## table as close to original as possible so curating is done here. setMethod("columns", "SIFTDb", function(x) { c("RSID", "PROTEINID", "AACHANGE", "METHOD", "AA", "PREDICTION", "SCORE", "MEDIAN", "POSTIONSEQS", "TOTALSEQS") } ) setMethod("select", "SIFTDb", function(x, keys, columns, ...) { sql <- .createSIFTDbQuery(x, keys, columns) if (length(sql)) { raw <- dbGetQuery(x$conn, sql) .formatSIFTDbSelect(raw, keys, columns) } else { data.frame() } } ) .createSIFTDbQuery <- function(x, keys, cols) { if (.missingKeys(x, keys, "SIFT")) return(character()) if (.missingCols(x, cols, "SIFT")) return(character()) if (missing(keys)) { sql <- paste("SELECT * FROM siftdata", sep="") } else { fmtkeys <- .sqlIn(.formatRSID(keys)) sql <- paste("SELECT * FROM siftdata WHERE RSID IN (", fmtkeys, ")", sep="") } } .formatRSID <- function(rsid) { gsub("rs", "", rsid, fixed=TRUE) } .formatSIFTDbSelect <- function(raw, keys, cols) { missing <- rep(FALSE, length(unique(raw$RSID))) if (!missing(keys)) { fmtkeys <- .formatRSID(keys) missing <- (!fmtkeys %in% as.character(raw$RSID)) } ## no data if (!nrow(raw)) return(data.frame()) ## create variable columns dat <- raw[,-c(1:3)] res <- matrix(t(dat), ncol=5, byrow=TRUE) colnames(res) <- c("PREDICTION", "SCORE", "MEDIAN", "POSITIONSEQS", "TOTALSEQS") ## create common columns id <- lapply(as.list(raw$RSID), function(x) rep(x, 4L)) rsid <- paste("rs", unlist(id, use.names=FALSE), sep="") protein_id <- lapply(as.list(raw$PROTEINID), function(x) rep(x, 4L)) aachange <- lapply(as.list(raw$AACHANGE), function(x) rep(x, 4L)) aa <- .createAAColumn(raw$AACHANGE) method <- rep(c("BEST HITS", "BEST HITS", "ALL HITS", "ALL HITS"), length(raw$RSID)) dd <- data.frame(RSID=unlist(rsid), PROTEINID=unlist(protein_id), AACHANGE=unlist(aachange), METHOD=method, AA=unlist(aa), res, row.names=NULL, stringsAsFactors=FALSE) ## reorder columns if (!missing(cols)) { if (!"RSID" %in% cols) cols <- c("RSID", cols) dd <- dd[,colnames(dd) %in% cols] } ## return keys not found df <- dd[!duplicated(dd), ] lst <- as.list(rep(NA_character_, length(keys))) for (i in which(missing == FALSE)) lst[[i]] <- df[df$RSID %in% keys[i], ] df <- do.call(rbind, lst) df$RSID[is.na(df$RSID)] <- keys[missing] rownames(df) <- NULL df } .createAAColumn <- function(x) { ## create single aa column, snp followed by ref lapply(x, function(elt) { ref_aa <- substr(as.character(elt), 1, 1) snp_aa <- substr(as.character(elt), nchar(as.character(elt)), nchar(as.character(elt))) c(snp_aa, ref_aa, snp_aa, ref_aa) }) } VariantAnnotation/R/methods-snpSummary.R0000644000175100017510000000570614614305321021403 0ustar00biocbuildbiocbuild### ========================================================================= ### snpSummary methods ### ========================================================================= setMethod("snpSummary", "CollapsedVCF", function(x, ...) { alt <- alt(x) if (is(alt, "CompressedCharacterList")) { alt <- .toDNAStringSetList(alt) if (all(elementNROWS(alt) == 0L)) { warning("No nucleotide ALT values were detected.") return(.emptySnpSummary()) } } gt <- geno(x)$GT if (is.null(gt)) { warning("No genotype data found in VCF.") return(.emptySnpSummary()) } if (ncol(gt) == 0L) { warning("No genotype data found in VCF.") return(.emptySnpSummary()) } ## Genotype count snv <- .testForSNV(ref(x), alt) if (sum(snv) == 0L) { warning("No valid SNPs found in VCF.") return(.emptySnpSummary()) } gmap <- .genotypeToIntegerSNV(FALSE) gmat <- matrix(gmap[gt], nrow=nrow(gt)) gcts <- matrix(NA_integer_, nrow(gmat), 3) if (ncol(gmat) == 1L) fun <- I else fun <- rowSums gcts[snv,] <- sapply(1:3, function(i) fun(gmat[snv,] == i)) gcts <- matrix(as.integer(gcts), nrow=nrow(gmat), dimnames=list(NULL, c("g00", "g01", "g11"))) ## Allele frequency acts <- gcts %*% matrix(c(2,1,0,0,1,2), nrow=3) afrq <- acts/rowSums(acts) colnames(afrq) <- c("a0Freq", "a1Freq") ## HWE HWEzscore <- .HWEzscore(gcts, acts, afrq) HWEpvalue <- pchisq(HWEzscore^2, 1, lower.tail=FALSE) data.frame(gcts, afrq, HWEzscore, HWEpvalue, row.names=rownames(x)) }) .HWEzscore <- function(genoCounts, alleleCounts, alleleFrequency) { a0 <- alleleFrequency[,"a0Freq"] a1 <- alleleFrequency[,"a1Freq"] expt <- rowSums(alleleCounts) * cbind(a0^2/2, a0*a1, a1^2/2) chisq <- rowSums((genoCounts - expt)^2 / expt) z <- rep(NA, nrow(genoCounts)) zsign <- sign(genoCounts[,"g01"] - expt[,2] * rowSums(genoCounts)) zsign * sqrt(chisq) } ## Maps diploid genotypes, phased or unphased. .genotypeToIntegerSNV <- function(raw=TRUE) { name <- c(".|.", "0|0", "0|1", "1|0", "1|1", "./.", "0/0", "0/1", "1/0", "1/1") value <- rep(c(0, 1, 2, 2, 3), 2) if (raw) setNames(sapply(value, as.raw), name) else setNames(value, name) } ## Expects 'ref' as DNAStringSet and 'alt' as ## DNAStringSetList. Returns a logical vector ## the same length as 'ref'. .testForSNV <- function(ref, alt) { altelt <- elementNROWS(alt) == 1L altseq <- logical(length(alt)) idx <- rep(altelt, elementNROWS(alt)) altseq[altelt] = width(unlist(alt, use.names=FALSE)[idx]) == 1L altseq & (width(ref) == 1L) } .emptySnpSummary <- function() { data.frame(g00=integer(), g01=integer(), g11=integer(), a0Freq=numeric(), a1Freq=numeric(), HWEzscore=numeric(), HWEpvalue=numeric()) } VariantAnnotation/R/methods-summarizeVariants.R0000644000175100017510000000563714614305321022754 0ustar00biocbuildbiocbuild### ========================================================================= ### summarizeVariants methods ### ========================================================================= setMethod("summarizeVariants", c("TxDb", "VCF", "CodingVariants"), function(query, subject, mode, ...) { grl <- cdsBy(query, "tx") callGeneric(grl, subject, mode, ...) }) setMethod("summarizeVariants", c("TxDb", "VCF", "FiveUTRVariants"), function(query, subject, mode, ...) { grl <- fiveUTRsByTranscript(query) callGeneric(grl, subject, mode, ...) }) setMethod("summarizeVariants", c("TxDb", "VCF", "ThreeUTRVariants"), function(query, subject, mode, ...) { grl <- threeUTRsByTranscript(query) callGeneric(grl, subject, mode, ...) }) setMethod("summarizeVariants", c("TxDb", "VCF", "SpliceSiteVariants"), function(query, subject, mode, ...) { grl <- intronsByTranscript(query) callGeneric(grl, subject, mode, ...) }) setMethod("summarizeVariants", c("TxDb", "VCF", "IntronVariants"), function(query, subject, mode, ...) { grl <- intronsByTranscript(query) callGeneric(grl, subject, mode, ...) }) setMethod("summarizeVariants", c("TxDb", "VCF", "PromoterVariants"), function(query, subject, mode, ...) { gr <- transcripts(query, columns="tx_id") grl <- splitAsList(gr, seq_len(length(gr))) names(grl) <- mcols(gr)$tx_id callGeneric(grl, subject, mode, ...) }) setMethod("summarizeVariants", c("GRangesList", "VCF", "VariantType"), function(query, subject, mode, ...) { callGeneric(query, subject, mode=locateVariants, ..., region=mode, asHits=TRUE) }) setMethod("summarizeVariants", c("GRangesList", "VCF", "function"), function(query, subject, mode, ...) { if (length(geno(subject)) == 0L) { warning("No genotypes found in 'query'.") return(.baseSE(query, subject)) } ## count hits <- mode(rowRanges(subject), query, ...) if (length(hits) == 0L) return(.baseSE(query, subject)) ## genotypes na <- c("0|0", "0/0", "./.", ".|.", ".") vcf_geno <- geno(subject)$GT[unique(queryHits(hits)), ] gtype <- as.numeric(!vcf_geno %in% na) ## summarize counts factor-by-sample fac_x_var <- table(subjectHits(hits), queryHits(hits)) var_x_smp <- matrix(gtype, ncol=ncol(subject)) fac_x_smp <- fac_x_var %*% var_x_smp rownames(fac_x_smp) <- NULL SummarizedExperiment(rowRanges=query[unique(subjectHits(hits))], colData=colData(subject), metadata=metadata(subject), assays=SimpleList(counts=fac_x_smp)) }) .baseSE <- function(query, subject, ...) { SummarizedExperiment(rowRanges=query, colData=colData(subject), metadata=metadata(subject), assays=SimpleList(counts=matrix(NA_integer_, nrow=length(query), ncol=ncol(subject)))) } VariantAnnotation/R/methods-VariantType-class.R0000644000175100017510000001177214614305321022576 0ustar00biocbuildbiocbuild### ========================================================================= ### VariantType class methods ### ========================================================================= ### 'show' methods setMethod("show", "VariantType", function(object) { cat("class:", classNameForDisplay(object), "\n") } ) setMethod("show", "AllVariants", function(object) { cat("class:", classNameForDisplay(object), "\n") cat("promoter: \n") cat(" upstream:", upstream(promoter(object)), "\n") cat(" downstream:", downstream(promoter(object)), "\n") cat("intergenic: \n") cat(" upstream:", upstream(intergenic(object)), "\n") cat(" downstream:", downstream(intergenic(object)), "\n") cat(" idType:", idType(intergenic(object)), "\n") } ) setMethod("show", "PromoterVariants", function(object) { cat("class:", classNameForDisplay(object), "\n") cat("upstream:", upstream(object), "\n") cat("downstream:", downstream(object), "\n") } ) setMethod("show", "IntergenicVariants", function(object) { cat("class:", classNameForDisplay(object), "\n") cat("upstream:", upstream(object), "\n") cat("downstream:", downstream(object), "\n") cat("idType:", idType(object), "\n") } ) ### constructors CodingVariants <- function() new("CodingVariants") IntronVariants <- function() new("IntronVariants") UTRVariants <- function() new("UTRVariants") ThreeUTRVariants <- function() new("ThreeUTRVariants") FiveUTRVariants <- function() new("FiveUTRVariants") SpliceSiteVariants <- function() new("SpliceSiteVariants") ### .checkArgs() takes the place of a validity method. ### 'upstream' and 'downstream' are forced to integers ### which changes the object and can't be done a validity ### method. .checkArgs <- function(x, argName) { if (!isSingleNumber(x) | x < 0) stop(paste0("'", argName, "'", " must be a single integer >= 0")) if (!is.integer(x)) as.integer(x) else x } IntergenicVariants <- function(upstream=1e+06L, downstream=1e+06L, idType=c("gene", "tx")) { upstream <- .checkArgs(upstream, "upstream") downstream <- .checkArgs(downstream, "downstream") if (missing(idType)) { idType <- match.arg(idType) } else { idType <- tolower(idType) if (!idType %in% c("gene", "tx")) stop("'idType' must be one of 'gene' or 'tx'") } new("IntergenicVariants", upstream=upstream, downstream=downstream, idType=idType) } PromoterVariants <- function(upstream=2000L, downstream=200L) { upstream <- .checkArgs(upstream, "upstream") downstream <- .checkArgs(downstream, "downstream") new("PromoterVariants", upstream=upstream, downstream=downstream) } AllVariants <- function(promoter=PromoterVariants(), intergenic=IntergenicVariants()) { new("AllVariants", promoter=promoter, intergenic=intergenic) } ### getters and setters setMethod("upstream", "PromoterVariants", function(x) slot(x, "upstream")) setReplaceMethod("upstream", "PromoterVariants", function(x, value) { slot(x, "upstream") <- .checkArgs(value, "upstream") x }) setMethod("downstream", "PromoterVariants", function(x) slot(x, "downstream")) setReplaceMethod("downstream", "PromoterVariants", function(x, value) { slot(x, "downstream") <- .checkArgs(value, "downstream") x }) setMethod("upstream", "IntergenicVariants", function(x) slot(x, "upstream")) setReplaceMethod("upstream", "IntergenicVariants", function(x, value) { slot(x, "upstream") <- .checkArgs(value, "upstream") x }) setMethod("downstream", "IntergenicVariants", function(x) slot(x, "downstream")) setReplaceMethod("downstream", "IntergenicVariants", function(x, value) { slot(x, "downstream") <- .checkArgs(value, "downstream") x }) setMethod("idType", "IntergenicVariants", function(x) slot(x, "idType")) setReplaceMethod("idType", "IntergenicVariants", function(x, value) { if (!value %in% c("gene", "tx")) stop("'idType' must be one of 'gene' or 'tx'") slot(x, "idType") <- value x }) setMethod("downstream", "IntergenicVariants", function(x) slot(x, "downstream")) setReplaceMethod("downstream", "IntergenicVariants", function(x, value) { slot(x, "downstream") <- .checkArgs(value, "downstream") x }) setMethod("promoter", "AllVariants", function(x) slot(x, "promoter")) setReplaceMethod("promoter", "AllVariants", function(x, value) { if (!is(value, "PromoterVariants")) stop("'value' must be a 'PromoterVariants' object") slot(x, "promoter") <- value x }) setMethod("intergenic", "AllVariants", function(x) slot(x, "intergenic")) setReplaceMethod("intergenic", "AllVariants", function(x, value) { if (!is(value, "IntergenicVariants")) stop("'value' must be a 'IntergenicVariants' object") slot(x, "intergenic") <- value x }) VariantAnnotation/R/methods-VCF-class.R0000644000175100017510000004221414614305321020741 0ustar00biocbuildbiocbuild### ========================================================================= ### VCF class methods ### ========================================================================= ### - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - ### Constructor ### VCF <- function(rowRanges=GRanges(), colData=DataFrame(), exptData=list(header=VCFHeader()), fixed=DataFrame(), info=DataFrame(row.names=seq_along(rowRanges)), geno=SimpleList(), ..., collapsed=TRUE, verbose=FALSE) { rownames(info) <- rownames(fixed) <- NULL if (collapsed) { class <- "CollapsedVCF" if (!length(fixed)) { fixed=DataFrame( REF=DNAStringSet(rep(".", length(rowRanges))), ALT=DNAStringSetList(as.list(rep(".", length(rowRanges)))), QUAL=rep(NA_real_, length(rowRanges)), FILTER=rep(NA_character_, length(rowRanges))) } } else { class <- "ExpandedVCF" if (!length(fixed)) { fixed=DataFrame( REF=DNAStringSet(rep("", length(rowRanges))), ALT=DNAStringSet(rep("", length(rowRanges))), QUAL=rep(NA_real_, length(rowRanges)), FILTER=rep(NA_character_, length(rowRanges))) } } new(class, SummarizedExperiment(assays=geno, rowRanges=rowRanges, colData=colData, metadata=as.list(exptData)), fixed=fixed, info=info, ...) } ### - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - ### updateObject() ### setMethod("updateObject", "VCF", function(object, ..., verbose=FALSE) { # Fix VCF-specific slots. object@fixed <- updateObject(object@fixed, ..., verbose=verbose) object@info <- updateObject(object@info, ..., verbose=verbose) # Call method for RangedSummarizedExperiment objects. callNextMethod() } ) ### - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - ### Getters and Setters ### .recycleVector <- function(x, value) { ## warns when number of values not a sub-multiple of length of x x[] <- value x } ### fixed setMethod("fixed", "VCF", function(x) { ## Fix old DataFrame instance on-the-fly. updateObject(slot(x, "fixed"), check=FALSE) }) setReplaceMethod("fixed", c("VCF", "DataFrame"), function(x, value) { slot(x, "fixed") <- value validObject(x) x }) ### ref setMethod("ref", "VCF", function(x) { fixed(x)$REF }) setReplaceMethod("ref", c("VCF", "DNAStringSet"), function(x, value) { value <- .recycleVector(ref(x), value) slot(x, "fixed")$REF <- value x }) ### alt setMethod("alt", "VCF", function(x) { fixed(x)$ALT }) setReplaceMethod("alt", c("CollapsedVCF", "CharacterList"), function(x, value) { value <- .recycleVector(as(alt(x), "CharacterList"), value) slot(x, "fixed")$ALT <- value x }) setReplaceMethod("alt", c("ExpandedVCF", "character"), function(x, value) { value <- .recycleVector(as.character(alt(x)), value) slot(x, "fixed")$ALT <- value x }) ### qual setMethod("qual", "VCF", function(x) { fixed(x)$QUAL }) setReplaceMethod("qual", c("VCF", "numeric"), function(x, value) { value <- .recycleVector(qual(x), value) slot(x, "fixed")$QUAL <- value x }) ### filt setMethod("filt", "VCF", function(x) { fixed(x)$FILTER }) setReplaceMethod("filt", c("VCF", "character"), function(x, value) { value <- .recycleVector(filt(x), value) slot(x, "fixed")$FILTER <- value x }) ### rowRanges setMethod("rowRanges", "VCF", function(x, ..., fixed = TRUE) { gr <- slot(x, "rowRanges") if (fixed) { x_fixed <- fixed(x) if (length(x_fixed) != 0L) mcols(gr) <- append(mcols(gr), x_fixed) } gr }) setReplaceMethod("rowRanges", c("VCF", "GRanges"), function(x, value) { ## This method does not manage the 'fixed' fields REF, ALT, QUAL, FILTER ## Use "fixed<-" instead. sub <- value[,!names(mcols(value)) %in% c("REF", "ALT", "QUAL", "FILTER")] slot(x, "rowRanges") <- sub x }) setReplaceMethod("mcols", c("VCF", "ANY"), function(x, ..., value) { ## This method does not manage the 'fixed' fields REF, ALT, QUAL, FILTER ## Use "fixed<-" instead. sub <- value[,!names(value) %in% c("REF", "ALT", "QUAL", "FILTER")] mcols(slot(x, "rowRanges")) <- sub validObject(x) x }) setReplaceMethod("dimnames", c("VCF", "list"), function(x, value) { rowRanges <- slot(x, "rowRanges") names(rowRanges) <- value[[1]] colData <- colData(x) rownames(colData) <- value[[2]] BiocGenerics:::replaceSlots(x, rowRanges=rowRanges, colData=colData) }) ### info setMethod("info", "VCF", function(x, ..., row.names = TRUE) { ## Fix old DataFrame instance on-the-fly. info <- updateObject(slot(x, "info"), check=FALSE) if (row.names && !anyDuplicated(rownames(x))) rownames(info) <- rownames(x) info }) setReplaceMethod("info", c("VCF", "DataFrame"), function(x, value) { slot(x, "info") <- value .valid.VCFHeadervsVCF.fields(x, info) validObject(x) x }) ### geno ### 'assays' extracts full list ### 'assay' extracts individual list elements setMethod("geno", "VCF", function(x, ..., withDimnames = TRUE) { assays(x, ..., withDimnames=withDimnames) }) setMethod("geno", c("VCF", "numeric"), function(x, i, ..., withDimnames = TRUE) { assay(x, i, ...) }) setMethod("geno", c("VCF", "character"), function(x, i, ..., withDimnames = TRUE) { assay(x, i, ...) }) setReplaceMethod("geno", c("VCF", "missing", "SimpleList"), function(x, i, ..., value) { assays(x, withDimnames=FALSE) <- value .valid.VCFHeadervsVCF.fields(x, geno) x }) setReplaceMethod("geno", c("VCF", "character", "matrix"), function(x, i, ..., value) { assay(x, i, withDimnames=FALSE) <- value .valid.VCFHeadervsVCF.fields(x, geno) x }) setReplaceMethod("geno", c("VCF", "numeric", "matrix"), function(x, i, ..., value) { assay(x, i, withDimnames=FALSE) <- value .valid.VCFHeadervsVCF.fields(x, geno) x }) setReplaceMethod("geno", c("VCF", "missing", "matrix"), function(x, i, ..., value) { assay(x, withDimnames=FALSE) <- value .valid.VCFHeadervsVCF.fields(x, geno) x }) ### strand setMethod("strand", "VCF", function(x, ...) { strand(rowRanges(x)) }) setReplaceMethod("strand", "VCF", function(x, ..., value) { strand(rowRanges(x)) <- value x }) ### header setMethod("header", "VCF", function(x) { metadata(x)$header }) setReplaceMethod("header", c("VCF", "VCFHeader"), function(x, value) { slot(x, "metadata")$header <- value .valid.VCFHeadervsVCF(x) x }) ## vcfFields setMethod("vcfFields", "missing", function(x, ...) { vcfFields(VCFHeader()) }) setMethod("vcfFields", "VCF", function(x, ...) { vcfFields(header(x)) }) ### - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - ### Subsetting and combining ### setMethod(parallel_slot_names, "VCF", function(x) { c("fixed", "info", callNextMethod()) }) setMethod("[", c("VCF", "ANY", "ANY"), function(x, i, j, ..., drop=TRUE) { if (1L != length(drop) || (!missing(drop) && drop)) warning("'drop' ignored '[,VCF,ANY,ANY-method'") if (!missing(i) && is.character(i)) { msg <- "[i,] index out of bounds: %s" i <- SummarizedExperiment:::.SummarizedExperiment.charbound(i, rownames(x), msg) } if (missing(i) && missing(j)) { x } else if (missing(i)) { callNextMethod(x, , j, ...) } else if (missing(j)) { callNextMethod(x, i, , info=info(x)[i,,drop=FALSE], fixed=fixed(x)[i,,drop=FALSE], ...) } else { callNextMethod(x, i, j, info=info(x)[i,,drop=FALSE], fixed=fixed(x)[i,,drop=FALSE], ...) } }) setMethod("subset", "VCF", function(x, subset, select, ...) { rowData <- rowRanges(x) mcols(rowData) <- cbind(mcols(rowRanges(x)), info(x)) i <- S4Vectors:::evalqForSubset(subset, rowData, ...) j <- S4Vectors:::evalqForSubset(select, colData(x), ...) x[i, j] }) setReplaceMethod("[", c("VCF", "ANY", "ANY", "VCF"), function(x, i, j, ..., value) { if (!missing(i) && is.character(i)) { msg <- "[i,]<- index out of bounds: %s" i <- SummarizedExperiment:::.SummarizedExperiment.charbound(i, rownames(x), msg) } if (missing(i) && missing(j)) { x } else if (missing(i)) { callNextMethod(x, , j, ..., value=value) } else if (missing(j)) { callNextMethod(x, i, , info=local({ ii <- info(x) ii[i,] <- info(value) ii }), fixed=local({ ff <- fixed(x) ff[i,] <- fixed(value) ff }), ..., value=value) } else { callNextMethod(x, i, j, info=local({ ii <- info(x) ii[i,] <- info(value) ii }), fixed=local({ ff <- fixed(x) ff[i,] <- fixed(value) ff }), ..., value=value) } }) .compare <- SummarizedExperiment:::.compare ## Appropriate for objects with different ranges and same samples. setMethod("rbind", "VCF", function(..., deparse.level=1) { args <- unname(list(...)) if (!.compare(lapply(args, class))) stop("'...' objects must be of the same VCF class") args <- .renameSamples(args) if (!.compare(lapply(args, colnames))) stop("'...' objects must have the same colnames") if (!.compare(lapply(args, ncol))) stop("'...' objects must have the same number of samples") fixed <- do.call(rbind, lapply(args, fixed)) info <- do.call(rbind, lapply(args, info)) ## rowRanges should not contain REF, ALT, QUAL or FILTER rowRanges <- do.call(c, lapply(args, slot, "rowRanges")) colData <- SummarizedExperiment:::.cbind.DataFrame(args, colData, "colData") assays <- do.call(rbind, lapply(args, slot, "assays")) elementMetadata <- do.call(rbind, lapply(args, slot, "elementMetadata")) metadata <- do.call(c, lapply(args, metadata)) BiocGenerics:::replaceSlots(args[[1L]], fixed=fixed, info=info, rowRanges=rowRanges, colData=colData, assays=assays, elementMetadata=elementMetadata, metadata=metadata) }) .renameSamples <- function(args) { Map(function(x, i) { idx <- names(colData(x)) == "Samples" names(colData(x))[idx] <- paste0("Samples.", i) x }, x=args, i=seq_along(args)) } ## FIXME: combine header info ## Appropriate for objects with same ranges and different samples. setMethod("cbind", "VCF", function(..., deparse.level=1) { args <- unname(list(...)) if (!.compare(lapply(args, class))) stop("'...' objects must be of the same VCF class") if (!.compare(lapply(args, rowRanges), TRUE)) stop("'...' object ranges (rows) are not compatible") if (!.compare(lapply(args, "fixed"))) stop("data in 'fixed(VCF)' must match.") fixed <- fixed(args[[1L]]) info <- SummarizedExperiment:::.cbind.DataFrame(args, info, "info") rowRanges <- rowRanges(args[[1L]]) mcols(rowRanges) <- SummarizedExperiment:::.cbind.DataFrame(args, mcols, "mcols") colData <- do.call(rbind, lapply(args, colData)) assays <- do.call(cbind, lapply(args, slot, "assays")) metadata <- do.call(c, lapply(args, metadata)) BiocGenerics:::replaceSlots(args[[1L]], fixed=fixed, info=info, rowRanges=rowRanges, colData=colData, assays=assays, metadata=metadata) }) ### - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - ### Coercion ### SnpMatrixToVCF <- function(from, seqSource) { if (!all(names(from) %in% c("genotypes", "fam", "map"))) stop("'from' must be a list with named elements 'genotypes', ", "'fam' and 'map'") ## match seqlevels style to seqSource map <- from$map chr <- as.character(map$chromosome) seqlevelsStyle(chr) <- seqlevelsStyle(seqSource) uniqueChr <- unique(chr) if (any(invalid <- !uniqueChr %in% seqlevels(seqSource))) stop("seqlevels not found in 'seqSource': ", paste(uniqueChr[invalid], collapse=", ")) ## ref gr <- GRanges(chr, IRanges(map$position, width=1)) ref <- getSeq(seqSource, gr) ## alt refIsAlt1 <- ref == map$allele.1 refIsAlt2 <- ref == map$allele.2 alt <- DNAStringSetList(as.list(map$allele.1)) alt[refIsAlt1] <- DNAStringSetList(as.list(map$allele.2[refIsAlt1])) if (any(refNotFound <- refIsAlt1 & refIsAlt2)) { df <- data.frame(rbind(map$allele.1[refNotFound], map$allele.2[refNotFound])) alt[refNotFound] <- DNAStringSetList(as.list(df)) } ## genotypes GT <- as.raw(from$genotypes) nrowGT <- nrow(from$genotypes) newGT <- c("./.", "0/0", "0/1", "1/1") GT <- newGT[as.integer(GT) + 1L] GT <- matrix(GT, byrow=TRUE, ncol=nrowGT) vcf <- VCF(rowRanges=gr, fixed=DataFrame(REF=ref, ALT=alt), colData=DataFrame(Samples=seq_len(nrowGT)), geno=SimpleList(GT=GT)) dimnames(vcf) <- list(colnames(from$genotypes), rownames(from$genotypes)) vcf } ### - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - ### Show methods ### setMethod(show, "VCF", function(object) { paste0("This object is no longer valid. Please use updateObject() to ", "create a CollapsedVCF instance.") }) ### methods for CollapsedVCF and ExapandedVCF .showVCFSubclass <- function(object) { prettydescr <- function(desc, wd0=30L) { desc <- as.character(desc) wd <- options()[["width"]] - wd0 dwd <- nchar(desc) desc <- substr(desc, 1, wd) idx <- dwd > wd substr(desc[idx], wd - 2, dwd[idx]) <- "..." desc } headerrec <- function(df, margin=" ") { wd <- max(nchar(rownames(df))) + nchar("Number") + max(nchar(df$Type)) + 7L df$Description <- prettydescr(df$Description, wd) rownames(df) <- paste0(margin, rownames(df)) print(df, right=FALSE) } printSmallGRanges <- function(x, margin) { lx <- length(x) nc <- ncol(mcols(x)) nms <- names(mcols(x)) cat(margin, classNameForDisplay(x), " with ", nc, " metadata ", ifelse(nc == 1L, "column", "columns"), ": ", paste0(nms, collapse=", "), "\n", sep="") } printSmallDataTable <- function(x, margin) { nc <- ncol(x) nms <- names(x) cat(margin, classNameForDisplay(x), " with ", nc, ifelse(nc == 1, " column", " columns"), ": ", prettydescr(paste0(nms, collapse=", ")), "\n", sep="") } printSimpleList <- function(x, margin) { lo <- length(x) nms <- names(x) cat(margin, classNameForDisplay(x), " of length ", lo, ": ", prettydescr(paste0(nms, collapse=", ")), "\n", sep="") } margin <- " " cat("class:", classNameForDisplay(object), "\n") cat("dim:", dim(object), "\n") cat("rowRanges(vcf):\n") printSmallGRanges(rowRanges(object), margin=margin) cat("info(vcf):\n") printSmallDataTable(info(object, row.names=FALSE), margin=margin) if (length(header(object))) { if (length(hdr <- info(header(object)))) { nms <- intersect(colnames(info(object, row.names=FALSE)), rownames(hdr)) diff <- setdiff(colnames(info(object, row.names=FALSE)), rownames(hdr)) if (length(nms)) { cat("info(header(vcf)):\n") headerrec(as.data.frame(hdr[nms,])) } if (length(diff)) cat(" Fields with no header:", paste(diff, collapse=","), "\n") } } cat("geno(vcf):\n") geno <- geno(object, withDimnames=FALSE) printSimpleList(geno, margin=margin) if (length(header(object))) { if (length(hdr <- geno(header(object)))) { gnames <- names(geno(object, withDimnames = FALSE)) nms <- intersect(gnames, rownames(hdr)) diff <- setdiff(gnames, rownames(hdr)) if (length(nms)) { cat("geno(header(vcf)):\n") headerrec(as.data.frame(hdr[nms,])) } if (length(diff)) cat(" Fields with no header:", paste(diff, collapse=","), "\n") } } } ### - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - ### genotypeCodesToNucleotides() ### genotypeCodesToNucleotides <- function(vcf, ...) { GT <- geno(vcf, withDimnames=FALSE)$GT if (is.null(GT <- geno(vcf, withDimnames=FALSE)$GT)) stop("no 'GT' data found in geno(vcf)") if (is(alt(vcf), "CharacterList")) stop("'ALT' must be a DNAStringSetList") ALT <- as.list(splitAsList(as.character(alt(vcf)@unlistData), togroup(PartitioningByWidth(alt(vcf))))) REF <- as.character(ref(vcf)) geno(vcf)$GT <- .geno2geno(NULL, ALT, REF, GT) vcf } VariantAnnotation/R/methods-VcfFile.R0000644000175100017510000000542014614305321020534 0ustar00biocbuildbiocbuild### ========================================================================= ### VcfFile, VcfFileList class methods ### ========================================================================= ### - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - ### Constructors ### VcfFile <- function(file, index, ..., yieldSize=NA_integer_) { if (is(file, "VcfFile")) return(file) tryCatch({ Rsamtools:::.io_check_exists(file) }, error=function(err) { stop(sprintf("VcfFile: %s", conditionMessage(err)), call.=FALSE) }) if (missing(index)){ index=paste(file, "tbi", sep=".") if (!file.exists(index)) index = NA } Rsamtools:::.RsamtoolsFile(.VcfFile, file, index, yieldSize=yieldSize, ...) } VcfFileList <- function(..., yieldSize=NA_integer_) { Rsamtools:::.RsamtoolsFileList( ..., yieldSize=yieldSize, class="VcfFile", classDef = VariantAnnotation::VcfFile ) } ### Methods ### setGeneric("indexVcf", function(x, ...) standardGeneric("indexVcf"), signature="x") setMethod("indexVcf", "character", function(x, ...) { stopifnot(!is.na(x), length(x) > 0) if(length(x) == 1L){ index <- Rsamtools::indexTabix(x, format="vcf4", ...) VcfFile(x, index) }else{ index <- sapply(x, FUN=Rsamtools::indexTabix, format="vcf4", ...) VcfFileList(x, index) } }) setMethod("indexVcf", "VcfFile", function(x, ...) { index <- index(x) newDx = NA if (is.na(index) || length(index) == 0L){ index <- paste(path(x), "tbi", sep=".") newDx <- index } if (!file.exists(index)) newDx <- Rsamtools::indexTabix(path(x), format="vcf4", ...) if (!is.na(newDx)) index(x) <- newDx x }) setMethod("indexVcf", "VcfFileList", function(x, ... ) { ind = index(x) dontExist = which(!file.exists(ind)) if (length(dontExist) != 0L){ fn <- function(dx, obj){ Rsamtools::indexTabix(path(obj)[dx], format="vcf4", ...) } newDx = sapply(dontExist, FUN=fn, obj=x) ind[dontExist] <- newDx index(x) <- ind } x }) setMethod("seqinfo", "VcfFile", function(x) { seqinfo(scanVcfHeader(path(x))) }) setMethod("seqinfo", "VcfFileList", function(x) { si = lapply(x, function(elt) as(seqinfo(elt), "GRanges")) grl <- as(si, "GRangesList") seqinfo(grl) }) setMethod("vcfFields", "character", function(x, ...) { stopifnot(!is.na(x)) hdr <- scanVcfHeader(x) vcfFields(hdr) }) setMethod("vcfFields", "VcfFile", function(x, ...) { vcfFields(path(x)) }) setMethod("vcfFields", "VcfFileList", function(x, ...) { vcfFields(path(x)) }) VariantAnnotation/R/methods-VCFHeader-class.R0000644000175100017510000001024614614305321022052 0ustar00biocbuildbiocbuild### ========================================================================= ### VCFHeader class methods ### ========================================================================= ### - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - ### Constructor ### VCFHeader <- function(reference=character(), samples=character(), header=DataFrameList(), ...) { new("VCFHeader", reference=reference, samples=samples, header=header, ...) } ### - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - ### Getters and Setters ### setMethod("reference", "VCFHeader", function(x) { slot(x, "reference") }) setMethod("samples", "VCFHeader", function(object) { slot(object, "samples") }) setMethod("header", "VCFHeader", function(x) { slot(x, "header") }) ## meta setMethod("meta", "VCFHeader", function(x) { dat <- slot(x, "header") nms <- c("INFO", "FORMAT", "QUAL", "FILTER", "ALT", "REF") dat[!names(dat) %in% nms] }) setReplaceMethod("meta", c("VCFHeader", "DataFrame"), function(x, value) meta(x) <- as(value, "DataFrameList") ) setReplaceMethod("meta", c("VCFHeader", "DataFrameList"), function(x, value) { dat <- slot(x, "header") slot(x, "header") <- c(dat[!names(dat) %in% names(value)], value) validObject(x) x }) ## fixed (QUAL, FILTER, ALT, REF) setMethod("fixed", "VCFHeader", function(x) { fixed <- c("QUAL", "FILTER", "ALT", "REF") lst <- slot(x, "header") lst[names(lst) %in% fixed] }) setReplaceMethod("fixed", c("VCFHeader", "DataFrameList"), function(x, value) { if (!all(names(value) %in% c("QUAL", "FILTER", "ALT", "REF"))) stop("names for 'fixed' can be 'QUAL', 'FILTER', 'ALT' or 'REF'") dat <- slot(x, "header") slot(x, "header") <- c(dat[!names(dat) %in% names(value)], value) x }) ## info setMethod("info", "VCFHeader", function(x) { info <- slot(x, "header")$INFO if (is.null(info)) info <- DataFrame(Number=integer(), Type=character(), Description=character()) info }) setReplaceMethod("info", c("VCFHeader", "DataFrame"), function(x, value) { slot(x, "header")$INFO <- value validObject(x) x }) ## geno setMethod("geno", "VCFHeader", function(x) { geno <- slot(x, "header")$FORMAT if (is.null(geno)) geno <- DataFrame(Number=integer(), Type=character(), Description=character()) geno }) setReplaceMethod("geno", c("VCFHeader", "missing", "DataFrame"), function(x, i, ..., value) { slot(x, "header")$FORMAT <- value validObject(x) x }) setMethod("seqinfo", "VCFHeader", function(x) { contig <- slot(x, "header")$contig if (is.null(contig)) Seqinfo() else Seqinfo(rownames(contig), seqlengths = if (is.null(contig$length)) NA else as.integer(contig$length), genome = if (is.null(contig$assembly)) NA else contig$assembly) }) ## vcfFields setMethod("vcfFields", "VCFHeader", function(x, ...) { fixed <- names(fixed(x)) if (!is.null(fixed)) fixed <- c("REF", "ALT", "QUAL", "FILTER") CharacterList(fixed = fixed, info = rownames(info(x)), geno = rownames(geno(x)), samples = samples(x) ) }) ### - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - ### Show ### setMethod(show, "VCFHeader", function(object) { selectSome <- S4Vectors:::selectSome scat <- function(fmt, vals=character(), exdent=2, ...) { vals <- ifelse(nzchar(vals), vals, "''") lbls <- paste(selectSome(vals), collapse=" ") txt <- sprintf(fmt, length(vals), lbls) cat(strwrap(txt, exdent=exdent, ...), sep="\n") } cat("class:", classNameForDisplay(object), "\n") samples <- samples(object) scat("samples(%d): %s\n", samples) meta <- names(meta(object)) scat("meta(%d): %s\n", meta) fixed <- names(fixed(object)) scat("fixed(%d): %s\n", fixed) info <- rownames(info(object)) scat("info(%d): %s\n", info) geno <- rownames(geno(object)) scat("geno(%d): %s\n", geno) }) VariantAnnotation/R/methods-VRanges-class.R0000644000175100017510000005530514614305321021675 0ustar00biocbuildbiocbuild### ========================================================================= ### VRanges: Variants in a GRanges ### ------------------------------------------------------------------------- ### ### Thoughts on the gVCF format. ## The gVCF format is essentially a run-length encoding of wildtype ## calls. We could represent the run as a range, but we would need ## somewhat to indicate "WT". Probably best to go the VCF route: have ## the first ref base, and an NA alt. We can distinguish this from an ## SNV or indel, because the width of the range is > 1, but there is ## only a single ref base. This will break the assertion that the ref ## length always equal the range width, but that is not a big deal. ### - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - ### Accessors ### setMethod("ref", "VRanges", function(x) x@ref) setReplaceMethod("ref", "VRanges", function(x, value) { x@ref <- S4Vectors:::recycleVector(value, length(x)) x }) setMethod("alt", "VRanges", function(x) x@alt) setReplaceMethod("alt", "VRanges", function(x, value) { x@alt <- as(.rleRecycleVector(value, length(x)), "characterOrRle") x }) setMethod("sampleNames", "VRanges", function(object) object@sampleNames) setReplaceMethod("sampleNames", "VRanges", function(object, value) { object@sampleNames <- as(.rleRecycleVector(value, length(object)), "factorOrRle") object }) setMethod("totalDepth", "VRanges", function(x) x@totalDepth) `totalDepth<-` <- function(x, value) { x@totalDepth <- as(.rleRecycleVector(value, length(x)), "integerOrRle") x } setMethod("altDepth", "VRanges", function(x) x@altDepth) `altDepth<-` <- function(x, value) { x@altDepth <- as(.rleRecycleVector(value, length(x)), "integerOrRle") x } setMethod("refDepth", "VRanges", function(x) x@refDepth) `refDepth<-` <- function(x, value) { x@refDepth <- as(.rleRecycleVector(value, length(x)), "integerOrRle") x } setMethod("softFilterMatrix", "VRanges", function(x) x@softFilterMatrix) setReplaceMethod("softFilterMatrix", "VRanges", function(x, value) { x@softFilterMatrix <- value x }) setMethod("hardFilters", "VRanges", function(x) x@hardFilters) setReplaceMethod("hardFilters", "VRanges", function(x, value) { x@hardFilters <- value x }) setMethod("called", "VRanges", function(x) { rowSums(softFilterMatrix(x)) == ncol(softFilterMatrix(x)) }) setMethod("altFraction", "VRanges", function(x) { altDepth(x) / totalDepth(x) }) setMethod("refFraction", "VRanges", function(x) { refDepth(x) / totalDepth(x) }) setMethod("isDeletion", "VRanges", function(x, ...) .dispatchSNV_ExpandedVCF(.isDeletion, x) ) setMethod("isInsertion", "VRanges", function(x, ...) .dispatchSNV_ExpandedVCF(.isInsertion, x) ) setMethod("isIndel", "VRanges", function(x, ...) .dispatchSNV_ExpandedVCF(.isIndel, x) ) setMethod("isDelins", "VRanges", function(x, ...) .dispatchSNV_ExpandedVCF(.isDelins, x) ) setMethod("isSNV", "VRanges", function(x, ...) .dispatchSNV_ExpandedVCF(.isSNV, x) ) setMethod("isSubstitution", "VRanges", function(x, ...) .dispatchSNV_ExpandedVCF(.isSubstitution, x) ) setMethod("isTransition", "VRanges", function(x, ...) .dispatchSNV_ExpandedVCF(.isTransition, x) ) ### - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - ### Constructor ### VRanges <- function(seqnames = Rle(), ranges = IRanges(), ref = character(), alt = NA_character_, totalDepth = NA_integer_, refDepth = NA_integer_, altDepth = NA_integer_, ..., sampleNames = NA_character_, softFilterMatrix = FilterMatrix(matrix(nrow = length(gr), ncol = 0L), FilterRules()), hardFilters = FilterRules()) { gr <- GRanges(seqnames, ranges, strand = .rleRecycleVector("*", length(ranges)), ...) if (length(gr) == 0L && length(ref) == 0L) { maxLen <- 0L } else { maxLen <- max(length(gr), length(ref), length(alt), length(totalDepth), length(refDepth), length(altDepth), length(sampleNames), nrow(softFilterMatrix)) } if (length(gr) != maxLen) gr <- rep(gr, length.out = maxLen) ref <- as.character(ref) ref <- S4Vectors:::recycleVector(ref, maxLen) alt <- .rleRecycleVector(alt, maxLen) alt <- as(alt, "characterOrRle") refDepth <- .rleRecycleVector(refDepth, maxLen) altDepth <- .rleRecycleVector(altDepth, maxLen) totalDepth <- .rleRecycleVector(totalDepth, maxLen) softFilterMatrix <- as.matrix(softFilterMatrix) storage.mode(softFilterMatrix) <- "logical" softFilterMatrix.ind <- S4Vectors:::recycleVector(seq_len(nrow(softFilterMatrix)), maxLen) softFilterMatrix <- softFilterMatrix[softFilterMatrix.ind,,drop=FALSE] sampleNames <- .rleRecycleVector(sampleNames, maxLen) totalDepth <- as(totalDepth, "integerOrRle") refDepth <- as(refDepth, "integerOrRle") altDepth <- as(altDepth, "integerOrRle") if (any(naToZero(refDepth) + naToZero(altDepth) > totalDepth, na.rm = TRUE)) warning("'refDepth' + 'altDepth' exceeds 'totalDepth'; using GATK?") new("VRanges", gr, ref = ref, alt = alt, totalDepth = totalDepth, refDepth = refDepth, altDepth = altDepth, softFilterMatrix = softFilterMatrix, sampleNames = as(sampleNames, "factorOrRle"), hardFilters = hardFilters) } ### - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - ### Coercion ### setMethod("relistToClass", "VRanges", function(x) "CompressedVRangesList") parseFilterStrings <- function(x) { x[x == "."] <- NA if (all(x == "PASS", na.rm=TRUE)) return(FilterMatrix(matrix(nrow = length(x), ncol = 0L), FilterRules())) filterSplit <- strsplit(x, ";", fixed=TRUE) filters <- unlist(filterSplit) filterNames <- setdiff(unique(filters[!is.na(filters)]), "PASS") filterMat <- matrix(TRUE, length(x), length(filterNames), dimnames = list(NULL, filterNames)) filterMat[cbind(togroup(PartitioningByWidth(filterSplit)), match(filters, filterNames))] <- FALSE filterMat[is.na(x),] <- NA filterMat } genoToMCol <- function(x) { if (length(dim(x)) == 3) { I(matrix(x, nrow(x) * max(ncol(x), 1L), dim(x)[3])) } else { if (ncol(x) == 0L) { if (is.list(x)) { x <- as.logical(x) } x <- Rle(x[NA], nrow(x)) } else { dim(x) <- NULL if (is.list(x)) x <- as(x, "List") x } } } setAs("VCF", "VRanges", function(from) { from <- expand(from) rd <- rowRanges(from) seqnames <- seqnames(rd) ranges <- ranges(rd) ref <- rd$REF alt <- rd$ALT if (is(alt, "DNAStringSetList") || is(alt, "CharacterList")) alt <- unlist(alt) alt <- as.character(alt) alt[!nzchar(alt)] <- NA alt[alt == "."] <- NA ad <- geno(from)$AD dp4 <- info(from)$DP4 refDepth <- NA_integer_ altDepth <- NA_integer_ if (is.array(ad) && ncol(from) > 0L) { if (length(dim(ad)) == 3L) { refDepth <- ad[,,1,drop=FALSE] altDepth <- ad[,,2,drop=FALSE] } else if (length(dim(ad)) == 2L) { RD <- geno(from)$RD if (is.matrix(RD)) { refDepth <- RD } altDepth <- ad } } else { if (is(dp4, "List") && info(header(from))["DP4","Number"] == "4") { dp4 <- as.matrix(dp4) } if (is.matrix(dp4) && ncol(dp4) == 4L) { refDepth <- dp4[,1,drop=FALSE] + dp4[,2,drop=FALSE] altDepth <- dp4[,3,drop=FALSE] + dp4[,4,drop=FALSE] } } totalDepth <- geno(from)$DP if (is.null(totalDepth) || ncol(from) == 0L) totalDepth <- NA_integer_ nsamp <- ncol(from) if (ncol(from) > 0L) { if (!is.null(colnames(from))) { sampleNames <- Rle(colnames(from), rep(nrow(from), nsamp)) } else { sampleNames <- as.character(seq_len(ncol(from))) } } else sampleNames <- NA_character_ meta <- info(from) if (!is.null(mcols(rd)$QUAL)) { meta <- cbind(mcols(rd)["QUAL"], meta) } if (is.integer(meta$END)) { end(ranges)[!is.na(meta$END)] <- pmax(start(ranges)[!is.na(meta$END)], meta$END[!is.na(meta$END)]) meta$END <- NULL } rownames(meta) <- NULL if (!is.null(rd$FILTER)) filter <- parseFilterStrings(rd$FILTER) else filter <- FilterMatrix(matrix(nrow = nrow(from), ncol = 0L), FilterRules()) if (nsamp > 1L) { meta <- meta[rep(seq_len(nrow(meta)), nsamp),,drop=FALSE] filter <- filter[rep(seq_len(nrow(filter)), nsamp),,drop=FALSE] seqnames <- rep(seqnames, nsamp) ranges <- rep(ranges, nsamp) alt <- rep(alt, nsamp) ref <- rep(ref, nsamp) } else if (nsamp == 0L && !is.null(meta$DP)) { totalDepth <- meta$DP meta$DP <- NULL } if (!is.null(geno(from)[["FT"]])) filter <- cbind(filter, parseFilterStrings(as.vector(geno(from)[["FT"]]))) otherGeno <- geno(from)[setdiff(names(geno(from)), c("AD", "DP", "FT"))] if (length(otherGeno) > 0L) meta <- DataFrame(meta, lapply(otherGeno, genoToMCol)) vr <- VRanges(seqnames, ranges, ref, alt, totalDepth, refDepth, altDepth, hardFilters = FilterRules(), sampleNames = sampleNames, softFilterMatrix = filter, meta) seqinfo(vr) <- seqinfo(from) vr }) setAs("VRanges", "VCF", function(from) { asVCF(from) }) optionalDescriptions <- c( GT = "Genotype", GQ = "Genotype quality", PL = "Normalized, Phred-scaled likelihoods for genotypes", MIN_DP = "Minimum DP observed within the gVCF block", END = "End position of the gVCF WT run" ) makeFORMATheader <- function(x) { fixed <- DataFrame(row.names = c("AD", "DP", "FT"), Number = c(2L, 1L, 1L), Type = c("Integer", "Integer", "String"), Description = c("Allelic depths (number of reads in each observed allele)", "Total read depth", "Variant filters")) df <- rbind(fixed, makeINFOheader(x)) df$Type[df$Type == "Flag"] <- "Integer" # Flags not allowed in FORMAT df } makeINFOheader <- function(x) { df <- mcols(x) if (is.null(df)) df <- make_zero_col_DFrame(length(x)) rownames(df) <- names(x) numberForColumn <- function(xi) { if (length(dim(xi)) == 2) ncol(xi) else if (is.list(xi) || is(xi, "List")) "." else 1L } typeForColumn <- function(xi) { if (is.integer(xi)) "Integer" else if (is.numeric(xi)) "Float" else if (is.logical(xi)) "Flag" else if (is.character(xi) || is.factor(xi)) { if (all(nchar(as.character(xi)) == 1L, na.rm=TRUE)) "Character" else "String" } } df$Number <- as.character(lapply(x, numberForColumn)) df$Type <- as.character(lapply(x, typeForColumn)) if (is.null(df$Description)) df$Description <- optionalDescriptions[rownames(df)] df[c("Number", "Type", "Description")] } makeFILTERheader <- function(x) { mat <- softFilterMatrix(x) if (is(mat, "FilterMatrix")) rules <- filterRules(mat) else rules <- FilterRules() df <- mcols(rules) if (is.null(df)) { df <- make_zero_col_DFrame(length(rules)) } if (is.null(df$Description)) { exprs <- as.logical(lapply(rules, is.expression)) df <- df[exprs,] df$Description <- as.character(lapply(rules[exprs], function(expr) { gsub("\"", "\\\"", gsub("\\", "\\\\", deparse(expr[[1]]), fixed=TRUE), fixed=TRUE) })) } rownames(df) <- names(rules[exprs]) df } ## If a row contains a FALSE, all FALSE filters are listed; ## otherwise, if a row contains an NA or it is empty, the result is ## NA (.), otherwise PASS. makeFILTERstrings <- function(x) { failed <- which(!x) ftNames <- as.character(colnames(x)[col(x)][failed]) ftStrings <- rep(NA_character_, nrow(x)) passedRows <- rowSums(x) > 0L failedRows <- rowSums(!x, na.rm=TRUE) > 0L ftStrings[passedRows & !failedRows] <- "PASS" ftStrings[failedRows] <- unstrsplit(splitAsList(ftNames, row(x)[failed]), ";") ftStrings } isGVCFRun <- function(x) { nchar(ref(x)) == 1L & width(x) > 1L } gVCFRunEnds <- function(x) { ifelse(isGVCFRun(x), end(x), NA_integer_) } vranges2Vcf <- function(x, info = character(), filter = character(), meta = character()) { if (!is.character(info) || any(is.na(info)) || any(!info %in% colnames(mcols(x)))) stop("'info' must be a character vector naming the mcols to include", " in the INFO column of the VCF file; the rest become FORMAT fields.") if (!is.character(filter) || any(is.na(filter)) || any(!filter %in% colnames(softFilterMatrix(x)))) stop("'filter' must be a character vector naming the filters to include", " in the FILTER column of the VCF file; the rest are encoded as the FT", " FORMAT field.") if (!is.character(meta) || any(is.na(meta)) || any(!meta %in% names(metadata(x)))) stop("'meta' must be a character vector naming metadata(x) elements", " to include as metadata in the VCF header.") metaStrings <- as.character(sapply(metadata(x)[meta], as.character)) if (any(elementNROWS(metaStrings) != 1L)) stop("The elements named in 'meta' must be of length 1") if (any(is.na(sampleNames(x)))) { stop("sampleNames(x) must not contain missing values (for VCF export)") } sampleLevels <- levels(sampleNames(x)) if (length(sampleLevels) > 1) { xUniq <- unique(x) } else { xUniq <- x } END <- gVCFRunEnds(xUniq) if (!all(is.na(END))) { if (!is.null(xUniq$END)) { warning("Replacing 'END' metadata/info column with computed gVCF END") } xUniq$END <- END info <- union(info, "END") } rowRanges <- xUniq mcols(rowRanges) <- NULL rowRanges <- as(rowRanges, "GRanges", strict=TRUE) colData <- DataFrame(Samples = seq_along(sampleLevels), row.names = sampleLevels) meta_vector <- c(fileformat = "VCFv4.1", source = paste("VariantAnnotation", packageVersion("VariantAnnotation")), phasing = "unphased", metaStrings) nms <- names(meta_vector) meta_df <- DataFrame(Value = meta_vector, row.names = nms) meta_header <- as(splitAsList(meta_df, nms), "SimpleDataFrameList") genoMCols <- setdiff(names(mcols(x)), info) header <- VCFHeader(reference = seqlevels(x), samples = sampleLevels, header = c(meta_header, DataFrameList( FORMAT = makeFORMATheader(mcols(x)[genoMCols]), INFO = makeINFOheader(mcols(xUniq)[info]), FILTER = makeFILTERheader(x)) )) metadata <- list(header = header) alt <- as.character(alt(xUniq)) qual <- xUniq$QUAL if (is.null(qual) || !is.numeric(qual)) qual <- rep.int(NA_real_, length(xUniq)) filtMat <- softFilterMatrix(xUniq) if (ncol(filtMat) > 0L) filtMat <- filtMat[,filter,drop=FALSE] filterStrings <- makeFILTERstrings(filtMat) fixed <- DataFrame(REF = DNAStringSet(ref(xUniq)), ALT = alt, QUAL = qual, FILTER = filterStrings) if (length(sampleLevels) > 1) { samplesToUniq <- tapply(x, sampleNames(x), function(xi) { match(xi, xUniq) }, simplify=FALSE) } genoArray <- function(v) { v <- as.vector(v) # handles e.g. Rle if (is.logical(v)) { v <- as.integer(v) } width <- length(v)/length(x) if (width > 1L) { v <- matrix(v, nrow=length(x), ncol=width) } if (length(sampleLevels) > 1L) { sample.ord <- order(sampleNames(x)) if (is.matrix(v)) { v <- v[sample.ord,,drop=FALSE] } else { v <- v[sample.ord] } ind <- unlist(samplesToUniq, use.names=FALSE) + (togroup(PartitioningByWidth(samplesToUniq))-1L)*length(xUniq) a <- matrix(NA_integer_, nrow=length(xUniq)*length(sampleLevels), ncol=width) a[ind,] <- v } else { a <- v } if (width > 1L) { array(a, c(length(xUniq), length(sampleLevels), ncol(a))) } else { matrix(a, length(xUniq), length(sampleLevels)) } } alleleDepth <- c(refDepth(x), altDepth(x)) filtMat <- softFilterMatrix(x) if (ncol(filtMat) > 0L) filtMat <- filtMat[,setdiff(colnames(filtMat), filter), drop=FALSE] ftStrings <- makeFILTERstrings(filtMat) geno <- SimpleList(AD = genoArray(alleleDepth), DP = genoArray(totalDepth(x)), FT = genoArray(ftStrings)) if (length(genoMCols) > 0L) geno <- c(geno, SimpleList(lapply(mcols(x)[genoMCols], genoArray))) info <- mcols(xUniq)[info] VCF(rowRanges = rowRanges, colData = colData, metadata = metadata, fixed = fixed, geno = geno, info = info, collapsed = FALSE) } setMethod("asVCF", "VRanges", vranges2Vcf) ### - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - ### Reading from VCF ### VRangesScanVcfParam <- function(fixed="ALT", info=NA, geno="AD", ...) { .Defunct("ScanVcfParam") } readVcfAsVRanges <- function(x, genome, param=ScanVcfParam(), use.names=FALSE, ...) { if (!isTRUEorFALSE(use.names)) { stop("'use.names' must be TRUE or FALSE") } as(readVcf(x, genome, param=param, row.names=use.names, ...), "VRanges") } ### - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - ### Writing to VCF (see methods-writeVcf.R) ### ### - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - ### gVCF support ### addTotalDepthRuns <- function(x, runs, genome) { mins <- viewMins(runs) gr <- as(ranges(runs), "GRanges") ref <- getSeq(genome, resize(gr, 1L)) vr <- VRanges(seqnames(gr), ranges, ref, alt, totalDepth=mins) } ### - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - ### Filtering ### softFilter <- function(x, filters, ...) { softFilterMatrix(x) <- cbind(softFilterMatrix(x), FilterMatrix(matrix = evalSeparately(filters, x, ...), filterRules = filters)) x } resetFilter <- function(x) { softFilterMatrix(x) <- FilterMatrix(matrix(nrow = length(x), ncol = 0L), filterRules = FilterRules()) x } setMethod("subsetByFilter", c("VRanges", "FilterRules"), function(x, filter) { ans <- callNextMethod(x, filter) hardFilters(ans) <- c(hardFilters(ans), filter) ans }) ### - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - ### Utilities ### setMethod("duplicated", "VRanges", function (x, incomparables = FALSE, fromLast = FALSE, method = c("auto", "quick", "hash")) { if (!identical(incomparables, FALSE)) stop("\"duplicated\" method for VRanges objects ", "only accepts 'incomparables=FALSE'") duplicatedIntegerQuads(as.factor(seqnames(x)), as.factor(alt(x)), start(x), width(x), fromLast = fromLast, method = method) }) setMethod("match", c("VRanges", "VRanges"), function(x, table, nomatch = NA_integer_, incomparables = NULL, method = c("auto", "quick", "hash")) { if (!isSingleNumberOrNA(nomatch)) stop("'nomatch' must be a single number or NA") if (!is.integer(nomatch)) nomatch <- as.integer(nomatch) if (!is.null(incomparables)) stop("\"match\" method for VRanges objects ", "only accepts 'incomparables=NULL'") merge(seqinfo(x), seqinfo(table)) altLevels <- as.character(union(alt(x), alt(table))) x_seqnames <- GenomicRanges:::relevelSeqnamesForMatch(x, table) matchIntegerQuads(x_seqnames, factor(as.character(alt(x)), altLevels), start(x), width(x), as.factor(seqnames(table)), factor(as.character(alt(table)), altLevels), start(table), width(table), nomatch = nomatch, method = method) }) setMethod("%in%", c("VRanges", "TabixFile"), function(x, table) { table <- readVcfAsVRanges(table, genome=genome(x), param=x) x %in% table }) setMethod("tabulate", "VRanges", function(bin, nbins) { if (!missing(nbins)) stop("'nbins' argument not relevant") m <- match(bin, bin) ## for dupes, 'm' always points to the subject with the lowest index tab <- tabulate(m, length(bin)) ans <- bin[tab > 0] ans$sample.count <- tab[tab > 0] ans }) setMethod("merge", c("VRanges", "VRanges"), function(x, y, ...) { ### FIXME: support softFilterMatrix xdf <- as.data.frame(x) ydf <- as.data.frame(y) bothAllNA <- function(nm) all(is.na(slot(x, nm))) && all(is.na(slot(y, nm))) ignore.cols <- Filter(bothAllNA, c("refDepth", "altDepth", "totalDepth")) ignore.cols <- c(ignore.cols, c("width", "strand")) xdf <- xdf[setdiff(colnames(xdf), ignore.cols)] ydf <- ydf[setdiff(colnames(ydf), ignore.cols)] by <- c("seqnames", "start", "end", "ref", "alt", "sampleNames") merged <- merge(xdf, ydf, by = by, ...) with(merged, VRanges(seqnames, IRanges(start, end), ref, alt, NA, NA, NA, sampleNames = sampleNames, merged[setdiff(colnames(merged), by)])) }) setMethod("liftOver", c("VRanges", "Chain"), function(x, chain, ...) { grl <- liftOver(GRanges(seqnames(x), ranges(x)), chain, ...) gr <- unlist(grl, use.names=FALSE) ans <- x[togroup(PartitioningByWidth(grl))] ans <- update(ans, seqinfo = seqinfo(gr), seqnames = seqnames(gr), ranges = ranges(gr)) relist(ans, grl) }) pileupGRanges <- function(x) { x <- x[!isIndel(x)] gr <- GRanges(seqnames(x), ranges(x), strand(x)) sm <- selfmatch(gr) uniq <- sm == seq_len(length(gr)) map <- integer() map[sm[uniq]] <- seq_len(sum(uniq)) pos <- map[sm] base <- match(c(alt(x), ref(x)), DNA_BASES) depth <- c(altDepth(x), refDepth(x)) samp <- as.factor(sampleNames(x)) pileup <- array(0L, c(sum(uniq), length(levels(samp)), length(DNA_BASES)), dimnames=list(NULL, levels(samp), base=DNA_BASES)) pileup[cbind(pos, samp, base)[!is.na(base),]] <- depth[!is.na(base)] ugr <- gr[uniq] ugr$ref[pos] <- ref(x) ugr$pileup <- pileup ugr } ### - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - ### Show ### .showHardFilters <- function(object) { cat(S4Vectors:::labeledLine(" hardFilters", names(hardFilters(object)))) } setMethod("show", "VRanges", function(object) { callNextMethod() .showHardFilters(object) }) setMethod(GenomicRanges:::extraColumnSlotNames, "VRanges", function(x) { c("ref", "alt", "totalDepth", "refDepth", "altDepth", "sampleNames", "softFilterMatrix") }) VariantAnnotation/R/methods-VRangesList-class.R0000644000175100017510000000127714614305321022530 0ustar00biocbuildbiocbuild### ========================================================================= ### VRangesList: Where there is an IntegerRanges, there must be a List ### ------------------------------------------------------------------------- ### VRangesList <- function(...) { new("SimpleVRangesList", GRangesList(..., compress=FALSE), elementType="VRanges") } setMethod("stackSamples", "VRangesList", function(x) { stacked <- unlist(x, use.names=FALSE) if (!is.null(names(x))) sampleNames(stacked) <- Rle(names(x), elementNROWS(x)) stacked }) setMethod("ref", "VRangesList", function(x) List(lapply(x, ref))) setMethod("alt", "VRangesList", function(x) List(lapply(x, alt))) VariantAnnotation/R/methods-writeVcf.R0000644000175100017510000002132514614305321021011 0ustar00biocbuildbiocbuild### ========================================================================= ### writeVcf methods ### ========================================================================= .chunkIndex <- function(rows, nchunk, ...) { if (missing("nchunk")) { if (rows > 1e8) n <- ceiling(rows / 3) else if (rows > 1e6) n <- ceiling(rows / 2) else if (rows > 1e5) n <- 1e5 else return(NA_integer_) } else { if (is.na(nchunk)) return(NA_integer_) else n <- nchunk } split(seq_len(rows), ceiling(seq_len(rows)/n)) } .makeVcfMatrix <- function(filename, obj) { ## empty if (length(rd <- rowRanges(obj)) == 0) return(character()) CHROM <- as.vector(seqnames(rd)) POS <- start(rd) if (is.null(ID <- names(rd))) ID <- "." REF <- as.character(ref(obj)) if (is.null(ALT <- alt(obj))) ALT <- rep(".", length(REF)) if (is(ALT, "XStringSetList")) { ALT <- as(ALT, "CharacterList") } ALT <- as.character(unstrsplit(ALT, ",")) ALT[nchar(ALT) == 0L | is.na(ALT)] <- "." if (is.null(QUAL <- qual(obj))) QUAL <- "." else QUAL[is.na(QUAL)] <- "." if (is.null(FILTER <- filt(obj))) FILTER <- "." else FILTER[is.na(FILTER)] <- "." INFO <- .makeVcfInfo(info(obj), length(rd)) FIXED <- paste(CHROM, POS, ID, REF, ALT, QUAL, FILTER, INFO, sep="\t") .makeVcfGeno(filename, FIXED, geno(obj, withDimnames=FALSE), dim(obj)) } .makeVcfGeno <- function(filename, fixed, geno, dvcf, ...) { if ("GT" %in% names(geno)) { geno <- geno[c("GT", setdiff(names(geno), "GT"))] } .Call(.make_vcf_geno, filename, fixed, names(geno), as.list(geno), c(":", ","), dvcf, sapply(geno, function(x) dim(x)[3])) } .makeVcfInfo <- function(info, nrecords, ...) { if (ncol(info) == 0) { return(rep.int(".", nrecords)) } ## Replace NA with '.' in columns with data. ## Columns with no data are set to NA. lists <- sapply(info, function(elt) is.list(elt) || is(elt, "List")) info[lists] <- lapply(info[lists], function(l) { charList <- as(l, "CharacterList") charList@unlistData[is.na(charList@unlistData)] <- "." collapsed <- unstrsplit(charList, ",") ifelse(sum(!is.na(l)) > 0L, collapsed, NA_character_) }) ## Add names to non-NA data. infoMat <- matrix(".", nrow(info), ncol(info)) logicals <- sapply(info, is.logical) infoMat[,logicals] <- unlist(Map(function(l, nm) { ifelse(l, nm, NA_character_) }, info[logicals], as(names(info)[logicals], "List"))) infoMat[,!logicals] <- unlist(Map(function(i, nm) { ifelse(!is.na(i), paste0(nm, "=", i), NA_character_) }, info[!logicals], as(names(info)[!logicals], "List"))) infoVector <- .pasteCollapseRows(infoMat, ";") infoVector[!nzchar(infoVector)] <- "." infoVector } .contigsFromSeqinfo <- function(si) { contig <- paste0("##contig=") } .pasteMultiFieldDF <- function(df, nms) { if (nrow(df) == 0L) return(character(0L)) prs <- paste(rep(colnames(df), each=nrow(df)), "=", unlist(lapply(df, as.character), use.names=FALSE), sep="") lst <- split(prs, row(df)) lns <- unstrsplit(lst, ",") paste("##", nms, "=<", lns, ">", sep="") } .makeVcfHeader <- function(obj, ...) { hdr <- header(obj) ## If fileformat is >=v4.2 or does not exist --> set GENO 'AD' field ## to Number 'G'. The Number field indicates the number of values ## contained in the INFO field. 'G' is a special character and means ## the field has one value for each possible genotype. fileformat <- "fileformat" %in% rownames(meta(hdr)$META) if (!fileformat) fileformat <- "fileformat" %in% names(meta(hdr)) if (fileformat && grepl(fileformat, "v4.2", fixed=TRUE) || !fileformat) { if (any(idx <- rownames(geno(hdr)) == "AD")) geno(hdr)[idx,]$Number <- "G" } ## Format all header lines dflist <- header(hdr) header <- Map(.formatHeader, as.list(dflist), as.list(names(dflist))) ## If fileformat, fileDate or contig do not exist --> add them fileDate <- any(grepl("fileDate", names(header), fixed=TRUE)) if (!fileDate) { fileDate <- paste("##fileDate=", format(Sys.time(), "%Y%m%d"), sep="") header <- c(fileDate, header) } idx <- which(names(header) == "fileformat") if (length(idx) && idx != 1) { fileformat <- header[idx] header[idx] <- NULL header <- c(fileformat, header) } contig <- any(grepl("contig", names(header), fixed=TRUE)) if (!contig) header <- c(header, .contigsFromSeqinfo(seqinfo(obj))) ## Last line before data colnms <- c("#CHROM", "POS", "ID", "REF", "ALT", "QUAL", "FILTER", "INFO") if (length(geno(obj, withDimnames=FALSE)) > 0L) { samples <- colnames(obj) colnms <- c(colnms, "FORMAT", samples[!is.null(samples)]) } colnms <- paste(colnms, collapse="\t") unlist(c(header, colnms), use.names=FALSE) } .formatHeader <- function(df, nms) { ## Support serialized VCF objects with old "META" DataFrame. if (nms == "META" && ncol(df) == 1L) { if (!"fileformat" %in% rownames(df)) df <- rbind(DataFrame(Value="VCFv4.3", row.names="fileformat"), df) fd <- format(Sys.time(), "%Y%m%d") if ("fileDate" %in% rownames(df)) df[rownames(df) == "fileDate", ] <- fd else df <- rbind(df, DataFrame(Value=fd, row.names="fileDate")) paste("##", rownames(df), "=", df[,1], sep="") ## Support VCF v4.2 and v4.3 PEDIGREE field } else if(nms == "PEDIGREE" || nms == "ALT") { if (!is.null(rownames(df))) df <- DataFrame(ID = rownames(df), df) if ("Description" %in% colnames(df)) { # VJC respond LTLA 20 Nov 2021 if (nrow(df) == 0L) return(character()) df$Description <- ifelse(is.na(df$Description), "\".\"", paste("\"", df$Description, "\"", sep="")) } # end response .pasteMultiFieldDF(df, nms) ## 'simple' key-value pairs ## (Rsamtools reports unstructured headers as one column named "Value") } else if(ncol(df) == 1L && names(df)[1] == "Value" && nrow(df) == 1L) { if (nms == "fileDate") { fd <- format(Sys.time(), "%Y%m%d") paste("##fileDate=", fd, sep="") } else paste("##", nms, "=", df[,1], sep="") ## 'non-simple' key-value pairs } else { if ("Description" %in% colnames(df)) { if (nrow(df) == 0L) return(character()) df$Description <- ifelse(is.na(df$Description), "\".\"", paste("\"", df$Description, "\"", sep="")) } df <- DataFrame(ID = rownames(df), df) .pasteMultiFieldDF(df, nms) } } ### - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - ### VCF methods ### setMethod(writeVcf, c("VCF", "character"), function(obj, filename, index = FALSE, ...) { con <- file(filename, open="w") on.exit(close(con)) writeVcf(obj, con, index=index, ...) }) setMethod(writeVcf, c("VCF", "connection"), function(obj, filename, index = FALSE, ...) { if (!isTRUEorFALSE(index)) stop("'index' must be TRUE or FALSE") if (!isOpen(filename)) { open(filename) on.exit(close(filename)) } scon <- summary(filename) headerNeeded <- !(file.exists(scon$description) && file.info(scon$description)$size !=0) if (headerNeeded) { hdr <- .makeVcfHeader(obj) writeLines(hdr, filename) } if (index) obj <- sort(obj) if (all(is.na(idx <- .chunkIndex(dim(obj)[1L], ...)))) .makeVcfMatrix(filename, obj) else for (i in idx) .makeVcfMatrix(filename, obj[i]) flush(filename) if (index) { filenameGZ <- bgzip(scon$description, overwrite = TRUE) indexTabix(filenameGZ, format = "vcf") unlink(scon$description) invisible(filenameGZ) } else { invisible(scon$description) } }) ### - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - ### VRanges methods ### setMethod(writeVcf, "VRanges", function(obj, filename, ...) { writeVcf(as(obj, "VCF"), filename, ...) }) VariantAnnotation/R/test_VariantAnnotation_package.R0000644000175100017510000000010414614305321023720 0ustar00biocbuildbiocbuild.test <- function() BiocGenerics:::testPackage("VariantAnnotation") VariantAnnotation/R/use_vep_api.R0000644000175100017510000000555114614305321020061 0ustar00biocbuildbiocbuild#' helper function to construct inputs for VEP REST API #' @import httr #' @import jsonlite #' @param chr character(1) #' @param pos numeric(1) #' @param id character(1) #' @param ref character(1) #' @param alt character(1) #' @note Produces a string used as an example in VEP API documentation. variant_body = function(chr, pos, id, ref, alt) { sprintf("%s %d %s %s %s . . .", chr, pos, id, ref, alt) } #' elementary vep/homo_sapiens/region call to ensembl VEP REST API #' @importFrom curl has_internet #' @param chr character(1) ensembl chromosome identifier (e.g., "7") #' @param pos numeric(1) 1-based chromosome position #' @param id character(1) arbitrary identifier #' @param ref character(1) reference allele #' @param alt character(1) alternative allele #' @note This function prepares a POST to rest.ensembl.org/vep/homo_sapiens/region endpoint. #' @return Instance of 'response' defined in httr package. #' @examples #' chk = post_Hs_region("7", 155800001, "chk", "A", "T") #' chk #' res = jsonlite::fromJSON(jsonlite::toJSON(httr::content(chk))) #' dim(chk) #' @export post_Hs_region = function(chr, pos, id, ref, alt) { stopifnot(curl::has_internet()) server <- "https://rest.ensembl.org" ext <- "/vep/homo_sapiens/region" tj = jsonlite::toJSON(list(variants=variant_body(chr, pos, id, ref, alt))) ans = httr::POST(paste(server, ext, sep = ""), httr::content_type("application/json"), httr::accept("application/json"), body = as.character(tj) ) httr::stop_for_status(ans) ans } #' Use the VEP region API on variant information in a VCF object as defined in VariantAnnotation. #' @param vcfobj instance of VCF class; note the difference between the CollapsedVCF and #' ExpandedVCF instances. #' @param snv_only logical(1) if TRUE filter the VCF to information about single nucleotide addresses #' @param chk_max logical(1) requests to ensembl VEP API are limited to 200 positions; if #' TRUE and the request involves more than 200 positions, an error is thrown by this function. #' @return instance of 'response' from httr package #' @examples #' fl <- system.file("extdata", "chr22.vcf.gz", package="VariantAnnotation") #' r22 = readVcf(fl) #' dr = which(width(rowRanges(r22))!=1) #' r22s = r22[-dr] #' res = vep_by_region(r22[1:100], snv_only=FALSE, chk_max=FALSE) #' ans = jsonlite::fromJSON(jsonlite::toJSON(httr::content(res))) #' @export vep_by_region = function(vcfobj, snv_only=TRUE, chk_max=TRUE) { stopifnot(inherits(vcfobj, "VCF")) if (snv_only) { dr = which(width(rowRanges(vcfobj))!=1) if (length(dr)>0) vcfobj = vcfobj[-dr] } if (chk_max) { if (nrow(vcfobj) > 200) stop("VEP API is limited to 200 positions") } rr = rowRanges(vcfobj) post_Hs_region( chr = as.character(seqnames(rr)), pos = start(rr), id = names(rr), ref = as.character(rr$REF), alt = as.character(unlist(rr$ALT)) ) } VariantAnnotation/src/0000755000175100017510000000000014614361054016024 5ustar00biocbuildbiocbuildVariantAnnotation/src/Biostrings_stubs.c0000644000175100017510000000003714614305321021526 0ustar00biocbuildbiocbuild#include "_Biostrings_stubs.c" VariantAnnotation/src/dna_hash.c0000644000175100017510000000626214614305321017736 0ustar00biocbuildbiocbuild#include "dna_hash.h" /* DNAStringSet -- hash */ #include "IRanges_interface.h" #include "XVector_interface.h" #include "Biostrings_interface.h" #include KHASH_MAP_INIT_STR(ref, int) struct dna_hash_t { khash_t(ref) *hash; int len, size, hash_idx, *offset; }; static const double DNA_GROW = 1.6; struct dna_hash_t *dna_hash_new(const int size) { struct dna_hash_t *dna = Calloc(1, struct dna_hash_t); dna->hash = kh_init(ref); dna->offset = Calloc(size, int); dna->size = size; dna->len = dna->hash_idx = 0; return dna; } void dna_hash_free(struct dna_hash_t *dna) { khiter_t key; for (key = kh_begin(dna->hash); key != kh_end(dna->hash); ++key) { if (kh_exist(dna->hash, key)) Free(kh_key(dna->hash, key)); } kh_destroy(ref, dna->hash); Free(dna->offset); Free(dna); } void dna_hash_grow(struct dna_hash_t *dna, int size) { dna->offset = Realloc(dna->offset, size, int); dna->size = size; } void dna_hash_append(struct dna_hash_t *dna, const char *value) { khiter_t key; key = kh_get(ref, dna->hash, value); if (key == kh_end(dna->hash)) { int ret; char *buf = Calloc(strlen(value) + 1, char); strcpy(buf, value); key = kh_put(ref, dna->hash, buf, &ret); kh_value(dna->hash, key) = dna->hash_idx++; } if (dna->len == dna->size) dna_hash_grow(dna, DNA_GROW * dna->size); dna->offset[dna->len] = kh_value(dna->hash, key); dna->len++; } SEXP dna_hash_as_DNAStringSet(struct dna_hash_t *dna) { int *iwidth, *istart, twidth, i; SEXP tag, start, width, ranges, xstringset; Rbyte *tagp; khiter_t key; istart = Calloc(dna->hash_idx, int); iwidth = Calloc(dna->hash_idx, int); twidth = 0; for (key = kh_begin(dna->hash); key != kh_end(dna->hash); ++key) { if (!kh_exist(dna->hash, key)) continue; kh_cstr_t cstr = kh_key(dna->hash, key); int idx = kh_value(dna->hash, key); istart[idx] = twidth + 1; iwidth[idx] = '.' == *cstr ? 0 : strlen(cstr); twidth += iwidth[idx]; } /* RAW */ PROTECT(tag = NEW_RAW(twidth)); tagp = RAW(tag); for (key = kh_begin(dna->hash); key != kh_end(dna->hash); ++key) { if (!kh_exist(dna->hash, key)) continue; kh_cstr_t cstr = kh_key(dna->hash, key); int idx = kh_value(dna->hash, key); if ('.' == *cstr) continue; for (int j = 0; j < iwidth[idx]; ++j) { if ('I' == cstr[j]) *tagp++ = DNAencode('.'); else *tagp++ = DNAencode(cstr[j]); } } /* ranges */ PROTECT(start = NEW_INTEGER(dna->len)); PROTECT(width = NEW_INTEGER(dna->len)); for (i = 0; i < dna->len; ++i) { int idx = dna->offset[i]; INTEGER(start)[i] = istart[idx]; INTEGER(width)[i] = iwidth[idx]; } PROTECT(ranges = new_IRanges("IRanges", start, width, R_NilValue)); /* DNAStringSet */ PROTECT(xstringset = new_XRawList_from_tag( "DNAStringSet", "DNAString", tag, ranges)); Free(iwidth); Free(istart); UNPROTECT(5); return xstringset; } VariantAnnotation/src/dna_hash.h0000644000175100017510000000052314614305321017735 0ustar00biocbuildbiocbuild#ifndef _DNA_HASH_H #define _DNA_HASH_H #include struct dna_hash_t *dna_hash_new(const int size); void dna_hash_free(struct dna_hash_t *dna); void dna_hash_grow(struct dna_hash_t *dna, int size); void dna_hash_append(struct dna_hash_t *dna, const char *value); SEXP dna_hash_as_DNAStringSet(struct dna_hash_t *dna); #endif VariantAnnotation/src/IRanges_stubs.c0000644000175100017510000000003414614305321020730 0ustar00biocbuildbiocbuild#include "_IRanges_stubs.c" VariantAnnotation/src/Makevars0000644000175100017510000000066314614305321017520 0ustar00biocbuildbiocbuild## This file uses GNU make syntax $(shell ...) so we need to ## have "SystemRequirements: GNU make" in the DESCRIPTION file. ## See Rhtslib's vignette for details. RHTSLIB_LIBS=$(shell "${R_HOME}/bin${R_ARCH_BIN}/Rscript" -e \ 'Rhtslib::pkgconfig("PKG_LIBS")') RHTSLIB_CPPFLAGS=$(shell "${R_HOME}/bin${R_ARCH_BIN}/Rscript" -e \ 'Rhtslib::pkgconfig("PKG_CPPFLAGS")') PKG_LIBS=$(RHTSLIB_LIBS) PKG_CPPFLAGS=$(RHTSLIB_CPPFLAGS) VariantAnnotation/src/R_init_VariantAnnotation.c0000644000175100017510000000114014614305321023122 0ustar00biocbuildbiocbuild#include #include "vcffile.h" #include "writevcf.h" #include "utilities.h" static const R_CallMethodDef callMethods[] = { /* vcffile.c */ {".scan_vcf_character", (DL_FUNC) & scan_vcf_character, 7}, {".scan_vcf_connection", (DL_FUNC) & scan_vcf_connection, 6}, {".tabix_as_vcf", (DL_FUNC) & tabix_as_vcf, 6}, {"matrix_pasteCollapseRows", (DL_FUNC) & matrix_pasteCollapseRows, 2}, {".make_vcf_geno", (DL_FUNC) & make_vcf_geno, 7}, {NULL, NULL, 0} }; void R_init_VariantAnnotation(DllInfo * info) { R_registerRoutines(info, NULL, callMethods, NULL, NULL); } VariantAnnotation/src/rle.c0000644000175100017510000000311514614305321016745 0ustar00biocbuildbiocbuild#include "rle.h" #include "utilities.h" /* rle representation */ static const double RLE_GROW = 1.6; struct rle_t *rle_new(const int size) { struct rle_t *rle = Calloc(1, struct rle_t); rle->size = size; rle->len = 0; rle->value = Calloc(size, char *); rle->length = Calloc(size, int); return rle; } void rle_free(struct rle_t *rle) { for (int i = 0; i < rle->len; ++i) Free(rle->value[i]); Free(rle->value); Free(rle->length); Free(rle); } void rle_grow(struct rle_t *rle, int size) { rle->value = Realloc(rle->value, size, char *); rle->length = Realloc(rle->length, size, int); rle->size = size; } void rle_append(struct rle_t *rle, const char *value) { if (0 == rle->len || 0 != strcmp(value, rle->value[rle->len - 1])) { if (rle->len == rle->size) rle_grow(rle, RLE_GROW * rle->size); rle->value[rle->len] = Strdup(value); rle->length[rle->len] = 1; rle->len += 1; } else rle->length[rle->len - 1] += 1; } SEXP rle_as_Rle(struct rle_t *rle) { SEXP value, length, nmspc, fun, expr, result; PROTECT(value = Rf_allocVector(STRSXP, rle->len)); PROTECT(length = Rf_allocVector(INTSXP, rle->len)); for (int i = 0; i < rle->len; ++i) { SET_STRING_ELT(value, i, mkChar(rle->value[i])); INTEGER(length)[i] = rle->length[i]; } PROTECT(nmspc = get_namespace("IRanges")); PROTECT(fun = findFun(install("Rle"), nmspc)); PROTECT(expr = lang3(fun, value, length)); result = eval(expr, R_GlobalEnv); UNPROTECT(5); return result; } VariantAnnotation/src/rle.h0000644000175100017510000000051714614305321016755 0ustar00biocbuildbiocbuild#ifndef _RLE_H #define _RLE_H #include struct rle_t { int len, size, *length; char **value; }; struct rle_t *rle_new(const int size); void rle_free(struct rle_t *rle); void rle_grow(struct rle_t *rle, int size); void rle_append(struct rle_t *rle, const char *value); SEXP rle_as_Rle(struct rle_t *rle); #endif VariantAnnotation/src/strhash.c0000644000175100017510000000114114614305321017634 0ustar00biocbuildbiocbuild#include #include "strhash.h" #include "utilities.h" khash_t(strhash) *_strhash_new() { return kh_init(strhash); } void _strhash_free(khash_t(strhash) *str) { khiter_t key; for (key = kh_begin(str); key != kh_end(str); ++key) { if (kh_exist(str, key)) Free(kh_key(str, key)); } kh_destroy(strhash, str); } const char *_strhash_put(khash_t(strhash) *str, const char *value) { int ret; khiter_t key = kh_get(strhash, str, value); if (key == kh_end(str)) key = kh_put(strhash, str, Strdup(value), &ret); return kh_key(str, key); } VariantAnnotation/src/strhash.h0000644000175100017510000000037214614305321017646 0ustar00biocbuildbiocbuild#ifndef _STRHASH_H_ #define _STRHASH_H_ #include KHASH_SET_INIT_STR(strhash) khash_t(strhash) *_strhash_new(); void _strhash_free(khash_t(strhash) *str); const char *_strhash_put(khash_t(strhash) *str, const char *value); #endif VariantAnnotation/src/utilities.c0000644000175100017510000000164214614305321020201 0ustar00biocbuildbiocbuild#include "utilities.h" /* get_namespace */ SEXP get_namespace(const char *pkg) { SEXP fun = PROTECT(findFun(install("getNamespace"), R_GlobalEnv)); SEXP nmspc = PROTECT(mkString(pkg)); nmspc = eval(lang2(fun, nmspc), R_GlobalEnv); UNPROTECT(2); return nmspc; } /* iterator to return null-terminated delimited fields */ char *it_init(struct it_t *it, char *str, char delim) { it->str = str; it->delim = delim; it->n_fld = (*str == '\0') ? 0 : 1; while (*str != '\0') if (*str++ == delim) it->n_fld += 1; return it_next(it); } inline char *it_next(struct it_t *it) { const char delim = it->delim, *curr = it->str; char *start = it->str; while ('\0' != *curr && delim != *curr) ++curr; it->str += curr - it->str; if ('\0' != *it->str) *it->str++ = '\0'; return start; } inline int it_nfld(const struct it_t *it) { return it->n_fld; } VariantAnnotation/src/utilities.h0000644000175100017510000000054514614305321020207 0ustar00biocbuildbiocbuild#ifndef _UTILITIES_H #define _UTILITIES_H #include #define Strdup(x) strcpy(Calloc(strlen(x) + 1, char), x) SEXP get_namespace(const char *pkg); struct it_t { char *str; char delim; int n_fld; }; char *it_init(struct it_t *it, char *str, char delim); char *it_next(struct it_t *it); int it_nfld(const struct it_t *it); #endif VariantAnnotation/src/vcffile.c0000644000175100017510000004647714614305321017623 0ustar00biocbuildbiocbuild#include #include #include "vcffile.h" #include "vcftype.h" #include "rle.h" #include "dna_hash.h" #include "utilities.h" #include "IRanges_interface.h" #include "XVector_interface.h" #include #include "strhash.h" enum { ROWRANGES_IDX = 0, REF_IDX, ALT_IDX, QUAL_IDX, FILTER_IDX, INFO_IDX, GENO_IDX }; enum { POS_IDX = 0, ID_IDX }; static const int N_FLDS = 7; static const int TBX_INIT_SIZE = 512; KHASH_SET_INIT_STR(WARNINGS) static khash_t(WARNINGS) *vcfwarn_new() { return kh_init(WARNINGS); } static void vcfwarn(khash_t(WARNINGS) *warnings, const char *fmt, ...) { static const int bufsize = 2048; char *buf = Calloc(strlen(fmt), char); int ret; memcpy(buf, fmt, strlen(fmt) + 1); if (kh_get(WARNINGS, warnings, buf) != kh_end(warnings)) { Free(buf); return; } kh_put(WARNINGS, warnings, buf, &ret); buf = Calloc(bufsize, char); va_list argp; va_start(argp, fmt); (void) vsnprintf(buf, bufsize, fmt, argp); va_end(argp); Rf_warning("%s", buf); Free(buf); return; } static void vcfwarn_free(khash_t(WARNINGS) *warnings) { khiter_t key; for (key = kh_begin(warnings); key != kh_end(warnings); ++key) if (kh_exist(warnings, key)) Free(kh_key(warnings, key)); kh_destroy(WARNINGS, warnings); } struct parse_t { struct vcftype_t *vcf; struct rle_t *chrom; struct dna_hash_t *ref; khash_t(strhash) *str; /* general purpose hash of strings */ int vcf_n, imap_n, gmap_n, smap_n, *smapidx; const char **inms, **gnms, **snms; khash_t(WARNINGS) *warnings; }; static struct vcftype_t *_types_alloc(const int x_n, const int y_n, Rboolean isInfo, SEXP map, khash_t(strhash) *str) { SEXP elt; const char *n; SEXPTYPE type; struct vcftype_t *types; const int map_n = Rf_length(map); const char *dot = _strhash_put(str, "."); if (map_n == 0) /* no INFO or GENO in header */ return _vcftype_new(VECSXP, NILSXP, '\0', NULL, 0, 0, 0, 0); types = _vcftype_new(VECSXP, NILSXP, '\0', NULL, map_n, 1, 1, 0); for (int j = 0; j < map_n; ++j) { elt = VECTOR_ELT(map, j); n = CHAR(STRING_ELT(VECTOR_ELT(elt, 0), 0)); type = TYPEOF(VECTOR_ELT(elt, 1)); if (type == NILSXP) { /* skip */ types->u.list[j] = _vcftype_new(NILSXP, NILSXP, *n, NULL, 0, 0, 0, 0); } else if (*n == '.' || *n == 'A' || *n == 'G' || *n == 'R') { types->u.list[j] = _vcftype_new(VECSXP, type, *n, dot, x_n, y_n, 1, 2); } else { /* array */ int z_n = atoi(n); int dim = (z_n == 1) ? (isInfo ? 1 : 2) : 3; types->u.list[j] = _vcftype_new(type, NILSXP, *n, dot, x_n, y_n, z_n, dim); } } return types; } static void _types_grow(struct vcftype_t *types, int vcf_n) { for (int j = 0; j < types->nrow; ++j) types->u.list[j] = _vcftype_grow(types->u.list[j], vcf_n); } static SEXP _trim_null(SEXP data, const char **cnms) { SEXP nms = PROTECT(NEW_CHARACTER(Rf_length(data))); int j = 0; for (int i = 0; i < Rf_length(data); ++i) { if (R_NilValue != VECTOR_ELT(data, i)) { SET_VECTOR_ELT(data, j, VECTOR_ELT(data, i)); SET_STRING_ELT(nms, j, mkChar(cnms[i])); j++; } } PROTECT(nms = Rf_lengthgets(nms, j)); PROTECT(data = Rf_lengthgets(data, j)); data = Rf_namesgets(data, nms); UNPROTECT(3); return data; } static struct vcftype_t *_vcf_alloc(const int vcf_n, SEXP smap, SEXP fmap, SEXP imap, SEXP gmap, khash_t(strhash) *str) { SEXP elt; SEXPTYPE type; const char *n; struct vcftype_t *vcf, *rowRanges; vcf = _vcftype_new(VECSXP, NILSXP, '\0', NULL, N_FLDS, 1, 1, 0); /* FIXED fields */ rowRanges = _vcftype_new(VECSXP, VECSXP, '\0', NULL, 2, 1, 1, 0); rowRanges->u.list[POS_IDX] = _vcftype_new(INTSXP, NILSXP, '\0', NULL, vcf_n, 1, 1, 0); rowRanges->u.list[ID_IDX] = _vcftype_new(STRSXP, NILSXP, '\0', NULL, vcf_n, 1, 1, 0); vcf->u.list[ROWRANGES_IDX] = rowRanges; const char *blank = _strhash_put(str, ""), *dot = _strhash_put(str, "."); for (int i = 2; i < Rf_length(fmap); ++i) { const char *nm = CHAR(STRING_ELT(GET_NAMES(fmap), i)); elt = VECTOR_ELT(fmap, i); n = CHAR(STRING_ELT(VECTOR_ELT(elt, 0), 0)); type = TYPEOF(VECTOR_ELT(elt, 1)); if (0 == strcmp(nm, "ALT")) { vcf->u.list[ALT_IDX] = _vcftype_new(VECSXP, type, *n, blank, vcf_n, 1, 1, 0); } else if (0 == strcmp(nm, "QUAL")) { vcf->u.list[QUAL_IDX] = _vcftype_new(type, NILSXP, *n, dot, vcf_n, 1, 1, 0); } else if (0 == strcmp(nm, "FILTER")) { vcf->u.list[FILTER_IDX] = _vcftype_new(type, NILSXP, *n, dot, vcf_n, 1, 1, 0); } else Rf_error("[internal] unknown 'fixed' field '%s'", nm); } /* INFO, FORMAT fields */ int nonzero = 0; for (int i = 0; i < Rf_length(smap); ++i) if (INTEGER(smap)[i] != 0) nonzero += 1; vcf->u.list[INFO_IDX] = _types_alloc(vcf_n, 1, TRUE, imap, str); vcf->u.list[GENO_IDX] = _types_alloc(vcf_n, nonzero, FALSE, gmap, str); return vcf; } static void _vcf_grow(struct vcftype_t * vcf, int vcf_n) { struct vcftype_t *elt; elt = vcf->u.list[ROWRANGES_IDX]; for (int i = POS_IDX; i <= ID_IDX; ++i) elt->u.list[i] = _vcftype_grow(elt->u.list[i], vcf_n); for (int i = ALT_IDX; i <= FILTER_IDX; ++i) vcf->u.list[i] = _vcftype_grow(vcf->u.list[i], vcf_n); _types_grow(vcf->u.list[INFO_IDX], vcf_n); _types_grow(vcf->u.list[GENO_IDX], vcf_n); } static void _parse(char *line, const int irec, const struct parse_t *parse, Rboolean row_names) { khash_t(strhash) *str = parse->str; struct vcftype_t *vcf = parse->vcf, *rowRanges, *elt, *info; const int imap_n = parse->imap_n, gmap_n = parse->gmap_n; const int smap_n = parse->smap_n; const char **inms = parse->inms, **gnms = parse->gnms; const char **snms = parse->snms; const int *smapidx = parse->smapidx; int fmtidx, imapidx; int j; struct it_t it0, it1, it2; char *sample, *ifld, *ikey; /* FIXED fields */ char *chrom, *pos, *id, *ref, *alt, *field; int alt_n; chrom = it_init(&it0, line, '\t'); /* CHROM */ rle_append(parse->chrom, chrom); rowRanges = vcf->u.list[ROWRANGES_IDX]; pos = it_next(&it0); /* POS */ rowRanges->u.list[POS_IDX]->u.integer[irec] = atoi(pos); id = it_next(&it0); /* ID */ ref = it_next(&it0); /* REF */ dna_hash_append(parse->ref, ref); alt = it_next(&it0); /* ALT */ alt_n = _vcftype_ragged_n(alt); _vcftype_setarray(vcf->u.list[ALT_IDX], irec, 0, alt, alt_n, str); _vcftype_set(vcf->u.list[QUAL_IDX], irec, _strhash_put(str, it_next(&it0))); /* QUAL */ _vcftype_set(vcf->u.list[FILTER_IDX], irec, _strhash_put(str, it_next(&it0))); /* FILTER */ if (row_names) { if ('.' == *id && '\0' == *(id + 1)) { /* construct ID if missing: chrom\0pos\0ID\0ref\0alt\0 ==> chrom:pos_ref/alt\0 */ *(pos - 1) = ':'; *(id - 1) = '_'; *(alt - 1) = '/'; while (*ref != '\0') *id++ = *ref++; *id = '\0'; id = chrom; } rowRanges->u.list[ID_IDX]->u.character[irec] = _strhash_put(str, id); } /* INFO */ field = it_next(&it0); info = vcf->u.list[INFO_IDX]; if (1 == imap_n && NULL == inms) { /* no header; parse as char */ elt = info->u.list[0]; elt->u.character[irec] = _strhash_put(str, field); } else if (0 != imap_n) { for (ifld = it_init(&it1, field, ';'); '\0' != *ifld; ifld = it_next(&it1)) { ikey = it_init(&it2, ifld, '='); for (imapidx = 0; imapidx < imap_n; ++imapidx) if (0L == strcmp(ikey, inms[imapidx])) { elt = info->u.list[imapidx]; _vcftype_setarray(elt, irec, 0, it_next(&it2), alt_n, str); break; } } /* missing data for type 'A', 'G' and 'R' need to be padded w/ NA's */ for (imapidx = 0; imapidx < imap_n; ++imapidx) { elt = info->u.list[imapidx]; if (elt->number == 'A' || elt->number == 'G' || elt->number == 'R') _vcftype_padarray(elt, irec, 0, str, alt_n); } } /* FORMAT */ if (0 == gmap_n) return; /* early exit */ field = it_init(&it2, it_next(&it0), ':'); int n_fld = it_nfld(&it2); int *gmapidx = Calloc(n_fld, int); for (fmtidx = 0; '\0' != *field; field = it_next(&it2), fmtidx++) { for (j = 0; j < gmap_n; ++j) if (0L == strcmp(field, gnms[j])) break; gmapidx[fmtidx] = j; /* gmap_n to ignore */ } /* SAMPLE */ struct vcftype_t *geno = vcf->u.list[GENO_IDX]; const int max_fmtidx = fmtidx; for (int j = 0; j < smap_n; ++j) { sample = it_next(&it0); if (0 == smapidx[j]) continue; for (field = it_init(&it2, sample, ':'), fmtidx = 0; '\0' != *field; field = it_next(&it2), fmtidx++) { if (fmtidx >= max_fmtidx) { vcfwarn(parse->warnings, "record %d sample %s: fewer FORMAT fields than GENO fields", irec + 1, snms[j]); continue; } if (gmap_n == gmapidx[fmtidx]) continue; /* unknown FORMAT */ elt = geno->u.list[ gmapidx[fmtidx] ]; _vcftype_setarray(elt, irec, smapidx[j] - 1, field, alt_n, str); } /* missing data for type 'A', 'G' and 'R' need to be padded w/ NA's */ for (fmtidx = 0; fmtidx < gmap_n; ++fmtidx) { elt = geno->u.list[fmtidx]; if (elt->number == 'A' || elt->number == 'G' || elt->number == 'R') _vcftype_padarray(elt, irec, smapidx[j] - 1, str, alt_n); } } Free(gmapidx); } static SEXP _vcf_as_SEXP(struct parse_t *parse, SEXP fmap, SEXP smap, Rboolean row_names) { SEXP result = PROTECT(_vcftype_as_SEXP(parse->vcf)); /* ref: DNAStringSet */ SEXP dna = dna_hash_as_DNAStringSet(parse->ref); SET_VECTOR_ELT(result, REF_IDX, dna); /* rowRanges: GRanges */ SEXP rowRanges, seqnames, start, width, names; PROTECT(seqnames = rle_as_Rle(parse->chrom)); rowRanges = VECTOR_ELT(result, ROWRANGES_IDX); start = VECTOR_ELT(rowRanges, POS_IDX); if (!row_names) names = R_NilValue; else names = VECTOR_ELT(rowRanges, ID_IDX); width = get_XVectorList_width(dna); SEXP ranges, nmspc, fun, expr; PROTECT(ranges = new_IRanges("IRanges", start, width, names)); PROTECT(nmspc = get_namespace("GenomicRanges")); PROTECT(fun = findFun(install("GRanges"), nmspc)); PROTECT(expr = lang3(fun, seqnames, ranges)); SET_VECTOR_ELT(result, ROWRANGES_IDX, eval(expr, R_GlobalEnv)); UNPROTECT(5); /* names */ SEXP nms, sxp = Rf_getAttrib(fmap, R_NamesSymbol), elt; PROTECT(nms = Rf_allocVector(STRSXP, Rf_length(result))); SET_STRING_ELT(nms, ROWRANGES_IDX, mkChar("rowRanges")); SET_STRING_ELT(nms, REF_IDX, mkChar("REF")); SET_STRING_ELT(nms, ALT_IDX, mkChar("ALT")); SET_STRING_ELT(nms, QUAL_IDX, mkChar("QUAL")); SET_STRING_ELT(nms, FILTER_IDX, mkChar("FILTER")); SET_STRING_ELT(nms, INFO_IDX, mkChar("INFO")); SET_STRING_ELT(nms, GENO_IDX, mkChar("GENO")); Rf_namesgets(result, nms); UNPROTECT(1); PROTECT(nms = Rf_allocVector(STRSXP, parse->imap_n)); if (1 == parse->imap_n && NULL == parse->inms) SET_STRING_ELT(nms, 0, R_NaString); else for (int i = 0; i < parse->imap_n; ++i) SET_STRING_ELT(nms, i, mkChar(parse->inms[i])); Rf_namesgets(VECTOR_ELT(result, INFO_IDX), nms); UNPROTECT(1); PROTECT(nms = Rf_allocVector(STRSXP, parse->gmap_n)); for (int i = 0; i < parse->gmap_n; ++i) SET_STRING_ELT(nms, i, mkChar(parse->gnms[i])); Rf_namesgets(VECTOR_ELT(result, GENO_IDX), nms); UNPROTECT(1); SEXP samplenms; int nonzero = 0; for (int i = 0; i < Rf_length(smap); ++i) if (INTEGER(smap)[i] != 0) nonzero += 1; PROTECT(samplenms = Rf_allocVector(STRSXP, nonzero)); for (int i = 0; i < parse->smap_n; ++i) if (INTEGER(smap)[i] != 0) SET_STRING_ELT(samplenms, INTEGER(smap)[i] - 1, mkChar(parse->snms[i])); PROTECT(nms = Rf_allocVector(VECSXP, 2)); SET_VECTOR_ELT(nms, 0, R_NilValue); SET_VECTOR_ELT(nms, 1, samplenms); sxp = VECTOR_ELT(result, GENO_IDX); for (int i = 0; i < Rf_length(sxp); ++i) { elt = VECTOR_ELT(sxp, i); if (R_NilValue != elt) Rf_dimnamesgets(elt, nms); } UNPROTECT(2); UNPROTECT(1); return result; } static void _vcf_types_tidy(struct parse_t *parse, SEXP result) { SEXP elt; if (NULL == parse->inms) { parse->inms = (const char **) R_alloc(sizeof(const char *), 1); parse->inms[0] = "INFO"; } elt = VECTOR_ELT(result, INFO_IDX); SET_VECTOR_ELT(result, INFO_IDX, _trim_null(elt, parse->inms)); elt = VECTOR_ELT(result, GENO_IDX); SET_VECTOR_ELT(result, GENO_IDX, _trim_null(elt, parse->gnms)); } static struct parse_t *_parse_new(int vcf_n, SEXP smap, SEXP fmap, SEXP imap, SEXP gmap) { struct parse_t *parse = Calloc(1, struct parse_t); parse->vcf_n = vcf_n; parse->str = _strhash_new(); parse->vcf = _vcf_alloc(parse->vcf_n, smap, fmap, imap, gmap, parse->str); /* FIXED */ parse->chrom = rle_new(parse->vcf_n); parse->ref = dna_hash_new(parse->vcf_n); /* INFO */ parse->imap_n = Rf_length(imap); if (1 == parse->imap_n && R_NilValue == GET_NAMES(imap)) parse->inms = NULL; else { parse->inms = (const char **) R_alloc(sizeof(const char *), parse->imap_n); for (int j = 0; j < parse->imap_n; ++j) parse->inms[j] = CHAR(STRING_ELT(GET_NAMES(imap), j)); } /* FORMAT */ parse->gmap_n = Rf_length(gmap); parse->gnms = (const char **) R_alloc(sizeof(const char *), parse->gmap_n); for (int j = 0; j < parse->gmap_n; ++j) parse->gnms[j] = CHAR(STRING_ELT(GET_NAMES(gmap), j)); /* SAMPLES */ parse->smap_n = Rf_length(smap); parse->snms = (const char **) R_alloc(sizeof(const char *), parse->smap_n); for (int j = 0; j < parse->smap_n; ++j) parse->snms[j] = CHAR(STRING_ELT(GET_NAMES(smap), j)); parse->smapidx = INTEGER(smap); parse->warnings = vcfwarn_new(); return parse; } static void _parse_free(struct parse_t *parse) { rle_free(parse->chrom); dna_hash_free(parse->ref); vcfwarn_free(parse->warnings); _strhash_free(parse->str); Free(parse); } static void _parse_grow(struct parse_t *parse, int size) { static const double SCALE = 1.6; if (0 == size) size = parse->vcf_n < 2 ? 2 : parse->vcf_n * SCALE; _vcf_grow(parse->vcf, size); parse->vcf_n = size; } /* --- .Call ENTRY POINT --- */ SEXP scan_vcf_connection(SEXP txt, SEXP smap, SEXP fmap, SEXP imap, SEXP gmap, SEXP rownames) { struct parse_t *parse; Rboolean row_names = LOGICAL(rownames)[0]; parse = _parse_new(Rf_length(txt), smap, fmap, imap, gmap); /* parse each line */ for (int irec = 0; irec < parse->vcf_n; irec++) { char *line = Strdup(CHAR(STRING_ELT(txt, irec))); _parse(line, irec, parse, row_names); Free(line); } SEXP result = PROTECT(Rf_allocVector(VECSXP, 1)); SET_VECTOR_ELT(result, 0, _vcf_as_SEXP(parse, fmap, smap, row_names)); _vcf_types_tidy(parse, VECTOR_ELT(result, 0)); _parse_free(parse); UNPROTECT(1); return result; } /* --- .Call ENTRY POINT --- */ SEXP scan_vcf_character(SEXP file, SEXP yield, SEXP smap, SEXP fmap, SEXP imap, SEXP gmap, SEXP rownames) { struct parse_t *parse; Rboolean row_names = LOGICAL(rownames)[0]; if (!IS_INTEGER(yield) || Rf_length(yield) != 1) Rf_error("'yield' must be integer(1)"); if (!IS_CHARACTER(file) || Rf_length(file) != 1) Rf_error("'file' must be character(1) or as on ?scanVcf"); if (!IS_LOGICAL(rownames)) Rf_error("'row.names' must be TRUE or FALSE"); parse = _parse_new(INTEGER(yield)[0], smap, fmap, imap, gmap); const int BUFLEN = 4096; char *buf0 = Calloc(BUFLEN, char); char *buf = buf0, *end = buf0 + BUFLEN; gzFile gz = gzopen(CHAR(STRING_ELT(file, 0)), "rb"); int irec = 0; if (Z_NULL == gz) { Free(parse); Rf_error("failed to open file"); } while (Z_NULL != gzgets(gz, buf, end - buf)) { int n = strlen(buf); if (n == end - buf - 1 && (*(end - 2) != '\n' && *(end - 2) != '\r')) { const int len0 = end - buf0, len1 = len0 * 1.6; buf0 = Realloc(buf0, len1, char); buf = buf0 + len0 - 1; end = buf0 + len1; continue; } if ('#' == *buf0 || '\0' == *buf0 || '\n' == *buf0) { buf = buf0; continue; } if (irec == parse->vcf_n) _parse_grow(parse, 0); /* trim trailing newlines */ int last = strlen(buf) - 1; while (last >= 0) { if (buf[last] == '\n' || buf[last] == '\r') buf[last--] = '\0'; else break; } _parse(buf0, irec, parse, row_names); irec += 1; buf = buf0; } gzclose(gz); Free(buf0); _vcf_grow(parse->vcf, irec); SEXP result = PROTECT(Rf_allocVector(VECSXP, 1)); SET_VECTOR_ELT(result, 0, _vcf_as_SEXP(parse, fmap, smap, row_names)); _vcf_types_tidy(parse, VECTOR_ELT(result, 0)); _parse_free(parse); UNPROTECT(1); return result; } /* --- .Call CALLBACK FUNCTION --- */ SEXP tabix_as_vcf(htsFile *file, tbx_t *index, hts_itr_t *iter, const int yield, SEXP state, SEXP rownames) { int irec = 0; kstring_t ksbuf = {0, 0, NULL}; Rboolean row_names = LOGICAL(rownames)[0]; SEXP sample = VECTOR_ELT(state, 0); SEXP fmap = VECTOR_ELT(state, 1); const int nrec = yield == NA_INTEGER ? TBX_INIT_SIZE : yield; struct parse_t *parse = _parse_new(nrec, sample, fmap, VECTOR_ELT(state, 2), VECTOR_ELT(state, 3)); const tbx_conf_t conf = index->conf; while (tbx_itr_next(file, index, iter, &ksbuf) >= 0) { if (ksbuf.s[0] == conf.meta_char) continue; if (irec == parse->vcf_n) _parse_grow(parse, 0); _parse(ksbuf.s, irec, parse, row_names); irec++; if (yield != NA_INTEGER && irec == parse->vcf_n) break; } free(ksbuf.s); _vcf_grow(parse->vcf, irec); SEXP result = PROTECT(_vcf_as_SEXP(parse, fmap, sample, row_names)); _vcf_types_tidy(parse, result); _parse_free(parse); UNPROTECT(1); return result; } VariantAnnotation/src/vcffile.h0000644000175100017510000000101114614305321017577 0ustar00biocbuildbiocbuild#ifndef _VCFFILE_H_ #define _VCFFILE_H_ #include #include "htslib/tbx.h" SEXP scan_vcf_character(SEXP file, SEXP yield, SEXP sample, SEXP fmap, SEXP imap, SEXP gmap, SEXP rownames); SEXP scan_vcf_connection(SEXP txt, SEXP sample, SEXP fmap, SEXP imap, SEXP gmap, SEXP rownames); SEXP tabix_as_vcf(htsFile *file, tbx_t *index, hts_itr_t *iter, const int yield, SEXP state, SEXP rownames); #endif /* _VCFFILE_H_ */ VariantAnnotation/src/vcftype.c0000644000175100017510000002166514614305321017655 0ustar00biocbuildbiocbuild#include "vcftype.h" struct vcftype_t *_vcftype_new(SEXPTYPE type, SEXPTYPE listtype, char number, const char *charDotAs, int nrow, int ncol, int ndim, int arrayDim) { struct vcftype_t *vcftype = Calloc(1, struct vcftype_t); vcftype->type = type; vcftype->listtype = listtype; /* VECSXP: ragged array */ vcftype->number = number; /* 'A' or '.' for ragged array only */ vcftype->charDotAs = charDotAs; vcftype->ncol = ncol; vcftype->ndim = ndim; vcftype->arrayDim = arrayDim; return _vcftype_grow(vcftype, nrow); } void _vcftype_free(struct vcftype_t *vcftype) { if (NULL == vcftype) return; int sz = vcftype->nrow * vcftype->ncol * vcftype->ndim; switch (vcftype->type) { case NILSXP: break; case LGLSXP: Free(vcftype->u.logical); break; case INTSXP: Free(vcftype->u.integer); break; case REALSXP: Free(vcftype->u.numeric); break; case STRSXP: if (NULL != vcftype->u.character) Free(vcftype->u.character); break; case VECSXP: if (NULL != vcftype->u.list) { for (int i = 0; i < sz; ++i) if (NULL != vcftype->u.list[i]) _vcftype_free(vcftype->u.list[i]); Free(vcftype->u.list); } break; default: Rf_error("(internal) unhandled type '%s'", type2char(vcftype->type)); } Free(vcftype); } void *vcf_Realloc(void * p, size_t n) { /* Realloc(p, 0, *) fails inappropriately */ if (n == 0) { Free(p); p = NULL; } else { p = R_chk_realloc(p, n); } return p; } struct vcftype_t *_vcftype_grow(struct vcftype_t * vcftype, int nrow) { if (NULL == vcftype) return vcftype; int ncol = vcftype->ncol, ndim = vcftype->ndim, o_nrow = vcftype->nrow, osz = o_nrow * ncol * ndim, sz = nrow * ncol * ndim; if (nrow < 0) Rf_error("(internal) _vcftype_grow 'nrow' < 0"); if (sz < 0) Rf_error("(internal) _vcftype_grow 'sz' < 0; cannot allocate memory?"); switch (vcftype->type) { case NILSXP: break; case LGLSXP: vcftype->u.logical = (int *) vcf_Realloc(vcftype->u.logical, sz * sizeof(int)); for (int i = osz; i < sz; ++i) vcftype->u.logical[i] = FALSE; break; case INTSXP: vcftype->u.integer = (int *) vcf_Realloc(vcftype->u.integer, sz * sizeof(int)); for (int i = osz; i < sz; ++i) vcftype->u.integer[i] = R_NaInt; break; case REALSXP: vcftype->u.numeric = (double *) vcf_Realloc(vcftype->u.numeric, sz * sizeof(double)); for (int i = osz; i < sz; ++i) vcftype->u.numeric[i] = R_NaReal; break; case STRSXP: vcftype->u.character = (const char **) vcf_Realloc(vcftype->u.character, sz * sizeof(const char *)); for (int i = osz; i < sz; ++i) vcftype->u.character[i] = NULL; break; case VECSXP: vcftype->u.list = (struct vcftype_t **) vcf_Realloc(vcftype->u.list, sz * sizeof(struct vcftype_t *)); for (int i = osz; i < sz; ++i) vcftype->u.list[i] = NULL; break; default: Rf_error("(internal) unhandled type '%s'", type2char(vcftype->type)); } vcftype->nrow = nrow; return vcftype; } #define TPOSE(to, from, nrow, ncol, ndim) \ for (int k = 0; k < (ndim); ++k) \ for (int j = 0; j < (ncol); ++j) \ for (int i = 0; i < (nrow); ++i) \ *(to)++ = (from)[i * (ncol) * (ndim) + j * (ndim) + k] SEXP _vcftype_as_SEXP(struct vcftype_t *vcftype) { if (NULL == vcftype || NILSXP == vcftype->type) return R_NilValue; const int ncol = vcftype->ncol, ndim = vcftype->ndim, nrow = vcftype->nrow, sz = nrow * ncol * ndim; SEXP ans = PROTECT(Rf_allocVector(vcftype->type, sz)); int *ival, idx; double *dval; switch (vcftype->type) { case LGLSXP: ival = LOGICAL(ans); TPOSE(ival, (const int *) vcftype->u.logical, nrow, ncol, ndim); Free(vcftype->u.logical); break; case INTSXP: ival = INTEGER(ans); TPOSE(ival, (const int *) vcftype->u.integer, nrow, ncol, ndim); Free(vcftype->u.integer); break; case REALSXP: dval = REAL(ans); TPOSE(dval, (const double *) vcftype->u.numeric, nrow, ncol, ndim); Free(vcftype->u.numeric); break; case STRSXP: idx = 0; for (int k = 0; k < ndim; ++k) for (int j = 0; j < ncol; ++j) for (int i = 0; i < nrow; ++i) { const int idx0 = i * ncol * ndim + j * ndim + k; const char * const s = vcftype->u.character[idx0]; const SEXP elt = (NULL == s) ? R_NaString : mkChar(s); SET_STRING_ELT(ans, idx++, elt); } Free(vcftype->u.character); break; case VECSXP: idx = 0; for (int k = 0; k < ndim; ++k) for (int j = 0; j < ncol; ++j) for (int i = 0; i < nrow; ++i) { const int idx0 = i * ncol * ndim + j * ndim + k; struct vcftype_t *t = vcftype->u.list[idx0]; const SEXP elt = (NULL == t) ? Rf_allocVector(vcftype->listtype, 0) : _vcftype_as_SEXP(t); SET_VECTOR_ELT(ans, idx++, elt); } Free(vcftype->u.list); break; default: Rf_error("(internal) unhandled type '%s'", type2char(vcftype->type)); } if (vcftype->arrayDim > 1) { SEXP dim = PROTECT(Rf_allocVector(INTSXP, vcftype->arrayDim)); INTEGER(dim)[0] = nrow; if (vcftype->arrayDim == 2) { INTEGER(dim)[1] = ncol * ndim; } else { INTEGER(dim)[1] = ncol; INTEGER(dim)[2] = ndim; } Rf_setAttrib(ans, R_DimSymbol, dim); UNPROTECT(1); } _vcftype_free(vcftype); UNPROTECT(1); return ans; } void _vcftype_set(struct vcftype_t *vcftype, const int idx, const char *field) { if (NULL == vcftype) return; switch (vcftype->type) { case NILSXP: break; case LGLSXP: vcftype->u.logical[idx] = TRUE; break; case INTSXP: vcftype->u.integer[idx] = ('.' == *field) ? R_NaInt : atoi(field); break; case REALSXP: vcftype->u.numeric[idx] = ('.' == *field) ? R_NaReal : atof(field); break; case STRSXP: vcftype->u.character[idx] = (strcmp(".", field) == 0) ? vcftype->charDotAs : field; break; default: Rf_error("(internal) unhandled field type '%s'", type2char(vcftype->type)); } } void _vcftype_padarray(struct vcftype_t *vcftype, const int irow, const int icol, khash_t(strhash) *str, const int ragged_n) { if (NULL == vcftype) return; const int offset = irow * vcftype->ncol + icol; if (vcftype->u.list[offset] != NULL) return; _vcftype_setarray(vcftype, irow, icol, "", ragged_n, str); } void _vcftype_setarray(struct vcftype_t *vcftype, const int irow, const int icol, char *field, int ragged_n, khash_t(strhash) *str) { struct it_t it; char *ifld; if (NULL == vcftype) return; if (VECSXP == vcftype->type) { /* ragged array */ /* 'G': one value per genotype */ if (vcftype->number == 'G') ragged_n = ((ragged_n + 1) * (ragged_n + 2))/2; /* 'R': one value per alternate allele + ref*/ else if (vcftype->number == 'R') ragged_n = ragged_n + 1; /* 'A': one value per alternate allele */ else if (vcftype->number != 'A') ragged_n = _vcftype_ragged_n(field); /* allocate and fill */ const int offset = irow * vcftype->ncol + icol; vcftype->u.list[offset] = _vcftype_new(vcftype->listtype, NILSXP, '\0', vcftype->charDotAs, ragged_n, 1, 1, 0); ifld = it_init(&it, field, ','); for (int k = 0; k < ragged_n; ++k) { if ('\0' == *ifld) ifld = "."; _vcftype_set(vcftype->u.list[offset], k, _strhash_put(str, ifld)); ifld = it_next(&it); } } else { /* array */ const int offset = (irow * vcftype->ncol + icol) * vcftype->ndim; ifld = it_init(&it, field, ','); for (int k = 0; k < vcftype->ndim; ++k) { _vcftype_set(vcftype, offset + k, _strhash_put(str, ifld)); ifld = it_next(&it); } } } VariantAnnotation/src/vcftype.h0000644000175100017510000000311514614305321017650 0ustar00biocbuildbiocbuild#ifndef _VCFTYPE_H_ #define _VCFTYPE_H_ #include #include #include #include "utilities.h" #include "strhash.h" struct vcftype_t { SEXPTYPE type, listtype; /* listtype for ragged arrays */ char number; /* 'A' or '.' for ragged array only */ const char *charDotAs; /* '.' enocding when in character vectors */ int nrow, ncol, ndim, arrayDim; union { int *logical; int *integer; double *numeric; const char **character; struct vcftype_t **list; } u; }; struct vcftype_t *_vcftype_new(SEXPTYPE type, SEXPTYPE listtype, char number, const char *charDotAs, int nrow, int ncol, int ndim, int arrayDim); void _vcftype_free(struct vcftype_t *vcftype); struct vcftype_t *_vcftype_grow(struct vcftype_t *vcftype, int nrow); SEXP _vcftype_as_SEXP(struct vcftype_t *vcftype); void _vcftype_set(struct vcftype_t *vcftype, const int idx, const char *field); void _vcftype_setarray(struct vcftype_t *vcftype, const int irow, const int icol, char *field, int ragged_n, khash_t(strhash) *str); void _vcftype_padarray(struct vcftype_t *vcftype, const int irow, const int icol, khash_t(strhash) *str, const int ragged_n); static inline int _vcftype_ragged_n(const char *a) { int n = (*a == '\0') ? 0 : 1; while (*a != '\0') if (*a++ == ',') ++n; return n; } #endif VariantAnnotation/src/writevcf.c0000644000175100017510000002300214614305321020011 0ustar00biocbuildbiocbuild#include "writevcf.h" #include #include /* write all elements of 'list' genotype field */ static void write_list_elt(SEXP v_elt, const char mv_sep, kstring_t *bufp) { SEXPTYPE v_type; int v, v_len; v_type = TYPEOF(v_elt); v_len = length(v_elt); switch (v_type) { case NILSXP: break; case LGLSXP: Rf_warning("'logical' is not a valid FORMAT data type"); break; case INTSXP: for (v = 0; v < v_len; v++) { if (NA_INTEGER != INTEGER(v_elt)[v]) kputw(INTEGER(v_elt)[v], bufp); else kputc('.', bufp); if (v < v_len - 1) kputc(mv_sep, bufp); } break; case REALSXP: for (v = 0; v < v_len; v++) { if (!ISNAN(REAL(v_elt)[v])) ksprintf(bufp, "%g", REAL(v_elt)[v]); else kputc('.', bufp); if (v < v_len - 1) kputc(mv_sep, bufp); } break; case STRSXP: for (v = 0; v < v_len; v++) { if (NA_STRING != STRING_ELT(v_elt, v)) kputs(CHAR(STRING_ELT(v_elt, v)), bufp); else kputc('.', bufp); if (v < v_len - 1) kputc(mv_sep, bufp); } break; default: Rf_error("unsupported 'geno' type: %s", type2char(v_type)); break; } } /* write all genotype fields for a single sample */ static void write_geno_sample(int i, int j, int k_last, Rboolean *k_valid, SEXP geno_zdim, int n_rows, int n_samples, int n_fields, SEXP geno, const char f_sep, const char mv_sep, kstring_t *bufp) { SEXP field, c_elt, v_elt; SEXPTYPE type; double d_elt; int k, z, z_dim, z_max, index, i_elt; for (k = 0; k < n_fields; ++k) { if (!k_valid[k]) continue; field = VECTOR_ELT(geno, k); type = TYPEOF(field); z_dim = INTEGER(geno_zdim)[k]; z_max = (NA_INTEGER != z_dim) ? z_dim : 1; for (z = 0; z < z_max; ++z) { index = i + j*n_rows + z*n_rows*n_samples; switch (type) { case NILSXP: break; case LGLSXP: Rf_warning("'logical' is not a valid FORMAT data type"); break; case INTSXP: i_elt = INTEGER(field)[index]; if (NA_INTEGER != i_elt) kputw(i_elt, bufp); else kputc('.', bufp); break; case REALSXP: d_elt = REAL(field)[index]; if (!ISNAN(d_elt)) ksprintf(bufp, "%g", d_elt); else kputc('.', bufp); break; case STRSXP: c_elt = STRING_ELT(field, index); if (NA_STRING != c_elt) kputs(CHAR(c_elt), bufp); else kputc('.', bufp); break; case VECSXP: v_elt = VECTOR_ELT(field, index); write_list_elt(v_elt, mv_sep, bufp); break; default: Rf_error("unsupported 'geno' type: %s", type2char(type)); break; } /* multi-value separator */ if (z < z_max - 1 && k_valid[k]) kputc(mv_sep, bufp); } /* field separator */ if (k < n_fields - 1 && k < k_last) kputc(f_sep, bufp); } } /* return TRUE if genotype element is NA */ static Rboolean valid_geno_elt(SEXP field, int index) { Rboolean valid = FALSE; Rboolean v_valid = FALSE; const SEXPTYPE type = TYPEOF(field); SEXP elt; int v; switch (type) { case NILSXP: break; case LGLSXP: valid = NA_INTEGER != LOGICAL(field)[index]; break; case INTSXP: valid = NA_INTEGER != INTEGER(field)[index]; break; case REALSXP: valid = !ISNAN(REAL(field)[index]); break; case STRSXP: valid = NA_STRING != STRING_ELT(field, index); break; case VECSXP: elt = VECTOR_ELT(field, index); for (v = 0; v < length(elt); v++) { if (valid_geno_elt(elt, v)) { v_valid = TRUE; break; } } valid = v_valid; break; default: Rf_error("unsupported 'geno' type: %s", type2char(type)); break; } return valid; } /* --- .Call ENTRY POINT --- * 'conn' : connection * 'fixed' : character vector of FIXED fields * 'format' : character vector of FORMAT names * 'geno' : list of genotype data * 'separators' : character vector of length 2 consisting of a field * separator (first) and multi-value per field * separator (second) * 'vcf_dim' : integer vector of length 2 (n rows, n cols) * 'geno_zdim' : integer vector of z dimension of genotype data */ SEXP make_vcf_geno(SEXP conn, SEXP fixed, SEXP format, SEXP geno, SEXP separators, SEXP vcf_dim, SEXP geno_zdim) { const char f_sep = *CHAR(STRING_ELT(separators, 0)); const char mv_sep = *CHAR(STRING_ELT(separators, 1)); int n_rows = INTEGER(vcf_dim)[0]; int n_samples = INTEGER(vcf_dim)[1]; int n_fields = length(format); int i, j, k, k_last, z, z_dim, z_max; int index; Rboolean fmt_search, fmt_found, *k_valid; Rconnection con = R_GetConnection(conn); kstring_t buf; buf.l = buf.m = 0; buf.s = NULL; SEXP field; if (n_fields != length(geno)) Rf_error("length(format) must equal length(geno)"); if (length(geno_zdim) != length(geno)) Rf_error("length(geno_zdim) must equal length(geno)"); k_valid = (Rboolean *) R_alloc(sizeof(Rboolean), n_fields); for (i = 0; i < n_rows; ++i) { /* reset buffer */ buf.l = 0; if (NULL != buf.s) *buf.s = '\0'; kputs(CHAR(STRING_ELT(fixed, i)), &buf); if (n_fields > 0) kputc('\t', &buf); fmt_found = FALSE; k_last = 0; /* write format names */ for (k = 0; k < n_fields; ++k) { fmt_search = TRUE; field = VECTOR_ELT(geno, k); z_dim = INTEGER(geno_zdim)[k]; z_max = (NA_INTEGER != z_dim) ? z_dim : 1; for (j = 0; j < n_samples && fmt_search; ++j) { for (z = 0; z < z_max; ++z) { index = i + j*n_rows + z*n_rows*n_samples; if (valid_geno_elt(field, index)) { /* avoid trailing f_sep */ if (fmt_found) kputc(f_sep, &buf); kputs(CHAR(STRING_ELT(format, k)), &buf); if (k == n_fields - 1) kputc('\t', &buf); k_valid[k] = TRUE; k_last = k; fmt_search = FALSE; fmt_found = TRUE; break; } else if (k == n_fields - 1 && z == z_max - 1 && j == n_samples - 1) { kputc('\t', &buf); k_valid[k] = FALSE; } else k_valid[k] = FALSE; } } } /* write genotype fields if present */ if (n_samples > 0) { for (j = 0; j < n_samples; ++j) { write_geno_sample(i, j, k_last, k_valid, geno_zdim, n_rows, n_samples, n_fields, geno, f_sep, mv_sep, &buf); if (j < n_samples - 1) { if (0 != buf.l) /* sample separator */ kputc('\t', &buf); } else { kputc('\n', &buf); if (R_WriteConnection(con, buf.s, buf.l) != buf.l) Rf_error("error writing to connection"); } } } else { kputc('\n', &buf); if (R_WriteConnection(con, buf.s, buf.l) != buf.l) Rf_error("error writing to connection"); } } free(buf.s); return R_NilValue; } /* paste-collapse character matrix by row, ignoring NAs */ SEXP matrix_pasteCollapseRows(SEXP x, SEXP sep) { /* VO: looks like 'nc' is not used? * int nc = ncols(x), nr = nrows(x); */ int nr = nrows(x); char c_sep = CHAR(STRING_ELT(sep, 0))[0]; SEXP ans = allocVector(STRSXP, nr); PROTECT(ans); for (int r = 0; r < nr; r++) { int len = 0; for (int i = r; i < length(x); i += nr) { SEXP str = STRING_ELT(x, i); if (str != NA_STRING) len += length(str) + 1; } char *collapsed = R_alloc(sizeof(char), len); char *dest = collapsed; for (int i = r; i < length(x); i += nr) { SEXP str = STRING_ELT(x, i); if (str != NA_STRING) { strcpy(dest, CHAR(str)); dest[length(str)] = c_sep; dest += length(str) + 1; } } SET_STRING_ELT(ans, r, mkCharLen(collapsed, len - 1 * (len > 0))); } UNPROTECT(1); return ans; } VariantAnnotation/src/writevcf.h0000644000175100017510000000040614614305321020021 0ustar00biocbuildbiocbuild#ifndef _WRITEVCF_H #define _WRITEVCF_H #include "vcftype.h" #include SEXP matrix_pasteCollapseRows(SEXP x, SEXP sep); SEXP make_vcf_geno(SEXP conn, SEXP fixed, SEXP format, SEXP geno, SEXP separators, SEXP vcf_dim, SEXP geno_zdim); #endif VariantAnnotation/src/XVector_stubs.c0000644000175100017510000000003414614305321020772 0ustar00biocbuildbiocbuild#include "_XVector_stubs.c" VariantAnnotation/tests/0000755000175100017510000000000014614305321016372 5ustar00biocbuildbiocbuildVariantAnnotation/tests/VariantAnnotation_unit_tests.R0000644000175100017510000000015514614305321024436 0ustar00biocbuildbiocbuildrequire("VariantAnnotation") || stop("unable to load VariantAnnotation package") VariantAnnotation:::.test() VariantAnnotation/vignettes/0000755000175100017510000000000014614361054017245 5ustar00biocbuildbiocbuildVariantAnnotation/vignettes/bioinformatics.csl0000644000175100017510000001135314614305321022756 0ustar00biocbuildbiocbuild VariantAnnotation/vignettes/ens.bib0000644000175100017510000000176114614305321020510 0ustar00biocbuildbiocbuild@Article{McLaren2016, author={McLaren, William and Gil, Laurent and Hunt, Sarah E. and Riat, Harpreet Singh and Ritchie, Graham R. S. and Thormann, Anja and Flicek, Paul and Cunningham, Fiona}, title={The Ensembl Variant Effect Predictor}, journal={Genome Biology}, year={2016}, month={Jun}, day={06}, volume={17}, number={1}, pages={122}, abstract={The Ensembl Variant Effect Predictor is a powerful toolset for the analysis, annotation, and prioritization of genomic variants in coding and non-coding regions. It provides access to an extensive collection of genomic annotation, with a variety of interfaces to suit different requirements, and simple options for configuring and extending analysis. It is open source, free to use, and supports full reproducibility of results. The Ensembl Variant Effect Predictor can simplify and accelerate variant interpretation in a wide range of study designs.}, issn={1474-760X}, doi={10.1186/s13059-016-0974-4}, url={https://doi.org/10.1186/s13059-016-0974-4} } VariantAnnotation/vignettes/ensemblVEP.Rmd0000644000175100017510000000712514614305321021711 0ustar00biocbuildbiocbuild--- title: "ensemblVEP: using the REST API with Bioconductor" author: "Vincent J. Carey, stvjc at channing.harvard.edu" date: "`r format(Sys.time(), '%B %d, %Y')`" vignette: > %\VignetteEngine{knitr::rmarkdown} %\VignetteIndexEntry{ensemblVEP: using the REST API with Bioconductor} %\VignetteEncoding{UTF-8} output: BiocStyle::html_document: highlight: pygments number_sections: yes theme: united toc: yes bibliography: ens.bib --- ```{r setup,echo=FALSE,results="hide",message=FALSE} library(BiocStyle) library(VariantAnnotation) library(jsonlite) library(httr) ``` # Introduction Ensembl's Variant Effect Predictor is described in @McLaren2016. Prior to Bioconductor 3.19, the ensemblVEP package provided access to Ensembl's predictions through an interface between Perl and MySQL. In 3.19 VariantAnnotation supports the use of the VEP component of the REST API at [https://rest.ensembl.org](https://rest.ensembl.org/). # Acquire annotation on variants from a VCF file The function `vep_by_region` will accept a VCF object as defined in `r Biocpkg("VariantAnnotation")`. ```{r dodemo,message=FALSE} library(VariantAnnotation) fl <- system.file("extdata", "chr22.vcf.gz", package="VariantAnnotation") r22 = readVcf(fl) r22 ``` In this example we confine attention to single nucleotide variants. There is a limit of 200 locations in a request, and 55000 requests per hour. We'll base our query on 100 positions in the chr22 VCF. ```{r lksnv} dr = which(width(rowRanges(r22))!=1) r22s = r22[-dr] res = vep_by_region(r22[1:100], snv_only=FALSE, chk_max=FALSE) jans = toJSON(content(res)) ``` There are various ways to work with the result of this query to the API. We'll use the `r CRANpkg('rjsoncons')` JSON processing infrastructure to dig in and understand aspects of the API behavior. First, the top-level concepts produced for each variant can be retrieved using ```{r doj1, message=FALSE} library(rjsoncons) names(jsonlite::fromJSON(jmespath(jans, "[*]"))) ``` Annotation of the most severe consequence known will typically be of interest: ```{r doj2} table(jsonlite::fromJSON(jmespath(jans, "[*].most_severe_consequence"))) ``` There is variability in the structure of data returned for each query. ```{r doj3} head(fromJSON(jmespath(jans, "[*].regulatory_feature_consequences"))) ``` Furthermore, the content of the motif feature consequences field seems very peculiar. ```{r lktaaaa} table(unlist(fromJSON(jmespath(jans, "[*].motif_feature_consequences")))) ``` # Transforming the API response to GRanges We'll consider the following approach to converting the API response to a GenomicRanges GRanges instance. Eventually this may become part of the package. ```{r lkmakeg, message=FALSE} library(GenomicRanges) .make_GRanges = function( vep_response ) { stopifnot(inherits(vep_response, "response")) # httr nested = fromJSON(toJSON(content(vep_response))) ini = GRanges(seqnames = unlist(nested$seq_region_name), IRanges(start=unlist(nested$start), end=unlist(nested$end))) dr = match(c("seq_region_name", "start", "end"), names(nested)) mcols(ini) = DataFrame(nested[,-dr]) ini } tstg = .make_GRanges( res ) tstg[,1] # full print is unwieldy names(mcols(tstg)) ``` Now information about variants can be retrieved with range operations. Deep annotation requires nested structure of the metadata columns. ```{r lkmc} mcols(tstg)[1, "transcript_consequences"] ``` # Further work An important element of prior work in ensemblVEP supports feeding annotation back into the VCF used to generate the effect prediction query. This seems feasible but concrete use cases are of interest. # References VariantAnnotation/vignettes/filterVcf.bib0000644000175100017510000000137414614305321021647 0ustar00biocbuildbiocbuild@article{drmanac2010human, title={Human genome sequencing using unchained base reads on self-assembling DNA nanoarrays}, author={Drmanac, Radoje and Sparks, Andrew B and Callow, Matthew J and Halpern, Aaron L and Burns, Norman L and Kermani, Bahram G and Carnevali, Paolo and Nazarenko, Igor and Nilsen, Geoffrey B and Yeung, George and others}, journal={Science}, volume={327}, number={5961}, pages={78--81}, year={2010}, publisher={American Association for the Advancement of Science} } @article{li2011tabix, title={Tabix: fast retrieval of sequence features from generic TAB-delimited files}, author={Li, Heng}, journal={Bioinformatics}, volume={27}, number={5}, pages={718--719}, year={2011}, publisher={Oxford Univ Press} } VariantAnnotation/vignettes/filterVcf.Rmd0000644000175100017510000002756714614305321021651 0ustar00biocbuildbiocbuild--- title: "2. Using filterVcf() to Select Variants from VCF Files" author: "Paul Shannon" date: |+ Created: 20 February, 2013 Last Modified: `r format(Sys.Date(), "%B %d, %Y")` vignette: > %\VignetteIndexEntry{2. Using filterVcf to Select Variants from VCF Files} %\VignetteEngine{knitr::rmarkdown} %\VignetteEncoding{UTF-8} output: BiocStyle::html_document: number_sections: yes toc: yes toc_depth: 4 bibliography: filterVcf.bib csl: bioinformatics.csl link-citations: true --- # Introduction Whole genome Variant Call Format (VCF) files are very large, typically containing millions of called variants, one call per line of text. The actual number of variants relevant to a particular study (of a disease such as breast cancer, for instance) will often be far fewer. Thus the first task one faces when analyzing a whole genome VCF file is to identify and extract the relatively few variants which may be of interest, excluding all others. This vignette illustrates several techniques for doing this. We demonstrate three methods: filtering by genomic region, filtering on attributes of each specific variant call, and intersecting with known regions of interest (exons, splice sites, regulatory regions, etc.). We are primarily concerned with the latter two. However, in order to create the small VCF data file we use here for demonstration purposes, we employed genomic region filtering, reducing a very large whole genome two-sample breast cancer VCF file of fourteen million calls, to a file containing fewer than ten thousand calls in a one million base pair region of chromosome 7. For the sake of reproducibility, and for completeness of exposition, we will illustrate this first step also. # The Data: Paired Tumor/Normal Breast Cancer Variants [Complete Genomics Inc.](http://www.completegenomics.com/public-data/cancer-data) states: > To provide the scientific community with public access to data > generated from two paired tumor/normal cancer samples, Complete > Genomics sequenced and analyzed cell-line samples of patients with > breast cancer (invasive ductal carcinomas). The cell line-derived DNA > are housed at ATCC. Samples have been sequenced to an average > genome-wide coverage of 123X for three of the samples, and 92X for for > the fourth sample.^[Data generated withversion 2.0.0.32 of the Complete Genomics assembly software, on high molecular weight genomic DNA isolated from the HCC1187 breast carcinoma cell line ATCC CRL 2322. Sequencing methods documented in [@drmanac2010human]] A small (1M base) subset of this data is included in the current package, and used in the code presented below. # Filter by Genomic Region We identified this subset in a prior exploration of the full data set (work not shown), learning that variants of biological interest suitable for our purpose are found in a 1M base pair region of chromosome seven. The appendix to this document (see below) shows the few lines of code required to extract variant calls in that small region, from the very large file obtained from Complete Genomics (*somaticVcfBeta-HCC1187-H-200-37-ASM-T1-N1.vcf.gz*) and write them to a new, small VCF file. # Introducing the `filterVcf` Method The `filterVcf ` method reads (by chunks, about which more below) through a possibly very large VCF file to write a new, smaller VCF file which includes only those variant calling rows which meet the criteria specified in `prefilters` and `filters`. Reading "by chunks" is accomplished using a tabix [@li2011tabix] file. The a `yieldSize` argument specifies how many variant lines are read into memory at each iteration. ```{r eval=FALSE} tabix.file <- TabixFile(file.gz, yieldSize=10000) filterVcf(tabix.file, genome, destination.file, prefilters=prefilters, filters=filters) ``` in which 1. *file.gz*: a gzipped vcf file with an accompanying Tabix index file. 2. *yieldSize*: the number of text (call variant) lines to read at a time. 3. *genome*: a string indicating the genome assembly, e.g., "hg19". 4. *prefilters*: one or more simple string-based filtering functions, each of which returns a logical vector corresponding to the vcf rows it will be passed (as simple character strings). 5. *filters*: one or more filtering function, each of which returns a logical vector, corresponding to the list of parsed vcf structures it will be passed. ## Prefilters Prefilters are conceptually very simple. They are functions which will be called with a single argument, a vector of character strings, each of which is an *unparsed* variant call line, as read from the input VCF file. We use `grepl` to return a logical vector equal in length to the incoming vector of unparsed VCF lines. Each prefilter and filter is called repeatedly, with lines supplied on each invocation. `filterVcf` calls these functions repeatedly until the input file is exhausted. Notice how the logic of these prefilters is very simple, using `grepl` to do fast, simple, fixed pattern matching: ```{r prefilters} isGermlinePrefilter <- function(x) { grepl("Germline", x, fixed=TRUE) } notInDbsnpPrefilter <- function(x) { !(grepl("dbsnp", x, fixed=TRUE)) } ``` ## Filters Filters are more sophisticated than prefilters in that they assess *parsed* variant call lines for possible inclusion. Such parsing is intrinsically expensive but will be performed only on those lines which passed the prefilters. Therefore it pays to eliminate as many lines as possible using prefilters. Filters are useful when there exists detailed criteria for inclusion and exclusion. This can be seen below, especially in the `allelicDepth` function. Each filter must be written to return a logical vector as long as the number of rows in the input VCF argument; be sure your filter works with 0-row VCF instances. ```{r filters} ## We will use isSNV() to filter only SNVs allelicDepth <- function(x) { ## ratio of AD of the "alternate allele" for the tumor sample ## OR "reference allele" for normal samples to total reads for ## the sample should be greater than some threshold (say 0.1, ## that is: at least 10% of the sample should have the allele ## of interest) ad <- geno(x)$AD tumorPct <- ad[,1,2,drop=FALSE] / rowSums(ad[,1,,drop=FALSE]) normPct <- ad[,2,1, drop=FALSE] / rowSums(ad[,2,,drop=FALSE]) test <- (tumorPct > 0.1) | (normPct > 0.1) as.vector(!is.na(test) & test) } ``` ## FilterRules `FilterRules` allow you to combine a list of filters, or of prefilters so that they may be passed as parameters to *filterVcf*. We use them here to combine the *isGermlinePrefilter* with the *notInDbsnpPrefilter*, and the *isSNV* with the *AD* filter. ```{r createFilterRules, message=FALSE, warning=FALSE} library(VariantAnnotation) prefilters <- FilterRules(list(germline=isGermlinePrefilter, dbsnp=notInDbsnpPrefilter)) filters <- FilterRules(list(isSNV=isSNV, AD=allelicDepth)) ``` ## Create the Filtered file ```{r createFilteredFile, message=FALSE, warning=FALSE} file.gz <- system.file("extdata", "chr7-sub.vcf.gz", package="VariantAnnotation") file.gz.tbi <- system.file("extdata", "chr7-sub.vcf.gz.tbi", package="VariantAnnotation") destination.file <- tempfile() tabix.file <- TabixFile(file.gz, yieldSize=10000) filterVcf(tabix.file, "hg19", destination.file, prefilters=prefilters, filters=filters, verbose=TRUE) ``` # Look for SNPs in Regulatory Regions We have now created a file containing 29 novel, Germline, SNP variant calls, each with a reasonable allelic depth, extracted from the 3808 calls in *chr7-sub.vcf*. We examine those variants for overlap with regulatory regions, turning to the ENCODE project and using Bioconductor's `r Biocpkg("AnnotationHub")`. The ENCODE project is a large, long-term effort to build a "parts list" of all the functional elements in the human genome. They have recently focused on regulatory elements. We use the Bioconductor `r Biocpkg("AnnotationHub")` to download regulatory regions reported for a breast cancer cell line, with which to identify possibly functional, and possibly clinically relevant SNVs in the breast cancer tumor/normal genome we have been examining. The `r Biocpkg("AnnotationHub")` is a recent addition to Bioconductor that facilitates access to genome-scale resources like ENCODE. ## Load CTCF Transcription Factor Binding Regions Identified in MCF-7 Breast Cancer Cell Line The [MCF-7](http://en.wikipedia.org/wiki/MCF-7) Breast Cancer Cell line was established forty years ago, and has since played a dominant role in breast cancer cell line studies. The University of Washington reports transcription factor binding sites (TFBS) for the CTCF protein, which often acts as a negative regulator of transcription via chromatin structure modifications Though the MCF-7 cell line is an imperfect match to the HCC1187 cell line sequenced by Complete Genomics, we combine these two breast-cancer related data sets here, didactically, in this exercise, to highlight the importance of cell-type-specific regulatory regions, and of the availability of such data from ENCODE. We shall see a SNP in the intronic binding region of the cancer-related gene, EGFR. ```{r mcf7regulatoryRegions, message=FALSE, warning=FALSE} library(AnnotationHub) hub <- AnnotationHub() id <- names(query(hub, "wgEncodeUwTfbsMcf7CtcfStdPkRep1.narrowPeak")) mcf7.gr <- hub[[tail(id, 1)]] ``` ## Find SNPs in CTCF Binding Regions ```{r findOverlaps} vcf <- readVcf(destination.file, "hg19") seqlevels(vcf) <- paste("chr", seqlevels(vcf), sep="") ov.mcf7 <- findOverlaps(vcf, mcf7.gr) ``` There is just one SNV which overlaps with the MCF-7 regulatory regions. Find out where, if anywhere, it fits within a gene model. ```{r locateVariant, message=FALSE, warning=FALSE} library(TxDb.Hsapiens.UCSC.hg19.knownGene) txdb <- TxDb.Hsapiens.UCSC.hg19.knownGene locateVariants(vcf[6,], txdb, AllVariants()) ``` # Conclusion This case study begins, somewhat artificially, with a very short region of chromosome seven, a section which we knew, from previous exploration, held an intronic regulatory SNV for EGFR, a receptor tryosine kinase implicated in some cancers. Though artificial, the case study illustrates all of the steps needed for broader, realistic surveys of whole genome variation data: 1. Filter by genomic coordinates. 2. Filter to extract only those variant calls which meet these criteria: Germline, novel (not in dbSnp), consisting of a single nucleotide, of sufficient allelic depth. 3. Intersect these variants with recognized DNA elements. In our case, we used short TFBS regulatory regions, but the same method can be used with exons, splice sites, DNaseI footprints, methylation sites, etc. # Appendix: Filter by Genomic Region The most basic form of VCF file filtering is by genomic region. We demonstrate that here, extracting variant calls in a 1M base region of chromosome 7, writing them to a new file, compressing and then indexing that file. These steps created the small VCF file which accompanies this vignette, and is used in the code shown above. Note that this code is *NOT* executed during the creation of this vignette: we do not supply the very large VCF file that it operates on. This code is here for tutorial purposes only, showing how you can filter by genomic region with a possibly large VCF file of your own. ```{r eval=FALSE} library(VariantAnnotation) file.gz <- "somaticVcfBeta-HCC1187-H-200-37-ASM-T1-N1.vcf.gz" stopifnot(file.exists(file.gz)) file.gz.tbi <- paste(file.gz, ".tbi", sep="") if(!(file.exists(file.gz.tbi))) indexTabix(file.gz, format="vcf") start.loc <- 55000000 end.loc <- 56000000 chr7.gr <- GRanges("7", IRanges(start.loc, end.loc)) params <- ScanVcfParam(which=chr7.gr) vcf <- readVcf(TabixFile(file.gz), "hg19", params) writeVcf(vcf, "chr7-sub.vcf") bgzip("chr7-sub.vcf", overwrite=TRUE) indexTabix("chr7-sub.vcf.gz", format="vcf") ``` This code creates the small gzipped vcf and index files used in the examples above. # Bibliography {-}VariantAnnotation/vignettes/VariantAnnotation.Rmd0000644000175100017510000004753314614305321023357 0ustar00biocbuildbiocbuild--- title: "1. Introduction to VariantAnnotation" author: "Valerie Obenchain" date: "`r format(Sys.time(), '%B %d, %Y')`" vignette: > %\VignetteEngine{knitr::rmarkdown} %\VignetteIndexEntry{1. Introduction to VariantAnnotation} %\VignetteEncoding{UTF-8} output: BiocStyle::html_document: number_sections: yes toc: yes toc_depth: 4 --- # Introduction This vignette outlines a work flow for annotating and filtering genetic variants using the `r Biocpkg("VariantAnnotation")` package. Sample data are in VariantCall Format (VCF) and are a subset of chromosome 22 from [1000 Genomes](http://ftp.1000genomes.ebi.ac.uk/vol1/ftp/release/20110521/). VCF text files contain meta-information lines, a header line with column names, data lines with information about a position in the genome, and optional genotype information on samples for each position. Samtools organisation and repositories describes the [VCF format](http://samtools.github.io/hts-specs/VCFv4.4.pdf) in detail. Data are read in from a VCF file and variants identified according to region such as `coding`, `intron`, `intergenic`, `spliceSite` etc. Amino acid coding changes are computed for the non-synonymous variants and SIFT and PolyPhen databases provide predictions of how severly the coding changes affect protein function. # Variant Call Format (VCF) files ## Data import and exploration Data are parsed into a `VCF` object with `readVcf`. ```{r readVcF,message=FALSE} library(VariantAnnotation) fl <- system.file("extdata", "chr22.vcf.gz", package="VariantAnnotation") vcf <- readVcf(fl, "hg19") vcf ``` ### Header information Header information can be extracted from the VCF with `header()`. We see there are 5 samples, 1 piece of meta information, 22 info fields and 3 geno fields. ```{r readVcf_showheader} header(vcf) ``` Data can be further extracted using the named accessors. ```{r headeraccessors} samples(header(vcf)) geno(header(vcf)) ``` ### Genomic positions `rowRanges` contains information from the CHROM, POS, and ID fields of the VCF file, represented as a `GRanges`. The `paramRangeID` column is meaningful when reading subsets of data and is discussed further below. ```{r readVcf_rowRanges} head(rowRanges(vcf), 3) ``` Individual fields can be pulled out with named accessors. Here we see `REF` is stored as a `DNAStringSet` and `qual` is a numeric vector. ```{r readVcf_fixed} ref(vcf)[1:5] qual(vcf)[1:5] ``` `ALT` is a `DNAStringSetList` (allows for multiple alternate alleles per variant) or a `DNAStringSet`. When structural variants are present it will be a `CharacterList`. ```{r readVcf_ALT} alt(vcf)[1:5] ``` ### Genotype data Genotype data described in the `FORMAT` fields are parsed into the geno slot. The data are unique to each sample and each sample may have multiple values variable. Because of this, the data are parsed into matrices or arrays where the rows represent the variants and the columns the samples. Multidimentional arrays indicate multiple values per sample. In this file all variables are matrices. ```{r geno_hdr} geno(vcf) sapply(geno(vcf), class) ``` Let's take a closer look at the genotype dosage (DS) variable. The header provides the variable definition and type. ```{r explore_geno} geno(header(vcf))["DS",] ``` These data are stored as a 10376 x 5 matrix. Each of the five samples (columns) has a single value per variant location (row). ```{r dim_geno} DS <-geno(vcf)$DS dim(DS) DS[1:3,] ``` DS is also known as 'posterior mean genotypes' and range in value from [0, 2]. To get a sense of variable distribution, we compute a five number summary of the minimum, lower-hinge (first quartile), median, upper-hinge (third quartile) and maximum. ```{r fivenum} fivenum(DS) ``` The majority of these values (86%) are zero. ```{r DS_zero} length(which(DS==0))/length(DS) ``` View the distribution of the non-zero values. ```{r DS_hist, fig=TRUE} hist(DS[DS != 0], breaks=seq(0, 2, by=0.05), main="DS non-zero values", xlab="DS") ``` ### Info data In contrast to the genotype data, the info data are unique to the variant and the same across samples. All info variables are represented in a single `DataFrame`. ```{r info} info(vcf)[1:4, 1:5] ``` We will use the info data to compare quality measures between novel (i.e., not in dbSNP) and known (i.e., in dbSNP) variants and the variant type present in the file. Variants with membership in dbSNP can be identified by using the appropriate SNPlocs package for the hg19 genome (GRCh37). ```{r examine_dbSNP, message=FALSE, warning=FALSE} library(SNPlocs.Hsapiens.dbSNP144.GRCh37) vcf_rsids <- names(rowRanges(vcf)) chr22snps <- snpsBySeqname(SNPlocs.Hsapiens.dbSNP144.GRCh37, "22") chr22_rsids <- mcols(chr22snps)$RefSNP_id in_dbSNP <- vcf_rsids %in% chr22_rsids table(in_dbSNP) ``` Info variables of interest are 'VT', 'LDAF' and 'RSQ'. The header offers more details on these variables. ```{r header_info} info(header(vcf))[c("VT", "LDAF", "RSQ"),] ``` Create a data frame of quality measures of interest ... ```{r examine_quality} metrics <- data.frame(QUAL=qual(vcf), in_dbSNP=in_dbSNP, VT=info(vcf)$VT, LDAF=info(vcf)$LDAF, RSQ=info(vcf)$RSQ) ``` and visualize the distribution of qualities using `r CRANpkg("ggplot2")`. For instance, genotype imputation quality is higher for the known variants in dbSNP. ```{r examine_ggplot2, message=FALSE, warning=FALSE, fig=TRUE} library(ggplot2) ggplot(metrics, aes(x=RSQ, fill=in_dbSNP)) + geom_density(alpha=0.5) + scale_x_continuous(name="MaCH / Thunder Imputation Quality") + scale_y_continuous(name="Density") + theme(legend.position="top") ``` ## Import data subsets When working with large VCF files it may be more efficient to read in subsets of the data. This can be accomplished by selecting genomic coordinates (ranges) or by specific fields from the VCF file. ### Select genomic coordinates To read in a portion of chromosome 22, create a `GRanges` with the regions of interest. ```{r subset_ranges} rng <- GRanges(seqnames="22", ranges=IRanges( start=c(50301422, 50989541), end=c(50312106, 51001328), names=c("gene_79087", "gene_644186"))) ``` When ranges are specified, the VCF file must have an accompanying Tabix index file. See `indexTabix` for help creating an index. ```{r subset_TabixFile} tab <- TabixFile(fl) vcf_rng <- readVcf(tab, "hg19", param=rng) ``` The `paramRangesID` column distinguishes which records came from which param range. ```{r} head(rowRanges(vcf_rng), 3) ``` ### Select VCF fields Data import can also be defined by the `fixed`, `info` and `geno` fields. Fields available for import are described in the header information. To view the header before reading in the data, use `ScanVcfHeader`. ```{r subset_scanVcfHeader} hdr <- scanVcfHeader(fl) ## e.g., INFO and GENO fields head(info(hdr), 3) head(geno(hdr), 3) ``` To subset on "LDAF" and "GT" we specify them as `character` vectors in the `info` and `geno` arguments to `ScanVcfParam`. This creates a `ScanVcfParam` object which is used as the `param` argument to `readVcf`. ```{r subset_ScanVcfParam} ## Return all 'fixed' fields, "LAF" from 'info' and "GT" from 'geno' svp <- ScanVcfParam(info="LDAF", geno="GT") vcf1 <- readVcf(fl, "hg19", svp) names(geno(vcf1)) ``` To subset on both genomic coordinates and fields the `ScanVcfParam` object must contain both. ```{r subset_ScanVcfParam_new} svp_all <- ScanVcfParam(info="LDAF", geno="GT", which=rng) svp_all ``` # Locating variants in and around genes Variant location with respect to genes can be identified with the `locateVariants` function. Regions are specified in the `region` argument and can be one of the following constructors: CodingVariants, IntronVariants, FiveUTRVariants, ThreeUTRVariants, IntergenicVariants, SpliceSiteVariants or PromoterVariants. Location definitions are shown in Table \@ref(tab:table). | Location | Details | |------------|------------------------------------------------------------| | coding | falls *within* a coding region | | fiveUTR | falls *within* a 5' untranslated region | | threeUTR | falls *within* a 3' untranslated region | | intron | falls *within* an intron region | | intergenic | does not fall *within* a transcript associated with a gene | | spliceSite | overlaps any portion of the first 2 or last 2 | | promoter | falls *within* a promoter region of a transcript | : (\#tab:table) Variant locations For overlap methods to work properly the chromosome names (seqlevels) must be compatible in the objects being compared. The VCF data chromosome names are represented by number, i.e., '22', but the TxDb chromosome names are preceded with 'chr'. Seqlevels in the VCF can be modified with the `seqlevels` function. ```{r locate_rename_seqlevels, message=FALSE, warning=FALSE} library(TxDb.Hsapiens.UCSC.hg19.knownGene) txdb <- TxDb.Hsapiens.UCSC.hg19.knownGene seqlevels(vcf) <- "chr22" rd <- rowRanges(vcf) loc <- locateVariants(rd, txdb, CodingVariants()) head(loc, 3) ``` Locate variants in all regions with the `AllVariants()` constructor, ```{r AllVariants, eval=FALSE} allvar <- locateVariants(rd, txdb, AllVariants()) ``` To answer gene-centric questions data can be summarized by gene reguardless of transcript. ```{r locate_gene_centric} ## Did any coding variants match more than one gene? splt <- split(mcols(loc)$GENEID, mcols(loc)$QUERYID) table(sapply(splt, function(x) length(unique(x)) > 1)) ## Summarize the number of coding variants by gene ID. splt <- split(mcols(loc)$QUERYID, mcols(loc)$GENEID) head(sapply(splt, function(x) length(unique(x))), 3) ``` # Amino acid coding changes `predictCoding` computes amino acid coding changes for non-synonymous variants. Only ranges in query that overlap with a coding region in the `subject` are considered. Reference sequences are retrieved from either a `BSgenome` or fasta file specified in `seqSource`. Variant sequences are constructed by substituting, inserting or deleting values in the `varAllele` column into the reference sequence. Amino acid codes are computed for the variant codon sequence when the length is a multiple of 3. The query argument to `predictCoding` can be a `GRanges` or `VCF`. When a `GRanges` is supplied the `varAllele` argument must be specified. In the case of a `VCF`, the alternate alleles are taken from `alt()` and the `varAllele` argument is not specified. The result is a modified `query` containing only variants that fall within coding regions. Each row represents a variant-transcript match so more than one row per original variant is possible. ```{r predictCoding, warning=FALSE} library(BSgenome.Hsapiens.UCSC.hg19) coding <- predictCoding(vcf, txdb, seqSource=Hsapiens) coding[5:7] ``` Using variant rs114264124 as an example, we see varAllele `A` has been substituted into the `refCodon` `CGG` to produce `varCodon` `CAG.` The `refCodon` is the sequence of codons necessary to make the variant allele substitution and therefore often includes more nucleotides than indicated in the range (i.e. the range is 50302962, 50302962, width of 1). Notice it is the second position in the `refCodon` that has been substituted. This position in the codon, the position of substitution, corresponds to genomic position 50302962. This genomic position maps to position 698 in coding region-based coordinates and to triplet 233 in the protein. This is a non-synonymous coding variant where the amino acid has changed from `R` (Arg) to `Q` (Gln). When the resulting `varCodon` is not a multiple of 3 it cannot be translated. The consequence is considered a `frameshift` and `varAA` will be missing. ```{r predictCoding_frameshift} ## CONSEQUENCE is 'frameshift' where translation is not possible coding[mcols(coding)$CONSEQUENCE == "frameshift"] ``` # SIFT and PolyPhen Databases From `predictCoding` we identified the amino acid coding changes for the non-synonymous variants. For this subset we can retrieve predictions of how damaging these coding changes may be. SIFT (Sorting Intolerant From Tolerant) and PolyPhen (Polymorphism Phenotyping) are methods that predict the impact of amino acid substitution on a human protein. The SIFT method uses sequence homology and the physical properties of amino acids to make predictions about protein function. PolyPhen uses sequence-based features and structural information characterizing the substitution to make predictions about the structure and function of the protein. Collated predictions for specific dbSNP builds are available as downloads from the SIFT and PolyPhen web sites. These results have been packaged into `r Biocpkg("SIFT.Hsapiens.dbSNP132")` and `r Biocpkg("PolyPhen.Hsapiens.dbSNP131")` and are designed to be searched by rsid. Variants that are in dbSNP can be searched with these database packages. When working with novel variants, SIFT and PolyPhen must be called directly. See references for home pages. Identify the non-synonymous variants and obtain the rsids. ```{r nonsynonymous} nms <- names(coding) idx <- mcols(coding)$CONSEQUENCE == "nonsynonymous" nonsyn <- coding[idx] names(nonsyn) <- nms[idx] rsids <- unique(names(nonsyn)[grep("rs", names(nonsyn), fixed=TRUE)]) ``` Detailed descriptions of the database columns can be found with `?SIFTDbColumns` and `?PolyPhenDbColumns`. Variants in these databases often contain more than one row per variant. The variant may have been reported by multiple sources and therefore the source will differ as well as some of the other variables. It is important to keep in mind the pre-computed predictions in the SIFT and PolyPhen packages are based on specific gene models. SIFT is based on Ensembl and PolyPhen on UCSC Known Gene. The `TxDb` we used to identify the coding snps was based on UCSC Known Gene so we will use PolyPhen for predictions. PolyPhen provides predictions using two different training datasets and has considerable information about 3D protein structure. See `?PolyPhenDbColumns` or the PolyPhen web site listed in the references for more details. Query the PolyPhen database, ```{r polyphen, message=FALSE, warning=FALSE} library(PolyPhen.Hsapiens.dbSNP131) pp <- select(PolyPhen.Hsapiens.dbSNP131, keys=rsids, cols=c("TRAININGSET", "PREDICTION", "PPH2PROB")) head(pp[!is.na(pp$PREDICTION), ]) ``` # Other operations ## Create a SnpMatrix The 'GT' element in the `FORMAT` field of the VCF represents the genotype. These data can be converted into a `SnpMatrix` object which can then be used with the functions offered in `r Biocpkg("snpStats")` and other packages making use of the `SnpMatrix` class. The `genotypeToSnpMatrix` function converts the genotype calls in `geno` to a `SnpMatrix`. No dbSNP package is used in this computation. The return value is a named list where 'genotypes' is a `SnpMatrix` and 'map' is a `DataFrame` with SNP names and alleles at each loci. The `ignore` column in 'map' indicates which variants were set to NA (missing) because they met one or more of the following criteria, - variants with >1 ALT allele are set to NA - only single nucleotide variants are included; others are set to NA - only diploid calls are included; others are set to NA See ?`genotypeToSnpMatrix` for more details. ```{r snpMatrix, message=FALSE} res <- genotypeToSnpMatrix(vcf) res ``` In the map DataFrame, allele.1 represents the reference allele and allele.2 is the alternate allele. ```{r snpMatrix_ALT} allele2 <- res$map[["allele.2"]] ## number of alternate alleles per variant unique(elementNROWS(allele2)) ``` In addition to the called genotypes, genotype likelihoods or probabilities can also be converted to a `SnpMatrix`, using the `r Biocpkg("snpStats")` encoding of posterior probabilities as byte values. To use the values in the 'GL' or 'GP' `FORMAT` field instead of the called genotypes, use the `uncertain=TRUE` option in `genotypeToSnpMatrix`. ```{r message=FALSE} fl.gl <- system.file("extdata", "gl_chr1.vcf", package="VariantAnnotation") vcf.gl <- readVcf(fl.gl, "hg19") geno(vcf.gl) ## Convert the "GL" FORMAT field to a SnpMatrix res <- genotypeToSnpMatrix(vcf.gl, uncertain=TRUE) res t(as(res$genotype, "character"))[c(1,3,7), 1:5] ## Compare to a SnpMatrix created from the "GT" field res.gt <- genotypeToSnpMatrix(vcf.gl, uncertain=FALSE) t(as(res.gt$genotype, "character"))[c(1,3,7), 1:5] ## What are the original likelihoods for rs58108140? geno(vcf.gl)$GL["rs58108140", 1:5] ``` For variant rs58108140 in sample NA06989, the \"A/B\" genotype is much more likely than the others, so the `SnpMatrix` object displays the called genotype. ## Write out VCF files A VCF file can be written out from data stored in a `VCF` class. ```{r writeVcf, message=FALSE, warning=FALSE} fl <- system.file("extdata", "ex2.vcf", package="VariantAnnotation") out1.vcf <- tempfile() out2.vcf <- tempfile() in1 <- readVcf(fl, "hg19") writeVcf(in1, out1.vcf) in2 <- readVcf(out1.vcf, "hg19") writeVcf(in2, out2.vcf) in3 <- readVcf(out2.vcf, "hg19") identical(rowRanges(in1), rowRanges(in3)) identical(geno(in1), geno(in2)) ``` # Performance Targeted queries can greatly improve the speed of data input. When all data from the file are needed define a `yieldSize` in the `TabixFile` to iterate through the file in chunks. ```{r eval=FALSE} readVcf(TabixFile(fl, yieldSize=10000)) ``` `readVcf` can be used with a to select any combination of INFO and GENO fields, samples or genomic positions. ```{r eval=FALSE} readVcf(TabixFile(fl), param=ScanVcfParam(info='DP', geno='GT')) ``` While `readvcf` offers the flexibility to define combinations of INFO, GENO and samples in the `ScanVcfParam`, sometimes only a single field is needed. In this case the lightweight `read` functions (`readGT`, `readInfo` and `readGeno`) can be used. These functions return the single field as a matrix instead of a `VCF` object. ```{r eval=FALSE} readGT(fl) ``` The table below highlights the speed differences of targeted queries vs reading in all data. The test file is from 1000 Genomes and has 494328 variants, 1092 samples, 22 INFO, and 3 GENO fields and is located at http://ftp.1000genomes.ebi.ac.uk/vol1/ftp/release/20101123/. `yieldSize` is used to define chunks of 100, 1000, 10000 and 100000 variants. For each chunk size three function calls are compared: `readGT` reading only GT, `readVcf` reading both `GT` and `ALT` and finally `readVcf` reading in all the data. ```{r eval=FALSE} library(microbenchmark) fl <- "ALL.chr22.phase1_release_v3.20101123.snps_indels_svs.genotypes.vcf.gz" ys <- c(100, 1000, 10000, 100000) ## readGT() input only 'GT': fun <- function(fl, yieldSize) readGT(TabixFile(fl, yieldSize)) lapply(ys, function(i) microbenchmark(fun(fl, i), times=5) ## readVcf() input only 'GT' and 'ALT': fun <- function(fl, yieldSize, param) readVcf(TabixFile(fl, yieldSize), "hg19", param=param) param <- ScanVcfParam(info=NA, geno="GT", fixed="ALT") lapply(ys, function(i) microbenchmark(fun(fl, i, param), times=5) ## readVcf() input all variables: fun <- function(fl, yieldSize) readVcf(TabixFile(fl, yieldSize), "hg19") lapply(ys, function(i) microbenchmark(fun(fl, i), times=5)) ``` n records readGT readVcf (GT and ALT) readVcf (all) ----------- -------- ---------------------- --------------- 100 0.082 0.128 0.501 1000 0.609 0.508 5.878 10000 5.972 6.164 68.378 100000 78.593 81.156 693.654 : (\#tab:performance) Targeted queries (time in seconds) # References Wang K, Li M, Hakonarson H, (2010), ANNOVAR: functional annotation of genetic variants from high-throughput sequencing data. Nucleic Acids Research, Vol 38, No. 16, e164. McLaren W, Pritchard B, RiosD, et. al., (2010), Deriving the consequences of genomic variants with the Ensembl API and SNP Effect Predictor. Bioinformatics, Vol. 26, No. 16, 2069-2070. SIFT home page: http://sift.bii.a-star.edu.sg/ PolyPhen home page: http://genetics.bwh.harvard.edu/pph2/ # Session Information ```{r sessionInfo, echo=FALSE} sessionInfo() ```