PWMEnrich/.Rinstignore0000644000175100017510000000014714614305422015675 0ustar00biocbuildbiocbuildinst/doc/Makefile inst/doc/PWMEnrich.tex inst/doc/references.bib inst/doc/PWMEnrich-plots.pdf .roxygen PWMEnrich/build/0000755000175100017510000000000014614351234014470 5ustar00biocbuildbiocbuildPWMEnrich/build/vignette.rds0000644000175100017510000000035714614351234017034 0ustar00biocbuildbiocbuilduP 0 @ vSZg7j擫nC+ɗKcBM(EQdKD|S*T,ONycNqb$'ÐzCPI~Q?X@2wn "ar#uk`JdR }bոVhH߮; PWMEnrich/DESCRIPTION0000644000175100017510000000271714614351235015107 0ustar00biocbuildbiocbuildPackage: PWMEnrich Type: Package Title: PWM enrichment analysis Version: 4.40.0 Date: 2015-09-25 Author: Robert Stojnic, Diego Diez Maintainer: Diego Diez LazyLoad: yes Description: A toolkit of high-level functions for DNA motif scanning and enrichment analysis built upon Biostrings. The main functionality is PWM enrichment analysis of already known PWMs (e.g. from databases such as MotifDb), but the package also implements high-level functions for PWM scanning and visualisation. The package does not perform "de novo" motif discovery, but is instead focused on using motifs that are either experimentally derived or computationally constructed by other tools. License: LGPL (>= 2) Depends: R (>= 3.5.0), methods, BiocGenerics, Biostrings Imports: grid, seqLogo, gdata, evd, S4Vectors Suggests: MotifDb, BSgenome, BSgenome.Dmelanogaster.UCSC.dm3, PWMEnrich.Dmelanogaster.background, testthat, gtools, parallel, PWMEnrich.Hsapiens.background, PWMEnrich.Mmusculus.background, BiocStyle, knitr biocViews: MotifAnnotation, SequenceMatching, Software VignetteBuilder: knitr RoxygenNote: 7.1.1 Encoding: UTF-8 git_url: https://git.bioconductor.org/packages/PWMEnrich git_branch: RELEASE_3_19 git_last_commit: fc31a1e git_last_commit_date: 2024-04-30 Repository: Bioconductor 3.19 Date/Publication: 2024-04-30 NeedsCompilation: no Packaged: 2024-05-01 05:26:53 UTC; biocbuild PWMEnrich/inst/0000755000175100017510000000000014614351234014346 5ustar00biocbuildbiocbuildPWMEnrich/inst/doc/0000755000175100017510000000000014614351234015113 5ustar00biocbuildbiocbuildPWMEnrich/inst/doc/PWMEnrich.pdf0000644000175100017510000552121614614351234017416 0ustar00biocbuildbiocbuild%PDF-1.5 % 78 0 obj << /Length 2604 /Filter /FlateDecode >> stream xr@V7dލ7ƱH倶IȴBˤPwcY2KדFHHRhDa%J`L.w)q먡2ؑE2e$2Ӧ?b_$O?`)(Upi B ?Dz;7˞ؾw[Wጤ0_7S sJ7wYٜOX.'4/ff2RmoFeTwnvD}ݯzԿiYں,wKt78uC FR~ۋ2/Z~^[|•q[4 8񃞼|{'{^q,yLyyJvnCFB:5ZeZ;Jӫ ܪt*xAN$ʀ?)`G̈́VNٱvy,kV~.j4A³Ί檬&,K?g17 p2 2T0"s 7MA~T*}=IYP1r!)p6PɨRRHA~S^v>-'L>%˘8 Մ9J_:TaIS\ШP:`߶Cnw?Shewk2`vZP_ nJM5cc86ޱq~2UC۲|KN(*h8̓)T Es0 6sXuhdZvAsBأ(S2ǂ̽^ԪEi9 F*cmOsq!>>h(4}cWe|?/DK^W6XjfW3R #aBk\2Px"GcD:dA]Ӆ<\6u |^^%;FxJ$pʶ_gW_H|w)%E__!{4#xƷQ,&6bk,XKHJWMށZyljJGWTM__QF0ԫվi:* VrdɱqܗZ^NDK4ba#(GGy8yRnkqiYp`w!k0L{HFGûJ޵70˘c*qM+e>ڧ&S-1sm|{W5`tT7c0E|k)z3 *~- _}0`Mx7wɟ'6M$lxjN% >M"^$O53<'ے%S|T}2\rBEOvI,0㠫P ֽ֗׫PkL=i/Ͼ}&3p R׃\{%2# os[bl- !ߠŹ܆kق[`_V/x.d@?ŕ4@;rS9y.mn m_UZOŷ@5%S&fvXӾϸg(ާJ-:}N"\b&Rq9"tMEVE=kG^דn5)8$NL#^qxDA@oNYnBI@;(ܐ7 @*$̩l9 tI.QiuN(7ɤ %h2*4^G,hvfop m9o>v,1ALqFz/m<\u)AsԎbe7srUTX ̴]SWD8f.AY *N3Ya.Gk zǒvc(%X_gWUƱ&%~qUTG`N *"x. 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----include=FALSE------------------------------------------------------------ library(knitr) opts_chunk$set( tidy=FALSE,dev='pdf', message=FALSE, warning=FALSE ) ## ----style-knitr, eval=TRUE, echo=FALSE, results="asis"-------------------- BiocStyle::latex() ## ----simple,echo=TRUE------------------------------------------------------ library(PWMEnrich) library(PWMEnrich.Dmelanogaster.background) # load the pre-compiled lognormal background data(PWMLogn.dm3.MotifDb.Dmel) # load the stripe2 sequences from a FASTA file for motif enrichment sequence = readDNAStringSet(system.file(package="PWMEnrich", dir="extdata", file="stripe2.fa")) sequence # perform motif enrichment! res = motifEnrichment(sequence, PWMLogn.dm3.MotifDb.Dmel) report = sequenceReport(res, 1) report # plot the motif with P-value < 0.05 plot(report[report$p.value < 0.05], fontsize=7, id.fontsize=6) ## ----stripe2visual,echo=TRUE,fig.width=4,fig.height=4,fig.align='center'---- # extract the 3 PWMs for the TFs we are interested in ids = c("bcd_FlyReg_FBgn0000166", "gt_FlyReg_FBgn0001150", "Kr") sel.pwms = PWMLogn.dm3.MotifDb.Dmel$pwms[ids] names(sel.pwms) = c("bcd", "gt", "Kr") # scan and get the raw scores scores = motifScores(sequence, sel.pwms, raw.scores=TRUE) # raw scores for the first (and only) input sequence dim(scores[[1]]) head(scores[[1]]) # score starting at position 1 of forward strand scores[[1]][1, "bcd"] # score for the reverse complement of the motif, starting at the same position scores[[1]][485, "bcd"] # plot plotMotifScores(scores, cols=c("green", "red", "blue")) ## ----motifEcdf,echo=TRUE,fig.width=4,fig.height=4,fig.align='center'------- library(BSgenome.Dmelanogaster.UCSC.dm3) # empirical distribution for the bcd motif bcd.ecdf = motifEcdf(sel.pwms$bcd, Dmelanogaster, quick=TRUE)[[1]] # find the score that is equivalent to the P-value of 1e-3 threshold.1e3 = log2(quantile(bcd.ecdf, 1 - 1e-3)) threshold.1e3 # replot only the bcd motif hits with the P-value cutoff of 1e-3 (0.001) plotMotifScores(scores, cols="green", sel.motifs="bcd", cutoff=threshold.1e3) # Convert scores into P-values pvals = 1 - bcd.ecdf(scores[[1]][,"bcd"]) head(pvals) ## ----tinman,echo=TRUE------------------------------------------------------ library(PWMEnrich.Dmelanogaster.background) # load the pre-compiled lognormal background data(PWMLogn.dm3.MotifDb.Dmel) sequences = readDNAStringSet(system.file(package="PWMEnrich", dir="extdata", file="tinman-early-top20.fa")) res = motifEnrichment(sequences, PWMLogn.dm3.MotifDb.Dmel) report = groupReport(res) report plot(report[1:10], fontsize=7, id.fontsize=5) ## ----tinman-alt,echo=TRUE-------------------------------------------------- report.top = groupReport(res, by.top.motifs=TRUE) report.top ## ----tinman2,echo=TRUE----------------------------------------------------- res # raw scores res$sequence.nobg[1:5, 1:2] # P-values res$sequence.bg[1:5, 1:2] ## ----parallel,echo=TRUE---------------------------------------------------- registerCoresPWMEnrich(4) ## ----parallel-stop,echo=TRUE----------------------------------------------- registerCoresPWMEnrich(NULL) ## ----bigmem,echo=TRUE------------------------------------------------------ useBigMemoryPWMEnrich(TRUE) ## ----bigmemoff,echo=TRUE--------------------------------------------------- useBigMemoryPWMEnrich(FALSE) ## ----readmotifs,echo=TRUE-------------------------------------------------- library(PWMEnrich.Dmelanogaster.background) motifs.denovo = readMotifs(system.file(package="PWMEnrich", dir="extdata", file="example.transfac"), remove.acc=TRUE) motifs.denovo # convert count matrices into PWMs genomic.acgt = getBackgroundFrequencies("dm3") pwms.denovo = toPWM(motifs.denovo, prior=genomic.acgt) bg.denovo = makeBackground(pwms.denovo, organism="dm3", type="logn", quick=TRUE) # use new motifs for motif enrichment res.denovo = motifEnrichment(sequences[1:5], bg.denovo) groupReport(res.denovo) ## ----bg-investigate,echo=TRUE---------------------------------------------- bg.denovo bg.denovo$bg.mean ## ----custombg,echo=TRUE---------------------------------------------------- library(PWMEnrich.Dmelanogaster.background) data(dm3.upstream2000) # make a lognormal background for the two motifs using only first 20 promoters bg.seq = dm3.upstream2000[1:20] # the sequences are split into 100bp chunks and fitted bg.custom = makeBackground(pwms.denovo, bg.seq=bg.seq, type="logn", bg.len=100, bg.source="20 promoters split into 100bp chunks") bg.custom ## ----sessionInfo,echo=FALSE,results='asis'--------------------------------- toLatex(sessionInfo()) PWMEnrich/inst/doc/PWMEnrich.Rnw0000644000175100017510000005173414614305422017407 0ustar00biocbuildbiocbuild%\VignetteEngine{knitr::knitr} %\VignetteIndexEntry{Overview of the 'PWMEnrich' package} %\VignetteKeywords{Motif enrichment, PWM} %\VignettePackage{PWMEnrich} \documentclass{article} \usepackage{float} \usepackage{a4wide} <>= library(knitr) opts_chunk$set( echo=TRUE,eval=TRUE,cache=FALSE,tidy=FALSE ) @ <>= library(knitr) opts_chunk$set( tidy=FALSE,dev='pdf', message=FALSE, warning=FALSE ) @ <>= BiocStyle::latex() @ %% colors \usepackage{color} \definecolor{Red}{rgb}{0.7,0,0} \definecolor{Blue}{rgb}{0,0,0.8} \hypersetup{% hyperindex = {true}, colorlinks = {true}, linktocpage = {true}, plainpages = {false}, linkcolor = {Blue}, citecolor = {Blue}, urlcolor = {Blue}, pdfstartview = {Fit}, pdfpagemode = {UseOutlines}, pdfview = {XYZ null null null} } \author{Robert Stojni\'{c}\footnote{ e-mail: \email{robert.stojnic@gmail.com}, Cambridge Systems Biology Institute, University of Cambridge, UK} } \begin{document} \title{Overview of the \Biocpkg{PWMEnrich} package} \maketitle \tableofcontents \section{Introduction}\label{sec:intro} The main functionality of the package is Position Weight Matrix (PWM)\footnote{In this vignette we use "PWM", "DNA motif" and "motif" interchangeably.} enrichment analysis in a single sequence (e.g. enhancer of interest) or a set of sequences (e.g. set of ChIP-chip/seq peaks). Note that this is not the same as \textit{de-novo} motif finding which discovers novel motifs, nor motif comparison which aligns motifs. The package is built upon \Robject{Biostrings} and offers high-level functions to scan for DNA motif occurrences and compare them against a genomic background. There are multiple packages with pre-compiled genomic backgrounds such as \Robject{PWMEnrich.Dmelanogaster.background}, \Robject{PWMEnrich.Hsapiens.background} and \Robject{PWMEnrich.Mmusculus.background}. In these packages the genomic distribution is calculated for motifs from the \Robject{MotifDb} database. The \Robject{PWMEnrich} package contains all the functions used to create these packages, so you can calculate your own background distributions for your own set of motifs. In this vignette we will use the \textit{Drosophila} package, but the other background packages are used in the same way (see Section \ref{sec:human} for minor human-specific differences). \subsection{Implemented algorithms} \Robject{PWMEnrich} uses the PWM scanning algorithm implemented by the package \Robject{Biostrings}. This package returns PWM scores at each position on one strand of a sequence. \Robject{PWMEnrich} extends this with a higher-level functions which automatically scans both strands for multiple motifs and sequences. The main goal of the package is to assess the enrichment of motif hits in a sequence (or group of sequences) compared to a genomic background. The traditional way of doing this is to use a threshold for the PWM score and count the number of motif hits in the sequence(s) of interest. Since this converts the sequence into a binary bound/not-bound string, the enrichment of binding events can be assessed using a binomial formula. The \Robject{PWMEnrich} package implements this algorithm, but by default uses a lognormal threshold-free approach \cite{stojnic_2012} which is related to the score used in Clover \cite{frith_detection_2004}. In the lognormal threshold-free approach average affinity is calculated over the whole sequence (or set of sequences) and compared to the average affinity of length-matched sequences from the genomic background. This approach performs better or same as the best threshold approach \cite{stojnic_2012}, with the added benefit of not having to choose a threshold or compare the results for multiple thresholds. We will use this threshold-free approach in all of our examples. Please consult the reference manual on how to use the fixed-threshold algorithms. \subsection{S4 class structure and accessors} As the \Biocpkg{PWMEnrich} package builds upon the \Biocpkg{Biostrings} package it uses the classes from this package to represent DNA sequences (\Robject{DNAString} and \Robject{DNAStringSet}). FASTA files can be loaded using functions from \Biocpkg{Biostrings} such as \Robject{readDNAStringSet}. The package introduces a new class \Robject{PWM} to represent a PWM together with the frequency matrix and other parameters (background nucleotide frequencies and pseudo-counts). All motif scoring is performed by the \Biocpkg{Biostrings} package which is why the \Biocpkg{PWMEnrich} package also returns log2 scores instead of more common log base \textit{e} scores. The results of motif scanning are stored in objects of class \Robject{MotifEnrichmentResults} and \Robject{MotifEnrichmentReport}. The package also introduces a number of classes that represent different background distributions: \Robject{PWMLognBackground}, \Robject{PWMCutoffBackground}, \Robject{PWMEmpiricalBackground}, \Robject{PWMGEVBackground}. In all cases, the classes are implemented with a list-like interface, that is, individual pieces of information within the objects are accessibly using \Rfunction{names(obj)} and \Rfunction{obj\$prop}. \section{Use case 1: Finding enrichment motifs in a single sequence} One of the most well-known example of combinatorial control by transcription factors in \textit{Drosophila} is the \textit{even skipped (eve)} stripe 2 enhancer. This well-studied enhancer has a number of annotated binding sites for TFs \textit{Kr}, \textit{vfl}, \textit{bcd}, \textit{hb} and \textit{gt}. We will use this enhancer as an example as we already know its functional structure. In order to predict which TFs are likely to functionally bind to the stripe 2 enhancer, we will calculate motif enrichment for a set of experimentally derived motifs from the \Biocpkg{MotifDb} database. We will do this by comparing the average affinity of each motif in the stripe 2 enhancers to the affinity over all \textit{D. melanogaster} promoters\footnote{For more information see \cite{stojnic_2012}}. These background distributions are already pre-calculated in the \Robject{PWMEnrich.Dmelanogaster.background} package which we will simply load and use. See the last section of this vignette for using your own motifs and background sequences. <>= library(PWMEnrich) library(PWMEnrich.Dmelanogaster.background) # load the pre-compiled lognormal background data(PWMLogn.dm3.MotifDb.Dmel) # load the stripe2 sequences from a FASTA file for motif enrichment sequence = readDNAStringSet(system.file(package="PWMEnrich", dir="extdata", file="stripe2.fa")) sequence # perform motif enrichment! res = motifEnrichment(sequence, PWMLogn.dm3.MotifDb.Dmel) report = sequenceReport(res, 1) report # plot the motif with P-value < 0.05 plot(report[report$p.value < 0.05], fontsize=7, id.fontsize=6) @ The main function we used is \Robject{motifEnrichment} which took our sequence and calculated motif enrichment using the lognormal affinity background distribution (fitted on a set of 10031 \textit{D. melanogaster} 2kb promoters). This function returns a set of scores and P-values for our sequence. We then used the \Robject{sequenceReport} function that create a ranked list of motifs, which we then plot using \Robject{plot}. The first column is the rank, the second shows the target name, which is either a gene name, an isoform name (such as ttk-PF), or a dimer name (such as tgo\_sim not present in this list). The next column in the plot is the PWM logo, and after that the motif ID. This ID comes from the \Robject{MotifDb} package and can be used to look up further information about the motif (such as the motif source). The next-to-last column is the raw affinity score, and the last column is the P-value of motif enrichment. As we can see, the top of the list is dominated by motifs similar to bcd. By further examining the list, we find we recovered the Kr, bcd and gt motifs, but not the vfl and hb motifs. These two TFs (vfl and hb) have the smallest number of annotated binding sites out of the five TFs in the stripe 2 enhancer. As a result, this affinity is not large enough to be picked up by motif enrichment. However, the other three motifs were picked up. We find this to be the typical case for many enhancers. \section{Use case 2: Examining the binding sites} We continue with our example of the eve stripe 2 enhancer from the previous section. We now want to visualise the binding sites for Kr, bcd and gt. <>= # extract the 3 PWMs for the TFs we are interested in ids = c("bcd_FlyReg_FBgn0000166", "gt_FlyReg_FBgn0001150", "Kr") sel.pwms = PWMLogn.dm3.MotifDb.Dmel$pwms[ids] names(sel.pwms) = c("bcd", "gt", "Kr") # scan and get the raw scores scores = motifScores(sequence, sel.pwms, raw.scores=TRUE) # raw scores for the first (and only) input sequence dim(scores[[1]]) head(scores[[1]]) # score starting at position 1 of forward strand scores[[1]][1, "bcd"] # score for the reverse complement of the motif, starting at the same position scores[[1]][485, "bcd"] # plot plotMotifScores(scores, cols=c("green", "red", "blue")) @ Here we used the \Robject{motifScores} function to obtain the raw scores at each position in the sequence. The result of this function is a list of matrices, each element of the list corresponding to an input sequence. In this case we had only one input sequence, and as a result we get a list of length 1. The matrix of scores is a 968 x 3 matrix, where the rows correspond to the two strands (2 x 484) and the columns correspond to motifs. It is important to remember that the scores are in real and not log space. In other words, a conventional PWM log2 score of 3 is represented as number 8 ($2^3$). The scores for the two strands are concatenated one after the other. Therefore, row 1 has the scores for the motif starting at position 1, and row 485 has the score at the same position, but with the reverse complement of the motif (i.e. motif score on the reverse strand). Note that there will be some NA values at the end of the sequence (e.g. position 484) because we do not support partial motif matches. Finally we use the \Robject{plotMotifScores} function to plot the log2 scores over the sequence. We colour-code the motifs with green, red and blue. The motif hits are shown as rectangles with the base being the length of the motif, and the hight being the log2 score of the motif hit. By default we show all motif hits with log2 scores larger then 0. The forward strand hits are shown on the top, and the reverse strand hits are shown on the bottom. We next might be interested in finding the P-value for individual motif hits so we can get an idea which sites are the most important. To do this we need to calculate the empirical PWM score distribution for single sites. We did not provide these values precalculated because they take up a very large amount of memory. To calculate it based on a set of promoter, we will need the \textit{D. melanogaster} genome sequence. Because the objects are so large, in this example we will determine the P-value only for the hits of the bcd motif, using only a small subset of promoters (controlled by the parameter \Robject{quick=TRUE}). <>= library(BSgenome.Dmelanogaster.UCSC.dm3) # empirical distribution for the bcd motif bcd.ecdf = motifEcdf(sel.pwms$bcd, Dmelanogaster, quick=TRUE)[[1]] # find the score that is equivalent to the P-value of 1e-3 threshold.1e3 = log2(quantile(bcd.ecdf, 1 - 1e-3)) threshold.1e3 # replot only the bcd motif hits with the P-value cutoff of 1e-3 (0.001) plotMotifScores(scores, cols="green", sel.motifs="bcd", cutoff=threshold.1e3) # Convert scores into P-values pvals = 1 - bcd.ecdf(scores[[1]][,"bcd"]) head(pvals) @ Here we have used the \Robject{motifEcdf} function to create an empirical cumulative distribution function (ECDF) for the bcd motif score on Drosophila promoters. This function returns an \Robject{ecdf} object which is part of base R. We can then use the quantile function to find which scores correspond to a P-value of 0.001, or we can use it to convert all the scores into P-values (not shown above). To plot the individual motif hits with P-values smaller than 0.001 we again use the \Robject{plotMotifScores} function, but now we apply the threshold so that only those motif hits above the threshold are drawn. In the last line we find out the positions of those motif hits where the P-value is smaller then 1e-3. Note that the values larger than the sequence length (484) indicate the reverse strand. Therefore, we find the four strong motif hits at positions 90 on the forward strand and 110, 354 and 475 on the reverse strand. Note that \Robject{plotMotifScores} can also plot multiple sequences on a single plot, and that the \Robject{cutoff} parameter can contain a vector of values if we wish to apply different cutoff to different motifs. \section{Use case 3: Finding enriched motifs in multiple sequences} So far we have only looked at motif enrichment in a single sequence, which was able to recover some but not all of the truly functional motifs. The power of the motif enrichment approach can be significantly boosted by performing it jointly on multiple sequences. For this example we are going to use the top 20 ChIP-chip peaks for transcription factor Tinman in \textit{Drosophila} \cite{jin_genome-wide_2013}. We are going to scan these 20 ChIP-chip peaks with all the motifs and then compare their enrichment to genomic background. Running on the whole set of peaks (i.e. thousands) is also possible but can take a long time (i.e. tens of minutes). The speed can be improved by using multiple CPU cores (see next section). <>= library(PWMEnrich.Dmelanogaster.background) # load the pre-compiled lognormal background data(PWMLogn.dm3.MotifDb.Dmel) sequences = readDNAStringSet(system.file(package="PWMEnrich", dir="extdata", file="tinman-early-top20.fa")) res = motifEnrichment(sequences, PWMLogn.dm3.MotifDb.Dmel) report = groupReport(res) report plot(report[1:10], fontsize=7, id.fontsize=5) @ As in Use case 1, the main function is \Robject{motifEnrichment} which took our sequences and calculated motif enrichment using the lognormal affinity background distribution (fitted on a set of 10031 \textit{D. melanogaster} 2kb promoters). We then applied the \Robject{groupReport} function to calculate the enrichment over the whole group of sequences. This produced a ranked list of motifs according to the estimated P-values. Then we used \Robject{plot} to plot the top 10 enriched motifs. The first three motifs are very similar and correspond to the tinman, which is the transcription factor for which the ChIP-chip experiment was performed. The first five columns are the same as before (see Use case 1). The sixth column gives the estimate P-value. The last column indicates the breadth of enrichment using a 5\% ranking threshold. This column helps to differentiate cases where the motif enrichment is strongly focused to a small subset of sequences (in which case breadth is small), versus being more widespread but weaker (in which case breadth is bigger). We can also sort by this column: <>= report.top = groupReport(res, by.top.motifs=TRUE) report.top @ This ranks motifs by breadth of enrichment, which is calculated by comparing enrichment \textit{between motifs} in individual sequences. This measure only makes sense when applied to a large number of sequence and when scanning with a large number of motifs (>20). The object returned by \Robject{motifEnrichment} has more information in it, as can be seen below: <>= res # raw scores res$sequence.nobg[1:5, 1:2] # P-values res$sequence.bg[1:5, 1:2] @ In these two matrices the rows correspond to the different input sequences and the columns correspond to motifs. The first matrix (sequence.nobg) contains the raw affinity scores, while the second (sequence.bg) contains the corresponding P-values. If you are using a fixed threshold background (e.g. scanning with \Robject{PWMPvalueCutoff1e3.dm3.MotifDb.Dmel}) the first matrix will contain the number of motif hits, and the second the corresponding Z-scores. \section{Using PWMEnrich on human sequences}\label{sec:human} Starting from PWMEnrich version 4.0 (October 2014) a new algorithm is used to better fit the background distributions in human sequences. The only difference from the usage perspective is in creating new background files - please make sure to set the parameter \Robject{algorithm="human"} in \Robject{makeBackground()} (and other related functions for creating backgrounds). This will instruct the function to fit separate parameters for different sequence lengths. The sequence lengths are obtained by multiplying the parameters \Robject{bg.len} and \Robject{bg.len.sizes}. The defaults are \Robject{bg.len=250bp} and \Robject{bg.len.size=c(1, 2, 4, 8, 16)}. This means that the P-values are the most accurate for sequences that are in the range of 250bp - 4000bp and the closest in size to 250bp, 500bp, 1000bp, 2000bp and 4000bp. \section{Speeding up execution} \subsection{Parallel execution} Motif scanning is the most time consuming operation. Because of this, the package has a support for parallel motif scanning using the \R{parallel} core package. Note that parallel execution is currently not supported on Windows. To turn on parallel scanning, simply register a number of cores available to the package: <>= registerCoresPWMEnrich(4) @ After this command is executed, all further calls to \Biocpkg{PWMEnrich} functions are going to be run in parallel using 4 cores (if possible). To turn off parallel execution call the function with parameter NULL: <>= registerCoresPWMEnrich(NULL) @ \subsection{Large memory backend} Motif scanning can be further speeded up by using large amount of memory. If you have an access to a machine with a lot of RAM, you can switch to the "big memory" backend: <>= useBigMemoryPWMEnrich(TRUE) @ From this point on, all motif scanning will be done using the optimised big memory backend. The memory requirement depends on the number of sequences scanned, and might require tens of GB of RAM. To turn it off: <>= useBigMemoryPWMEnrich(FALSE) @ \section{Customisation} \subsection{Using a custom set of PWMs}\label{sec:custom-pwm} Background motif distributions for a custom set of PWMs can be easily calculated for all model organisms. We will illustrate this by creating a new lognormal background for two \textit{de-novo} motifs in Drosophila. To load in the motifs the package provides functions to read standard JASPAR and TRANSFAC formats. <>= library(PWMEnrich.Dmelanogaster.background) motifs.denovo = readMotifs(system.file(package="PWMEnrich", dir="extdata", file="example.transfac"), remove.acc=TRUE) motifs.denovo # convert count matrices into PWMs genomic.acgt = getBackgroundFrequencies("dm3") pwms.denovo = toPWM(motifs.denovo, prior=genomic.acgt) bg.denovo = makeBackground(pwms.denovo, organism="dm3", type="logn", quick=TRUE) # use new motifs for motif enrichment res.denovo = motifEnrichment(sequences[1:5], bg.denovo) groupReport(res.denovo) @ We load in the count matrices and then convert them into PWMs using the genomic distributions of the A, C, G, T nucleotides. Next we use these PWMs to calculate the properties of the affinity distribution on the set of \textit{D. melanogaster} promoters. In this example we used \Robject{quick=TRUE} for illustrative purposes. This fits the parameters quickly on a reduced set of 100 promoters. We strongly discourage the users to use this parameter in their research, and instead only use it to obtain rough estimates and for testing. The resulting object \Robject{bg.denovo} can be used same as before to perform motif enrichment. The background object \Robject{bg.denovo} contains the two PWMs and their background distribution parameters. All of these can be accessed with the \$ operator. <>= bg.denovo bg.denovo$bg.mean @ %$ \subsection{Using a custom set of background sequences}\label{sec:custom-bg} Low-level functions are available for constructing custom backgrounds. We start with the two de-novo motifs from previous section and fit the background to first 20 \textit{D. melanogaster} promoters. <>= library(PWMEnrich.Dmelanogaster.background) data(dm3.upstream2000) # make a lognormal background for the two motifs using only first 20 promoters bg.seq = dm3.upstream2000[1:20] # the sequences are split into 100bp chunks and fitted bg.custom = makeBackground(pwms.denovo, bg.seq=bg.seq, type="logn", bg.len=100, bg.source="20 promoters split into 100bp chunks") bg.custom @ The resulting \Robject{bg.custom} object can be used as before for motif enrichment with the \Robject{motifEnrichment} function (as described before). \section{Session information} <>= toLatex(sessionInfo()) @ %\bibliographystyle{apalike} %\bibliography{references} \bibliography{references} \end{document} PWMEnrich/inst/extdata/0000755000175100017510000000000014614305422015776 5ustar00biocbuildbiocbuildPWMEnrich/inst/extdata/bg.seq-test.RData0000644000175100017510000025113114614305422021052 0ustar00biocbuildbiocbuild Gu0\=;ZVC궬-XK}Zd˖j̬ٙl\B$@ $!܄&r}!'@B@_UwtJ+g~W^zUuݶ6!DHhgޱ! #&a뉁L%wZfi1s`X=|v(g?v m-MrT*fBRNu^ 1݆_0S]D2e%J%d2LOd²%R Jvece҉ND,x,$A X6~*4R~P ؔб_&!䛂G g'7I po'@l)v.ɤ[P3҃(k},5@)I++K! dr(˔SNG) c '%9;T6ea)6;dRNar)i9Q@~vbH)ج$A&o]>(#g9RR/_(S@X 8UYKA$r!6tTˑ*)XR`:rdmٔuѻ,0n9j;قAU82w'(8ҀRӖ65-'!)w s447d.dBS2)tHZOJ6JR'KKXp4IGc%pJ++ $J LIQ@Zh G!&>!I$e2,|9qG:RR+PN:Eͱ.=:X G-?)G[ASzZM9#y998/'{Yvh@IQ8 :y%%%Pꎕt J#!4rVj!: S`e}F[MmBpK&QN dqDR>Jq9 X6rR#֞LHͤJCg%v̤l+ KNvשYl",gJ*)N":[ )GJc8:0A1/0QOX*Rwl&CPĶ4K݄B6v! jq `sK9hc;>%vf@:D!+BK$QR8rlVU)ir-HIݥcɝh7EtJҩmeII{hSe9 {*+9}N\$,/:>u{i84#ބcR4hIḺ3$CqD"E &䄎˂\'ؘ$_R_n%E崰hd˓hI_S)I@GLccaOaɖ);W &\%6+U!D\K#EE@3L0pzo|;IDR: N#|XOΑY4yB ON{I6 M;)ʙhl8 OI9ƥ}LR4S_KFniNz$VUʚ|^<҄6zK'}㔜#+!LI#w^Iv4e-&0~LrTDѽ=,IqJeOəٔƖM&Qxq&-r'e. 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S/>#_dAK/3D3|!%a&퓎N*× et7i_2|+%ỄewS ̀o2nEk[pyw_x: ,\<ο9w~}:q~ݎ/[XX-XB*T,Z Aݢ -U\f? -\81^vyKjk\KE9٨[~y~.(>;.7N_~B=kk[R˟sa΋yA,\X+|`^ݼ%"㛗s̫WWxnA-G%y)gyuKj~,W[Ot`6SW[_fIZmkn9)-ninjxBd1?Mw}F#^-w>C2?W [uζ;[Ƅ!:2Ft" Z PWMEnrich/inst/extdata/example.fa0000644000175100017510000000106214614305422017740 0ustar00biocbuildbiocbuild>tinD CATGTCAAGTGGCACTAAACATGACCTAATGGACCACACTGACCA GCCACGCCCCGTCGCGAAGGCGGAATATGAATATGCGACCCATAA ACAATTACTGTCTCGTCTTTCAATGTCGGCGGCAATGTTGCGGCG ACGTTTGCCCCTCCGTCGAGGATCCGGGACCCCGGTCTGGAGTCT AGACCGGCGCAGCTCTCAAGTGGAGAATTTGTCATTCCCATATAT CACACGAAGGATCGGGGAGTTGGCGATAAGCCAGGATAGAGTTCG CCCTGGAGCGCCGTTTAATTCATATACCGTTACAGGATCAAAGGT TCAAGAGCCGCTGTCGCAGCTGCGAGCCTCCCACC >tinB-180 GTCAACATGTGTGATTCGCATGTGTGGACCGCCGCACAGGGGCGT CCTTAATTGCCTGATGAGCCATGAAATGATGTCACCATGGATCCT GTCGCGCCAGCAGGAAGTGGGCAAAAAGTCCTCGTCCCAGCTCCC CGGGCTGTGTCCTCCGTGATGCAACATATGGCGGCCATATACGAG ACT PWMEnrich/inst/extdata/example.transfac0000644000175100017510000000303314614305422021153 0ustar00biocbuildbiocbuildAC oligo-analysis.asmb_m1 XX ID tin_like_motif XX DE vkcTCAAGTGgctw PO A C G T 1 12 10 10 5 2 5 7 13 12 3 2 24 6 5 4 1 0 0 36 5 0 36 0 1 6 36 0 1 0 7 37 0 0 0 8 0 1 36 0 9 0 0 0 37 10 0 0 36 1 11 5 6 22 4 12 4 19 7 7 13 8 8 6 15 14 10 4 8 15 XX CC program: feature CC matrix.nb: 1 CC matrix.nb: 1 CC sites: 37 CC consensus.strict: agcTCAAGTGgctt CC consensus.strict.rc: AAGCCACTTGAGCT CC consensus.IUPAC: vkcTCAAGTGgctw CC consensus.IUPAC.rc: WAGCCACTTGAGMB CC consensus.regexp: [acg][gt]cTCAAGTGgct[at] CC consensus.regexp.rc: [AT]AGCCACTTGAG[AC][CGT] XX // AC oligo-analysis.asmb_m2 XX ID gata_like_motif XX DE wmmAGATACam PO A C G T 1 17 7 6 12 2 17 12 6 7 3 13 19 7 3 4 42 0 0 0 5 0 0 42 0 6 42 0 0 0 7 0 0 0 42 8 42 0 0 0 9 0 42 0 0 10 21 5 8 8 11 12 16 5 9 XX CC program: feature CC matrix.nb: 2 CC matrix.nb: 2 CC sites: 42 CC consensus.strict: aacAGATACac CC consensus.strict.rc: GTGTATCTGTT CC consensus.IUPAC: wmmAGATACam CC consensus.IUPAC.rc: KTGTATCTKKW CC consensus.regexp: [at][ac][ac]AGATACa[ac] CC consensus.regexp.rc: [GT]TGTATCT[GT][GT][AT] XX // PWMEnrich/inst/extdata/jaspar-insecta.jaspar0000644000175100017510000004026414614305422022112 0ustar00biocbuildbiocbuild>MA0010.1 br_Z1 A [3 1 5 7 3 6 4 7 1 9 8 5 4 2 ] C [1 1 2 0 0 0 1 0 8 0 0 0 0 3 ] G [4 1 1 1 0 0 4 1 0 0 1 3 0 2 ] T [1 6 1 1 6 3 0 1 0 0 0 1 5 2 ] >MA0011.1 br_Z2 A [ 3 5 0 0 12 1 2 1 ] C [ 1 2 10 1 0 1 0 2 ] G [ 1 1 0 0 0 2 1 1 ] T [ 7 4 2 11 0 8 9 8 ] >MA0012.1 br_Z3 A [ 3 9 10 12 0 4 10 6 6 4 2 ] C [ 1 2 0 0 10 0 0 0 1 3 3 ] G [ 1 1 0 0 1 0 0 4 2 2 5 ] T [ 7 0 2 0 1 8 2 2 3 3 2 ] >MA0013.1 br_Z4 A [1 4 2 1 5 6 5 1 3 5 3 ] C [1 0 0 0 0 0 0 3 0 0 1 ] G [0 0 4 0 1 0 1 1 1 0 0 ] T [4 2 0 5 0 0 0 1 2 1 2 ] >MA0015.1 Cf2_II A [25 1 74 0 78 1 41 2 53 12 ] C [13 2 2 9 2 4 2 9 3 29 ] G [40 1 4 4 0 1 36 1 20 15 ] T [ 2 76 0 67 0 74 1 68 4 24 ] >MA0016.1 usp A [ 0 1 0 0 0 0 35 5 5 6 ] C [ 1 0 0 0 0 36 0 25 8 10 ] G [37 36 38 38 0 1 3 3 22 16 ] T [ 0 1 0 0 38 1 0 5 3 6 ] >MA0022.1 dl_1 A [ 0 0 0 0 1 1 1 0 0 1 1 3 ] C [ 5 0 1 0 1 0 0 0 3 9 9 5 ] G [ 6 12 11 10 3 2 0 0 0 0 1 5 ] T [ 2 1 1 3 8 10 12 13 10 3 2 0 ] >MA0023.1 dl_2 A [0 0 0 0 4 2 0 0 0 1 ] C [1 0 0 0 1 1 0 1 9 8 ] G [7 7 9 8 0 0 0 0 0 0 ] T [1 2 0 1 4 6 9 8 0 0 ] >MA0026.1 Eip74EF A [ 2 5 0 0 17 17 5 ] C [12 12 0 0 0 0 1 ] G [ 2 0 17 17 0 0 10 ] T [ 1 0 0 0 0 0 1 ] >MA0049.1 hb A [ 1 6 9 4 13 16 16 14 15 9 ] C [ 5 8 3 3 1 0 0 0 1 2 ] G [ 8 2 4 1 0 0 0 2 0 2 ] T [ 2 0 0 8 2 0 0 0 0 3 ] >MA0085.1 Su(H) A [ 1 0 0 0 0 3 0 10 10 5 1 1 4 3 4 2 ] C [ 3 4 0 0 0 0 0 0 0 5 7 2 2 1 3 1 ] G [ 3 0 10 0 10 7 10 0 0 0 1 4 2 4 2 2 ] T [ 3 6 0 10 0 0 0 0 0 0 1 3 2 2 1 5 ] >MA0086.1 sna A [ 0 39 3 2 0 0 ] C [39 0 0 0 0 0 ] G [ 1 0 37 38 0 38 ] T [ 0 1 0 0 40 2 ] >MA0094.1 Ubx A [ 3 72 79 1 ] C [ 0 4 1 5 ] G [ 0 3 6 4 ] T [85 9 2 78 ] >MA0126.1 ovo A [10 1 4 20 21 0 7 0 5 ] C [ 4 4 2 0 0 21 5 0 4 ] G [ 0 14 2 0 0 0 3 21 2 ] T [ 7 2 13 1 0 0 6 0 10 ] >MA0165.1 Abd-B A [ 1 0 5 21 0 3 13 ] C [ 0 0 0 0 3 0 0 ] G [ 0 0 0 0 0 11 5 ] T [20 21 16 0 18 7 3 ] >MA0166.1 Antp A [ 1 0 16 16 0 0 15 ] C [ 1 0 0 0 0 0 0 ] G [ 0 0 0 0 0 9 1 ] T [14 16 0 0 16 7 0 ] >MA0167.1 Awh A [ 3 1 33 40 0 0 34 ] C [16 0 0 0 0 0 0 ] G [ 0 0 7 0 1 8 4 ] T [21 39 0 0 39 32 2 ] >MA0168.1 B-H1 A [ 4 0 21 21 8 1 1 ] C [ 4 0 0 0 0 7 0 ] G [ 0 0 0 0 0 0 20 ] T [13 21 0 0 13 13 0 ] >MA0169.1 B-H2 A [ 6 0 21 21 5 3 0 ] C [ 2 0 0 0 0 1 0 ] G [ 1 0 0 0 0 2 21 ] T [12 21 0 0 16 15 0 ] >MA0170.1 C15 A [ 1 0 17 19 6 0 10 ] C [ 1 0 0 0 2 3 0 ] G [ 0 0 0 0 0 6 9 ] T [17 19 2 0 11 10 0 ] >MA0171.1 CG11085 A [ 0 0 13 13 1 2 1 ] C [ 3 0 0 0 0 1 0 ] G [ 0 0 0 0 0 2 12 ] T [10 13 0 0 12 8 0 ] >MA0172.1 CG11294 A [ 0 0 15 15 0 0 15 ] C [ 3 2 0 0 0 0 0 ] G [ 0 0 0 0 0 0 0 ] T [12 13 0 0 15 15 0 ] >MA0173.1 CG11617 A [ 1 0 10 17 0 17 0 ] C [ 0 0 0 0 17 0 0 ] G [ 0 0 3 0 0 0 0 ] T [16 17 4 0 0 0 1 ] >MA0174.1 CG42234 A [ 2 2 6 16 0 0 13 ] C [ 0 0 0 0 0 2 0 ] G [ 0 0 2 0 0 6 3 ] T [14 14 8 0 16 8 0 ] >MA0175.1 CG13424 A [ 2 0 21 21 0 3 8 ] C [ 7 0 0 0 0 0 0 ] G [ 1 0 0 0 0 4 13 ] T [11 21 0 0 21 14 0 ] >MA0176.1 CG15696 A [ 2 2 21 32 0 0 16 ] C [ 3 3 0 0 0 0 0 ] G [ 3 0 6 0 0 2 16 ] T [24 27 5 0 32 30 0 ] >MA0177.1 CG18599 A [ 4 0 25 25 0 0 23 ] C [10 0 0 0 0 1 0 ] G [ 1 0 0 0 0 8 2 ] T [10 25 0 0 25 16 0 ] >MA0178.1 CG32105 A [ 2 2 17 19 0 0 15 ] C [ 4 0 0 0 0 0 0 ] G [ 0 0 0 0 1 1 4 ] T [13 17 2 0 18 18 0 ] >MA0179.1 CG32532 A [ 2 0 23 23 0 0 12 ] C [ 6 0 0 0 0 0 0 ] G [ 2 0 0 0 0 2 10 ] T [13 23 0 0 23 21 1 ] >MA0180.1 Vsx2 A [ 1 2 0 13 13 0 0 10 1 ] C [ 1 2 0 0 0 0 0 0 2 ] G [ 5 0 0 0 0 0 0 3 10 ] T [ 6 9 13 0 0 13 13 0 0 ] >MA0181.1 Vsx1 A [ 1 0 22 22 0 2 15 ] C [ 8 0 0 0 0 0 0 ] G [ 1 0 0 0 0 3 6 ] T [12 22 0 0 22 17 1 ] >MA0182.1 CG4328 A [12 7 13 30 0 0 15 ] C [ 2 1 0 0 0 0 0 ] G [ 3 0 0 0 0 6 15 ] T [13 22 17 0 30 24 0 ] >MA0183.1 CG7056 A [ 7 0 0 21 26 0 7 22 ] C [ 3 7 6 0 0 3 0 0 ] G [ 2 0 7 2 0 3 1 4 ] T [14 19 13 3 0 20 18 0 ] >MA0184.1 CG9876 A [ 2 0 18 20 0 0 14 ] C [ 9 0 0 0 0 0 0 ] G [ 3 0 0 0 0 1 6 ] T [ 6 20 2 0 20 19 0 ] >MA0185.1 Deaf1 A [ 0 0 0 0 0 1 ] C [ 3 0 10 0 0 3 ] G [ 0 0 0 10 5 4 ] T [ 7 10 0 0 5 2 ] >MA0186.1 Dfd A [ 1 0 24 24 0 1 21 ] C [ 4 0 0 0 0 0 0 ] G [ 3 0 0 0 0 19 3 ] T [16 24 0 0 24 4 0 ] >MA0187.1 Dll A [ 1 22 23 0 3 10 1 ] C [ 0 0 0 0 0 0 12 ] G [ 0 1 0 0 3 9 4 ] T [22 0 0 23 17 4 6 ] >MA0188.1 Dr A [ 1 0 21 21 0 0 21 ] C [13 16 0 0 0 0 0 ] G [ 6 0 0 0 0 0 0 ] T [ 1 5 0 0 21 21 0 ] >MA0189.1 E5 A [ 5 3 43 42 1 5 34 ] C [15 0 0 0 0 0 0 ] G [ 6 0 0 0 1 16 9 ] T [17 40 0 1 41 22 0 ] >MA0190.1 Gsc A [ 0 22 22 0 0 0 ] C [ 0 0 0 0 22 14 ] G [ 0 0 0 0 0 2 ] T [22 0 0 22 0 6 ] >MA0191.1 HGTX A [ 4 0 20 20 0 1 15 ] C [ 2 0 0 0 0 0 0 ] G [ 3 0 0 0 0 6 5 ] T [11 20 0 0 20 13 0 ] >MA0192.1 Hmx A [ 1 0 20 20 1 0 4 ] C [ 3 0 0 0 0 3 0 ] G [ 0 0 0 0 0 0 16 ] T [16 20 0 0 19 17 0 ] >MA0193.1 Lag1 A [ 0 0 17 1 0 18 12 ] C [19 9 2 18 12 0 0 ] G [ 0 0 0 0 0 1 4 ] T [ 0 10 0 0 7 0 3 ] >MA0194.1 Lim1 A [ 0 0 18 18 0 0 17 ] C [ 2 0 0 0 0 0 0 ] G [ 0 0 0 0 0 0 1 ] T [16 18 0 0 18 18 0 ] >MA0195.1 Lim3 A [ 3 4 16 20 0 0 16 ] C [ 7 0 0 0 0 2 0 ] G [ 2 0 3 0 0 4 4 ] T [ 8 16 1 0 20 14 0 ] >MA0196.1 NK7.1 A [ 5 0 33 35 4 6 13 ] C [ 5 0 0 0 0 0 0 ] G [ 1 0 2 0 5 7 22 ] T [24 35 0 0 26 22 0 ] >MA0197.1 Oct A [ 2 8 1 22 22 0 2 15 ] C [ 4 3 0 0 0 0 3 1 ] G [ 7 0 0 0 0 0 6 1 ] T [ 9 11 21 0 0 22 11 5 ] >MA0198.1 OdsH A [ 0 0 22 22 0 0 13 ] C [11 0 0 0 0 0 0 ] G [ 4 0 0 0 0 0 8 ] T [ 7 22 0 0 22 22 1 ] >MA0199.1 Optix A [ 0 0 27 0 26 ] C [ 5 0 0 0 0 ] G [ 0 27 0 0 1 ] T [22 0 0 27 0 ] >MA0200.1 Pph13 A [ 6 0 21 21 0 0 13 ] C [ 8 0 0 0 0 1 1 ] G [ 3 0 0 0 0 0 6 ] T [ 4 21 0 0 21 20 1 ] >MA0201.1 Ptx1 A [ 0 0 20 20 0 0 0 ] C [ 5 0 0 0 0 20 19 ] G [ 1 0 0 0 0 0 0 ] T [14 20 0 0 20 0 1 ] >MA0202.1 Rx A [ 3 0 27 27 0 0 17 ] C [13 0 0 0 0 0 0 ] G [ 0 0 0 0 0 0 10 ] T [11 27 0 0 27 27 0 ] >MA0203.1 Scr A [ 3 0 25 25 0 0 23 ] C [ 7 0 0 0 0 0 0 ] G [ 0 0 0 0 0 18 2 ] T [15 25 0 0 25 7 0 ] >MA0204.1 Six4 A [ 0 0 20 0 20 1 ] C [ 0 0 0 7 0 19 ] G [ 0 20 0 4 0 0 ] T [20 0 0 9 0 0 ] >MA0205.1 Trl A [ 4 6 12 0 3 1 13 5 7 9 ] C [22 23 6 64 0 70 0 54 13 33 ] G [ 9 11 30 3 14 0 2 7 11 10 ] T [36 31 23 4 54 0 56 5 40 19 ] >MA0206.1 abd-A A [ 1 0 16 18 1 0 15 ] C [ 3 0 0 0 0 0 1 ] G [ 0 0 0 0 0 6 2 ] T [14 18 2 0 17 12 0 ] >MA0207.1 achi A [ 0 0 23 0 23 3 ] C [ 0 0 0 22 0 0 ] G [ 0 23 0 0 0 12 ] T [23 0 0 1 0 2 ] >MA0208.1 al A [ 0 20 20 0 0 17 18 ] C [ 0 0 0 0 0 0 0 ] G [ 0 0 0 0 0 3 2 ] T [20 0 0 20 20 0 0 ] >MA0209.1 ap A [ 2 1 19 19 0 0 16 ] C [10 0 0 0 0 0 0 ] G [ 0 0 0 0 0 6 3 ] T [ 7 18 0 0 19 13 0 ] >MA0210.1 ara A [14 23 34 0 34 ] C [ 0 0 0 34 0 ] G [ 5 0 0 0 0 ] T [15 11 0 0 0 ] >MA0211.1 bap A [ 0 0 23 23 0 1 7 ] C [ 0 0 0 0 0 0 0 ] G [ 1 0 0 0 23 0 16 ] T [22 23 0 0 0 22 0 ] >MA0212.1 bcd A [ 0 20 22 0 0 0 ] C [ 0 0 0 0 22 21 ] G [ 0 0 0 1 0 0 ] T [22 2 0 21 0 1 ] >MA0213.1 brk A [ 1 0 0 0 0 1 1 0 ] C [ 5 4 0 0 10 0 8 5 ] G [ 4 0 10 10 0 9 0 1 ] T [ 0 6 0 0 0 0 1 4 ] >MA0214.1 bsh A [ 1 0 16 16 0 0 7 ] C [ 3 0 0 0 1 3 0 ] G [ 2 0 0 0 0 6 9 ] T [10 16 0 0 15 7 0 ] >MA0215.1 btn A [ 5 0 22 23 0 0 19 ] C [ 1 0 0 0 1 0 0 ] G [ 4 0 1 0 0 15 3 ] T [13 23 0 0 22 8 1 ] >MA0216.1 cad A [ 3 1 8 37 3 3 18 ] C [ 2 0 0 0 0 0 0 ] G [ 3 0 2 1 0 9 19 ] T [30 37 28 0 35 26 1 ] >MA0217.1 caup A [ 4 14 19 0 19 ] C [ 1 0 0 19 0 ] G [ 2 2 0 0 0 ] T [12 3 0 0 0 ] >MA0218.1 ct A [ 0 1 9 20 17 0 ] C [ 6 2 0 0 0 19 ] G [ 0 0 11 0 1 1 ] T [14 17 0 0 2 0 ] >MA0219.1 ems A [ 3 3 19 19 0 0 14 ] C [ 7 0 0 0 0 1 0 ] G [ 1 0 0 1 0 10 6 ] T [ 9 17 1 0 20 9 0 ] >MA0220.1 en A [ 2 0 23 23 0 0 13 ] C [ 8 0 0 0 0 0 0 ] G [ 2 0 0 0 0 2 10 ] T [11 23 0 0 23 21 0 ] >MA0221.1 eve A [ 3 1 22 22 0 0 17 ] C [10 0 0 0 2 2 0 ] G [ 0 0 0 0 1 11 4 ] T [ 9 21 0 0 19 9 1 ] >MA0222.1 exd A [ 4 1 0 0 0 17 0 11 ] C [ 4 0 0 0 0 0 12 0 ] G [ 4 2 0 0 17 0 0 6 ] T [ 5 14 17 17 0 0 5 0 ] >MA0223.1 exex A [ 0 0 23 23 0 1 20 ] C [ 7 0 0 0 0 1 0 ] G [10 0 0 0 0 0 3 ] T [ 6 23 0 0 23 21 0 ] >MA0224.1 exex A [ 0 0 23 23 0 1 20 ] C [ 7 0 0 0 0 1 0 ] G [10 0 0 0 0 0 3 ] T [ 6 23 0 0 23 21 0 ] >MA0225.1 ftz A [ 1 1 18 18 0 0 14 ] C [ 2 0 0 0 0 0 0 ] G [ 0 0 0 0 0 9 4 ] T [15 17 0 0 18 9 0 ] >MA0226.1 hbn A [ 2 0 17 17 0 0 9 ] C [ 2 0 0 0 0 0 0 ] G [ 2 0 0 0 0 0 7 ] T [11 17 0 0 17 17 1 ] >MA0227.1 hth A [ 0 0 17 0 14 0 ] C [ 0 0 0 17 0 0 ] G [ 0 17 0 0 3 8 ] T [17 0 0 0 0 4 ] >MA0228.1 ind A [ 0 0 21 21 0 0 19 ] C [12 0 0 0 0 0 0 ] G [ 0 0 0 0 1 8 2 ] T [ 9 21 0 0 20 13 0 ] >MA0229.1 inv A [ 4 1 0 16 16 0 0 11 ] C [ 0 9 0 0 0 0 0 0 ] G [ 3 0 0 0 0 0 0 5 ] T [ 9 6 16 0 0 16 16 0 ] >MA0230.1 lab A [ 1 0 16 16 1 0 16 ] C [ 3 0 0 0 0 0 0 ] G [ 0 0 0 0 0 6 0 ] T [12 16 0 0 15 10 0 ] >MA0231.1 lbe A [ 0 22 22 1 4 19 ] C [ 0 0 0 10 5 0 ] G [ 0 0 0 2 3 3 ] T [22 0 0 9 10 0 ] >MA0232.1 lbl A [ 0 23 23 0 1 17 ] C [ 0 0 0 5 3 0 ] G [ 0 0 0 0 7 6 ] T [23 0 0 18 12 0 ] >MA0233.1 mirr A [20 29 41 0 41 ] C [ 1 0 0 41 0 ] G [ 1 2 0 0 0 ] T [19 10 0 0 0 ] >MA0234.1 oc A [ 0 19 19 0 0 0 ] C [ 0 0 0 0 19 17 ] G [ 0 0 0 2 0 1 ] T [19 0 0 17 0 1 ] >MA0235.1 onecut A [ 1 0 0 15 0 0 5 ] C [ 2 0 0 0 0 0 0 ] G [ 0 0 15 0 0 0 4 ] T [12 15 0 0 15 15 6 ] >MA0236.1 otp A [ 1 0 20 20 0 0 15 ] C [ 6 1 0 0 0 1 0 ] G [ 2 0 0 0 0 4 3 ] T [11 19 0 0 20 15 2 ] >MA0237.1 pan A [ 4 2 2 2 0 17 8 7 ] C [10 3 0 0 2 0 0 11 ] G [ 3 0 1 0 20 5 0 3 ] T [ 8 20 22 23 3 3 17 4 ] >MA0238.1 pb A [ 4 0 24 24 0 0 24 ] C [ 9 0 0 0 0 0 0 ] G [ 0 0 0 0 0 11 0 ] T [11 24 0 0 24 13 0 ] >MA0239.1 prd A [10 1 4 20 21 0 7 0 5 ] C [ 4 4 2 0 0 21 5 0 4 ] G [ 0 14 2 0 0 0 3 21 2 ] T [ 7 2 13 1 0 0 6 0 10 ] >MA0240.1 repo A [ 1 1 27 28 0 0 21 ] C [ 4 0 0 0 0 0 0 ] G [ 4 0 0 0 0 0 7 ] T [19 27 1 0 28 28 0 ] >MA0241.1 ro A [ 1 0 23 23 0 0 19 ] C [12 0 0 0 0 0 0 ] G [ 3 0 0 0 0 3 4 ] T [ 7 23 0 0 23 20 0 ] >MA0242.1 run::Bgb A [12 27 28 0 0 5 0 28 20 ] C [ 2 0 0 29 28 1 29 0 0 ] G [ 0 1 1 0 1 23 0 0 9 ] T [15 1 0 0 0 0 0 1 0 ] >MA0243.1 sd A [ 3 8 4 14 1 3 0 3 4 5 5 4 ] C [ 0 1 8 0 1 0 8 5 1 6 1 3 ] G [ 8 1 1 0 0 1 0 3 2 3 7 4 ] T [ 3 4 1 0 12 10 6 3 7 0 1 3 ] >MA0244.1 slbo A [ 9 0 0 3 2 7 6 11 ] C [ 1 1 0 2 10 2 5 1 ] G [ 1 0 3 7 0 2 1 0 ] T [ 1 11 9 0 0 1 0 0 ] >MA0245.1 slou A [ 3 0 22 21 0 2 11 ] C [ 5 1 0 0 0 1 0 ] G [ 1 0 0 1 0 5 11 ] T [13 21 0 0 22 14 0 ] >MA0246.1 so A [ 0 0 27 0 27 0 ] C [ 0 0 0 0 0 10 ] G [ 1 27 0 1 0 0 ] T [26 0 0 26 0 3 ] >MA0247.1 tin A [ 0 1 0 16 16 0 0 3 ] C [ 9 0 11 0 0 0 0 0 ] G [ 3 0 1 0 0 16 0 13 ] T [ 4 15 4 0 0 0 16 0 ] >MA0248.1 tup A [ 4 1 16 16 0 1 2 ] C [ 2 0 0 0 0 0 0 ] G [ 1 0 0 0 2 7 13 ] T [ 9 15 0 0 14 8 1 ] >MA0249.1 twi A [ 1 3 1 0 11 0 7 0 0 1 1 2 ] C [ 4 7 3 14 3 4 1 0 0 0 2 3 ] G [ 3 3 10 1 1 1 5 0 12 2 2 8 ] T [ 7 2 1 0 0 10 2 15 3 12 10 2 ] >MA0250.1 unc-4 A [ 1 0 21 21 0 1 6 ] C [ 5 0 0 0 0 0 1 ] G [ 2 0 0 0 0 0 14 ] T [13 21 0 0 21 20 0 ] >MA0251.1 unpg A [ 1 1 21 21 0 0 14 ] C [ 6 0 0 0 0 0 0 ] G [ 4 0 0 0 0 2 7 ] T [10 20 0 0 21 19 0 ] >MA0252.1 vis A [ 0 0 22 0 22 2 ] C [ 0 0 0 22 0 4 ] G [ 0 22 0 0 0 8 ] T [22 0 0 0 0 2 ] >MA0253.1 vnd A [ 4 1 0 0 19 19 0 2 9 ] C [ 0 8 0 15 0 0 0 0 0 ] G [ 2 1 0 2 0 0 19 0 10 ] T [13 9 19 2 0 0 0 17 0 ] >MA0254.1 vvl A [ 0 11 2 0 3 11 ] C [ 2 0 2 0 8 0 ] G [ 0 0 0 6 0 0 ] T [ 9 0 7 5 0 0 ] >MA0255.1 z A [11 1 1 41 1 8 2 18 10 14 ] C [ 3 7 0 0 0 10 6 7 5 6 ] G [ 5 3 40 0 40 0 29 11 12 2 ] T [22 30 0 0 0 23 4 5 14 19 ] >MA0256.1 zen A [ 2 0 16 16 0 0 16 ] C [ 9 0 0 0 0 0 0 ] G [ 0 0 0 0 0 11 0 ] T [ 5 16 0 0 16 5 0 ] >MA0257.1 zen2 A [ 3 1 26 26 0 0 22 ] C [ 7 0 0 0 1 0 0 ] G [ 5 0 0 0 0 10 4 ] T [11 25 0 0 25 16 0 ] >MA0094.2 Ubx A [ 3 0 0 17 20 0 0 14 ] C [ 5 0 0 0 0 0 0 0 ] G [ 3 0 0 0 0 0 6 6 ] T [ 9 20 20 3 0 20 14 0 ] >MA0443.1 btd A [14 10 4 2 0 0 2 0 0 15 ] C [ 3 0 0 0 0 0 28 0 0 1 ] G [ 7 20 18 28 30 30 0 30 20 10 ] T [ 6 0 8 0 0 0 0 0 10 4 ] >MA0444.1 CG34031 A [ 0 0 22 25 2 7 3 ] C [ 1 0 0 0 0 0 0 ] G [ 1 0 0 0 0 1 22 ] T [23 25 3 0 23 17 0 ] >MA00445.1 D A [ 1 0 0 20 0 0 2 0 1 4 6 ] C [ 8 25 17 0 0 0 0 0 2 10 1 ] G [ 7 0 0 0 0 3 27 0 4 6 1 ] T [13 4 12 9 29 26 0 29 22 9 21 ] >MA0446.1 fkh A [ 3 5 0 0 0 13 4 6 0 23 15 ] C [ 0 0 0 0 0 0 13 7 11 0 4 ] G [ 0 22 0 0 1 14 2 3 2 1 4 ] T [24 0 27 27 26 0 8 11 14 3 4 ] >MA0447.1 gt A [28 0 1 54 0 7 0 55 60 2 ] C [ 5 0 1 0 53 0 6 3 0 25 ] G [25 0 3 6 0 53 0 1 0 5 ] T [ 2 60 55 0 7 0 54 1 0 28 ] >MA0448.1 H2.0 A [ 6 2 16 31 0 8 24 ] C [ 3 1 1 0 0 0 0 ] G [ 4 0 1 0 0 9 7 ] T [19 29 14 1 32 15 1 ] >MA0449.1 h A [ 1 3 1 22 1 2 1 1 7 1 ] C [ 2 7 31 1 30 1 10 1 17 30 ] G [30 17 1 10 1 30 1 31 7 2 ] T [ 1 7 1 1 2 1 22 1 3 1 ] >MA0450.1 hkb A [ 3 8 0 0 0 0 0 0 24 ] C [ 0 0 0 0 32 0 0 0 3 ] G [18 24 32 32 0 32 3 31 2 ] T [11 0 0 0 0 0 29 1 3 ] >MA0451.1 kni A [19 25 16 5 0 21 0 17 1 0 25 5 ] C [ 1 1 0 9 4 0 0 0 3 26 0 12 ] G [ 2 0 0 6 1 5 26 8 18 0 1 7 ] T [ 4 0 10 6 21 0 0 1 4 0 0 2 ] >MA0452.1 Kr A [ 3 11 24 30 18 0 2 2 0 4 20 ] C [17 4 0 0 6 0 0 0 1 0 4 ] G [ 5 10 7 0 7 31 29 29 7 5 3 ] T [ 6 6 0 1 0 0 0 0 23 22 4 ] >MA0453.1 nub A [ 2 29 0 0 0 24 28 28 4 6 19 4 ] C [ 1 0 0 0 19 0 0 0 1 5 5 3 ] G [ 1 0 0 25 1 0 1 0 0 8 1 15 ] T [25 0 29 4 9 5 0 1 24 10 4 7 ] >MA0454.1 odd A [ 7 10 0 12 0 0 15 0 0 11 5 ] C [ 5 5 14 0 0 0 0 0 15 3 4 ] G [ 0 0 1 3 15 0 0 15 0 1 6 ] T [ 3 0 0 0 0 15 0 0 0 0 0 ] >MA0455.1 OdsH A [ 0 0 22 22 0 0 13 ] C [11 0 0 0 0 0 0 ] G [ 4 0 0 0 0 0 8 ] T [ 7 22 0 0 22 22 1 ] >MA0456.1 opa A [ 0 10 1 1 0 0 0 0 4 0 3 2 ] C [ 1 7 16 15 16 18 18 14 0 14 1 0 ] G [15 1 0 1 1 0 0 0 13 2 4 16 ] T [ 2 0 1 1 1 0 0 4 1 2 10 0 ] >MA0457.1 PHDP A [ 4 0 16 17 0 1 8 ] C [ 6 0 0 0 0 0 1 ] G [ 0 0 0 0 0 1 4 ] T [ 7 17 1 0 17 15 4 ] >MA0458.1 slp1 A [ 8 0 3 0 0 0 27 1 17 4 15 ] C [ 3 0 0 0 0 0 0 22 8 13 3 ] G [14 0 38 0 1 2 4 7 15 3 4 ] T [16 41 0 41 40 39 10 11 1 21 19 ] >MA0459.1 tll A [20 29 33 31 0 0 0 33 31 17 ] C [ 4 0 0 1 0 2 33 0 0 10 ] G [ 4 4 0 1 33 0 0 0 1 2 ] T [ 5 0 0 0 0 31 0 0 1 4 ] >MA0460.1 ttk A [10 22 0 0 22 0 21 22 1 ] C [ 8 0 0 0 0 5 0 0 5 ] G [ 3 0 22 22 0 0 0 0 3 ] T [ 1 0 0 0 0 17 1 0 13 ] PWMEnrich/inst/extdata/pfm_vertebrates.txt0000644000175100017510000010452014614305422021731 0ustar00biocbuildbiocbuild>MA0004.1 Arnt 4 19 0 0 0 0 16 0 20 0 0 0 0 1 0 20 0 20 0 0 0 0 20 0 >MA0006.1 Arnt::Ahr 3 0 0 0 0 0 8 0 23 0 0 0 2 23 0 23 0 24 11 1 1 1 24 0 >MA0009.1 T 2 1 40 0 0 0 0 0 1 40 31 28 1 0 0 0 0 0 2 7 0 5 8 0 0 40 40 0 40 0 28 0 0 2 38 0 0 0 40 0 38 4 0 4 >MA0017.1 NR2F1 0 1 12 6 0 0 0 1 2 6 6 1 3 0 0 0 0 7 13 3 2 0 0 4 5 10 6 3 2 12 1 0 0 0 0 0 11 3 1 1 0 3 11 0 0 0 0 10 11 12 0 0 1 1 4 7 >MA0019.1 Ddit3::Cebpa 14 11 18 0 0 4 38 36 0 14 4 0 7 7 3 1 0 33 1 2 6 17 23 26 12 14 15 0 38 0 0 1 0 5 9 6 6 7 3 38 1 2 0 0 33 3 3 7 >MA0025.1 NFIL3 1 0 22 0 2 0 23 22 0 7 5 0 0 0 8 0 0 0 0 11 5 5 0 2 0 0 21 0 0 1 4 7 3 22 21 1 15 0 23 0 0 8 4 10 >MA0027.1 En1 4 5 3 0 4 3 3 2 1 1 1 1 2 0 0 0 0 0 1 3 4 6 2 2 7 2 3 7 0 4 3 1 1 3 1 0 8 3 0 7 3 3 4 2 >MA0028.1 ELK1 7 10 9 5 2 0 1 27 21 13 7 6 4 19 24 0 0 0 5 0 10 6 10 1 1 24 27 1 0 14 4 6 5 3 1 4 0 0 2 1 >MA0029.1 Mecom 14 20 0 27 1 27 26 0 27 0 24 23 6 15 2 1 1 0 10 0 0 0 0 3 1 0 7 6 6 2 25 0 0 0 1 27 0 0 0 4 7 3 5 4 1 0 16 0 0 0 0 24 2 0 7 3 >MA0030.1 FOXF2 1 10 17 13 3 7 0 27 27 27 0 27 16 7 10 7 4 5 11 0 0 0 0 0 25 0 4 4 7 5 2 5 8 20 0 0 0 0 0 0 2 6 9 5 4 5 0 0 27 0 0 0 2 0 5 10 >MA0031.1 FOXD1 1 0 19 20 18 1 20 7 1 0 1 0 1 18 0 2 17 0 0 0 1 0 0 3 1 20 0 0 0 1 0 8 >MA0032.1 FOXC1 4 2 4 6 7 0 0 16 4 5 4 4 3 0 0 0 5 6 3 5 2 16 0 0 3 3 5 1 4 0 16 0 >MA0033.1 FOXL1 7 10 6 13 4 21 0 22 1 4 3 4 10 0 2 1 4 2 6 4 2 2 0 0 11 7 8 2 7 0 21 0 >MA0038.1 Gfi1 9 28 53 53 1 1 31 8 19 7 20 16 0 0 0 52 7 28 11 13 9 7 0 0 1 0 2 11 2 28 15 2 0 0 51 0 13 6 21 5 >MA0040.1 Foxq1 4 13 5 3 0 0 0 0 17 0 6 4 1 2 0 0 0 0 0 0 1 0 3 3 0 0 18 0 0 0 1 4 3 7 1 11 15 0 18 18 18 0 13 9 >MA0041.1 Foxd3 11 30 24 1 12 0 1 5 14 6 1 0 3 2 1 4 0 0 0 0 0 7 2 12 26 0 0 0 34 0 0 10 21 0 4 0 7 15 22 42 1 47 46 32 12 34 40 35 >MA0042.1 FOXI1 6 4 11 0 13 0 0 0 15 0 8 2 1 8 4 0 0 0 0 0 0 0 1 1 14 12 8 0 18 0 0 0 16 7 4 4 10 7 8 31 0 31 31 31 0 24 18 24 >MA0043.1 HLF 1 6 1 0 13 0 6 0 13 15 2 5 4 0 0 0 1 15 0 9 4 0 3 5 8 12 0 3 2 1 12 0 1 1 1 3 5 0 17 15 2 2 0 9 0 2 12 5 >MA0046.1 HNF1A 5 1 1 1 20 16 1 8 14 2 0 13 8 5 0 0 0 0 0 2 0 2 0 0 4 1 8 13 14 20 0 0 0 1 0 4 1 0 0 3 3 0 2 0 20 20 1 2 20 7 6 19 17 4 2 3 >MA0048.1 NHLH1 13 13 3 1 54 1 1 1 0 3 2 5 13 39 5 53 0 1 50 1 0 37 0 17 17 2 37 0 0 52 3 0 53 8 37 12 11 0 9 0 0 0 0 52 1 6 15 20 >MA0051.1 IRF2 0 2 12 11 12 2 0 0 12 12 12 0 0 5 6 6 5 3 4 0 0 0 0 0 6 0 0 0 0 6 7 2 0 1 2 6 7 10 0 0 0 10 0 12 0 0 0 6 2 3 2 1 1 1 1 0 0 1 0 0 6 0 0 0 0 0 3 2 4 4 4 2 >MA0056.1 MZF1_1-4 3 0 2 0 0 18 5 0 0 0 0 0 4 19 18 19 20 2 8 1 0 1 0 0 >MA0057.1 MZF1_5-13 1 2 15 0 0 0 0 3 10 8 4 0 1 0 0 2 0 1 0 2 7 7 0 11 15 14 14 8 4 4 4 7 0 5 1 0 2 4 2 2 >MA0059.1 MYC::MAX 7 15 2 1 21 0 1 0 0 1 3 1 1 9 20 0 20 0 1 0 1 5 9 4 9 0 0 0 20 0 21 18 0 4 1 1 0 0 1 0 20 0 1 13 >MA0063.1 Nkx2-5 7 0 17 17 0 4 2 0 4 0 0 0 2 1 1 0 0 0 7 0 11 9 13 0 0 10 11 3 >MA0066.1 PPARG 3 3 19 0 1 0 2 26 5 5 4 1 2 22 1 0 3 22 5 7 8 0 0 1 0 1 23 1 15 7 2 0 5 5 27 25 12 5 12 0 14 0 9 27 26 4 3 0 4 10 18 2 20 0 0 0 0 0 6 1 3 25 0 0 1 23 0 1 4 6 4 25 1 1 0 3 13 1 5 20 >MA0067.1 Pax2 10 7 3 0 26 2 2 1 7 1 2 28 1 17 0 11 5 21 0 1 3 1 19 11 9 2 26 2 1 11 10 8 >MA0068.1 Pax4 7 20 16 11 13 11 6 9 5 5 6 7 8 6 10 4 3 7 4 9 5 5 7 6 3 7 3 1 6 3 2 0 2 1 1 3 1 1 3 11 11 7 7 5 5 8 6 8 9 6 7 6 11 9 12 9 12 11 11 13 11 1 1 1 3 1 2 1 6 1 4 5 2 1 4 4 4 3 3 2 5 2 0 1 2 2 2 3 1 2 1 0 2 8 4 6 12 10 7 4 0 2 4 9 2 5 8 3 5 4 4 8 3 5 4 3 4 6 3 3 >MA0069.1 Pax6 2 2 4 39 3 1 1 21 1 2 36 11 1 1 4 2 26 2 34 0 37 2 4 14 0 11 5 0 4 0 1 1 1 41 4 2 1 25 6 13 3 17 33 39 12 1 5 1 1 18 37 2 1 8 34 25 >MA0070.1 PBX1 5 3 16 1 0 17 17 0 0 16 12 8 6 9 1 1 18 1 0 0 18 1 0 2 2 3 1 0 0 0 0 1 0 0 1 2 5 3 0 16 0 0 1 17 0 1 5 6 >MA0071.1 RORA_1 15 9 6 11 21 0 0 0 0 25 1 1 12 2 0 0 0 0 25 0 2 0 4 5 4 25 25 0 0 0 7 15 3 7 0 0 0 25 0 0 >MA0072.1 RORA_2 9 17 15 35 23 2 0 28 0 0 0 0 36 15 8 2 0 1 0 12 0 0 0 0 0 36 0 6 8 7 3 0 0 13 0 8 36 36 0 0 0 10 11 10 18 0 13 9 36 0 0 0 36 0 0 5 >MA0073.1 RREB1 3 1 3 0 7 9 8 4 0 11 4 1 3 4 2 4 4 4 1 4 8 10 8 11 4 2 3 6 11 0 7 10 8 6 9 5 5 6 7 4 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 1 0 3 2 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 2 1 1 0 1 >MA0074.1 RXRA::VDR 3 0 0 0 0 9 4 2 2 5 0 0 1 0 7 0 0 0 0 9 0 2 4 0 0 0 0 0 9 1 7 10 9 0 0 1 0 2 8 5 10 0 0 0 2 0 0 1 10 1 0 4 2 0 0 0 10 9 1 0 >MA0075.1 Prrx2 52 59 0 0 58 2 0 0 0 0 4 0 1 0 1 1 0 58 59 0 >MA0077.1 SOX9 3 8 71 2 7 2 13 9 14 55 38 1 1 0 2 0 7 31 9 6 0 2 4 72 4 6 7 9 24 4 71 65 0 59 54 24 >MA0078.1 Sox17 7 8 3 30 0 0 0 0 0 9 8 18 0 1 0 0 0 17 6 4 1 0 0 0 31 2 10 9 11 9 1 30 31 0 29 4 >MA0081.1 SPIB 31 4 24 2 1 48 47 0 14 16 0 0 0 0 0 29 7 47 47 0 0 18 2 2 0 1 1 2 >MA0084.1 SRY 5 8 15 18 25 0 28 27 7 5 3 0 3 0 26 0 0 0 10 4 3 3 0 0 0 1 2 8 13 10 4 3 2 0 0 19 >MA0087.1 Sox5 23 0 0 1 0 1 8 0 0 0 0 1 1 3 0 1 0 22 0 0 4 0 22 23 0 22 21 8 >MA0088.1 znf143 0 6 2 0 2 1 0 0 9 0 4 6 0 0 0 4 3 2 1 3 3 2 3 3 1 7 10 10 1 1 4 2 0 1 10 5 1 3 1 6 4 1 1 2 0 0 0 0 0 4 2 2 1 6 0 0 1 0 7 1 3 1 4 5 7 2 0 0 0 5 0 0 9 3 0 1 5 5 1 0 >MA0089.1 NFE2L1::MafG 7 29 0 1 34 0 11 2 0 0 0 26 10 3 0 32 0 2 6 0 34 1 0 6 >MA0090.1 TEAD1 1 9 0 12 0 0 0 0 5 1 2 0 6 0 12 0 0 0 12 11 0 7 4 2 1 3 0 0 0 0 0 0 0 4 3 8 4 0 0 0 12 12 0 1 7 0 3 2 >MA0091.1 TAL1::TCF3 13 9 39 20 0 44 0 12 0 0 0 4 14 10 0 24 43 0 1 32 0 0 3 4 8 20 3 0 1 0 11 0 0 43 20 2 9 5 2 0 0 0 32 0 44 1 21 34 >MA0092.1 Hand1::Tcfe2a 4 10 2 0 0 0 0 9 16 5 8 0 2 28 0 0 3 14 0 4 10 15 1 0 0 29 25 1 3 4 7 4 24 1 29 0 1 5 10 16 >MA0101.1 REL 0 0 1 5 6 5 1 2 0 1 5 1 0 1 5 1 0 0 15 16 8 15 15 9 3 1 0 0 0 0 4 1 1 2 3 10 16 15 2 0 >MA0107.1 RELA 0 0 0 11 10 2 0 0 0 0 4 0 0 0 3 0 0 2 18 18 11 17 18 7 4 0 0 0 0 0 3 1 0 0 1 16 18 16 0 0 >MA0108.2 TBP 61 16 352 3 354 268 360 222 155 56 83 82 82 68 77 145 46 0 10 0 0 3 2 44 135 147 127 118 107 101 152 18 2 2 5 0 20 44 157 150 128 128 128 139 140 31 309 35 374 30 121 6 121 33 48 31 52 61 75 71 >MA0109.1 Hltf 16 20 26 0 25 0 24 0 15 12 10 12 33 59 0 0 5 0 14 12 12 5 0 0 0 0 12 22 14 10 13 16 0 0 34 59 18 37 8 15 >MA0111.1 Spz1 9 0 2 0 1 8 6 1 10 0 2 0 2 0 0 0 0 0 9 0 1 8 3 9 10 11 1 0 2 2 2 9 2 0 1 0 1 10 4 4 0 0 2 0 >MA0115.1 NR1H2::RXRA 17 17 20 0 0 0 0 25 24 25 0 0 0 0 25 20 0 5 1 0 0 0 0 25 0 0 0 0 0 0 25 0 1 15 0 5 5 25 24 0 0 0 0 0 25 25 0 0 0 2 6 3 2 0 0 1 25 0 0 1 0 0 0 25 0 0 2 4 >MA0116.1 Zfp423 7 0 16 17 0 0 1 24 13 0 0 0 0 0 11 4 16 17 16 33 33 17 0 0 0 0 0 0 8 22 22 17 0 0 0 0 0 0 16 33 33 17 16 16 0 0 0 0 0 0 0 15 9 4 0 0 16 17 9 0 >MA0117.1 Mafb 0 1 0 1 12 3 3 2 0 12 3 0 2 8 1 5 15 1 1 12 0 1 7 5 0 1 11 2 1 3 4 3 >MA0119.1 TLX1::NFIC 0 0 0 0 14 2 2 7 4 0 0 0 16 14 0 0 0 16 1 8 8 1 3 0 16 16 0 0 0 16 16 0 0 5 4 5 2 16 0 0 0 1 16 0 0 0 1 1 2 3 7 0 0 0 0 1 >MA0122.1 Nkx3-2 4 1 13 24 0 0 6 4 9 7 4 1 0 0 0 0 6 7 4 5 7 0 24 0 18 12 5 9 14 3 0 0 24 0 2 3 >MA0124.1 NKX3-1 13 0 20 0 1 1 19 1 1 0 19 0 0 0 3 0 0 0 0 0 1 3 19 0 1 19 19 0 >MA0125.1 Nobox 0 36 38 1 2 15 4 2 1 0 0 1 0 0 12 13 0 0 0 2 4 22 18 6 37 2 0 34 32 1 4 17 >MA0130.1 ZNF354C 7 3 0 0 16 0 6 2 16 16 0 15 3 0 0 0 0 1 0 11 0 0 0 0 >MA0131.1 HINFP 2 13 20 2 0 0 0 0 0 2 6 1 0 17 0 1 19 18 0 13 5 0 0 1 19 0 0 2 19 1 7 6 0 0 1 19 1 0 1 4 >MA0132.1 Pdx1 1 0 31 31 0 1 19 0 0 0 0 1 5 0 0 0 1 10 6 31 0 0 30 19 >MA0133.1 BRCA1 15 4 41 36 7 19 3 11 35 1 2 29 14 22 10 2 1 4 6 7 15 7 2 0 1 1 3 3 >MA0135.1 Lhx3 9 16 19 0 0 19 20 2 0 16 9 2 2 0 2 0 0 0 0 0 2 1 1 0 8 9 3 2 0 0 0 0 0 0 0 0 3 0 3 8 0 1 20 20 1 0 16 19 3 8 10 6 >MA0136.1 ELF5 12 29 2 14 0 0 0 4 4 9 3 20 0 1 44 44 3 8 3 7 5 1 0 0 0 11 11 20 5 17 29 43 0 0 26 21 >MA0139.1 CTCF 87 167 281 56 8 744 40 107 851 5 333 54 12 56 104 372 82 117 402 291 145 49 800 903 13 528 433 11 0 3 12 0 8 733 13 482 322 181 76 414 449 21 0 65 334 48 32 903 566 504 890 775 5 507 307 73 266 459 187 134 36 2 91 11 324 18 3 9 341 8 71 67 17 37 396 59 >MA0142.1 Pou5f1::Sox2 63 579 11 47 118 415 206 1235 17 10 14 1050 887 1200 198 846 58 50 12 363 18 365 30 5 16 1029 16 68 33 119 66 28 36 79 825 29 186 24 15 1239 129 30 315 75 207 389 701 1270 1231 63 907 612 80 1332 104 196 269 92 53 837 >MA0149.1 EWSR1-FLI1 0 2 104 104 1 2 103 102 0 0 99 105 0 0 100 102 5 3 0 0 0 0 0 0 0 0 0 2 4 0 0 2 3 0 0 3 105 103 1 1 104 102 2 3 104 103 2 0 105 103 0 2 97 97 0 0 0 0 0 1 0 0 1 0 0 0 0 0 2 1 3 2 >MA0062.2 GABPA 32 70 0 0 991 989 94 56 154 264 233 768 914 0 0 1 2 32 261 137 262 357 188 4 990 991 1 0 866 37 603 415 224 1 1 1 0 0 2 0 637 96 49 176 >MA0039.2 Klf4 1468 88 14 14 277 22 42 264 123 254 81 9 13 36 1922 15 80 46 130 2858 1022 4237 4304 4281 11 4273 2179 3902 3789 280 1765 6 13 16 2139 34 2042 125 289 932 >MA0138.2 REST 211 58 76 1452 34 122 1575 3 35 913 219 39 20 1406 13 1574 42 205 366 213 179 174 269 1366 30 44 978 7 1586 1481 201 375 7 5 112 1280 9 13 1007 31 933 1114 366 146 50 94 1516 323 12 12 20 162 124 1551 1577 34 233 7 1530 183 688 319 37 840 1124 105 26 10 182 12 5 70 329 886 7 2 51 74 10 9 198 507 125 260 >MA0002.2 RUNX1 287 234 123 57 0 87 0 17 10 131 500 496 485 1072 0 75 127 0 42 400 463 158 696 467 149 7 1872 70 1987 1848 251 81 289 521 814 656 1936 53 1716 13 93 1339 1325 1053 >MA0047.2 Foxa2 1 221 1 1 1 736 2 338 50 417 131 129 1 3 1 3 1 4 725 77 265 6 53 195 1 582 1 36 175 67 2 3 58 24 482 290 805 3 806 769 634 3 81 391 436 360 140 189 >MA0112.2 ESR1 122 107 64 83 134 308 36 19 33 4 398 58 63 64 32 21 305 10 59 26 120 80 173 229 232 28 8 18 41 394 13 250 276 258 19 22 106 436 353 165 154 164 149 65 47 89 387 420 91 53 27 107 53 97 8 426 31 12 2 22 71 117 82 93 57 48 43 18 310 24 37 60 83 56 416 6 33 17 61 262 >MA0065.2 PPARG::RXRA 94 101 390 100 139 145 71 819 522 713 82 41 22 54 676 317 166 23 3 15 129 547 21 48 5 2 9 99 555 58 320 127 368 671 674 395 179 15 270 137 767 693 263 144 47 126 464 79 87 34 193 66 8 23 9 12 120 479 109 81 >MA0151.1 ARID3A 27 0 1 27 27 20 0 0 9 0 0 0 0 0 0 0 0 1 0 27 17 0 0 6 >MA0152.1 NFATC2 3 1 1 0 1 0 18 1 2 1 0 25 26 3 2 2 0 0 0 0 1 20 21 24 26 0 0 4 >MA0153.1 HNF1B 0 0 8 7 0 5 3 0 0 8 9 0 3 3 1 1 0 0 0 0 0 0 0 8 0 0 0 1 0 4 1 1 0 1 0 0 6 6 0 0 9 0 5 8 9 0 0 1 >MA0155.1 INSM1 1 0 0 6 16 0 0 0 0 0 3 10 0 0 8 15 0 0 1 0 0 2 16 0 4 20 3 0 0 24 23 24 24 16 0 12 19 4 13 3 8 0 0 0 0 6 5 2 >MA0156.1 FEV 2 9 0 0 13 12 7 0 9 3 0 0 0 0 0 0 1 1 13 13 0 0 6 0 1 0 0 0 0 1 0 13 >MA0157.1 FOXO3 0 1 3 13 12 13 0 12 0 2 0 0 1 0 12 0 5 10 1 0 0 0 0 0 8 0 9 0 0 0 1 1 >MA0158.1 HOXA5 2 7 0 6 14 14 0 0 13 0 7 0 0 0 0 1 0 5 5 1 0 2 0 6 0 4 4 9 2 0 16 9 >MA0159.1 RXR::RAR_DR5 12 0 1 0 0 22 4 5 5 13 5 17 1 0 2 1 21 0 0 0 0 18 0 7 8 4 1 6 3 1 1 1 17 0 11 23 13 1 3 1 5 9 11 7 12 3 20 16 3 3 1 0 0 9 22 2 0 7 1 3 2 0 0 1 6 17 2 1 >MA0160.1 NR4A2 8 13 0 3 2 0 14 3 1 0 0 0 2 13 0 8 3 1 13 11 0 0 0 2 1 0 1 0 10 1 0 0 >MA0161.1 NFIC 358 81 81 91 1226 3298 1832 67 67 88 5364 981 176 300 6713 6643 160 1186 4546 6464 51 90 162 1447 >MA0163.1 PLAG1 0 3 0 0 0 0 4 12 11 2 0 0 0 2 0 0 0 0 14 15 10 0 5 0 0 0 0 0 18 14 18 17 4 1 1 0 2 14 18 18 16 16 0 1 0 1 0 2 3 6 0 2 0 0 2 0 >MA0164.1 Nr2e3 4 23 23 0 0 0 0 12 0 0 1 23 0 0 2 0 0 22 0 0 0 5 0 0 0 0 23 23 >MA0018.2 CREB1 0 0 11 0 1 0 2 8 1 1 0 9 0 3 7 0 1 10 0 2 10 0 1 1 9 0 0 0 0 8 1 2 >MA0099.2 JUN::FOS 0 0 16 5 3 0 16 1 0 2 12 0 15 0 0 15 0 1 1 3 1 17 3 0 0 14 0 1 >MA0259.1 HIF1A::ARNT 27 10 78 0 0 0 0 18 28 29 2 103 0 0 0 51 49 34 23 0 104 0 104 20 0 31 1 1 0 104 0 15 >MA0442.1 SOX10 0 8 0 0 0 0 19 2 1 0 3 0 0 2 4 0 19 1 3 10 17 22 0 21 >MA0141.2 Esrrb 1055 673 403 260 312 3347 3590 33 12 180 9 3478 801 832 1212 1230 2653 32 2 2 11 43 3394 21 984 1352 1212 449 630 248 60 3597 3622 243 167 128 773 767 807 1702 51 27 6 29 16 3192 85 26 >MA0145.2 Tcfcp2l1 8 285 2437 24 710 402 707 1462 2585 1612 16 253 2199 49 3768 3304 21 98 1380 1183 1630 922 286 212 3811 3324 30 118 255 22 1117 3949 103 253 616 1026 780 1263 223 12 1232 3881 40 465 507 12 1890 2246 1134 681 441 1003 34 491 612 17 >MA0146.2 Zfx 50 59 91 72 10 6 30 191 8 0 0 0 0 84 178 170 151 49 297 361 124 153 2 3 481 480 1 122 179 173 199 297 144 2 183 122 470 477 0 0 0 218 70 75 37 61 30 112 144 15 1 1 0 1 480 56 >MA0461.1 Atoh1 0 7714 0 4249 1557 0 0 0 7714 0 0 3465 0 0 0 4780 0 0 7714 0 0 7714 7714 0 0 0 0 0 6157 0 0 2934 >MA0462.1 BATF::JUN 3082 4782 4519 8258 0 0 10522 586 0 1291 10264 481 553 170 1478 0 0 0 5026 0 8590 0 5136 2889 2219 0 0 10374 0 4910 0 641 0 1823 2298 3614 786 10522 148 0 0 10522 0 258 >MA0463.1 Bcl6 191 35 5 30 0 0 446 142 941 642 615 101 189 369 171 22 80 876 819 0 86 0 0 101 7 92 494 188 245 28 30 24 0 0 404 814 11 175 213 630 116 166 349 871 841 26 137 956 20 0 4 38 121 133 157 233 >MA0464.1 Bhlhe40 1626 4869 0 15804 0 2875 0 0 3859 6629 2028 6868 524 15804 0 14493 1107 0 0 11919 4255 6293 6311 3146 0 0 0 11822 0 15804 26 345 3681 999 7265 0 0 1311 0 15804 0 0 4575 3802 >MA0465.1 CDX2 613 665 308 0 697 1597 0 1597 1597 1597 1013 319 8 136 1048 832 0 0 0 0 0 150 480 502 1153 0 0 0 0 0 0 0 313 185 422 0 549 68 0 1597 0 0 0 121 >MA0466.1 CEBPB 13006 75198 0 0 4556 0 74715 8654 60151 99494 0 10026 5868 0 0 0 99494 5478 51954 39343 0 36038 33617 18428 0 0 75531 0 10015 0 0 0 2043 42845 0 99494 99494 19407 0 9286 38886 0 0 61413 >MA0467.1 Crx 1207 1454 347 943 515 0 1632 0 0 1995 544 297 43 55 222 0 0 465 0 29 0 52 458 417 1470 823 1574 2097 0 0 0 0 1326 135 183 225 109 8 0 0 2097 2068 102 175 >MA0468.1 DUX4 7297 37001 38217 5228 0 0 35278 38217 0 0 33798 2495 0 0 11675 16478 13951 2889 0 0 37408 0 979 1216 0 2002 4981 6127 0 0 0 0 0 27446 0 0 19312 16758 18139 50 0 38217 809 4419 >MA0469.1 E2F3 0 0 0 0 0 0 0 0 365 275 196 811 109 441 305 1400 0 2549 2469 2549 0 2549 1703 1327 903 1495 329 1075 640 718 0 0 0 80 0 2240 0 607 397 742 556 512 582 200 644 1149 2549 0 0 0 309 0 239 460 629 302 274 160 645 259 >MA0470.1 E2F4 272 155 0 0 0 27 15 1803 1253 712 503 370 510 11 1878 0 218 41 0 0 349 393 848 1213 1867 0 1878 1633 1822 75 625 720 616 388 0 0 0 0 0 0 0 0 97 366 >MA0471.1 E2F6 812 442 0 672 7 0 20 2757 1686 988 771 80 406 0 1954 0 0 29 0 0 454 706 1406 1909 2757 0 2750 2757 2708 0 1071 1135 909 459 0 0 131 0 0 0 0 0 180 371 >MA0472.1 EGR2 169 337 168 387 26 448 0 152 0 1059 0 460 0 558 127 572 460 572 722 1186 0 1246 1094 1169 187 1246 0 1041 302 617 291 180 216 65 0 602 0 0 0 0 0 579 114 199 251 214 269 290 72 34 196 0 0 77 0 0 207 91 187 251 >MA0473.1 ELF1 4134 6990 7978 4086 924 8929 0 0 13518 13518 1183 1571 2634 2167 1502 1207 4589 10007 4589 0 0 0 0 0 2094 1735 5587 3563 2318 3111 2587 0 13518 13518 0 0 12335 179 8125 1630 1463 2015 1732 0 0 0 0 0 0 0 9674 1024 >MA0474.1 Erg 9430 1473 16007 0 0 16727 16580 3244 1570 4953 4558 231 10483 693 0 0 0 0 0 4038 1498 2474 5874 4736 27 16727 16727 0 0 13483 2146 8204 8042 1192 35 0 0 0 0 147 0 8973 2072 1653 >MA0475.1 FLI1 1830 295 2712 0 0 3667 3520 620 95 850 1014 205 2815 955 0 0 0 0 0 1001 300 462 1432 557 0 3667 3667 0 0 3047 300 2130 1875 200 0 0 0 0 0 147 0 2271 387 316 >MA0476.1 FOS 7879 7475 0 0 29396 998 0 0 29396 258 4006 712 10177 0 0 0 14079 0 29396 0 5823 8236 9686 10841 0 27108 0 11206 0 0 0 1538 7897 11119 903 29396 2288 0 3113 29396 0 0 21777 9257 >MA0477.1 FOSL1 1791 1568 0 0 5272 124 0 177 5272 37 1184 119 437 0 0 0 3120 0 5095 0 811 1684 2384 3109 0 5272 0 2028 0 0 0 1734 1875 978 158 5272 0 0 0 5272 0 0 2690 529 >MA0478.1 FOSL2 833 1525 2861 0 0 5075 0 0 205 5318 0 994 592 55 0 0 51 3286 0 5113 0 1399 2086 2707 2328 0 5318 192 1838 0 0 0 1933 1405 494 74 5318 0 0 194 5318 0 0 1986 >MA0479.1 FOXH1 939 1578 973 8211 7547 0 2374 1414 8211 0 7138 2840 2333 3261 0 0 0 5773 6797 0 8211 0 1490 2137 3156 0 229 0 0 0 0 0 0 2942 2163 821 0 435 8211 64 0 0 0 1073 >MA0480.1 Foxo1 468 127 87 0 0 0 0 0 1871 0 1072 277 1279 1406 0 0 0 0 0 157 2007 573 478 752 209 0 2490 0 0 287 0 0 71 1267 332 788 2490 0 2490 2490 2203 462 483 774 >MA0481.1 FOXP1 103 131 155 180 138 79 71 311 311 311 16 311 176 243 102 108 57 39 42 6 11 4 0 0 0 278 0 77 18 42 48 55 40 53 88 221 0 0 0 0 10 0 57 23 129 52 68 77 36 79 0 236 0 0 0 7 0 1 27 38 >MA0482.1 Gata4 10 0 0 0 2707 0 0 547 0 601 386 1039 2165 89 0 39 0 2746 0 1240 1004 929 374 306 0 0 0 0 0 0 940 157 682 1323 275 2657 2746 0 2746 0 2199 566 984 749 >MA0483.1 Gfi1b 1124 1761 1759 0 0 1033 61 1116 57 142 740 438 0 0 338 1761 21 1312 0 1 1328 433 104 0 2 28 0 0 388 1 1676 76 130 95 0 0 1395 0 707 0 644 27 215 458 >MA0484.1 HNF4G 3514 3259 4696 1103 1944 1511 0 9343 7889 8464 99 324 225 159 6785 1864 1531 102 201 713 2870 8827 55 82 0 6 143 4338 8377 794 2011 3830 3811 7480 3301 2279 221 54 1481 938 9098 3624 1107 85 556 2063 832 843 668 3494 2792 404 0 0 50 249 5361 3782 831 1317 >MA0485.1 Hoxc9 235 112 88 224 661 0 745 881 879 18 48 567 141 110 171 605 468 3 9 84 0 6 0 715 72 329 401 536 40 1 205 0 35 0 0 0 5 6 160 139 66 152 192 16 876 21 4 0 867 117 240 255 >MA0486.1 HSF1 25 8 3 1 4 128 0 187 186 44 39 13 8 0 6 82 26 11 222 71 7 0 24 6 63 62 16 5 218 88 56 12 8 2 6 89 225 3 15 73 67 9 4 2 18 62 179 203 0 144 1 0 11 18 45 57 187 208 5 113 >MA0488.1 JUN 8141 7116 6496 16526 0 0 20967 0 0 938 6904 20968 203 2792 2938 1903 729 0 0 0 3438 608 3531 14064 0 3704 4894 4985 9682 3713 0 20849 0 4512 20360 0 0 0 1016 5141 5929 2887 0 20968 119 1 13018 0 16499 0 0 16045 >MA0489.1 JUN (var.2) 4852 3927 3726 4396 4146 5930 0 0 10953 382 199 956 10956 376 1250 1324 1127 1122 593 993 0 0 0 5704 0 10000 0 2509 2818 4110 4330 3461 4213 4033 0 10956 0 4090 0 0 0 2645 2036 1595 1773 1977 2004 0 10956 0 3 780 10757 0 0 5426 >MA0490.1 JUNB 3605 5055 8384 0 0 16992 0 0 1256 16992 736 3366 1057 582 0 0 0 9859 0 15736 0 2962 5983 8427 8026 0 16992 0 6281 0 0 0 6117 4038 2453 0 16992 0 0 852 16992 0 0 7177 >MA0491.1 JUND 12389 15163 0 0 38710 0 0 1547 38710 0 5786 759 4824 0 0 0 22857 0 37163 0 5136 14956 13640 18723 0 38710 0 14078 0 0 0 2301 12429 11922 0 38710 0 0 1775 38710 0 0 31273 5539 >MA0492.1 JUND (var.2) 11448 13436 11261 13616 25347 0 0 33631 0 5124 0 8793 33631 267 10255 4326 3228 4836 2452 169 0 0 0 10406 1824 3935 24697 0 5470 10841 7996 8721 8110 15761 8115 0 33511 0 8287 26683 0 141 0 2407 6351 9861 8246 9424 1802 0 33631 120 0 14938 0 29696 0 0 25487 6184 >MA0493.1 Klf1 150 167 67 13 404 0 400 0 0 0 280 76 40 396 461 118 526 0 526 526 508 35 202 305 32 0 0 0 97 0 0 0 0 98 14 31 52 4 0 29 0 0 18 211 >MA0494.1 Nr1h3::Rxra 0 140 738 246 58 78 358 374 794 278 77 559 900 255 117 11 135 281 237 50 30 243 971 1080 313 318 257 0 19 0 77 177 962 1040 502 508 196 290 28 1089 137 0 3 128 343 336 321 903 4 440 192 20 0 42 196 333 439 1191 10 151 52 128 750 250 302 154 69 1188 193 0 32 112 714 430 459 303 >MA0495.1 MAFF 6531 13157 12632 14736 32728 8513 998 3238 53327 387 0 48740 20032 21358 18219 14137 10417 12741 12389 29577 10615 6027 4789 16391 322 50520 0 0 53701 381 7624 3349 4014 7157 11010 10791 26849 2157 2570 21695 5877 25722 313 0 0 47337 0 746 9364 2603 1898 3512 8426 9498 7989 8867 27941 11300 10364 3132 52125 0 431 6034 57 3891 16738 26448 29627 28952 23905 20728 >MA0496.1 MAFK 17128 15239 17367 34765 10333 0 2669 60790 0 0 57894 23471 27164 20865 17282 29664 13278 9271 4862 17432 261 58121 0 0 60742 430 9775 3962 5229 9289 4539 3886 21340 8031 29271 0 0 0 55539 48 0 9853 1268 1575 2699 9459 28387 12812 13132 3754 60529 0 0 5251 0 2466 17691 28396 33121 31520 >MA0497.1 MEF2C 705 733 431 382 0 1616 1706 2107 2131 2135 56 2177 389 975 1009 321 151 196 1412 985 256 32 0 0 0 62 0 120 836 450 676 573 822 78 0 74 241 87 2 4 0 32 1671 148 126 507 752 760 337 1224 263 230 15 76 70 2091 0 29 250 624 >MA0498.1 Meis1 1341 171 121 0 0 85 0 2458 261 334 175 1059 515 472 529 122 759 2411 0 0 50 2607 0 1115 537 1472 547 802 1007 736 499 1414 3 0 2607 0 0 0 1075 263 494 145 696 536 480 645 263 72 2607 0 2472 0 149 156 1473 466 856 594 592 862 >MA0499.1 Myod1 6862 4142 0 24353 0 0 0 0 9 16 5861 1913 4129 5589 6513 24514 0 6423 24514 0 0 9087 11010 7591 11622 7657 5003 11000 0 0 18072 0 0 24514 654 2905 5945 4319 4423 7060 2859 0 161 19 0 24514 0 14764 10583 5117 6660 8305 >MA0500.1 Myog 7838 11572 0 19356 0 67 0 0 2832 5343 4069 2870 331 19356 0 0 16261 0 0 8524 3764 4770 8648 7453 0 0 19356 3028 0 19356 5881 5168 7519 0 0 0 0 0 0 19356 0 2119 5081 2998 >MA0501.1 NFE2::MAF 726 0 0 1090 16 110 80 978 8 0 931 383 361 292 211 26 0 0 0 871 1 937 0 4 1063 17 203 100 102 283 334 0 1082 0 166 12 22 51 1041 22 73 283 130 91 106 4 1090 8 0 37 967 51 61 37 5 69 221 499 605 490 >MA0502.1 NFYB 2965 2816 2821 655 674 2832 3509 0 0 7020 7003 0 413 5454 725 1580 1759 1760 2228 2076 1091 139 7020 7020 0 0 0 4780 97 943 1984 1078 1176 1089 2707 2978 2921 0 0 0 17 0 1709 1457 5136 491 1367 1263 3048 1563 119 451 0 0 0 0 7020 118 12 216 >MA0503.1 Nkx2-5 (var.2) 1790 1282 344 0 2467 0 0 0 2858 2265 502 327 192 1777 3429 6 3429 0 3391 431 3 956 821 1467 1292 0 0 0 0 0 0 825 1795 491 488 16 0 956 0 3429 38 140 336 176 >MA0504.1 NR2C2 129 82 193 0 10 3 6 343 187 324 10 11 0 9 290 126 70 6 8 3 0 309 0 0 0 0 0 27 315 0 90 221 195 366 355 79 54 48 208 71 385 336 89 35 92 50 22 1 21 27 313 26 4 0 0 0 48 279 36 13 >MA0505.1 Nr5a2 645 985 276 122 71 8 1685 1679 39 0 169 0 1437 271 249 224 170 198 378 351 1616 14 0 0 0 584 1568 20 345 693 634 336 990 366 23 74 0 23 1663 1702 61 12 90 740 454 199 211 238 836 1257 4 3 0 0 0 888 122 155 346 306 >MA0506.1 NRF1 602 623 196 0 1514 0 0 0 0 74 2245 375 4001 189 4016 1862 403 0 4338 349 4550 804 3647 0 4239 468 935 528 4624 0 4275 0 1575 0 0 0 140 313 3693 0 286 0 0 0 >MA0507.1 POU2F2 415 530 605 2016 0 256 0 158 0 2257 0 1159 594 635 266 825 19 0 2 0 0 2271 30 2 39 361 402 593 415 1 0 0 0 1997 0 0 0 613 107 835 898 442 251 2287 2029 2287 132 16 0 2285 476 1225 >MA0508.1 PRDM1 1779 1649 3148 3486 4173 422 728 211 4603 4161 4497 159 117 1057 2042 471 298 345 439 0 0 110 0 0 0 0 142 458 498 778 1037 2331 688 442 430 4038 930 4392 0 430 39 4186 520 2239 973 1316 325 422 236 0 143 2835 0 0 12 67 116 3508 809 810 >MA0509.1 Rfx1 290 58 145 421 37 0 1254 138 604 186 287 1793 2082 0 157 222 833 433 1748 2048 402 115 0 0 1710 0 0 2138 1532 377 131 762 96 0 73 96 1534 1952 12 244 56 0 159 1481 1029 522 257 90 409 1789 0 0 129 101 0 0 >MA0510.1 RFX5 343 595 6 181 1189 0 1844 245 0 3057 3692 0 2084 956 871 1572 796 1870 3687 2171 832 1 0 2815 497 176 3868 782 361 1388 561 862 1283 0 61 0 2023 3514 408 314 0 0 0 2184 1239 1392 1615 709 0 447 3036 0 109 645 0 0 0 1002 367 370 >MA0511.1 RUNX2 99 165 151 121 124 75 9 0 15 0 0 0 42 258 126 133 119 79 172 155 392 4 1 3 0 0 132 190 8 126 443 444 426 477 245 133 11 1030 0 1062 1060 90 158 143 462 387 334 406 292 538 462 1038 31 1044 0 2 840 672 653 348 >MA0512.1 Rxra 278 4154 2623 4877 0 0 0 0 5348 1150 1640 4521 418 252 0 0 0 583 5348 0 1070 742 305 9 2473 471 5348 3882 643 0 0 2336 1569 244 767 0 0 0 1466 4122 0 0 792 1397 >MA0513.1 SMAD2::SMAD3::SMAD4 166 196 14 33 0 0 106 211 0 765 30 117 110 432 75 8 45 899 0 231 125 896 19 736 583 214 168 34 877 103 0 0 539 280 3 18 133 47 35 133 594 0 718 0 899 23 283 0 97 0 152 540 >MA0514.1 Sox3 0 0 262 0 0 0 0 0 19 42 1487 1552 0 0 0 144 0 698 853 654 330 0 0 0 0 1923 0 254 22 382 250 515 1805 2067 2067 0 2067 1115 1173 989 >MA0515.1 Sox6 4 0 161 0 0 0 0 0 4 14 139 221 0 0 0 0 0 115 112 76 50 0 0 0 0 249 0 5 0 31 56 28 88 249 249 0 249 129 133 128 >MA0516.1 SP2 160 39 52 0 0 174 0 0 0 206 115 187 226 228 203 253 811 1386 1679 1643 0 1686 1656 1108 1196 971 554 717 794 745 1028 257 1 7 0 1280 0 0 0 0 99 325 401 408 422 245 579 247 0 43 232 0 30 578 284 501 620 342 256 316 >MA0517.1 STAT2::STAT1 69 150 430 3 0 3 3 0 255 107 4 10 4 15 28 63 230 7 148 2 2 13 581 75 111 13 2 170 489 365 77 84 151 467 0 0 4 0 191 161 10 0 4 9 36 411 156 32 2 618 615 600 39 99 241 593 608 442 107 191 >MA0518.1 Stat4 211 12 26 632 307 1450 733 45 2873 2873 1573 486 632 889 1019 2 0 2229 1491 28 0 0 0 0 279 785 721 443 484 0 529 12 72 1223 2134 2828 0 0 833 685 1028 1117 1159 2859 2318 0 1003 172 6 0 0 0 188 917 492 424 >MA0519.1 Stat5a::Stat5b 6172 2073 0 0 0 263 7044 11543 0 14888 14886 2326 3596 0 1600 16341 12795 6137 0 0 93 0 4425 2901 0 0 0 0 0 4140 16507 0 1621 3584 7937 16507 14907 166 3449 3326 824 0 1526 0 >MA0520.1 Stat6 257 665 152 0 13 122 70 664 328 1153 26 1852 1849 621 345 663 328 566 0 0 1680 1204 21 534 3 0 0 0 540 490 482 372 436 0 0 0 0 219 898 416 1796 0 3 398 235 450 487 698 1852 1839 50 578 948 92 280 30 0 0 293 782 >MA0521.1 Tcf12 6170 8820 0 12895 8 184 0 0 1875 3912 2863 1585 124 12895 0 0 10822 0 0 5527 2416 3005 5123 3951 0 0 12887 1889 0 12895 3811 2925 4836 17 0 0 0 0 0 12895 0 1682 3642 2191 >MA0522.1 Tcf3 2717 8100 0 17261 0 0 0 0 0 6957 2351 8489 6433 17261 0 4101 16850 0 0 12529 4204 4115 4689 1756 0 0 12403 411 0 17261 3397 96 6625 1366 972 0 0 757 0 17261 0 1335 6004 4170 >MA0523.1 TCF7L2 1525 1808 3070 85 2795 138 155 4188 4103 4169 303 1035 1398 1782 756 840 136 1947 26 0 3304 0 15 0 37 849 1123 826 1140 1153 559 2008 0 0 729 0 70 19 3848 2008 1210 757 767 387 423 148 1367 4050 0 0 0 0 0 296 457 823 >MA0524.1 TFAP2C 4801 6477 2550 4918 421 0 0 0 1082 16442 191 0 285 4343 8905 6752 4693 4943 2009 6040 18426 14817 9317 14790 0 0 0 7908 9235 3259 2123 3347 2643 6098 11033 0 0 1237 1768 1195 18235 18426 10222 1788 2536 4750 3909 8290 5401 932 0 3609 7872 786 789 0 0 11 3060 3726 >MA0525.1 TP63 3613 2402 4027 19 6536 2926 82 896 2058 437 5045 2812 4726 0 5101 953 225 528 2379 1446 1752 1337 544 9153 1640 255 0 4400 4119 7000 2050 989 198 9632 1856 817 0 5454 4089 3775 2372 3599 3802 102 616 1263 9550 74 998 1417 208 4555 3530 0 66 695 9394 213 408 1621 1895 2294 1259 358 840 5188 0 4262 2457 778 2329 1276 1178 0 2609 7167 13 3437 2756 2790 >MA0526.1 USF2 3976 1495 0 13819 0 0 0 0 10453 0 2184 2073 4490 13819 0 4863 0 0 0 745 9929 6633 7125 1909 0 0 1370 13819 0 13819 2568 729 455 645 5925 0 0 7586 0 13819 0 53 3161 4547 >MA0527.1 ZBTB33 82 6 28 10 0 35 2 0 461 8 416 85 19 79 90 256 66 659 89 705 0 668 0 80 57 94 232 328 145 233 146 32 11 5 0 667 0 705 118 450 141 136 43 109 266 221 601 7 601 0 3 35 0 46 190 54 252 315 372 116 >MA0528.1 ZNF263 2366 4596 11118 453 1011 13919 1569 1552 8113 5132 3096 6598 4175 4106 7943 3823 3132 8305 5226 4067 7264 293 0 0 0 1738 551 844 1427 968 1543 1069 590 620 1618 950 1 0 0 219 650 907 11078 10639 2802 13819 12444 43 11252 11451 5513 7042 9471 6730 8903 9042 4780 9659 11444 5892 8923 9819 6141 1498 0 1315 963 42 722 1570 805 641 1518 1599 1317 1537 469 1562 1752 659 1038 867 699 923 >MA0007.2 AR 6277 6497 0 6462 11206 26 10426 1478 4353 3312 3241 0 2214 2656 1599 1112 0 0 1304 0 11115 0 2976 2096 3249 142 525 1262 2434 7032 3049 4709 11206 1254 0 65 107 4023 2151 3291 305 10681 671 2460 298 768 0 0 2186 0 0 673 2729 2606 1354 7518 0 7059 3656 2277 >MA0102.3 CEBPA 10351 0 0 705 0 11956 2660 12226 15318 0 5804 1332 0 0 0 15318 1274 7654 3092 0 5688 4963 3635 0 0 11304 0 1066 0 0 0 1804 890 0 15318 15318 3309 0 1022 5004 0 0 7826 3661 >MA0024.2 E2F1 259 218 144 0 0 0 0 0 1059 508 305 317 0 274 0 1059 0 337 286 0 0 269 280 628 641 1059 0 1059 722 773 0 551 485 203 213 0 0 0 0 0 0 0 0 0 >MA0154.2 EBF1 4513 0 0 886 0 9191 21366 1085 8427 0 22124 7097 10267 33855 22776 33781 22317 0 0 0 0 2891 12581 2643 0 0 0 0 0 32770 25294 33855 7215 9664 20945 0 10193 74 2347 12489 0 134 0 1625 >MA0162.2 EGR1 1098 1506 1160 1335 233 2911 0 0 0 3652 0 2368 0 3026 5728 6847 6049 10431 11574 0 12256 11852 10154 8361 11952 0 9849 5595 3083 1359 2250 0 0 6840 0 0 611 0 0 6599 1420 1913 2347 2544 2797 490 449 2505 0 404 1491 243 304 3289 987 1722 >MA0076.2 ELK4 298 391 2034 0 8 0 0 0 0 0 290 1458 1905 145 2688 0 0 3427 3427 0 501 1253 886 570 1038 16 0 0 0 0 2915 2843 668 785 561 210 723 3419 3427 0 0 512 83 1216 >MA0258.2 ESR2 5410 429 59 74 0 8098 502 2092 1808 1602 1121 3278 434 1111 834 96 0 0 147 7621 1 4202 3235 2510 975 398 2056 6469 5854 2708 2563 7170 8143 989 533 57 2738 1688 2173 533 6658 1391 363 0 606 174 644 41 7033 89 87 801 1228 1752 5133 66 1518 977 1278 4095 >MA0098.2 Ets1 431 145 263 1179 0 329 0 0 0 0 0 65 146 306 265 668 1160 993 117 1507 0 0 1868 1868 0 329 579 678 542 629 420 244 187 451 4 0 0 0 0 156 1422 62 437 309 472 349 319 425 121 357 1539 1868 0 0 1712 117 1162 607 711 502 >MA0148.3 FOXA1 4328 4659 3062 7135 70 5883 0 0 0 18252 0 9193 827 10702 4807 4827 6851 8558 4317 0 0 0 0 0 0 20556 2655 7296 1 2603 4124 4731 5314 3641 12 16125 0 1292 2859 3756 0 0 1392 63 9415 8729 5767 5074 6915 21926 0 22008 20716 19149 0 1452 10160 12493 11242 5183 >MA0035.3 Gata1 2035 506 0 0 0 17955 0 0 2166 2502 1886 4321 6406 14869 697 0 0 0 17955 0 6166 5072 2530 2792 437 0 0 0 0 0 0 6687 1598 9069 8251 2649 17258 17955 0 17955 0 15789 2600 9399 >MA0036.2 GATA2 1569 1125 1555 1051 32 9 0 51 4380 0 0 1225 104 1256 567 768 729 525 1057 3015 192 0 0 0 4380 0 1765 1185 754 1709 1220 427 765 914 0 0 0 0 0 0 1967 224 1490 778 876 2377 2526 442 4188 4329 0 4380 0 3155 544 1715 >MA0037.2 GATA3 4628 0 4628 0 4628 4628 1290 2796 0 0 0 0 0 0 0 712 0 4628 0 0 0 0 3338 1120 0 0 0 4628 0 0 0 0 >MA0114.2 HNF4A 2766 1731 837 6075 11026 117 26 178 0 864 5270 6119 1156 1870 2070 5971 720 51 1846 4425 16535 1630 2637 173 35 3745 5653 13491 6498 6450 3974 907 15810 8370 329 67 0 367 407 15741 5340 1662 352 415 3001 4057 13410 70 477 988 49 15112 13586 16188 128 2413 3334 1769 7985 5247 >MA0050.2 IRF1 316 251 173 138 735 24 12 0 0 0 913 20 16 7 11 17 513 218 145 198 166 320 284 324 430 101 792 0 31 56 1249 39 787 1 14 58 1023 245 464 186 193 331 266 251 212 148 361 506 0 0 4 0 238 345 0 5 0 28 263 291 114 99 151 460 576 653 646 165 40 1350 1331 1302 113 172 210 1345 1336 1293 294 341 389 917 872 714 >MA0058.2 MAX 12557 13557 5014 0 24565 0 19618 0 0 122 741 1112 3587 24565 0 24565 0 0 0 2889 8243 6781 12571 0 0 0 4947 0 24565 14194 3024 3115 3393 0 0 0 0 24565 0 7360 >MA0052.2 MEF2A 481 169 116 0 1272 1008 1383 1352 1390 6 1472 231 587 530 345 148 149 1179 473 68 0 0 5 0 0 0 49 632 409 449 379 625 32 0 14 119 39 0 0 0 0 1160 112 110 203 465 530 146 1000 119 346 51 116 83 1467 1 33 142 424 476 >MA0100.2 Myb 89 227 979 910 0 53 0 150 30 725 374 452 0 0 979 0 0 416 949 8 190 0 0 69 0 0 979 9 0 0 326 300 0 0 0 926 0 404 0 246 >MA0147.2 Myc 1181 0 5335 0 0 0 0 49 848 82 3650 5335 0 2295 0 0 0 2297 1087 1877 504 0 0 0 5335 0 5335 2081 556 801 0 0 0 3040 0 5335 0 908 2844 2575 >MA0104.3 Mycn 528 0 0 1403 0 168 0 0 146 1135 1403 0 1403 0 0 0 566 268 0 0 0 1235 0 1403 163 0 0 0 0 0 1403 0 >MA0150.2 Nfe2l2 166 401 118 203 488 0 0 726 20 84 65 577 10 4 623 338 78 239 247 28 0 0 0 533 20 549 3 11 699 26 109 159 275 152 205 0 711 0 118 16 46 90 698 19 31 113 88 94 124 5 726 15 0 55 606 66 56 7 4 46 >MA0105.3 NFKB1 570 55 121 3141 2228 0 0 0 0 0 1580 319 0 0 224 1204 0 55 1526 4858 5026 2395 2682 5057 4902 1635 812 0 84 0 0 0 269 1541 0 89 112 868 5112 4973 3586 254 86 868 >MA0060.2 NFYA 4708 1187 3548 1203 1826 192 237 16 222 8555 5 10 29 12 377 460 1537 4261 1515 987 2256 2973 2344 834 7384 993 1018 2 52 5 10 70 2677 3943 4176 1136 983 4580 2168 3978 313 4788 481 242 7181 150 8 5 8720 8683 15 1873 2227 2707 1562 2014 796 614 4285 2954 666 7517 347 61 8703 8748 9 3 5699 2492 828 664 >MA0014.2 PAX5 261 315 308 245 0 39 766 152 9 174 542 537 0 3 317 153 225 587 152 228 59 30 148 169 736 21 163 538 419 8 17 215 751 17 41 6 33 667 333 222 546 336 455 0 83 533 35 294 344 185 679 17 540 155 659 222 25 74 300 12 167 272 121 26 48 314 9 2 157 2 125 22 547 6 54 52 >MA0080.3 Spi1 24957 27071 38966 42251 38682 12577 45356 4801 0 63665 63715 3436 8250 3893 21911 10470 7875 3311 3103 534 8120 3209 0 0 0 0 10145 3223 12038 12028 17557 22021 13786 12032 11483 43018 14683 58914 63715 50 0 50134 3385 45501 21003 10731 6748 7652 6329 13016 0 467 0 0 0 0 0 48857 2283 8773 >MA0143.3 Sox2 0 0 669 0 0 0 0 0 1476 1476 0 0 0 0 0 419 0 0 0 0 0 1476 0 297 0 0 807 1476 1476 0 1476 760 >MA0079.3 SP1 857 0 99 0 0 1359 0 0 0 1054 652 1786 6215 6703 8734 8661 0 8734 8734 6679 6357 4969 4271 642 0 0 0 4624 0 0 0 0 734 1820 1877 1932 0 73 2751 0 0 2055 1323 2379 >MA0083.2 SRF 426 1017 515 270 477 49 0 1767 1184 2226 590 2048 1452 68 15 608 1353 1299 813 446 285 257 681 2144 2105 66 0 0 120 12 10 0 7 864 504 178 281 331 206 1000 533 0 0 23 73 16 16 51 87 2209 2255 441 230 460 757 483 1271 750 586 84 172 421 1020 35 1551 166 728 0 0 364 190 340 >MA0137.3 STAT1 366 0 0 361 109 1739 163 29 3620 3624 1879 731 52 6 3262 2009 0 0 7 0 5 311 785 0 110 6 0 1637 3466 3426 9 0 725 1747 3577 3513 0 1511 253 0 167 0 0 714 >MA0144.2 STAT3 1936 708 250 1006 840 3692 1574 2532 21620 21620 9311 8571 0 346 19585 7780 0 0 0 0 0 962 3801 0 5850 0 0 11456 20046 18686 0 0 6579 7312 20912 15174 1029 13000 6472 0 402 0 0 4768 >MA0140.2 TAL1::GATA1 1114 206 318 4955 0 0 2049 658 963 1191 1778 1038 1352 1885 1381 110 4659 160 2038 807 0 0 0 4955 63 1331 1214 1127 1130 1237 1142 1033 1237 4822 0 890 967 159 0 0 0 0 55 2214 1244 1520 990 1712 1466 820 1884 23 0 3544 836 3783 4637 0 4955 0 2788 752 1534 1117 1057 968 995 1217 453 0 296 361 >MA0003.2 TFAP2A 1387 2141 727 1517 56 0 0 62 346 3738 460 0 116 451 3146 1630 1060 1506 519 1199 5098 4762 1736 2729 236 0 0 1443 3672 690 851 792 884 985 3712 0 0 85 1715 920 4638 5098 3455 465 168 1230 1105 1981 2077 131 0 336 3215 308 204 0 0 84 510 1094 >MA0106.2 TP53 736 3 963 269 0 73 175 80 810 337 837 0 1013 178 36 0 1191 45 11 0 597 818 919 162 45 0 1226 26 43 6 469 5 29 33 1231 0 47 52 87 790 377 3 12 25 1186 26 32 194 918 0 561 191 180 172 59 17 2 180 985 3 >MA0093.2 USF1 4846 1472 43 16573 188 1605 0 0 14762 0 1821 3764 7448 16799 0 10279 595 0 0 0 13166 9926 7162 2983 0 0 932 14642 0 16842 1375 0 1113 1070 4939 0 269 5443 0 16842 0 705 3676 3982 >MA0095.2 YY1 1126 6975 6741 2506 7171 0 11 13 812 867 899 1332 4583 0 99 1117 0 12 0 0 5637 1681 875 4568 801 181 268 3282 0 0 7160 7158 38 2765 4655 391 661 15 63 266 0 7159 0 0 684 1858 742 880 >MA0103.2 ZEB1 856 0 1197 0 3555 0 0 0 0 1455 2991 333 3170 0 3555 3555 0 0 830 0 431 0 0 0 0 0 3555 414 564 1594 385 0 0 0 3555 0 >MA0591.1 Bach1::Mafk 45 14 15 66 2 0 113 0 1 5 108 0 0 98 12 14 36 41 16 1 0 0 94 0 107 1 1 114 4 42 41 46 54 32 0 107 0 20 4 2 3 113 0 6 38 14 18 4 0 111 7 1 0 109 0 2 0 0 6 22 >MA0592.1 ESRRA 17 18 200 198 0 0 6 0 197 23 70 118 174 2 0 0 0 0 205 1 108 54 11 15 6 10 203 207 22 3 10 44 40 62 1 0 0 5 1 180 0 0 33 44 >MA0593.1 FOXP2 330 340 160 59 766 766 766 0 766 297 332 113 7 0 0 0 0 0 757 0 154 124 190 142 606 0 0 0 0 0 0 293 184 133 277 0 707 0 0 0 9 0 22 126 >MA0594.1 Hoxa9 34 12 38 161 0 101 157 171 5 7 131 53 110 117 3 0 58 6 1 0 154 11 81 5 0 8 0 8 0 0 1 0 0 4 45 17 0 172 5 9 0 166 11 30 >MA0595.1 SREBF1 28 0 0 58 0 6 0 2 58 0 14 0 58 0 51 38 44 45 0 26 16 0 0 0 7 13 0 11 0 7 0 58 0 0 0 1 14 0 0 25 >MA0596.1 SREBF2 24 0 0 10 2 2 0 0 47 2 9 0 3 0 10 9 0 0 0 16 14 0 44 37 27 36 0 47 0 2 0 47 0 0 8 0 47 0 0 27 >MA0597.1 THAP1 18 15 20 7 3 58 47 30 116 93 47 0 182 195 136 17 71 34 27 12 137 0 0 1 77 41 17 61 125 42 10 1 4 58 57 32 >MA0598.1 EHF 703 0 0 0 0 0 0 0 724 1427 0 0 1427 1427 0 707 0 0 0 0 0 0 0 720 0 0 1427 1427 0 0 1427 0 >MA0599.1 KLF5 1429 0 0 3477 0 5051 0 0 0 3915 2023 11900 12008 9569 13611 0 13611 13611 13135 5595 7572 0 0 0 0 5182 0 0 0 0 2587 1711 1603 565 0 3378 0 0 476 4101 >MA0600.1 RFX2 373 62 192 431 34 1 1567 164 581 193 214 2095 2312 0 760 588 406 585 620 141 184 736 317 1936 2162 432 222 0 5 1932 8 0 2294 784 403 831 630 668 1716 317 64 910 121 7 41 94 1765 2146 0 170 28 0 539 1178 733 761 678 116 1783 1354 688 255 176 306 1866 0 2 200 73 6 52 263 177 376 370 380 >MA0113.2 NR3C1 248 70 207 401 0 385 152 234 249 108 0 47 120 0 60 0 0 94 0 465 0 136 71 107 0 0 8 162 407 150 150 370 109 36 0 14 177 81 109 19 465 0 0 0 0 67 25 55 28 0 66 0 79 0 338 0 410 183 58 255 PWMEnrich/inst/extdata/stripe2.fa0000644000175100017510000000077514614305422017707 0ustar00biocbuildbiocbuild> eve_stripe2 GGTTACCCGGTACTGCATAACAATGGAACCCGAACCGTAACTGGG ACAGATCGAAAAGCTGGCCTGGTTTCTCGCTGTGTGTGCCGTGTT AATCCGTTTGCCATCAGCGAGATTATTAGTCAATTGCAGTTGCAG CGTTTCGCTTTCGTCCTCGTTTCACTTTCGAGTTAGACTTTATTG CAGCATCTTGAACAATCGTCGCAGTTTGGTAACACGCTGTGCCAT ACTTTCATTTAGACGGAATCGAGGGACCCTGGACTATAATCGCAC AACGAGACCGGGTTGCGAAGTCAGGGCATTCCGCCGATCTAGCCA TCGCCATCTTCTGCGGGCGTTTGTTTGTTTGTTTGCTGGGATTAG CCAAGGGCTTGACTTGGAATCCAATCCCGATCCCTAGCCCGATCC CAATCCCAATCCCAATCCCTTGTCCTTTTCATTAGAAAGTCATAA AAACACATAATAATGATGTCGAAGGGATTAGGGG PWMEnrich/inst/extdata/tinman-early-top20.fa0000644000175100017510000017674414614305422021673 0ustar00biocbuildbiocbuild>tinman-early_885 CCCCCCCGCCGCCACTCAGCGCCGCTTTTCGCCAACAACTACGTACGCATCTGAACCGTGAGATTTTATTTCATGCAAAAAAGCCAACACAAGAGCCACCGAGCAGAGGCGCAGGCAGAAAAAAGTAGAAGTACTCGGCTGCGCATGCGCAACACAGAACGAGACGGCAATTGCGATGGCAGACAGAGAGGGGGCAACATAGTCCGCTGGTCGACCTGTGCAGGTGTTTTGCGCTTGCAACAAGGAAACCAGCATATAAATTATAAGTTCGGCTCACTGCAGTTGCAACCTGGTTTTTTTCGTTTGTTATATGCCCGAATCGTTGGCCGTTAATGGCTTATTAAAAGGGTTCACACGTTGAGGTTGAAGTTTGAAGTTCAATTTGAACTAAGAGCGAAAAGAATCACATTATATGTAATTACATTTTTAGGAAATTTATTAAATAACTATAAATAATGAGACATATTCGGCCTGTAAACTTGAAGATATTATTTATGCCCACGATACACTATAATGGGACGTGACCGTGAACGCGGTTTTCATTAAATGTGATGATTACCTTTTACGAATCGGTGAAGGGGAGATTATTTTTGGAACAGCGAGAGCCAAATTATTTTTTTGCACGCGTATGAATCACTGTGTCTTATCGGTTGATAAGACCGTAAAAACAGTACATAATTATAAGCAACTGCACACAGCTGGGCCTATAATCAACTGGGGTTTTTCCAACTTGCGACAAAATGAGAAAAGATACGCATGCATTAGTATCCTGTTTTGGGACATCGCTTCCAGCGAAACAGGAACCGGCTAAGTGGCCAGTCGAGGATCGCGACATATCCCATTATTCGAGTCGAGTGTCTGGCCCTTAAAAAAACATATTTTCGTCATATTTTTGAGAGCACATAAGAACCGGCTGGCAGGTAAGGAGCTCGCCCACGTCTCAGGTTCGTACTTTGCTTTCTCCTTGCGGAGCAGAGTATCCGATTTCATAAAGGACATGCCACATTTCGTTGAGCTCCGTTCACGGCGGATGCACAGCCCAGAAAGTTCCCATGTCCTTTTAGCTGGAAAATGGAAAACCACTTGAGCTGCATGCGCCAGCCGCATGTCTGTGATTATAGAAGAAGAGCTCACTTGAGAGCCACCAGCATTTCTCGCATCTTGGTTCTCCCACTCAATTGCATTCTATAACTTGAGCATGCTACTGTTACTGTATCTACAAGATACATCTCTGCCCTTTGTTTTTTTGGGAAACGACGCCGCTGCTCTGCACTCCGATTCTCACGTTTGTGGATGGCCAGATGGGAAGTTGGGGGGGCAAAGAGGCCTGCAATCATAAATAATAATCAATTTCCTGTGCCCAAAATGTTTCCCATCGCTCCGCAAGTTTTTCATTTCCATTCTCCAGCTGGATAGCGTATGCAACTGCAAGTTTTTCCTTTCTGCTGAGAAAATGGGAAAAAATAAAATCTGACAGCACAACGCGAGGTGAGGGGCCAATCAGCCGATTGTTTAAAATGTATTTATTGTTTGCCGTTTTCCCTCTGCCACCCGTGCGTCGCTCTCCTCCATCGCTGCGTTTGTTTGATTTGTCGAACATTTGTGCACATTTGTTTCCCCCATTTACATGCGCAGCCCACCACCCCCATCCACCCAAAAATGGAGGCTGCCAATGGGGGCGGCCATCGGCTCGATTCTGAGTGAATCCGCACTGCTTCGGCTCGCTGCCTCGTTTTGTGGCCGCCGCTCGGCTGCGGAGGTTAAGTGTTGGCCCAATCAACGGCTGCCCCATGACGCATGCGTTCTCCTCGTGGAGCCCTAAACTAGCGAACTGATGGAAAGCCCTAAAGACATGGGATTACTTACTATTATTTACTGTTAAATATTTAAAAAGTTGCGTTTAATTTTATAAACAAAGCAATCAACAGTATGTTTGGAATAAATAGCCATTCTTAGATTTTCCTTAACCAAATAAAATTTAAAAAAAAAATGTAAATATCTCATTTAATAGCTTTCTTGCGAGCATTAAAAATATCTTAATCCAGGGTATTCCTCGCTCTTCTACCCACATTCAGCTGCAATTTATTGTTATCACAGGGCATGCAGTCCCCCAAATGTATCTTTTTCCTGGCAACATCTCTGTTTTGTTTATGACTTGGCCAAAAATACGAAAGAAGCAAATTGCGCCAGCTGTTGCCGTCTTGGATGCGATGGCTTGAGATGAGAATGAGTTGCGAAGAGGATTCGATGCGGAGCTGGTGCGATCTCCTTACCCTTTTGCCACATTGTGCGGATCAAGAGATAATGCAATTAGTGAAGGGGGCACTTGCAACATCGACGAGTGTGGGTGGTGTTGCCGCGCGAGGGATGGGGCCTGCACCATATGTCACTTCGGTAAGCTGAGCCCTCGCATCGGATTACCACTTAGCCATAGCTGCTCCCCCCCCCCGCGAGCTCTCTCGATCCTCCGGCCATCAAGCTGGGTGCTGTACCTACATATATGTATGTATGTATATATGTATGTTTCTTCTTGCGCTGCTTCCATAAACATAAATCCAAGCCGGACGAGCTGTTGTTTTTCTTCTTCGCTTGTTTGTTGTTCTCTGACTTTATGCTGCCGAAGGTTCTGGGCCGGAAAACGCCAAAGAGCATAATAAGTTCAGATCCAGCATCGGAATCGGACTTTTAGCGGCAGCGTCAAACAGACAGTGCGCGGCACGAATGTAAGGTCAACGGCGTTGACAGTGATCTTGCATAATTGCAGGGCCACGGAAAAGGTATGGCCCCACCCCGGCTTAAGGTTAAGTTTCCAACGCCCCTCGATCTTTTTTTCGTTTTTTCGTTTTTTTCGTTTTTGCTCGTCTGCTTGAGAGCGAGAGTTAACCCCTTGATGGCCGCGGGGTCAGCAACGGACGCATGTGAATCATTGACAGTTTTTATGAAGCGCCGCATGCATCACTCAACGACATCTCAAGAAGCGCCCGCTGTCGCCTCATGTCGCTCAGTATCCCGCAATGGGTGTGATGAATTCCATGTTGCTACTTCGACACTGTGAGAAATCGGAGCATGGTTGGTATCTTTAGAAAAGATACGCACCTCATTCTGCTGGTTAAGTAACTTAGTGAGCTAAGTTCATAAAATTACCTGGTAAAGTTCA >tinman-early_2150 TGGATGGGGAGATACACTGTTAAAATACATATTCCCAAATATAAATGTTATATATGCTAAACATAAGACGTTTCGCAAAACTGTATCACACCAGCTGCAACTCTCGTAATTTACATACGATACGAAATTTACATACGATAAAATCAAGACTGTATTGCGGGCTGGCAGTTACGAATCATTCTGGCCAAAGTATAATAAAAATGACGAAAAATAAAAAACACGTATTTTTAAACACTGAAAACTTATGGTTAAGTACATATACTTATTTCAAAGACCTTTTTTTCGTGTGCACACACATAGGTGAATGATGCATTTCCGGCGAGAGAACAATACACAATCCCTTTGGCAAAACTCGTTTCCATTTTTTTGGTTTTTGTAAATCACTTCCGCGCAACGAATTGGAGACCCGAAATTGTCATGGACGTCAATTGACAACAGGAGCCGGCCAAAACCAGGGGCGAGAAGACGAGAAGGCGGGAGGGGGCCTGCAGAGCTCTAGGGCTATAGAGTAGAGGGTTAGGTCAAAAGGGAGACCATAGGCAGATCCGGAAGGTAAATTAAAATGGATGACAATGGGATCAGCGAGGGAGGGGGGAGGGGCTGCAGCCAAACGAGCATTAAATGAATTACTGAATGAATAAGTGTGAATAAGTGTCAGTGGTAGTGTGAGTATGTCTGTGAATGGCTCCGAGTGTTCACATGTGTGTGTGTGACTGTGCGTTCGTGGCACGCTGCTCGAGGCTGCTGCAACATGGCCGCCTAATTGTATTCGCGTTAAGCAAACACCTTTTGCCAAATACCTCGATTTTTACGCCTGGCGAGGCGTTTAATTGGGGTTGTTATGTTCGGAGTTCCCGCTCACATATACTCAAATGTATGGGCCGGGTCTTTTGCGCGGTTTGCCTGCGATTAACAGCGTCGATTTGTGACACCCAACATACTTTATTGCATTCGAATGCGAGTGGTTTGGTGCATTGTGTATCTGTCGGATGCGAATGCGATTGGGCCAGCACTGTCACACTTCAAATCAAACTGCCAAAGGACAGCTGGCCCTAACTGTGTAGGAAGGATCCTAATGCGGACTCCAAAGATCCCAGCAGATGGACTTTTATGAACCAGCATTTAATTTTGAATTTCTACTTACATAACTGATATAGAATCTATTGAAACAGCGACTAATTAGCAACACAAAGTGCAAGCTAATTAAAACCATTCGACGATGAAAAGTTTTCGACAAATATAAGAGAGTCAGTGTCAAGTAGACACTTTTAAGTTGAAGAAAACATCTTGCTGTCTTTTCTCATAAACTCTATAGCTGTTTATGTATGTCAGCCGCAAATAGCCCTCTGCTGTCGGTGGACCTCCACCCATTGCAACGCAAATATATTGAAAATGTATTCACAAAAAAATAAAAATAAACTAGGCAGTGCCCAAATCCCTAAAGGCCGAGCAGGAGGACGACAAGGACGAGAGCTACAATTGAAGTTGTTTGTACTTAGAAATAAGCACAAACAAAGTAGTTTGAAGTGCGGAAAATACAGAAGCATGCATACAAAAATAAATAAATACACAAATAAAGGAGCAGAGGGCAATAAAAAACAAACGAGTTTTTGCGGGAATCGAATTGAATTCGTGTGTAATTCGAATGCCAGCGCTCCACACAAACACAGATAAATCCATGAGATACTAAAACTTCCTGCGGAAGCTAAGCCATAACAACAACGGCGACGCACACAAAGGTTTTACTTTGATATTAAATGCAACATCATTATTAAGCCCAATAACAATGGCCAACAACGAACGCCCGGGAAGATGGGCGTGCGCACATGGGAGAAATTTCCCCGAAAAGTCCTTTGGCGTAACCAAAATTTCCCAAACATGCGCGTCAGCGCACGGTGCGCACGGTATAACTCTCCTGCTCGCACGCTCTTCCCATTGGCCAGGCGGCCATGACGCGCCCACTTGACGGCGACGTCGTCGTCGGCAGAGGTTATTACAAGATAAAATTCTACCCCCTCCACTTACCACCTCCATTCCCCCATGGGGAGAATAATGGTGCGAAGTCATGAGCGACAGTGGGGTGCCCACTTTTAGAGGGCGGATGCTCGTGGATAGGGATCATGGAGCTTCCTTCATATACACAACATATATTTATATATATATATATATATATATATATATATATATATATGTATATGCATATTCTGATATATGTATATAGCCCCCAGCCCATATGTAAATGGGGCCAGCCCCAACGGACTCTTGCCATGTTTTCAATTGCCTCGGGCGGATACCCCCAGTTCAGTCTGTCCTGTCGTTTACTGCTCGTGCTGATTATGTATATTCCCATACTTTACTTTCGACCTCCTCGCTCGCACTCTCCCTATCTGTGAGTGTCCGGGGTGTGTCCCGAAGCCACCTCTCAAGGAACGGCGCCATGTATTGGCGTTTGAAGGATCTGCACTAATCACGAGTTGGCTTACGAGTATGAAATCGAAATTCAAATTGCACGAACAGCGACACTCCTTGTACATAAGGCCCCTATGTCAGTGCCAAAGTGGTCTATAACAGTTCCGTTGCACGCAAGAAATTTATTGCAATTCCCAAAGAACGTACCGTGAAGTAAACTAAATGTCCAAGTATAATAGCATACCACTAACACGGCAGAAGAAAATAATGCCTTCGCAAAAATAACGTTCTTTCACCGGTATGGTAATGTGAGAATGCCTTTAAAAGGGTTTTCGCAGTGCGCCTTTTAAGTATTAAAATAATAGTTCGTGGTTCAAAATAAGATTTCGTTGCTGACTAATATAAAATCACAAATATGTGATCAATCCTCCCCAGATCTAATCATGGTTGGTACTCAAAATCTCAAAGTATTACAACCCGACTGGGATTTTAAATGATGAATTGATCTGATTCTTAAATAGCCAACTACATATAGAAAAGGCGAGAGCGTAATTCCAGAAAATTTTTCTTATCGCCCTCGTTCCTCAATAGGATAATGGCTGATATCACCATCTGATATTATCGTGCTAAATGCCGGTCACAATCGCATGTCTTGCCCTGTCCTGTCCTCTCCTTGGACCCACCGCATAGGCGCCACAAGTCGGGGCTGATGTTCCCGCTCCGGAAGTCCAGAGTTGCAGAGAGCTGATAATGATAAATACAATTAAATGACGCGGGGAATGCTGACAACATCAAGTGCAGAGCACTGCTGGAAGAGAACGGCCCAATCCCAGAATCCCTATCTGGGAACGTCAATCCGACGTGCCTGAGGAGGTTCGTGAGGCGCTACAATGGCCTCGCATCCACACTCACTCTCGCGCACACACAAGCAAACATACACAGAGGCTGCAGCACACACACACTCTCACTCAAGTGGCACTCTTGAGAGCGGCAGAGAAAGCAACAGGAAAGAGTTAGAGCGGGATGACGTGCGGCATAAGGACTCGGTTCACATCGTACGTCTCGCTCTGGCGACGAATGGTGGTTGGTTCTCTCGGTGGTTGAATAGGCCACTCACATTGTTGCCGTATCCTTGTTGAAAAGGACATAAGAAGCCACTTAGCGCGCCCAACAAAGCCGAGAGTCTCTCCAGTCTGGCTGTGTGCGTGTGTGTGTTTGTGTATGTGTGTGTGTGTGTGCGACTATCCGAGTATCTGCGTTATGTACCCAATGGCAGAGGGATGCAGATACAGATATGCACCACATACATAGTGATACCCAAAGACACCCCGAGCATACGACCTGGTACTTTTCTGCCGTTGGTTGTTGGGTTTTGGTTATTGGCTGTTCTGAAGCGACTTGGACTCGTGTTGAGTTCTTGATGCCTCGTTAGTTGTTGATGACACTTCAGTTTGGAATGTGTAGAGTGCGCTGCTCCGCGGACCCGTGCAACTGAGAAATCGAGAAACGACTCGCGACCCTTGAACTATCGAACTTTCCCGGTGATAAACTTAAACTTAAGCCTAAATCGAACTATGTCTGGAATTTAAGTTGCCCTACCAGGATACCCGCTTAATTCATCGCAGTGCTACCCAGTAACTACACATAAACATATTTTTTTTCCGTGCTCCGAACACCGTAAAAGCTCCAAAGTGGCGGAGAAACGCAAAAGTCCGCCCACGCCGCCACCCGCGTCGCAGCGTAGTAAATCAAACAGTGGTTCCCCGATATCGCAAAGCGATATGACCACGTCGGCGTCCTTGGAGAGGACCCCCTCAAAGCGGGATCGAGATCGCGAGCGGGACAACAGCAGCGGTCTGGGCAGCGCTGGCAGCTTGCCCGCATCGCCCCAAAGCGCTATCACGGTGAGTCCGTCCTCCCCAGCCACGCCGAAGCGCCCGCTGCGCACCTCAACGCCCTCGCTGGAGCGGAAGAGGGAGCGTGAGGATCGAGAGGACCGGGAGGATCGCAAGGAGCGGCAGGAACGGCACGAGCGAGACAGAGATCACGAGAGATTCGCCGCAGTCTTCAGCACCGCCAGCACCACCGTGCCCACGAACACAAGTTCCAGTTCCGGATTGGCGCCCGAACAGCTCCGCATTCCGACGGGTGCAGCCGCCTTCAGCGGATTTCCGGGGCTCCACAGCATGAGTAGTCTTATGCTTCCATCGTCGGCGGCTGTGGCCGCCGCAGCAGCGGCCCCGTTTCTGCCCTGGTCGCCCATCCTGCTGCCGCCGTGGAACCACGCCCTCCTACCAGCCGCCTTTTATCCGGCGGCCCTGCGAAACGCTTTGCCTGGGTAAGCACAAGGATTCCAATGGAATAATTAAAACATTGATACCTCTGGAGTGGTGAACACAAACCTATGATTCATATGTCTTGGTTACGAAAAGCATATTTTGGTGATACGTTGACACAAGTCCATTCTAGTTCTTCTAATTATGACCTCTGTTTTGTGGGCTATTTATAAACGCTTAAGCATTGGTAACACTTTGATGGCATGGATCTGGCCAAGATATGGATAATAAGGACATGCCTCAGATTGAAACAAAATTACTGACCACCGACAATACGTTTGCCTTTCATGGCTGACCACTTTGTAAAAAACAGATATGGTTTTCAAGGTTTTGGGTTCCCGATTAATTTTCTATGTCTTTCTATTAATTCTCCTAATCTAATAATTTGGAAGATTGGGCGTTGAAAGCAGAATCTGCAGAATACGCAAGACCATTAATTAATTCTCCTAATCTAATAATTTGGAAGATTGGGCGTTGAAAGCAGAATCTGCAGAATACGCAAGACCAGCCATACTTTGCAATTGCCTTCGCCTAAGCTCCGACTTAAAATCACACCCTGCGAGCCTCTGCCTCCATATTAGTGTTTAGATCCCATATAGCTGATGGACTTGTCCGGCTAATCCTGGTAGCTCTTTAAATTAACCAAGCGGGCACAGCGCGCAAGTACAGGACACAGGGTATAATTCCCCGCCTCTTTGATCTGGCAAGTACTCAAGGTCCTGGCGAATGGCGGTTGGGAAATTCTGGCTTGTTGTTCGACCCTGGCTTAGAAATTCCCGTAGGTGAGAGCCAGGGAAACCCCAATCGGGAATGACATGTGTACGACAGACATGGGACTCAGATGCCTTCGAGATACTGGCGTCACACTGTCTGGCAATGGGATTTCCGCTCAGGAGGACGGGGAATGCCCGTGTAGCCTGTCCATAGCGTGGGAAATTCGCGAGTCGGGGTCTTCGGGAAAACTCGAAATGGGAAAACCGGAAGCAAGCAAACTTGCGCCAACATGTGGCACGACCTGTTTCGACCCGTAAAGAGTCCCTGCTGACCTGTGCTGACCTGCACTGACCCGACCAGGTAGCTGCGATCCTTACGAGGCGGATTTGCGTTTAATTGTTGATGGTATTAGGCAAATCAAAACTCGGGGTCTGACCGGGACTAGGTGTCAATAATCCAGCGATTTGGGTGCACTTATTCAAAGTTAATTCCGGGGGAAATGTGCGCGTTTTCGGTTCCGAAGCATGCCTGCAGGATGCACACCCCCCACCTCCTTATCTTCTTAACAACGGCAAGTGCAAAAATCTGTGAAAGTCAGAGCGCTACAGGTAGTGCAGGTAGTTTCCTTTGCATATCCCGACCAACAGGGACCTCCTTTTGTTAAACCTTCCGGCCATTCACAC >tinman-early_280 GCTTAGCACAATCATTTTTTGAGGACTCGCCGCCAATGTACCTAATAATACAAAAGTAAAGAAAAAACAAAGAATAATAATTCTATTTAAAATAATATAGATACATGTTTCAGTAATTATAAAGAAAAAAGAATTCTTGGAAGAGGGGGAAAAATACTTGCCTTGATCATATAACATTTCCTAACAATGCGGGTGGAATCAGCATTCAGAATGAATTCCACCCATTGTTTAAGAGATTTTCATTATATTATATTATATATTATATCATATTATATAGGCGCTAAGTACATTTCATGGTTGAAATCTCCCTATGTGTAGACCTTTAATTCTTTCAACACTTCTGATCCAAAAAGCTTTAAGTAGCCAAATCTATATTACGAAATTATTGCTTGTTGACACTTGTAAGGATATCTATTTAACGCCAGGATTCAAAAATTATAATTTTCAAGGTTTTTTTTCCCTGCCAACCATTGGAAAATTGTATTGTAGTGATGCAACATCGGTATGTTAATTTAGTTATCCACTCCAAGCATGGTTATTTTAATTTCAAGGTCTTGTATCTTTTAACCTATATAAACGAACGATTCCTTCGCTGTCGAAACTAAGTTTTGCAACGTAATGACTATCGTATTAAATCTTCCGGTCTGTGTTTTGTTTTTACTCGGTACTTTGGTACCCAGCAACTCTCAGAAATGGATCCCATATGCGAATCCTTTGGATGAACAGTCGCAGCTACAGGTAAACACAAGAACGCAAAGCAGCTCATAAGCACCTAAAGCACAATCCAATGTTTTAATATTAGGAAATTGGATTAAGCGATCAAAATCCGGATAATTTTGAGCAGTCGCAATCTCAATCACAAGATCAGATAGTTGCGCAGATTCTGCCATCTGATGCTTCTCTAAGTGGAGGGCAACCACCTAATAATATTATACAACTTGTGGATGAAGGGATGGATCGACCTTTGCTCCTACAGCCACAAAATCGCCCAATCGAGACTGTCGATCAATCACAGAACTTAATTCAGAGGAAGTTACAGTCACAGTCGCAGGCACAAGCGCTGGCCGAGTCACAGGCACAGTCACAGGCTCAGTCACAGGCTCAGTCACAGGCTCAGTCACAGGCTCAGTTACAGGCTCAGTTACAGGCTCAGTCACAGGCGCAGTCACAGGCTCAGTCACAGGCTCAGTTACAGTCTCAGTTGCAATCACAGATACAATCACAGTTACAGTCTGATTTACAGTCCCAGTCGCAGTTACAGGCACAACTCCAGTCTCAGTTAGATGCACTTAAACAACAGTCTGGGCTCCAGAAGCTCGCATTTGGTCAGTTGAAACCCCAAGTATCCGATCAACCAATTTTAATCTTAGATTCCATACCAAAACCACGACCTCAGTTTGTACTACTTCCTCCTCAGAATGGGCCAGTTCAGCAGGACCCAAATCTTCGTTACGTGATAATTCCGAGTCCGCCAAAACGTAAACGCCGAATTTCACCTGATAGCAGCACAATTCCCAAGGTTTATATATTGACTCCAGACAAACAGATAAAATCGAACACCAGTATTAATATTGATCAATTACTAAAGTTGTATAAGTACAAAACGGATAAGGAGCGCAAAATTGCCGAATTGAAAAGACAGATAAAGGAACTAAAGAAACAAAATCTTTTTAATTTGGTTCCGGCTCTTCAGCTAAGACCAGCAATCCCAGCCATTGAACTCCAATTACCAGTAGAATCACAGTCCGAAATACAATCCCAAATAGCATCACAGCAATCAGGGTCATTATCCCAATTACAGTCGCAGCAAAATTCAAAGTCTTCGGGATCACAGTCAAACGCTCAATCATCTGGATCACAGTCCCAGTATCAGTTACAGTCAGATTCACAGTCGTCTGGTTCACAGTCCCAGTTGCAGTCACAGTCAAACCCTCAATCATCTGGAACGCAATCCCAATTGCAATCACAGAATAGAAGATTACAATCCCAATTGCAATCACAGAATAGAGGATCACAATCCCAATTGCAATCACAGATAAACCCTCAATCATCTGGATCACAATCCCAATTGCAATCACAGATAAACCCACAATCATCTGGGTCACAATCCCAATTGCAGTCACAGAATAGAGGATCACAATCCCAATTGCAGTCACAGAATAGAGGATCACAATCCCAATTGCAATCAGAGATAAGCCCACAATCATCTGGTTCACAGTCCCAATTGCAATCACAAATAGACTTACACTCTGATGCCTCACAGTCCCAGCTGCAGTCACAGTTAGGCTATGATTCAACTGGATCACAGTCCCAATTGCAGTCACAGTTAGACTCACAATTATCTGGCGCACAGTCTCAGTTGCAGTCACAGTTTAATTCCCAGTCTTCAGAATCACAGTCCCAATTGCAGTCACAGGCCAAATTCCGGTCTTTAGGAGCCCAGTCCCAATCGCAGTCACAGCTTAATTCCCAGTCTTTAGGATCACAGTCGCAATCGCAGTCACAGCTTAATTCCCAGTCTTCAGGATCACAGTCGCAATCGCAGTCACAACTTAATTCCCAATCTTCAGGATCACAGTCGCAATCGCAGTCACAGCTTAATTCCCAGTCTTTAGGATCTCAGTCCCAATCGCAATTACAGATAAGGTCACAGCTAGGAACACAGACTTTAGAGTCACAGTCTCCATCCGAGGTGCTACTACACATTCAGGGGCCTTCCACATATCAGGACAATTTGGCTGAATCGAATATACAGACGCAAAAAAATCGAATCAATGTGGTTACGCCTGAGTACAAAGTATTCAAAACACCAAATTACCTTACCCAGGAGGACATCTTGGATAATACTAAGCTGACAACTGTGAGTTGAAACATCTATTCCTCTGATTCCAATCGCTCCGAATACCTTTTTAACAAATCCAATAGGTTGATCTGATTGAGAAGTACGGCTATCCATCTGAGACCAATTACGTGACCTCCGAAGACGGCTACAGGCTTTGTTTGCATCGTATTCCGCGTCCTGGGGCTGAGCCAGTTTTGTTGGTCCACGGCCTAATGGCCAGCTCTGCCTCATGGGTGGAACTTGGGCCCAAGGATGGACTAGCATATATCCTTTACCGCAAGGGTTACGACGTCTGGATGCTGAATACCCGTGGCAACATATACTCCCGCGAGAATTTGAATAGACGCCTCAAGCCGAATAAGTACTGGGACTTCTCCTTCCACGAGATCGGCAAGTTTGATGTGCCCGCTGCCATTGATCATATTCTGATTCACACACACAAGCCAAAGATCCAATACATTGGCCATTCGCAGGGCTCCACCGTTTTCTTCGTCATGTGCAGTGAGAGACCCAATTATGCACATAAGGTGAATCTAATGCAGGCACTTTCTCCAACGGTTTATTTGCAGGAAAACCGCAGTCCAGTGCTAAAGTTCCTCGGAATGTTCAAGGGAAAGTATTCGGTGAGTGAAAGTCCTTGTTTGGGAGTATATAATTCCACTATCATTAGGATTACCTTATAATTTATAAGATAAGACAATAAGAAGATAACATGACAAAGAGTTGACATATCAGCAATTTTAACTGCTTTACAGCATACTTATTCCGTTTATCTTTATAGATGCTTCTGAACCTCCTGGGCGGTTACGAGATCTCAGCGAAAACGAAGCTTATCCAGCAGTTCCGCCAGCACATATGCT >tinman-early_1353 TTGCAGCTCCTCCTCCGGGCTCGTCTCCCAGGTGATGGCGTTGTCCAGCACCACCTTGAGGTGCTTCATGTCCTTGGCCAGCTGCTCTATTCCCGTCTGAATGCTGCTTGTCAGTTGGACGAACTGGCTCGTCCTCGGATTGAGCTGCTGCCGCTGGTTGAGGTATACCAGCAGCTGATGCCGCAGTCGCTCGCAGCCCTCGTACTCGATGTCCCAGGAATCGTGATCCACCAGCGCCATTTCTCTGCTTTTGCTATGAGTAATTATCAAAAGCAGAAAACTACGCCTCCAGCAATCCAAGTAACAAAAACAAAAACAATAGCCGAGCAACAGCTGATGGCTGGTAGCAAAATAAGTAAGCGCGAGTACGGTCACATTGCCAGTAAGAAGGGATTCGGAAATAATCTTCAATGTTTGAACTGATTTAGATATTATACTTATGTAGCTGTAGATAAGCTGGAAAAATGCACCGCGATTAATCCCCAATCCTTTATCAATTCACCTCGCAGGTGCGTTGCTAGTGTGAACTATTTACTAAATAATTTAAGCTGGTCACTCGAAAATCGTCACTATGATGTGAAGTATGATTATTCGGCCCGAATTCCTGAAACTACTCACAAAAATTAAGAAATGCTTTCCAAATTGCTTCCAATTCTGATTCTGCAAGGTGGCCGAAGAACGTATTCCTTGTGCTCGAGAGATTACACCATCCCCACAAGTAACCACACCCTAACTGGTCAAATTTTAAAGGTTCCAAACCTAAATTCGCAGGAAAGATGTTTTGGTACGCACATTGTTGTCCGGCATAAGAGAGGTCGGTTCTACACCGACATTGGCGAGCTCCGGGCTGTATTGGTATACTCCATCAGGGACGGTGTGATGACCATCAACAGCACCAACGTCCCGGAGGAACTGGGCGGACATGGCATTGGGAAGCTACTGGCCAAGTCGGCACTGGACTACGCACTGTTGAATGGGCACTTCATTATTATAAGGTGTCGGTTTGTCCAGCACTACATCGACAAGTATGAGCCGCAATACGCCAAGTACATATTAAATTAGGGAACCGAAAACAAAACTTCAAAATTGTCGACACAATCAAACTGAACACAAATAAATCCCCCCACTATGCACATAGACACCATACATATAAACGTATATTTGCGTTCAGATTTGTTGAATTCCTTCGGTTTTTGAAAAATTACATGAAAATAATTTGAATTGGCTTATTTTTAAATTATTTCAAAATTTCTAAACCGAAAGGTAACAGATTATATTTAATGACGAAATTACGGTTTCTTATATAAGCTATTTTGTTATGCTTCTAAAAACAAATGTTTTATTACCTCTTTAAAATATGTAAACTAGTTTTCACAATATATTTTAAAAAATTGTTAATTTCCATATATACCACCTTGCATTGTGTGACCACCCTGAAATTAGCAAAGGGCGTTGCCAGACTCCGGCCACCCACGCACATACGATAGTTGGGACCGGCCCGGCGCGGTGGACAAAGTCACGGTATCGATATGGTGACACGATTCACCCACACATGCATGCATTGGGCTACGATGGCTGTGACACAAGGTGTGAAGAAATCACTGGGTTACAATGAGCGCTATTTATAAATATTTTGGAAGTATGCTGCAGGATTTCATAAAAGTAGTATGCAATTGGTATGCTTGATTCATATTGTTTAAAATTTGCTGTGCTACGTCAATTTTGTACTTGAAAAATATGGAGTCGTGACATGCCGCCAAAGCGGTGACTTGTCACGGTGAAATAATCGCGACTAGTAGCAGGTACGCAACTATCGCCCATCGCTAGCTCACACGTACACACAATTTATTTAGTCGTGATTTCTATTTTATTTGTTTCAAATTACAACCAAGAAGGAAACAAATCGCGACCAAAATCCGCTGGCCACAATGCCGAGTGCCTGTACAAGCGTGCGCGAGCATTCCCACTAATCGGAAGCGAGCGCATCCGCCGTTTTGAGCGTTTTCCGCGCGGGAGTGGGAAATCGAAAAGGCAAGGCAAGCCATCGGTCCGCTGCAAAATAGGGATAAATAGGATATAGTAACGTAGTTCAGCACACAATCGGGGGGCCAGCATGGAGCGGCGCGTGAAGCTGAAGACGATCAATCCGCACATAACGTGCAAGATCTGCGGCGGCTACTTCATCGATGCCACCACGGTGACGGAGTGTCTGCACACATGTGAGTTGGTCCGGCATGGAACACTACCTGCCCAATAACGTCAGTGTTTGTTGCAATTACAGTTTGCAAGAGTTGCCTGGTGAAGCACCTGGAGGAGAAGAAGACCTGCCCCACCTGCGACAACATCATCCATCAGTCCCATCCCCTGCAATACATCAGCTTCGATCGCACCATGCAGGACATTGTGTACAAACTGGTGCCGAAACTGCAAGAAGGTTGTCTTGAGCGTGCTATTATTATTATTGTTTCTTCTACTAACTCACTCACACTCCAACCCGAAACATTCCAGATGAATCGCGCCGAGAGCGGGACTTCTACAAGAGCAGGAACATGCCGTGTCCCAAGGATATCACGCAGAACCACGAGGACGACAACGAGAAGGTGATGGACGCCCACGCCGAGTCCGACTTCCATCGCCTGGACGAGCAGGTGAACGTGTGCCTGGAGTGCATTAGCAATAACTTCAAGAACCTGCAGAGGCGATTCATTCGCTGCAGCTCGCAGGCGACGATAACGCATCTCAAGAAGCTGGTGGCCAAGAAGATCCTCAACGGCATCGAAAAGTACAGAGAGGTGAGAGGCGAAAGCCAAGTTTCGGAACAGTAACTGCTCTCAGTGAAAGCTCAGACTGGCTTAGTTACTTGTATACATGGCACATACTTATATGATAACTACGTATCTTATCTTTCAGATCGACATACTGTGCAACGAGGAGCTGCTCGGCAAGGATCACACGCTGAAGTTCGTCTACGTGACGCGCTGGCGCTTCCGGGATCCGCCGCTTCGGCTGCAGTTTCGTCCGCGGGTCGAGCTCTAACCACTGTTTTAGTATAATGCCACAACAAATGATCTATTTTTATAATTTATTGACAATGCGTAAATATCGTAATTAGGAGAGTCGCTAGTGTTAGTCCCCTCGAAACCA >tinman-early_1624 TCGTCTAATCTTTAGGGCGCGAAATTTAAATAATGAACTGTAATTTGTAATTTAATATACAAACAATAATATTCACAAACCAGGGGTTAAACTTTAATGCAACCAATGATTCAATTATTTTTTTTAGTTCTTAATAATGCGTCAAATAGTTTTCTCATAGTAACTTCTGTGTTTTGCGCAATGTATATATTATACGCTTACTTTTGTAATTTTTGATAATAATACAATAGTTGTAGAGTTATTTAAGACAATGAAAAAAACATTAAAAACAACATTAAATAATGTATAAAAAATATGCATTTTATTTTGTTTTAAACCATAAATTATCTAACGACGGCAATATATTTTTATACGTGGGTTTCAATATCAATTTCATACAACATGGATTTGTATGGAATTTCATATGAATATATATGCTCATATGTATATTAACTATGTATGCAAGCAAATTGTCCACATGTGTAATTGATATTTTTACATAAATCCCATGTTCTGTTATTCATCCCCCTAGCCACTACTTTCTTGGGCCGGATGCTGTCTCAATACATATTTATCGCCTGCAGCGAAATTTTAGTATAAACATAAACTGTCAAAACGATACATAAGCCATCGGATGGCCCATGTTCCAAAGCCGTTTGACCAGTTTCGGCTGATGTTGATACAAAAAAGAGTTGAAACCTGTATTCATACAGCTTGTACCTCTTATGCCAATCGAGTTTTCATTATGTATACCGATTTCTTCATGGTTTTTGTTCAACACAAAGGCACATTTATGGATTGTACGAGCGTATTTATAAGTCGACACTCCAGACGACACCCCTAAAAAGAAATCGCTGAAAACCGTTAAGGAACCCATAGGGGTAACACAGAGATTTGGATGCGGTGTCGGCTTTCAAGCGTTCCACACTGCTGGGAATTCCCTGAGTACCATCTATGGGGGGTACGACGATATCGTTACCCCGAATACCCGCCCCCACAATTCACCCGACAAATGGTACTCGTAACGTCCTGATGATGCCGTTCTCCTCGGGATCGCCCCATTCCATTTCCCACTTAACTGCATATGGTTTTTGTTAGTGGTTGTTTCGGGTTTCGGAAATTTCTGCCCGCGTTCGGGACCCTACCCTCCACTTCATCTATTAGCAACGGCTCTGGCAGCTGTTCTACCCTATTAAAAGGTCCCTTAGTTCCTCGATTCCGACTACCTGCATCTGCTGAATGGAAATGCACTGAAAACTTTATCGTATTTTCTTTTTTAAGTATTTTATTACTGTTATTTTGATTAGATTAAATGAATTGCTATGCAAGAAATGTAGATTCTACTGATAGGAAATCTACATCAATATCAATAAATCAATACTTCACGAAAATGATTGCAGGTGCAGTTTTTTAGGGAAAAATAAAACAAATAAAAATCAAGCAGAATTTTTGTTTAGGGTAAAATACTTATTTATCATGATTGCTTACTCTTCTAGGTCTAACTTAAATTTTTTGCAGTGTACAAAGGCATTCCCAGCGTGTGTAACGTACTTTCCAGCCTTAACGAAACCGGGCACCGAAGGGAATTTAGGGTATTCTACCCCTGGAATACCTCCCCTCGCATACCTGTAGCAATTCTGGGCATTGCAGTGAAATCGAATAATAGACAGGAAGTCCCTTCGATGTCTTTTATTCCTTTCAAGAGCCGAGCCGCTCTAGTAGATCATGGTGCAGCGATTGTGTCGACCCATGATGCTGGTCTTTGTTCTGTTTTATCCTAAAACTAAGTTCCTAATGTGGTGGTCCTAGGCATCTGAGCCCGACTCAGGTGGAGTTATCCAGAACAACCACCTGTGTGTTTTGGTATCTTCGCCAGGCACTCTCCGCCCTTTCCAAGTATTCACCGATCTCCTGTCGCTTGTTCAGAAGGTCGTACTTCTCTAGAGCCAACTTCAGGAGTACGTACTCCAGGCTTCGAATGCCTCCGAGATTGTTTGGATCCCTTAGGTTATCGACTAGGATTACGAATTTCTTGAGCAGCTCCACCAGCTGCCCAAAGTTGCTCAAATCGTCCAGATTAGCATCCACCTCGTTGACGTATCGCATACTCTGCTTAATCCTTCTGGCCAAATCGGTGCTCCTGCCAACCAGGGGTGAGAGCTCCGTATTCAGCTGTTGCACCGATCTGCGCGTATCGGTCACCTTCAGCAATGCGTCCTGATTATACTGATCCTGATCTGTCCACTGGGTCATCGGAGCCTTAGTAGTCGCTCCTTCGTTGTCCACCATAGATATCCGATCGTTCTCCACAATAATGGTCTTGTCGATGGTGTCGGATGCATTCGGCGGCTGCTGTACGTGAAAGTCCAAGGTGAAGTGAAACCGCATGCTACCCTATAAGGATTAGAAATTTTGTTAAGAAACATCCAGCAGGCTTGCACAATCGGAAACATGCCTGGACCAAAGTAAATGCTGCTAGGAAAACCAAAATAGTCTTAGAGGACCACATCTTGTCGGTGAACTGGGAAATGTTTGACTGATTCCAAGCTGAAATCTCACGATCAGACCACTTCATGCAATCCAAGCTCGACATCGACGCTTATCAAACCACGGGGTTATCACACATCGTTCCCACTTGACCGATAACAGATTCATTCACATTCAATGAGTTTTCTAGCTATGGGAATACATATTGCCAGACAAAGCGCAAGTCACGTGTTCGCCGAGATGAAAAGTCAATACAGTGAGGTGAGCGATTGCCAACCTAAAACCATTTCAAGGCGCTTGGAATAGCAAGTATTTCAATTTGCTGAAAGCTTCGTATCCAATATGACCTCTATGGAAACTAGTAAGTTCTTTGAACCTGCCGAGGGTATACAGAATTCAGAATTATCTTAAACTGACTAAACGAATGCCATATCATATGTGGTCTATTAATATTAATTAGTGGTAGTTGTATTGTAATAAAACAATGAGTATGCAGATTGTATGCATTATTGAGGGTGATCCAAAAAGCAGTTAATATTAATTGCCTGATATGGATATAGTTCCCATTTGGTATAAAAATTTAAACAATTTAAGAAAAAATGTGTGCTATATAAATATAATCTTAATACATGTGACATAAAAGATGGACTGGTTAATAGCCCGCATGGGTATTTGGAGTCCGTGAATCATCCACCTTTTTTGGGAACCCATTTCACAGAGTCATTTCCGTTACGAACTCAACCGAATTTCCGACTCTCTAAGGGTTAACATCTGCTTGACTACACTACGCTTATCATCCGTCTGAGGTAACAACAACCGCTCTTATCGCTATATATGATGTTTACGCGTGCAATAATAATTATAATGTACACAGAGCCACTATAGCGATAAGGTGCGAATGCTATCAGGGTCCGCAAGTACCCCACATATATGTACATATGTATATGTACACAGTCGAATGTTTGTTTGTGCGAAAGTATGCCTTAATTTAGAAAATGGTATCAAAGCAGGGCAGTTCATGGAAGCGAAGCCATGGCGATAGGGGCAGGCCAAAGAAAAGCATAAAATGTGGGAATACGAATGGGAATCAATAGTAGAAATTGCGCAGCAGAATAAAATTATTCCACTGCCTGAAAATAACAAAAACCGAAAGGAAAAAAAATAAAATAACCGTTGACACACACTCACATGAGATTGCCCATAA >tinman-early_1977 CACGACGGGTGGCAAAATGTGAGTGCAGTGCGCAATAAGCACGGAATTAGCCTAGCCGCAAATCGGAACCCAGCTGTCTGATATAGAAGACGAATTCGGAACAATATCAGCAATAAAATCTCATCTAAATGGTGTGTTTGACCTCCATACCTCAGATTTAACAGGCATTTCATTGTGTTCCTATATCAAAGTAGGCTTACTTTTATCACAAAATAATACCTAGACCAAATATTAAGTGTACTATTAAAGCATTACAAGTTCCGTTTGGTAGAGTTATCTCACATTTTTTCCTAGAATTAGTCATTTATTTGGCGTAAGACTGGGTTTTCACTTCATTTGTGTTGCTAACAATACTAAATGTTAAGTAATTTCTGATTAATCGTGATTAATAAGTCAATATTTCGGTTTTTACAAATAATTTTTAAGTCACTTTGTCACAATGATTCCGATAAGCGAAACTTTCCCAATGATGTATTACAAATTTTGTTACTAGCTTACGGTGTTTGAGGGTTTTCCTTACCTTGCTCATCAAGCGATCCGCTCCGGTGCCTTCCTCGTCGCGGAAAAGGATGTCCCTGTTGTAGTTGGGCACGAGGTCCTTGAATTTGGGCGAGTCCCGACGGATCACACCCTCCAGAGGTCCGGAGGCGCTGTTCGTGTACTCGGATAGATTGGGAATTGTCTGCTTGAGGACCAGCGGATACAGGTTGCGCGCCCTATGACGACCCAATCCTCGGCCAGGACCGCAGCTGTGAGCCGGGCTAAAGACCATCGGCAAGACGATCAGCAGCAGGGCCACCAGAGAGGTCAGCCTGCTGAGGCAACGCTGCGTATGCGCAATGTGGCGCATCGTTTGCGTTTCTTCTTGCGGGATTGCGGAGATGGAGCTCGCTGCGGATTTGGAGCTGGAACTGGAACTGGAACTGTGGCATTTGGCATCCAGGGAGAGACAGGTGACACTGGCGGCACTGGCCCAAGGCACTGAGCTGTGGTTATCCATGATTTATCTAAGACTCGTTGTTTGCTGTTTCTCTTGATTGGATTTTGTATCTCCTTTAATTTTTTTTATTTATTTTTTTGGTTATTTAGCACTTCACTTTTGGCACACAGACACGCTTATTTGGCGACACGTTTGTTATTCAGATTTATGGTATTATTTGGATTTAAACTGGCTAAATCGAAATACTTGAGTCGGCGAAATCGCAGGTCCTAACTGACTTAACTACGGTCTTAACTGTTCTCCGCTGAACTGGCAGTTAGCTCTCGGTTCGGACAACCGTTGCGCTCGCACTGTGATTGACAAATGACATTTCCGAGCGGAGTATCGTGCGCGCAGAGTGTTCGAGTGCGAGTACGAGTACGGCAGCGAATACGAATGCGAGTATAAGTACGAGTACGACGAGGGTGCTTTCAAGAACCCTAGGCCTTTTAGCTCACACAAACACACACGCACACACACACTATCGCCTCGAGTTCATTCCGCGTTGTGATTGTATGCGTATGCCACTCGACGTTCGATCGGCTGTGGTGTTCATTTTTATGCTGCCTTATGCGCAAGGGAAGTGGAAGCGGGACAACCAGATGATGCAGCATACTCACAAGACTGGTTTGCATGTGTTAGTGCCTGAGTCTTCGGCTCTTATGTACAAGCAATGTTGGCAACAGATGTTGGATGGCATTGGCATAGTGTGCTGCGGTCCATTATGGCCATGGATGAAAGTGTGTGCCTGTTGCCAATTGGATGTGTGATAGTGTGCGTTGCTGGCGCAGAAGAGCTGCAGCATCCTCGACTGCAACTGCAATTTCTCTTAACACTTGCACTACTCGGACTCGACTCGACTCGAGTCTTGTGGCTTGTTAGCCGCTTCTGGCCCTGCAACTCCTCCATCGCAGGCCATTGTTCACTTTTTTCCGTTGCCTTTCTTCGTTTTGTTGTTCGTTTCGCTTTATTGCTTGTAAATTAATAATGTTCCGGTTTTTTGTGTATTGTGTGACTCCTGGGCAGCCTTATATATATACATACATATATATGTATATTGTATATGGTATGATATGTGGAGTACATATGGGTTCAATGCTGCTTCCGTTTGCCAGTCTTCAAGGAACGGAAAAAGTGCTTAAAACTAAAGTACTTTATGATCATCGAAGGCTCCGAGCTTATGTCACCCAGTGAGTCATTCATATGCCCCTCAGGTAGTGGTCATGTTCCGATTAGATATCGCCTTAGGACCTACAAATTACTGGTCAATTTAACAATTTCCTTCTCCGCCAGAGGTTCCGTTCAACGCCATGTCAATAATCGCAAATATGTCTATATATATTGCAAAAACCGATAACGATTTTGGCAAATACAATTTGCAATTAGGTACCTTATATTTATGCACAGCAAATAACTAAGTTGCGGATTGATGCCGCACTCTGGAAATCAATAAATTTCCAGTTTTATCTCTGTTGACTTTGGCTAATATAAAATTCCCCACAACTGGGAAATCTCCAATTTAACTTTTTAGCTTTTTCGCACTTTCAGAACTTGGATCTGTGGCAAGCGCTAATCTTTTGTTTTGGCTCTTTTTCCAGACCTTATTTATTTTTTTGCGGTTGCACCAGGTCTATTTGCACTGTCACTTGGCAAGCGCGAATTGTGGAATACGCCCGTCAATGTGTGCCCAAGAAATTGTCACTTTCCAACGGGGTAGAACCAAGGGCCAAGGTGCACCTGCTCATGTAACGGATTCCATGGGGGGGCATAGCATTTTTTTTTTTTTTGGTAACGCTCTGGAATGCAAGGCATAGCATAATAATTTCGACAAAAAACGAAATGAATCGAAGAAAAAAAATAAAACGAATCGAAAGAGGAGAAATACAATAGCTGGTCGTGGCGGCAAAAGATATAAGAGAAGGCAAAACGCCATCAACAAGATACACGGAACCAAATTAAAGATGAAGAAGCTGAAATGGAAGGGGGTATGGAATATACGAGTCTTGGATGCTGGGCCATCGGGCATTCGAGTGTGTGTGCGAGAGTCTATGTGGCTGCCACATAAATTTCTGCCTTCTTTGTGTGCGCACCGAACTCATTACGTTCGTCGCTGTGTGTATCTGTGTGAGTTTCGAATGTGTCTTTGGGCCTCCGCATCTGTATCTGTATCTGTATCTGTGGGGCGGAGCTGCTTAAGGCGCTCATTTGTGAATGAACACACAACACACCGTGAACAAGCCAGATACAGATGCGAAGACCATCCCGGGCCCATGGGCAAACAACAATCGCTCCAGCACTTAATGAGCATGAATAAATGGCATCGCAGACCGTGGAGCAAGCGAGACGGCCAGCGGTCCTCATAATCACGATCATATAGATCGCGCTCTTAGCCGAAACGGCTCACAAAAAATACAGAAAAGAGTTAAAACAAAAAAAAAAGAACACTAAATAAATAAACTAACAAAAAATGCCTTTCCAGGCCCTTCGAGATAAAAAGCCGCAGATTAGTTTCTGTAATTAGATTTGGTCCAGCTTTGATTTATGTGAATATTAATGGGACAACAGCTATAAGTCGAAATTGAATTTTAATGGCAACCTGTAATGCTGCACGGTGAAAAATGTAAAAAAGCAGTTCTTAATGTCAAAACATTAAAAAAAAAAATGAATACA >tinman-early_406 TCTTAACACTTTAAGTTGTCAAAAATTTGCAAAAATATTAAAGAGTAAATTTGTATTTTAAGCTTTTTCATAAATACATATATGAAAAAAAAGTTCCTGCATATATCTATTAAAACAAAAAGCGCATAAAGAAGTTTCGTGTTTTTAAATGAAAGAACAATTATTTTATTTCCTCTTTTTGATTAACATCTGAATAAGGCTGCTCTTCGCCCATGTCAGTACTGTGAGAAGTATAAGGTTAACTACTTAACCTGACAAATTGTTTAGCAGCGCTTGGCTTTCTCCTTGTAGTTTCGATTGAACGTTTGAAGATGAGTTCGACGTTGACTGCGACGATGAATGTGACGATGATTGAGACGACGACTCGGACTTCGACTCCGACTGAGTCTTGGAGCTCGATTGATCTAGTAGCAAGGTATTTCGATTGCCAGCCTCCTGTCGTTGTGCTTCAGACTGTGATGCCTGCGATGACTGTGATGACGAACTCTGCGAGTTTTGTGAGTTACTGTGCGAGCTTGAAGAAGAGGTTTTCTGATCATTTTCCGTCTGAATTCGCAGTTGTTCCTGCTCCTTCAATCGCTGAAGAGCGGCGCTTTGAGATGCAGAACTTTCTGTGCCTTGTAGGGAACTAGTCTGCAGGCCAGTGGATCCAGCTTGAAGACCCTGCTGTGAGCCTTGAGCTCCAGTACTTTGTATATCTTGCTGTAACTTGGTCTGAGAACTAGAGCCAGCAGATGAGGACTGTTGTTTATTGATTGATTCCTGAGCTTGTTGTTCCTTCTGTTGTTGTCCTAGCAACAGCTGTGCGTTTTGTTGTTTTTTAATATCATTTTGTCTACGCTGCAAAAGGCTCAATATATTTTGATGCTTTTTTAATTCTTGCTGTAGACGAAACTGATCTCGGTAATCCGACAAATCATTGAAATCCGGCGATAAAATATACAGTCCTGTTGAAGCTGGCTTGCCAATTAGCCCTCCGCCTGATGGCAATTTGTCCAGTGAACCGGGTTGGGGGACCAGCTTAGGCTCAATTGAAGGACCTTGATTTCCAGGAGCTGGCTTTCCTTCGGAAGGCAATTGTCCGATAATAAATAGATTGGATGGGTTACTTCCCTTTGATATTTCTGCCTGCGACTTCTTTTGGTCTTCCAATTCTTTTAGTTTTAACTGTTTCAATTGGTAGCTCAACTTCTCCTTATTTAGCTGCAGAATATTTTGGTATAGCTCATGTATACGTCGCTGAGAGTCCGATTGGGTTTGTAGCTCAGACTTGCCCTTCTGCAGCAGCTCTCGAATATTTTGCATTTCCAACTTTATTTGTTCCAATTGCCGAAGATTCTGATCCCGCTGCAACTGTAACTGCTCCTGCGATTGTGACTGTGACTGTGACAGTGAGTTAGATTTTGACTCTTGCAACTGGCTTTGGGTTTGTGTATCTGATTTTAGCTGATTCTGCTGAAACTGTAGAAGATTTTGCAGCTGTAACAGAATTTGCTGCTCTAGCTGCCCAGGCGACTGTGATTTTGAACTGGGTCGTAAGGTGGGTTGCTCAGCCAACTGTTTCTGTTGCACTTCTGTAAGTTTCTTTAGTTGCAAAAGGATTTGTTGTTCTAAATCTGATTTCAATGCCGACTGCGTCTTCAATCTGGATTGCAGTAATCGAAGAAGTTGCAAATGTGACTGGATCTGTTGCGCTACCTGTGACTGTGGCTGCGACTGTGAAGCAGATTGCTCATCTAATTGCTTCTGCTGCAGTTGCCGAAGGCTTTGTAATTGCAATTGCATTTGTTGCTCCAGCTGTGACTTGGATTGTGACTGAAACTGTGAAGCAAATTGCTTATCCAACTGATTCTTTTGTAGCTGTGACTGGATTTGCTGTCCCAGCCCTAGCCGTGACTGTGATAGTGAAGACGATAGCTCCTCAAGTTGTTGCAAATTCAACTGTTTTTGTTGCTCTTGCCGTGTCTTGGATTCGGACTGCGACTGTGATGCAGATTTGTCATCTAACTCTTTTTGCTGCAGCCCTTGAAGAATTTGAATTTGTGACTGGATTGGCTTACCTTGCTCTGACTGCGATTGTGATTGTTCTTGTAATTGTAAGTTGGATTGAGAATCCGATTGCCTCTGCGATTGAATCTGAGAAGCAGATTGTGCGGCAGATTGCTCATTTGACTTTTGTTGCAACTGTCGCAGTATTTGCAATTGCGTCTGTATCTGTTGCTGTCGCTGTGACTGCGACTGTGACTGCTTCTGTGACTCGGACTGTGACTGTGACTCTGATTGTGACTGAGATTCTGAAACAGGTGGGTTTTCCAACAGTCGATTGCTTTGCTCCTGTGCCTGGAATTGTGAAGATGCATTTGATTCAGATTGTGAGGCAGATTGTGACTGCGTCTGGGATTGCGACTGAGAATCTGCATATCTCAACAGTTGCAACTGCAATGCAGGTGATATTTTATCTACTTTATCCCGAGAAATCGTTTCCAAACTAGGATTATTTCTTCCTTCTCTTAGTGCTGACACACCGAATCGAATATGTAAGTTATTGTTCTGAACTTGCGTCTGTTTCTGATCTTTGTCTGAGAGCACGTCTTGTGCATTAATTAGTTGTTGATTTGGAATTCCAAGTAAATTGGCTGACGGCAGGAGGGATGGATACTGACTGTACGGCCCATGTATTCCTATCTGATTTTTCGTAGAAATAGTATCGACTTTTGGATAGTAGATAGGCTTAAGTATCACTTGTGGTTGATGTACGTGAATCACTTGTGAAGACTGCAAATATTAATACACTTAGTTTATGGACTCAAGATGGGTGCAGATTGTCTAATTACCTCGGGCTTTGGGGTCACTTTCGGTTGCTGCACTTCAATCAATGGCTGTGGGTTTACTCCAAATATCGGAGGACTAAGAATGTCCCTAAACGATTCGTGAACTTTTTCAAGATTTTTCCTGAAGCAATGCTTGCACAAAAAAGACTCACTGGGCACCAAAGTGCCGAGGAGAATTAGGGCAATAAACTGTTGGCACGTCAAAGTCCACATCTTTGGACCAAGTTCGAGATCAAGTCGAAAATGTGTCTTATATAGGGCATGAGGCCATAACACCGTGAAATAAAATATGCCATGGTTTAAATGGGTTCTCCTACCGAAGATAAGCACATTATTATGCTTTTACATCACAAAAGGTTATAGATCTAGAAACCTTGGCCATAAAAATGTTCTGAAAAGGAAAAAAAAAAATTATTTGCAGAACATTTTAATATTTTAAAAATAAATTGCTTGTTGAAGGAACGGTCAAATATGAATTCATTTACATATCAGTTCATTTTCAACAAAATTGATAAAGTTTC >tinman-early_976 ATTCCCAAAACTAATATACAAGTCGAAATGCAGACATACTTACATACTTACATTCATATATATTCTGATCAAAAACTTACCATTTCAAAGATGTGGTTTATCTTTTTAATAAACACGTACATATATAAACTTAGAGCGTTAACTACGAATTAATGTATCTATCTGTAAGCAGTTTTACAACCATACCCGTGCATTCACAACTACGAACGACGAAGTCGAGGAGAACAACAGCTCAGTCACAGCATTCAAATTCATAGTATGAATTTTATACACATCATCCGTAGGGGCAGTTATCTTCTTTAAGTCTTTGAATTCCGTAAGGAACAGCATGAAAGCGACTCCCCCAAAGGGAAGTGAAATTTGTTTACGTACGTATACAACACAAGAAAAAGTAAATGAACTTTAATAAATACAGATATTACGAAATAAAACACTCTACCATTTTAATACAAGTATGCATTCGGTTTATGATTATGAATTTCGTTCCTACTGCAAATATCTCTCGGGTTGTTTGTTATGCTGCGCTTTTAACTGCGTGCTAGCAGTTCCCTCTTTCTTTTGAGGAATGACCATGCGCACTTCGACTGTCGTGGGATAAAAATGGTAAATCCGGCTTTTTCTTAAAGGTGCCAAATGATGTATTGTAGATAGCCATCCCACAATGCGGGGTTAAGTCGGTAAGGCTGGATGGTAAAAAATTAGAACTAGGTCCCCGATCTTACACATATACTATATACAGGTACGTGAGTGTTAATTAATTACTTTAAAACATTAGGGACTTAGATTGGCATTTATACAAATCAAAGTAGCTCTTTTCGTACTTGTAATTCAAAACCTTTTTCTGTTCTTTTTTATACCATACTATCACTTTAAAGTGGTTTTAACCAAAATGCATACATTTCGTTTTTTGTTTTTCGTTTTCCTTGTCTGCGTCCGCTCTTTGTTTTTTTAACCAAAGTAAATCTGACTCAGGGAAGTCACTCAAAACATGTTTGCCTGAGCTCTCGTATTCCAAATGGAACTCCGGGAAAAATATATGCTCGTATACACTCAAAAGTGGCTCAAAACTGTATCTATTCGTATTTTGATATACACAATAATAAAACTGCTCGAAGAACTCCAATCTGTTACACAATGCCTGGTCATTGTTTACATTTAGCTTACTTGTGTGCTTATATGCACCTATACACATACTATGTGTGTAGATAAAGGATGGCTTATCTTATCACTGACGAAACAGCGGAGCATGCAAGAATGCTGTTTGTCTTTGTACATTGGCTTCGATTAAGTAGTCAAAAAGGAAAACGGTAAACGGCATGCATAGCTTTGCCTCCGCACTCTCTAATCTCTAGGGTCGGACGTTATGACCTGTCCTGCTTTGATAAATCTTTTCTATATTTCGTTTATTTCGCATACTTCCCTACTTATTTCAAATGCACTAATGCGAAAAACATTATTTTTTCAAATCAAACGGTCAAGTGTGCTTCTAATGAAATCTCCGAAAAGGTCTATACTCCTCATACATTTCTGCAGTCGGCAAAAAAGGAGGCACATACTCGTAGAAATTTTTCATCGCTGTGTATATGGATTTGTAAATGGTATAAAAAGCATGTATCAGTATCAGTGTCAGTTCTCTTCCCGCTGCTCTCATTGGGCTCTCTGGGTTGAGCAGTTTTCGAGCCTCTCGAGTTCGAGTTCACCGAGTTCGAGTCGCGGCGGTTCGCCCACTCAGTTTGCCCACAGCTCTGAATGCGAGCGTAACGATCGCAAGCCGCCTGTCTTACAGTGAAGTGCATTTTCTCAAGCCGCTTCCTTGATTTGTATTTGGTTGGACAACAGCCTATAACTTGAACCCGTCTTTAATGCCCGTATCTCTGTGAGAGTGAGTTTCCAATGGCGATTTGGCGAGTGCGCGTGTGCAGGGCACCCCTTCCTGTGTTGTCGTCCGGAGAGGATTCGCGTTGAGTGGAACAGCCGTATAAGAGTGGTTGGGCTAGCGGATTTAAAGGAATGTGGGTGCTGTGCCTTTAAGTTAGGACTCCTTGTGACTCGGGATACGAGAGAAGAGGCTGGATCGCGAGATATTTCTAAAAATATATGGCAATGAATGGGGATATACAAAATGTATCTGCACGAAAACTCATATGCTCGCCAGATGTTTAGCTTGAGTAAAAACTTCCTTAGAATTTCCGTTTTTATTATTTCTGTTTTATTATTTCAGGTTTTAATCAGAGCTTCTTTCCCTTAATGTAATTTCATGTGTGTATTTGCGCAATGTTCGGGCTCCAATTAACAGTGCTCTACAAGTTTTTCTGTTTTTCAGTTTCTGTTTTTTTCCCCTTTTCGTTTTTAATAAAAACATTTTGAAGTTTGATTACACTCTTTCCTATATCTTTCGATTCTATTACCTTTATATATTGGCCTAGACGCGAGGTTTCATCCACTTGAGAGTGAACTATTAGATTTCTTATATTAAGTTGATGTCTTGCGAATTTGATACTCTCCTTCGGTGCCTCAGACACAGAAGTCGAGGACGAAGCTTTAAAAAACACACGACCCGAACAACTAGACATACTTTCGAAAACTGGTGATTGCCGTTTTTTGCATAAGAACGCTTTTTGTTGTTTCGTCAAATTGGAAAAATATTGGTTTTTCTTTAGGTGGGCTTTAGACTTCGCTTTTTCGTTTGCAATAAATATTTTTTTAAGACGTCTGCGTCTGTTCTTTGTGCTGTAATTAATGTAGTTAATCGGGGTGCGCAACAAGATGTGATAACCTGCTTTCGTCTGAAAAATCTCAGATAGTTCGCCATCTTTCAGCAACAAAATGTTTCTCTCAAAGACGAAAGGCGTTTGTGTAAGACTCAAGGGGCCCAAGTCACCGC >tinman-early_417 TTCTGGATGTGAATCTGAATCCGATTCCGATTCCTGCCGATCTGGCTGCGTTCCAATAAATTGCTCTGTTTTATGGCAATTTAAGTAGGCTGCTTTATGGCCCTCCAACCCGTCGCTCTTCCCTCTCACATTCCCCGACTATCCCGCCTAGTTGCGAATCGGGTTCGGATTCTCGCTTCTCGGATCTCAGATCTCGATCTCCATCTCCATCTTCACCTCCATCGGGTAAACCGCATGATTCACTTGCTGATCTGTGTGGTCGGCCGGGGAAGATGTATTACGTGGTCTACCCACTTTTAGGCGGCGACAAAACGCTCGCCGCCAACTTCCTCATAAATTAAAGAAAAATACAAATTCGAAAAAAGGCGGGCAGTCGCTGGAAGATCTGCAAACTCCTCAACTTGTTTGGTCGGTTTTCAGCTCGCCTTGTAGGCAACAATTCAATTTGACATAATGAGCTACATTTTGTTTACAACGGCAAACGCATGGCTGCCAGTTGTTTCTCTTTGTAGTGGGAGGAATTAAGATATGAATTTCATAGGTCCATGTACAATGGCTTGAAGGTTAGCTATGCTGATTCGAAGACGAATCACTTAACTGTCATTGGATCTTAGTAATCCCTGGAAAGCGAATTCGTCTAAAGAAAATGTATCTTATTCAACTTTAGTGGTTTTAGGCAGCTAGAATGCTTCCCACTCGCGAAACAACTTTTGGTGTGATCCAATTTGGGGAGCATTATGACTTCCTATGATTCGTGGCAGACCGAGAAATCAACGGTGTTTTGGACATAAACCAGAGGCAGCAACTTGTTTGGCCTGTTTCCCAGCCACATCCCCTCCTCTGCCCGCAGGTGAATGCCTCTCTTTCCGTCTGGCCGTGTGGCAACATTTCAATTTGACATAATGAGCCGCAAGCCCGGGTTGCAGGTAGAGATGTTTGAGCTGGCTTAGTGGTTGCGACCATTACCATCCGTCGGGATATGCTATCCCGTCAAAATATTGTCAGGTAACTAACGGCAATTGCGGCTACCGACTGGCGACGTGTCTGCCCAAAGTCGCGATCCCTAATATCCATATGTATATATGGTATATATATATAATAACCGAACCGAACGAGTCGCTGAATGAGTCGCCCCGTGGCGCGACAATCGAAATTGGTCTCGCCTTTCGAGACCGGGGTTTTGGGTTCAGTTCATCAGTCTGGTCCTCGGCGGGCGGTCCAACCTGTTGAACACTCCACTTGACATTGAGTGGAGTGGTTCTAAGCGGGCCAACAAGATGGATCTGGTGCACCCCTGAGTGTAATGGGATATGGCCACTGTTGCACTTCAATTCGATCTCGATTTTTTGGTCGGATTGTGCAATCTGCTCAAGTGGCGTCTGGCCTGTGTGATTGGATTCTCCCTCTTGGATTCCAGCTGACGCCAGCGGCAAGTATTTGAAGTCCTCATCCTCACTTTATGGATCTCGCATAAAAGTTTGTTTTTCTAGGCAGCCCGTGGTTGTATCTGTTTTTTTAGGGGTCCTACCTAGGCTCTTTTATTGTCTTTCATGCTTAATTAGTCATTAAAACAAGGGTTATAAAAGTGACACTCATAAAGCCAGCGACTAGAGTGGGTATGGGCCAGAATAAAGCTTTAAAAACGACGCCATTAAAGTGGCCACCGCATGGCATGCAACACGCCAAAATGCCACACGGGAGCTTGGGTATATCCCGGTTGCACCTGAACGGGGCATACATATATGTGGGCTTGCATGTGTACTGGTATTACGTATGTATGTATGTATTTGGCCAGGTGTCATATGGCGCTGCGGGTGCTCCATCGAAAATCCCAGCGGGAAATGCAAAACGCTCGCCACTCTTAATGAGCGCCTGGAGAGAGGCACGAGTTTCCCCCATTTTCCCTTTCGCCTTTCCCTCGATTTCCCGCGCCCTGGAGCTCTGGGGCTCCTCCTGTTTTCCAAGTCCTCACGCGATAAACGGGCGTGGCCGTCAGTTGTTCCCTTATTTTGTGGCCTGGTTAACTCCCGACTAATTTCTGGCCTGCACATTTATCAGTTCTCCGCTTCTCTGGTTTCTCCCTCATGGCTTCTCGCGGCGATTTCCCCAGCTGAAATTTAATATTTTTCTACAGCCGAGCAAACAGTAGCCGCCTCTCACCTTGACAAGCTGCTGACACCTTGAATGAACTTTTCAAAAATGCGAGAGGGAAAAAGGCACGGAGAATTAAAAAAAAAATTTAATTGCACTTTAATAGTCAGGCGGCTGCTTTAATATGCCCAATTCCAGCAGGCGAGGGCTTTAAGAATATCTTGAATTGCATTAATTAGGTTAGAGAGCTTCGACGGAATCACTGGGCTTTAAAAACATTTTAGCTCGAAAATAACATGGAATATGGAATATTTAGCCTTTTTTCGAGTCAGTACTTTAATCAGGTCGAGGCTGTGCAATCACATCGATACTTAAAGTTAGCAGCATCCACTTGACTTAAAGTGCTCGTAAAACTTAAAG >tinman-early_1924 TTAAGCCAACTGACTTATTAAATTAATTCATTGTTTACCAATTTTTCTGGCATACTTATTTTTAACTGCTGCTTATGTATGGTTTATGCTGATTATGTAAGGAATCATGAAATGCATGGCTCACAACCCAGTTTACAACACAGATAGTAACCAATTAACTTTAATCACGTATTTTATTTTAACACATAAATATAAAAGAAAATACATTTTATATTATAAAAATACATGAATACTAATATTGATATTATTTTCAAGAACGGAATAAGCAGCCGTACTTATGAATTTAAATATTTTCTATTTTGTGAGAGACAAAATCAATGCAAACTTAACGTAGAAAAGCACTTGTCCCCATTCAACATTAAAGTGGCACAAGGAGATCAGGTTTCCCAGCCAGATCCATGAATGGTAATGAAAAGCAGGGAAAGGCAAACATTTGAAGTGGCTTTTGATAAACCATTAAATAAAACTCAATACCACAAGGACAACAAGTCTGCAAGAATTCTGGGCGATGGGCATTCGGAAATCAAACAAGAAACTGTCGAATTTAATTGAAAATGCCAATTCATCATAGGGGTGGGATGGATTTACTCGGCCCCCTCCACTTTTATGTAATTGAATACCTGCTCCATTCGAGGGTAATCGCATCAGCAAAAGAAGCTAACAAAATTAGCGACAACACAAGCGGAGAGTCGCGTCTCTTGGGCGCGTGCGTCTGCCACAAGCATCATTATATACTAATTACTCGAGTATTATCGAAACCCATTCAGGCGAAATGGTATAACGAGTAGCAGTTCCTGCCCCGATCCATTCCAATTGCACACCGCACTTGGTCCTGAATTGGTTACTCAAATCTGACCGCAAAACACGGAACCCTCACCCACTCACCCCGCTCACTTCCATCACTTTCGCGCTGGTCTTCCATTGTTGGCTTGACTTTGCGGCGCCCGCGGCTTTAAGAGCCACTGAATTGGCAATAAAAAGATTATAGCCCGAAGGTGTGAAGTGCAGTGGAGAGCAGAAATCGCACCGAGATCCGGAATCAAAAGGCACAAAAGCCACCCCAGGCGGCGAGGCGATGAGAACGCGTGCTCGTTTCGCTCGGATTTGTTCCCAGCATCCGAACATCCCAGCATCCCACATGCCACTCGATTCGAGTGAATCAGTTGAGTGCCTTTCGGACTGAGTGGGCAGCCATGTTGCCACAGCCTCCGGGCATCCAATCAGCGCTCTTTGGGCTCCATGCCAACCAATGAGAAGAACTTACCTTGAGTACTCTTTGAGTGATCTCTGGAATATTTGCCCAGGTTAACTACTTAACCAGTTGCTTATAATGTGAACTATAGTTTCTGAAATCTCAGTCTTAGCCATTTTCGCTGGTTTGATTTAGTTATGTTCATGTTAAAAGCACTATTATTTTAGTTTCATTTGCTTAGTTAATACACATTACGGCCTGTATAAAAAATTATGAAACACTTTGCAAATAAGTGCCTAAATTTTTATCTATTTTAAAACTATAAAACCATCTGTTTTAGATTTTATTTATTAATTTAAAAGCTATGTAAGGTAAATACTTCATATGGCTTTCTTACTTTTCCACCAGCTGGCTGGATTTATTAAAATAATATTTGGTTCAGTAAAATAATTTATAGAATTTTAAGATATTAGCTGCCTAATAATTTGTCATAATGTAAGCAAGTGGCATACGTGATTATATTATGTATTGTAGTTCGAATGCTCGACTTGAAGTAGGCAGCTACGAAGAAATATTTTTGGTTAGACCCATTAAAGTATTGTTTAGTTTTTGGCTAACGATTCCAAACAATCCCATGACCCAACTAACCTACTTACACTAAAAAGTTGGAGTTTACGGTTGATTGTTTCCGAATGATTTAAATTCTAAACAAATCGTTCGTTTAAACAATGAGTTGAGATGGGAATGGGTTTAGTATGTAAACCCATTCGGAGGACTGTACCCAAAATGTGGAAGACAAAACACCGAGGAGCAGTTAGCGGGCTATAAACAATAAACAGGATGCATGCACTCCCCGCGTGTTAAGGCGCACCATACAAGTACGTGGCAGCAACCGTAGCCATTTACGACCGACTTTGTTTACGCACTTGAGAGGAAACTCAAAGGCTCTCCAGCTAGGTTTCGGATCCACCAATGGCAGGAGTCGAATGCCAGTTTCCACCCCCACGGATGCCGGCGAGCCACTTGAGTACGGCGCACATTTCCACTCCGTGAGACCGAGTGAATCTCGCCGGGGATCTCGGCTACTATTTAAAGACAGCCCGGAGCGCCCAGTTGAAGCACATTCGCTGCGCAGCCCTCGAGTCGTGAAGATCGGAAAATCAATAGAAATCCGTAGTGAACAAGTGTCGGAAAGGCCAAAGACAATACCTATATATATATATCCGAAATATTCAGAAAATTCGAAAAATGCTGAATATGGAAAGCGCGGGTGTCAGTGCTGCGATGGCGGGTCTGAGCAAATCGCTCACCACGCCCTTCTCCATCAACGATATCCTAACGCGCAGCAATCCGGAAACACGTCGCATGTCCAGCGTGGACTCCGAACCGGAACCGGAAAAGCTGAAACCCTCCTCCGATCGCGAGCGATCGATCTCCAAGTCGCCGCCACTCTGCTGCCGAGATCTCGGCCTCTACAAGCTGACCCAACCCAAGGAGATCCAACCAAGTGCCAGGCAACCCAGCAACTATCTGCAGTATTATGCGGCGGCGATGGACAACAATAACCACCATCACCAGGCAACGGGCACATCGAACTCCAGTGCCGCCGACTACATGCAGCGCAAATTGGCCTATTTTGGATCCACCCTCGCTGCTCCTTTGGACATGAGACGCTGCACCAGCAACGATTCCGGTAAGTAACCTGCACGAAATTAACGCCATTCAGGCTCTAATGGACTCTGAAAAGAACGCTACTTATTCATTGGCCTTTTGTATAGGATGTATGCTAACTTTTGGTAATTTTCCCTTTACAGACTGCGACTCACCACCGCCATTGAGCAGTTCCCCCTCGGAGTCGCCGCTATCCCACGACGGCAGTGGATTGAGCCGCAAGAAGCGGTCGCGTGCCGCCTTCAGC >tinman-early_778 AAATTTCAATTATAACTTGCTATAACAAGAACATACAAAACAAATTTTAAAATTAATATTTAAAGAAATTATTTGAAAAACAGTTTTAATAATGAATATCTAAGTCATACACTGAGATACATAAAAATATGGAATGGAATCTGATTAAATTATAATGACTTTAAAAAATATATTTAAGAAGCATATTTTACCGTTCGACTTATATTAGTACATTTTCGTGAGTGGTATTTCAAGCCAAAAGCCACACGGTCACACTGTACGTTGGCGTCGGCGTCGCGCGCGGATCGTTTTCGGCTAATTCGCTGCTGAATTTTCGTTGTTCTCGAGTTTTAAAACAAACGAACGAAAGGTGAAGCCAATAAGCGTGGTGAATAGTAATTAAAAAAGCGCGACGCAGTGGCTGTCATCGCATGTACATCGATTATTTGATATAAAATGCACCAGCAGCTGGAGCTCGAAGTGAGTTGAAAGTGCGGGTTACATAGGTGGGGCGGCGAAAGCCTATAAATAACAATATGCATTGCTATTTCGGATTTGGGCGTCGCTATCTTCGTCCATTTGTCGCCCCAAATGCTCCAAGATCCAAGTGGCAAGTGCAATGCGTTGTGCCATTTTTTTTTGTAGTTTGTCGGGGACTGATTACGAAACTACGCCCAAGTGGGTGGTGGGTGGTTGGTTGGTGAGTGGGCGCCACACTTTGCATACAATGAGCTTAGAACCCCAAACGACAGCAAATTGCGTGAGAGTGGCTGCCTGTGTATCCGTGTTTATGTTCGGCTTCCCGTTTTCCGCTTTTCGCGTTCAATTCTATCGATTTATTTCCTCCGCGAAACAAACATTGATTATATTCGAACATACTCGTAAATGTCTCGAAATGTGAATCATCGGGTGGCGGCGGCCAGCGAAACCCACTTGCCACTTCCTTTCGGCGTTAGATCAGGTGATAAGCGGCTTGATAAGAAGATACTCGCAGTCGCAGGCAACGCCTCTGGATTTTTAAGAGCCAGCTGCTGTTGATAAGCACCGCAAAAAACGATGTGCTCCATTCGTATCCCAAATAAATAAAGCATGGAAACACTGCAAAATGTGATATTCATCATTAACTTTGTGGTCTGCCGTTCAAAAATCCAAATGTACTCGTATTACATGCAAGTACTAATTGCAACTTTGCTCTTCTTGGAAATGTAGTGTATATTCTGTAGTATACTACTGTAAAAAAAACCGTCGAATTATTAACATTACATTCCCCAGTCCAAATTCATATCAAGTACGAAGTATTTACTAAATATTCAAACTGCATGCAATTTTCTTTCGGTGCATCTGTTTTGCCCGGACTCGTCCAACAGGATATGTGCGACAGATGTTTTTGAATGCGTTGCCAAGATGCTGAAATGCTGCATTTCAAGGTTTCTCGCCCGTTTCCAATTGCCTTTTGTTCATGCTTATTCCTTGGACTTAAGTACAGCCACTCGACGCAGAAATTGGGTAAGTGGCTCAGTTCTAGGCTCGCTGCTCCGCGACTGGCTGGATGCTCACCAAGTGACCCACTTTGGTGGCTCACATGTGCCATAGATCGCATGTACATATGTATGTATGTACATGTATAAGCAGCTCATGAAACAAATACAAAACACGAGAATAGGAGATGAAAGAAGAAACCCCAGCTCGATTGCTTGGCCTTCCTTGGCATTGTTTTTGTTCAAGCTACTACCCTACAAGTGGGTATTATAATTGCACGCATAAGTTGGCAACGTTATGCTGCAACGGTGAAATCTTAGAAGTCGTATCTTGCATTTGAAACTACTTACATATCTACCTGGCCAAAAATCATGTATCTACCTGTTTTTCCGCGAAAACAGATCTTCAAATCGAAATGCCATCTGTTTAGAAAAAATATGCACAGAACCCAAATTCCGTTTTTCATTTTGTATACAAATTCGGAAGAACCGGAAACGGATGACTATATCACTGGCCTGCCATAAATAACAATTTACGTCTTGTTTATGTGGGAGTATGTTGTGTTTAAGGATGGTATAGGACAAAAGTTGCAAAGATCAAAAGTGGGCTAAAAGTTCTCTTACAAATTGGCTCTGAATGGGATCAGTATAACATAATATATAGAATTGTGCCAATTTTATCTCATAAATAAAGATGTGTTTAGGGTACTCCGCCTTCGATCCTCGAAAGCAGGCTTTCTTGCCTTGCTGCTGCTTTGGCACCTTTCCATTTGTAGTTTCTCTCTTTGTTGTTAACCAGCGTAATTACATGTATTTATTCTGGCATGCATGTGTAAGCAAATAGTTAGACGACTGTCTGATAACGCCCCTCGCTTTTCAATGGGAGATTAGCTAAATGTTTTCCCACTCGTGTAGAAAACCCAAGTCGAAGGCTGATCGTAATAGAGGCTAAGTATTCAGTTTTAATCGCAATCTTTCGAGGCGGTTATTGAACCTGTCAGTTGAATATTTCAATCGCGAATATCACCGGGCGACAGCCACTCGCTTGCCGTTGACCTTGCGAAACGGTCGCTTCTGTAAATATGCATTTCGCCACCCCGCTTTTGAACGGCAAAGAAAGCTGCTCCAGAGATCTTTAACCCCCCTCTTCTAGAGCTGGAGGTCAGTGCGAGTCAGCGAATGGGTTGAACTGCAAATTCCCTTGATGGTCAG >tinman-early_2164 GCCTGACCTACATACATCCACGTCCACATTTACGTCCAACTGCAACAGCACAAATCCGAAAAAGAAAAACCGAAGCCATGGCTTCGCCAGCTGACCTGAATTTTCGCACGCACACGATTTTCGCGCTGGAAGCCGGCTTTTCCCAGCATCGGGAAAACGGAATGGAGAATGAAGAAATCAACTAATTTTAATCAAATTTTCACGCTGCGATTTGAGCAAAGATCTCTCCTGGGCGTGAAGAGGGTAGTCGAACGGAGTGGCCCGGGACAGGAGCTCGCAATTTGCATATGTTGATTGCGAAGCCGGCTAAAAGGGCGTCGTCCTGCCTTGCCCCGCCCCCCCGTTAAAAAATAGTACGTAATGCGGTGAGGATACAAAGCAGATAAGAAGGAGAAGCACGAGGAGGAGGAGGAGAAGGAGAAGGATGCGGATGATGATCGGCTGGTCTGGCAAAGAAACAAAGGAAGAATAGGGGGAATAACAAGGGAACAAGGCCGAAAAGGAAGCCGGAAGTGTGCAGCTGCCGCTCGCCCTACCCCCTCCAGTTCCCTTCCAACCGTTCCGTGTCGTTCTCACACCACAACCCACACCACCACCCCCACCTCATCCTCTTTGCATGGCCAGAGGCACATGGAGCGCACATGGCAGGAACGAATCGGAAGCGAAACCAGCTAGAGTCGCTCTTTAACAACAGAGCAGAACAGAAACAGAAATCGGGTCTCTCTCGCACGAGAGCGGAAAAACAAAATCGGGAGACTGCACTGCGAAACGACTGGCAATGGGCCGACTAGAGGCGGGGCAAAACGAACGAGCAAGAATAGGAATAGGAAGAGCGGTGGAAATTGAAATTTATCCACGCGCTTTTCTCGCCTTTGTATATTGTGTCTCGTCTCGTGTATCCTGCGCTCGATTCCTCATGTCCTAGTCCCATGCAACTGCAAGCGGAACAAACCCCGAGGGCAGGTAAAAACGCCTGGTTTTTAAAGCAAAGGAGGCAATTGAAAGGGGGAAGCCCAGGGAGAGGCAACAAAGGGCACAGTATAGCAAACGCACCCACACACGCACCAAATTCCCAAATAGACACGGCGGTACGGGAATCCGAATCTCGAGTGGATATGCTCGAGTTCCGCTTAAGTGGTACTTGCGACTGGGATTGGTGTCGCAGGCGCAGGAGGAGCCGATTGCAAGTAGTGCCCACACAATAATATGCAGTCGGGGGTACAAACTATGGAATCTCATATGGGGTACTTGGCATTTGTTTACAACTACGAGCAGAGTAGCAGAGTGGGAATGCCTTTGGAGTGGCGCTTGTGTTTATCAATTAGGATCGAGTAGCCAGCTGCATTCCCAATAGCACTAATTTATTGCAGTGTAGGCACATATTAAATGAGTGGTTTTCCGACTTTGCCACATATGAATCATTTACCTACCCCGCCTCTTAACCCAAATGGCTTTCTACTTCTATGCCCAAAGTAACCGTTATGGGCAGTTTCCAACCGCTAAAGGAAAAGATACAATCCATATGCCCTAGTGGTCAAACGCAGAGTTACGTTCGGTCGAAAGGAAAGAGATCTCTGAATCGAATCGAGATCTTTGCCATTCGAACTGTGTGTCTGGATCGAAATGGTGTCAACAGAGTGGGAAATCGCGGCGGTAAGGAACTGTTCCTGAGCATTGGAAACGGACTGGACGGGTACTCACCTCGAGGTAAACATCACTGTCCGGATTAAAGGTGTCGTTAACCCGACGGAATGTGTAATTCACGTGCGGAATCGAGTGCAGGATTTTGACGAGAATGCTCGGCTGTTCCATTTTTGTTGCTATATGTTGATGTGCTTAGTTGTTGTTGTTGGTGCAGTTGCAGTTTCAGTTGTAGTGGTGCGGCTGCCTCGAAACACACACACACATATACACTCGCGTTGCCACAAAGGAATATAAATGTGCTCTCTCTTTAATTTGTTGCTGCTGCTGCTGCTGGCGAAGCTGTTGTCGTTCTTGTTGTTGTTGCTGCTGAGGGGGAGGGTTCTCTTGGTGGGAGTTTCTCTAAGTGTGGTATTGTGTCTGTCAGTGAGCGTGCGTGCGTGTGGGTGTGTGTATGTGTGTGCGTCTGCCAGCGAAGGACGTCGCTGCTTCTGACGTCACAGAACGCTGCCGCCGCTGAGAAAGAGAGAGCCGCTGCTGTAAGAGAGAACAAGGACCGTTAACAAGAGCGTTTGCGAGAGCGTGTGTGCGGGGGAGAGTCCTGCCGAAAAAACAGGAAAAAAAACAACAAAAACAGAAACAACAACGCATGCTCTCACTCCCTTTCTCTCTGTGTTTTAAGCGCGTCCTGCCCGTTGCTCTAAATTTTCACTTTCACTTTTCACCCAAACAAAATATATAAGAAAATTCGTCAAATCGAAAAAAATATGCACACACAGTAAACAGCGCTGCAATTTGCAGCCTTTGATTAAAAAAAATATATATGGTATACTACTAAGTAGTATGCTCCCAAGTCACGCGGCTTACATTCGGCACATTTGATTCAAGCCAATTGCATTCGCCAGATTTTTAGAATTTTTTGATTGGATTTTCACTGCAAGACACCGCGATCGGGGTTTTAGCTCGATTTAGTAGCCGTAAGTCGGGCGTTCAGGGCGTTTTTTCATTTACCGTTTCGTTTCAAGCCGCGACCCGTTTCGTTTCCGATTCGTTCCGATTCGCAACCTCCTCCTGCTTTTCTACTCCTTCCCCAACTTCTACCGTCTGAGGTGCTTAGGAAATATATCAGA >tinman-early_491 GCGTTATCCCCTTTCCGCGTGGATTGCATTTTTGTTCGATCTCTGACCGCCTCCAAATTGGCTTTGTTTATGGTATTGTTGTATCGTGGAACTAAAGGACCTGCGGGTTAATTTCTTAGCGCTACTTAATTCTCGGTTCTCAATCGCCAGCCGGCAGGTACGAATTGCAGCCGAAAGCTTATCAATCCCATTTTGATTTGGATTCTTTGATTAGATGTTTGTGTTACGGTTCTAACGAAATTGTATTGTGTGTTTCTGGAGCCGAACAAGCATTGCAAGTTGTTTTTTGAGATCGTCACGAACAGAAGGAGTTGTCGATACGCTCGATCGCCGATAGTTAGCGCAACAAGGAGAGATAGTAAAATGGCCAGGGATAAGCATTTATGTACATACATTCATTACAAATTGGGAAGCTGGTGAAAGTATTGCTCGAGAGACTTGTTTGCATTTGTGCCAGGAATGCAAAGCAAAATCTGACAGAACCGCATAAGGTGTAAAATTACAACGCAATGACAGATTTTTCCCCTTAATAAGTTATATAATTGGAACAAGGGACTATCTCTGGACGTTAAAATTTCATTTCGCGGCAATATCGGCGGGTATTTTTCGACCCCATTTAGTACAGAAAGCATAAAAACCAGCTGCAACTGTAACGTACGGAACCTTCAAGTGTCTCAGATATGTTAGCGAACAAAAAACGACAAGGACGAAAAATAATCAAAAAATATAACCGAAAGGAAGGCCAAAACATCCTTTCTTCCTGTCCTGCGCTCGAGGACAGTCGACGGCTGAGAGCTGCCAAAACCCAATCCGAATGTGTGTGCGGTGAATTGTTTCAGAACGGCAGGAGGCTGTTTTCCAAAACGGAAAGCGACGCCAAACGAAGGCACACCGCAGCGAAGTGAAGCGAGAGAAAAACGAATTTTTCGTCTATTTCGATGGCAATAATTCAAAATATGAAGACGGCTCGATAGTCATAAATCTGTCAAAAATCCATTTCTTTCATTTTATCCCAAAAGCAGCACCGCCTGGCGTTTCCTTGCCTTTATTGTGTGAGCGCGCCTGCTTGCTAAGCGTATCTTTCAGATACACAAACTGGCACTTCAAAAACGGGCAGAGGGAAAAGAAAATGAAATAGTGTCCGCCTGTCGCCGTCTCTTTTGGGTTCTGTTTTCAAAAGATCCACTTGAGCCAGCAGACTTTCCTCCACCCAGTATGATTTTTCCGAACTACGAAGACGAGAAGAAAACCCATCCATCCAATCAACTCGAGCTGAAACGCAACTGAACTGAAAGTGCACCAAAGTATCTGGTAAGATGCTCGTTTGCTCACTTCAATCCAAAAGATTCCAACCCTCCCGCCACTCAACCAGACCCAAGCAGTTCAAGATGACGGAAAACAGCCATCGCACCATATTTCAGTCAATTAGAACTGTCATAATTCATTCCTCAGAGCTATTTTGTTTTACTGTGCTGCCGATCTTGCTTTTGGGTTATTTCCTATGAACGGCGCAATAATAATGCTCGCCCTCATGATGAGTTCCAGTAAATTCAAACATTTTCGATCTCCCTGAACCCAAAAAATATAAGAAAACTGTTGTAAATGGCTACGTATAAGCGCAAATGAAGGAAGGAATGTTGGAGATGGCGGTTCACCAAGTGGACTCTATGACGATGTGAAACAGTATATCCAATTACGGAAAAAGGTAAATATAATATAATATAAATATAATATAGTTAAATGCATCGACTGTGAAATACCTATTGCACAGCACATCAAGCATACATGAGTATATGTATGTATATGTACATATGTGGTACTATGTATATATGTATGTTCCTATCTACTGGCCATGACCAAGCCTTGCCCTTCGATCCGAAACTTGATGAACTGAATTACATTTGACTCTGACTCATCTCTAACTACATGTGCTCTTGTGGTAATCCTGCCCAAGTTCATTTTCAAAATTAAAACATAATTAATGCAAGTTCTTTTAGTTAAATTACGTTTACTCTAGAACCCCTCCCTACACTACATATTACAAATATTTGAATTACCACTGTATTATCGCTTCCACTCTCTGTGGGGAAGTCAATTATATGAAACTATTAAAGTTGCCACGCGTATCGCCCTCATATAATCCAGCTCGCTGCATAATTCATGTGGCTCCAACTTACTTTAATACCCTTCCATCCATCTATTGTATACCCCCAATGGCTGCCTCCACTTTCCGTCTACAACAAGGACTGGCTATCGAAATTGATGCCGCCAATTAACAGGCGTTGAAAAACATTTCGACCAAATTGCATAAGCCTCAGGACGGAACATAAACTGCTGTGTCGCGGGATCTGGCAGTGGGTGGTGGCAGATGAACACCCCTCAGTCACGGTTCTCCACATCGAAACCACACGCATATCAGCACTCCAAACATCCGACACAATCCGATGTTGATTACCACATAAAAATCCAGCAAAAGGACCTGACTCCTTCCGTCCACTTGCCTGGGTCAACAATTTTTGATTGCCTTTCTGTGTGAGCAAGCGCGGCGTGAAAATTATTCCTAAAACAAATACAAATTCAT >tinman-early_676 TACACTGGATTGTACATTGTAACATATGTAGCTTAAAGTTCTAAATGTTTAATGGTTCGAATGCATTTCTGGTTTCAGTACTAAGCATTCCCCATTCTACAATACAAAAAAACATCTATCGCCAATATCCAAAATGCAATAACACTGAAGATAAAATCTTAAATAACTTTGCAGATTAAACAAAGCGATCCAATTTAATATTTCTACCTGACCATGTTACCCCTTTTTACTAAGCCCGTTTTTATTTGAATATCAGACACCCCAATGTGTAACCACCAGCTTTCCGAACGACTGTCAATTTCAGACTTTGCAGCGGCAGAGTCATAACTTTAAAGCCATACAAGGAGTACGGGTTGCGGGACATGGGAAGACCCACCCCCTGGCGAATAACCCTCGGCCAATATAAATTCTTGTAATTTAATTTCGCTATTGCAAAGCAAGCGCACTCACATACTCGTAATCCGTGCCATACAGCGTCAGCAGTGCCACGTAAAATCTCGCATTTATTTTCCCGGAGAAGAGATATTAAAAGGCAACTGTGATGCTATCGGAGACCACATCCCATTGAAGCCGAAGCCCAGCTTCCTTTGCTTTTTTACCGCCAACCCTCGGAATAACTGTGAAAATGCTCGACATTCCAGGGCCCAAGCAACAATAACAAACGGGGACGGGACGACTATCAGTTTCAGTTTCTACAGACGAGAGGAATGACAAAATTTCGCCTCTGGCCTGTCACTGTATCTGAACCTCGTCGTCTATGCATGTATCCGTATCCGTGTTCGTATATTTGTATCCGTATCTCAGTCTCTGTATCTTTTCGCAGGGCACATCATTTAGCTGGCAACGAATGAAGCCGTTTTGTAGAAGGGCTCTACCAGCATATGTACATAGGTTTTCATATGCCGCCGTGTGGTACATAGGGGAAAATTTCTATATACAACAAAGGCGATACAATTAGTTGAAAAAGCCCAAGTGCGATTCGATGTCGTTATATCTGTGTGTGGAGTATGGTGTGTGTTCTGTGCGAAAGGGAGGGTTGCTCTGCCGACGTCGCTGCTACGAAGGTATTACTTTTTTTTTAGACCAGCAGTACGGGTCTTCATCCTGTTTCGTTAGCGAACAACGGGGCAAAACGAAAAACTGAAAAATGTACATAAAATGGAAATGAGAGGCTTTTGTTGAACGCACGGCGATGGGCGTTTCGGTATATAAATCGACACCGAAACCGAAAACCTTTCTCGAATTTAAAACCCCGGCTCTCGACGCCTACGTCGATCCGGGCAGCGACGCCAGCGCAGGCAGCGCATGAAGCTGTACACTCAGACAAATAGCGCGACCCATTAAAGGCATATAATGCAAAAAATATGAATGCTCCCTAGTAATATTGTGTTTTATATTAATTTGCAATTATGTATCTTGTTATACAAATTATTGTGGATATAATTACATTTCTGAAGAAACTGTATGCAGATAGGAAATTTTTGGCAGTGCATCGCAGGCAATTTGGAAAATAATAATCTCGATCCTTTCTTTGTTATGTAATCTACTCAGATTCAAGTTCGAAATAATTATCTAATTCCTGCAAGTGTAGACGCCAAGCGAAGACTTCGACAGCGCTTCTTCTAATTAGTTTTCGGAGTCTAAAAATAAGTTTTAGTAGATCTGCGGGGCAGCGGGTGCGGTGCGTCTGAGGGTTAGAGTGATATGGTACATGTTTCTCATCTGCTGCGTGATAATTCAATTAGGAATGTGTGAATCAGCAACAACAACAAACAGAGCTGAAGCAGAGTTGCAGCGTTGCAGCGTTGCAGCGGAAGTGCAGGCGAAAGAGGGAGGAAAACAAGCAAAGGGGGCGACGGAAAGCGGGAAAGAGGAAGAGAGGCAAGAAGGAAAAGTGTCTTTCCTCAATAGAAAGACCAGAGTAAAGAGGAATATGCGTGAAGAGAGGGTGAGATAGAGATAGAGAGAGAAACAAGGAGAGAAAGAGTGGGAAGGCGGAAAGAAAAACCATCCATGTCCATGTCCATTCCATGTTGCATGCAACCCTGTTTTGCTTTTTTTTTTCCAACCTGGATGTGGCAGGAACAGAAAGTTGGTGCCTGTGCTATGCTGCTGTTGCTGCTGCTGCCTTTGCCACGGCAACAATTTATGTGCATGTATTTGTGCTCGTACATAAAAATAGCGGTCGAGGCAGCAACGACGCCAGCAGAGCCACAGCCAAAAGCAGAGCAAAAGCAGAGGCGGAGAGGCAGACGCAAATACCCTGTGCCTGTGTCGTGGAAGGGGCACGGGGTGGGGGTGCTGCAGAGTGGAACGCACAGAGGCACAGAGAGTGTGGGAAAGGGGGGTTGAGGGCACTATGTGCGCAGCTTGTGCGCCTCGAAAATACATATTTCAGCATGAAGCATAGGCATGGGACATTGAGTGCGACACAGTGGCGCCGACTAGAGGGGATCACTTTGGTACGGTATCCACTGAATAGCTCTTAAAATATCATTACTAATTTCTTAAAAAAAATAAAATAATTGTTAACTATGCCATAAAACCAATAATCATTTTCATTTATCTAATAATATTATAAAATAAATATTTAAATCTAAAAAAAACTTTTTTATTTTATCCGTTAAATAACTTTGTAATATTAGATGCTAGCCAGAAACCAGAAACCTCTGCTTAACTTATGCTATAGCATGGATATTAACTCTCACCCCAGCACCCTGTTAACCTCGAAGCCACCCCTACCACTGCGCCCCTGGCCGAGTATCTTGTATCTTGCAGTTTGCTGTGCATACACAGGCACATGCGACTTTGTATGTACAAAGTGCCATGTACAGCATTTGGTAGGTAGGTATGTATGTGCACACATATGTGGCTGCATTTACGCTGTGTTCGCTATGTACTCAAGTGTGCGATAGTGTCGAGGACGTTAACGTTGGAAAATGCCAGCAACAACGACAACAACAACAAGAGCCAAGAGTTCGGCAACGGTCGCATTACCAATTTAAATTCCCATCCAATGTCCGCACGTAACGTAACGAAATCGGCGGACGAAAGAGTATTTAATGAGCGCGTTGCCAGAACAACGTGATATATTCGTTTGCCTTTGTTGTTGCCTCCAAGTGGTATATGTATGTATGTATGTATTTGTACGGTATATACGGTACAGCTGTCTGTGTGCGCTATCAAATTAGGTTCATTTGCTGCCTTTGGCAGTACGGCTGCTGACGCTTCTTTGCCTGCCACTCACTCGCCTCATACACTTTCTGTCGCTCCAGTGGCCAAAAGCACCCTTCGTGCAACTGCTCAGCGATGCCCCTACCCCAGTTTTGTTCTTCTTGGACGTCAAGTGGGCGGGCGAATGTGAGTGTGTGCGTGTGTGTGTGTGTGTGTGTACATGAACGTACTTATCGCCTATGTAAACCGCACGGCTCACTGGCTGCGCGTTGCTTTCGTTACTGCCTTCGTCACTGCGGTTTCTGACTTTTTTGTACATGTACTGAGTACATAGAAAGGCATCGCCTGAGTGGTTCACATAGTCGTCAATGAAGAACCCCCACCCCTCGCCAGCTCGTAAAAACAGCCCCTCAAAACGCTGCGGCATGCTGATAATTCAATAAGCAGAGCCCACGAAATAAAGAACTCAGACCCACGTTGATGAGTCGCTCCTGCGTAAATACAGTCATACTTCCAAAGTTGGCTACAAAATGAGGACCCTTTTCATTTTTTTTTTATTCAAAAAAATTGAGCACAAGAAAGACTTTTTTTATAAAAGAAAAAAAAGGCTTAAAATAGGGTGACCATTCCGGCTGACACTTTTATATTAAAAAATATGCAAAAAAATTGAACACAATAAGTAGCAAAACGTGTTATGTTAAACCTTTACAAAGTACAAAAGACAAAAAATTAAATTGGCATCGTAGAAAGCATTTCCATGGATCGTGGATTGCTTATTTAAGACATTGTACAGGGTTTAATTCGAAATTAACACTCACTTTTTTTTTTAGAGTCTATACCATTTTTTCAAGACTTTTTTTCAACCATTGATATCAGTTCTTTTTAAAGGGGGTAAGCTTGTATTATTAGTAAGACTGTGTCCTGAATAAATTAAGCCAATATGAAACCCATGTCCTGAACAAATGTCTAACAATGACCAAATTTCGTACTTCTTGCTGTATAATTTGAAATTCAAAACTTTCAAAATGGAAAAACTTCAAAACTCAAATCTTTGAAGTGCAGGCTACCTCAGACTTTCGGCCTGATGAAGTTCGGGTTATAGAGATGCTAAGGTAAAGCATAACCACAAGAAAAAGCCCAAGTACTTTGTGTGGTCGTTTGGCGTATTCCTTGCGCTTGTGTGGACTCTCGACCCAACACTCGACATCATTATGGCCAAGGGAACGGGAAAAGACTCACTCGGTTCAGACGACTGAGCTGCTGCCTGCAAATAGCAATGTCATGCAGCTGTCTCAGCATCAGCTTCTGCACCACTCCA >tinman-early_1037 ATGAAATAATTCACGATCAAAATTTATCACCATTAAGAACAAAACCGGACCAAAGAAGTTGGACAGATGGGACCAGCTGTGAGTTTTAGCTGAGAACTTTCAGGCTGAAAAAAAAACCGCGAAAACCGTGTCATTATAAAGTCCATGGACAAAGGCATAAATATATGAGCACATGAGTAATTTCAGCATACACTGGCCTACAAAAGAGAGTACAGTTTGCTTGTTCCGAAAATGTATAGCTCAAGTAATTGGAACGCGGAACAGTAGAAGTATCTTCTTGCTGATATTTATATTGAATTTCTATTTTATACCATCTATTGAATGGTTAAAGAGTTTAAGCACTTAATTACTTTTAGATAAACCCCAGCCTAGTGACATAGTGTTCCAGTAGCTCAGCCGACATGTGAGGCAGTTGGTCAATGAAAGCCCTGATAATGATGTTGCGGATGGCGATGATAAAGCAAGGAAGATGCGTCGTAAAACTGAAGCGTCAATTGTCCAGGAGGAAGAGGAAGGGCGGAGGAATATAGCTCCAAATTGGCGTTGCACTATCGCGACGCGTATCTTGTATCTTGTAGATACCCTGTCCATATTGACATGTCAACTGTTGCTCCTCTCGGTGAAACCGCGAAATTTACAACTTTATCTATGTGTGTCGTCTGTAGTGAAGGGGTGGGTAGCAACGGCGGCGGGGGGAGCGGCTTATAACCAGTGGCTCTACAGAGGGGGAGGGCTATATGGTGGGGTAGGGGTCTATAGTTGAGATCTCTGCGTGCCATTTTCTCGTTTGACGGCCTTTTGTATAGCGTCCGATCTCCTCGCTCCTGTCAACCTTAATTGTCCGTTCGTGCCGTCTCTTTCTCTGCAGCATATCCCCCTCTCTTTCGCACTCTCCCATACAGTTTTTTTTTTTTTTTTTTTGTTTCCTGTTTGCAGTCCGCTGATCGCGACTGAGAGACAAAACGCTGCGCACTACTCGCTTATTTATTTATCTGTCTGTCTGCGTGTATCTGTGTGTGCCGCTTTGTTCGCGTGTATTGGTATGTGTATTGTATCTGTGTGTGAGTGTGCGATCAAAAGCCGCGTTTGTAACATGTGAACATCTGCTCCTTTCCTTTCCTTTTATTATTGTTTTCGTTGCCTCGTTTCGTTATTATCGTTATTTTATTGTTGTTCTTATTATATGTATATATATGTGTGTATATGGCATATTGGGCATTTGTTGCTGCTGCTGTTGTTGTTGCCATCGGACGTCTGTATTTGTGTTCTGAGGCCCCGAGAAAATAGCGTCGCAATGTGGAAGCGTCTGTCAATATTGGACATACTCCAAATACCCTGATTATCTGGCGGGGCTTCGAGATTTGCCAACAGTTCGGGGGAAATTGGAACAAGACCTTGGGTAACCGGCATACGAATGAAATTCTAATTTTATGAGGCCCAGCCAATTATAAAGTATGTGGGAAAAGTTTAAGGGTAATCCTAAAACTGGACACCAGTTTACCAAGGAAAGAAATTCTAGTTAAATCGAGTACCATAGACTTTACGACCGTAGGTAGGATTATAAAAAGTCATGTTAGTACCTTCAAATTTAGTATCGTAATATGACTTTTTACGAGAGTCGTTTGTAATAAAGTAAAGTCGTTCTTAAAGAAGTCGTATTTCTTTTAATAAGATACGTACTCCGCTCATTCAACTTTAAAATTGATATGTGTGGAAAATTAAGATGTACACATATCCTTGGCAACAGATCACTTGAAAATCCTTAGTGGTCCTTGGGCCATTGAACTTGTTCTCAAGCCCGCACTTCACCTCGAATCCACCCAATCCAAACTTCAAACTGACGACATGCAGCGCAATTTGAGATCCCAGATTCGGGAACCTTTCACGCACACATGTGCAACCTGATTTCCAAACGCTTAAAGGACACGCTGTGCCTGTGCCAGCGGCATCTTAAGTGATTTCGAAAAGGAGCTGGGAAGCGGTAAAAGTATCTGGAGGATGCGCGGATGTCGGATCTCAGATACTCAGATACAAGAATTCTTGAGTGCACGTCGTACGCAGGCACTTGGGCACACACACACTCCACTCATTGTAGGTTACTAGTTAAATCTGAAATCGAGGCAGCTGGAGAAAGAGACGGCTGCCGCAGGGGCCGACAAAAAGAGACGGGACGAGGCACTTGGCCAAGGAACATCTGTTGCGCTGACATAAATCCTGAGTGACTGACCGCTGGTGCGAAAGACCGCGAATCTCGAATGCGAATGTTCATGTGGATGCGACTTGGCCGCCGGCGCTCGTTTGAGGACCAAAACCAGTTTGCACCGATTCCCAGCTGCCCGTTTGGGTACAGTGGTTCAAAAGGTAATCTATCTGATTCAAATCTTGTCCACCTAACAACAATTTGAAGTCCGAAGATAAAGCTCTTATTTCAATTAAAAAGAATGGCAGGATAGTAGGATAGCAGGAGAAACATTTCATTATTAAATTTGATTATCTACATTACATTTCGTACAGAAAAGAAGTTAAAAGTGATTTAATGCCCGTAAAACCACTGTGCCCGCCAATTTCATTGAGTAACTTTTGCGCTGCCCGTGAATGAATCTGTGGGATACTCCCCACCGCTTTCGTACTTCGATTCGGTGGGACAGGATCTGAGAATGGAAAACCAGTGAGCAGACTTACCTTCTTCTCGGTGAGCTTCTCGCAGCGGCGCTGCTTGCAGATCTGGTGGCTCTTGTCGTTTCGGCAGGGGGCGCACTCCCCACAGTTGTCCTTGCGCTGGCATCCCACGCATTCGCCGCACCGCTTGCGCTTCTTCTTGGACTGGAAAAGAGAGCAGGAAGAGGAGTTGTTGTCGTTGGAGTGGTTGTGGTTGTGGTTCTGGTGGTTATGGTTGTGAGTGGTGGGTGTGATTGTGATTGTGGGTGGTGGCAGTGTCATTGGCGGGTGGTTCAGTGTGGGTGGCGGAAGATGGTGGCGACGACCGTAGTCCTGTTAATCGCAGCGGTTGCCAGTTCGCCCCGTTTCGCAAGTTTATCGGTGGGGTTGTCGAAGATATCCGATATTTATCGATCAAAAACTATTCACTTCTTTTGCTTCTCGTTTGTAATGCAAATGGATCGTACAATCGATATATACCTTCGGCTTCAACATCCCCACCCTTCGATCACCTACCTGCTTGTCGCATCCACTGGCATTGGCATCGTTGCTATCCCCCATGCCCAGGGCACTCAACGGACTCTTGAAGTTGGCCGTGCTGGAGACGGCGTCCACTTGGCCAGGATTGCTGGGGTCTACGTCCATGCCGCTGGCCTCGCTCTCGGCACTGGACTCCAGCGAATTGGGCCGCTGTTTCCGG >tinman-early_1066 AAGCCCTGACGACTGTGTTCCGATGAGGCGGGGTGACCTAAGTCAGGATGACCACCGTTCAAGGTATCCGTGGAGGAGCTGTCGTCGACGGCCACCACTCCCACTGTGGACGCCGCCCCCGCACTGGTGGGCAGTGAGTTGGAAGAGCTAATCTCAAATGTGCTGGACTCGTCGTCCAAAACTACGGCGAAACCATGTTGTGAGTTTGACGACACACACACGTCCTCCACATCCTCCCGCAGCGTTGACATAGATACAAAGGCTTCTCTGCCGGCTCCTACTTCTGCGTCTACTGCTCCTAATCCCGATTCTTCTGATGCTTCTGTTTTTCGGCTGGTTGAGGCCTGACTCTTGACCCACGTGGAGGTGGAGCTGGTGACTGCTGACTGAGTACGCTCCCCTGCACTGGTCACGGATTCACTGCTAGCAGCTGGGCATCCTCTTTCCATAATACTTTACAGGCATTCAAAAGTGGGTAATGCGTTCTCCTGCAAATGTTAAAGATTGGTTATCGAATGGTTGGAAGCTGGTAACACGAGGAAGCACTTATGGAAAAGTGAATAATAGTGTTGAAAATAGACGCTATGAAAATTTCAAACCCAAGGTATGACAAATTTTACACCGCTTACAATGAACGATCCTTTTGTGCCCTGAGAATCCAAGATGATCCGAAATTCCCACGCAACAAGCGCAAACTGATGGTTGCAAAAAAAAATTAAATCCAAGGGAATGAAAAGCAGTGGGAAAAATTTGTTGTAACGACGCTGCCTCTGCCGGCGTCGTTGGAACTCGAAAAAGGAAATCACAAAAGTGCATCGAGGAAGAGGCTAAAACGGCGCGAAGTAGAATGGGCAAAAGAAGACGAAACGGGTATTACAACAAAAACACCACAAACAGACACACACACATACCCTAAAAAGAGAGACAACAGCAAAAGGTATTGTAAAGAGAGTGAAGAGCGAGAAGTAACGGACGTGAGAAGAAGAAGCACCAAAGATACACAGCAGCCGCAGCACCAAACAAGAACAACAATAACAACAACACCACGTACACCTTGCAATTCAAGCCGCTATCTTTTCCCTCCGCTTTTTACTGACTTTCAGAGATGATCAACGCGGGAAAGTCACTGGTGATCGGGAAACAGTCACCGAACTAATAGGAAAAGCCTTCCAAATAATTTTTATACTAAAGAAGTGTATGACCATATTCAAATAGTTTCGACACCCAACTAAAGTTAAAATGTACATTAGTAAATCGTTTTAATAAACTGCGTTGGTTGTTACTAATAGAACTGTGAAATCTATAGTCACGAAACATTTCTTGCTGACTAGAGCTGAGCCAAGCCCAAGCTAAGATTACTTCTGCCAATATGCTGAGTCACAGAGTTGTTGACTTTCCAAAATACATACGTATTAGTCATCCATTTTAAAAAGTTACATTAAAACGTGAGTTTTTTACATGGGCTATATAAAATCGCAAACATTTAGTCGAAACCGAAATTCCAAATAACAAGATATTAGTCATTCTGTAAAACAAATTATTTGTGCAAACTGACTTTCTTTTCCTGGAGAACGGAAAGAAATCATTAATTATTAGGGCTAAGATGTTTGTAAATTGTATGATAGTAGTCCCTATTCTCTAGAAATTCATTGATCCTAGCCAGCTTCCATTCACCGTGACTGAAAAATCGCAGCTGCGCATCTCTGATGTTTGACAGCAGCTTGACTGGCCATCACATCCTACCAGCGCACAGACACACACACGCACCCGAGAGAGTAGAAAAAGGGATCGCCGTAGGCAGAGATTGGAAGATCCAGGGGAGGGGACAAGAAAACCAAGGAGAAGGAGAAGAAGCAAAGATCAGCCATTGCAGCGCAGCATCCGCAGAACAAAAGGGGCGAGATCACAAAGGAATTTTCAGCGCTCCTGCTGTTGCATCACTGCCGAAACAGATCAGATGTGCAGGCATTTCTCCCGAACATTTTCCCATATTCGTATTCATTCTCTTCCCACCCTCCTTCTACTGCTTCTCCTCCTCCTCCTCCTTCCCCACCCCTGGCTCGTGGCCTTTGGCTGTTGACCTCCCGAAAAAACGTAAAATATGTTTTTAGGGTCTCCGCTTTCACACCGCATTTCGATACGCGTTTTTACCTGTGCCACACAGGACACTTTGGTCGAACATCGGGGATGCGCTCGAGAACATTTTCTCTCTGTGGATTTAGCCACGGGTGTGGAGTCGTCATAATCGCTGCATACACACAGACGCACACCAACGCGGATAATGGGCAGAGACCTGTGGTCCCAAATCCCCCAAAAATAGATTACTTTCGTTGCAAATTCAGAGCAGCGATTGCAGCACGCTGAGCGTTCCTTACGGACTCTTTGACCATCCTAACCCCACTTTTCTGCACCTGCAAATGCAGCGGTTGGAACACGGAGGGGATAGCCCCATAATTCGTGGAGCAAACAACAAAAGAAAAATGTGGACATTGTTGTACACACAGGCAGAGTCACGCACACAGACGAACCCACTCGCTCGCTCGCTCGCAGCTAAAGTTTGAGTTCGCGTGGTGCCTTGCACATTGTTTATAAACTTTAAGGGCAGCCAACGTGGAGGGGGCACTAAAAAAACACACCCCACTTTAGCGGGCACAATTTGTTTTCAGGTTTTCAACTGCACATTTAACGCGTGTAAAACTTTTTGCGTTTGGGGCCCACTCTCACACATACAGGCGCAACACAGCACACACACGTTCAATCGAATTTTTCACATACGTTTTAAGGACGTTGCTCCTCGGAGATTGCCTAATTTTTGCGTTTTTCAGATGACTCTGTACCCAGTCTATCTAAATTTCTGGTTTTCAGAT >tinman-early_548 AATGGTTTCGGATGGTCATAAATGTTAAATGTATTAATAAAGCTTAAAATAAAGTTTGATAAAGAATGAAAAAGAATTCATTAAATAACTTTTTAGAACAAAAAATATGTATTGAGATCATAAAATGCAATCGACAATCAAATTAGTAAGAATAACAAGAAACAAGGGGATCTCTAGCCACAAAATTCCACAGAACTTCCCTCGATTTACCTTTTCCCTTGACGACAACAACTGTAAATCCTCCAATAAGTACAGTCATCCCCTAGGGTAAAAATCGAGCCACCGCGAGTCATATCAATTCATAAATGCATATTAATTCCGATAGAGGCTAAAATTAAAACAGGAGTCGCTTTGGAATAATGTGAGAAAAAAACTTTCTCACGATCACCTTGTTGTCGCGCAGACGGCGTTTGGTGGAAATCCACGTGCAAAAGGGGGAAGTTCGGAAGCAAGGAAGGCTGGATGGTAGGAAGGAAGGAAACCACCACCACCAAGCAGGCGGAGTAGGACGCAGGACCCTCTTAATGGTGGTGGTTAGTACCTCCCTCTCCCCCTCCCCCTCTACCAGTGACGATCGCCAGAAGTGCATGAGAATTTAAATTACAAACAAACCGAAACGCAGAGCGCACAATCAACGGGCTTTGTCCGTGTGCGTGTATGCATCGGCATGCGATGACGCGTAGAGGGTGGGGTAGAGAGGGTGCCAGGACGAGGCGTGTGTGGGAACTTTCGACGAGGACACTCGAGCATGAAGCGGGTCAAACGACAGAGGCCAGTTATTATCCAGCTCCTTTTTATGGCACTTCTCACACGCCCACAAGGGGCGTTAGTTAGCTCACTAACCTCTTTCCAAACAGAGTCGAATGGAATTATAGCAAGTGTGAGAGAGAAGCGAAAGTCGAGTTCTCAGCGTCACAATAACAACACCCACGCGACAGAAGCATCTTTAGTTCTCAGCGAGAGAGATAAGCTCAGCTTCCTCTTCTTCTTTCTCTGCTCCTTCTGCTGCTCTCACGGGCATGCGCAACATTGCAACAAAAATTAAAGGGAACCCGTAACCACAAAAGTCATGGGCGTAGTGAACAATTTTATTTAAAAACCCGTTATGGTGTACCATAGAAATTAATTCCCTATAAAAATGGACAAACATAAACTGAAAATGTTTTAAAATAAATATAGTTCGATATGTAGTCAAAAAACCCCTCAGTAATCCACCCCTGCTTAAGGCAAAAGTCAAAGTCATCTAGACGGATAATTTCTTCCTGTGCATCAGCGGGCGATTGTAATGCGATCCGTGGCCACTCCAGTGCAGTCATTCAGGCCAGAAATCTGGAATCCCAAGTGCTGGGACACATGAATGCGTTTCATTGCCTATTTTTAAACATAACATTCCATTTCAAGTGATAAGGCCACCAGAAAATAGTTCTCTTTGCCCGTTTGCATGTGCATTGCTGCGGCAGTATGGGTGCGTGTATCTCTGTGTGTATGTGTCGCATTACTCATGCGCTCTCACTCCAAGCGTCGTTTGGTATATTCGCGGCTCTCGTCCGGTATTTTCCAGGGTCACACTGCGCCTGAGCTGGGGCGTTGTTGTTGCCTGCGAGAGCGGAGCAGAAGTATTTATCGCGTTGCAGCGCACGGTCCTACTGACACAGAAAATGCAAGAAAAGTAAGAGCGTAAAAGAGCTGGCGTAGGCGTGACAAAAGCGGTGAAAAAGCAACGGTTCTGTGCAAGCAAAGTGAATAAATACAGTGAAGCCCACCTGCGAACGAATATCGAAGGCTCAAAGGCGAATTTGGCGCTATTCCAATGAATAGTCCTCGCTCGAACGCGGTTAATGGCGGCAGCGGCGGCGCCATCTCCGCTCTGCCCAGCACTCTGGCCCAACTGGCTCTGCGCGACAAGCAGCAGGCTGCGTCGGCATCGGCGTCCTCTGCCACGAACGGCAGCAGCGGCAGCGAGTCCTTGGTGGGCGTCGGTGGACGACCGCCCAATCAGCCGCCCAGTGTGCCAGTGGCCGCCAGTGGCAAGCTGGACACCAGCGGCGGAGGCGCCTCCAACGGCGACTCGAACAAACTGACCCACGATCTGCAGGAGAAGGAGCACCAGCAGGCGCAGAAGCCACAGAAGCCACCGCTCCCGGTGCGCCAGAAGCCCATGGAGATCGCCGGCTATGTGGGCTTCGCCAACCTGCCCAATCAGGTCTATCGCAAGGCCGTCAAGCGGGGATTCGAGTTCACTCTGATGGTGGTGGGCGCCAGTGGGCTGGGCAAGTCGACGCTGATCAACTCCATGTTCCTGTCGGACATCTACAACGCGGAAC >tinman-early_648 CTCGCGTAGATTTTTTACGGTTCTTCATGGGACGAAGTCGCCTTAACACACCGCACCACAGCAGTTGCTAATTTCGTGGCGTCACAAATGTCTTCTAGAGGAGTGCTACTAAAATACGGAATGTGCCGACATTTTACTCACTTGCTGGATGCAAACCGGCCAAACTGGCGGCCTGGGAAGCGGCCACTGCCATCGTGTTCTGGTGGTGATGGGCTCCCGCCGCAGCCGCCGCTGCTGCTGCCATGGAATAACGGTCATTGGCAGAGGCGCCTCCAGCTTGTGGCAATAAACCCAATCCGGCAGCAGCGGCGGCATTGAACTGCGAGTTGAAGAGCGGATTGGAAGCCGCTGCAGCCGCCGCTCCAGTTGGATCACGTCCCCAGTAAGCTGAAAATGGAAGAAACATTTAAGATCATCATTTGCAACTGAAAAAATATATAATGTCTTACCGAACAGGGAAGCTGCATCTAAAAGGCCGGTGGGGTCGTGTGGCCCCCCGGCTCCACCACCCCCTCCCGCATTCGAGTTTTTGTTTGAGTTTTTGCCATCGCTGCCATCGCCGGCATTTTTGTTCATGTTCAAAATCCCCTAGTTCTTAGGACCAACTATTGTGCGTAACATGCGTTGGGAGTCTGCAAAGGATTTAGAAAATGCAATACTTATAAGATATTTACTTGTATTCTTTTTTTACATATGTTGCCATATGTATAGAATCTTTACAGTAATAGTAACTTAAGTGAATACACAACACTTGTATTTTGAAGATACCATTCATCAATTGGTTTCATTTTTCAACTTCAATCTTCGTTATTACTCCGCTCTCTATGTTGGCAATGCCGCACCGCTTGGGCGAAACTCAAGCATAAAGCGGAATCGGCCGAAATATGGAAAGCAAAACAAAACTAAAAAAAACTACACGCCAGCGAGAGGCCGTACACGCACAAAACGGAAGCAGAAGCTGACTGCTCTCTTTTACCACTACACCTGCACGTCGGCGGAGCGGCGAATTTACCACTCAGCCAAGTCGTCGGCGACGGCGAATTGACAAATTACGCACAATATTGCATTTTGTTTAACAATAAAAGAAAAAAATATCCCTCCTTCAAGGAAAACTTGAACTTTACAAGTCATGCGCAACGTTTACCACATCCACTAATGAAAAAAGGGAAAGAAATAAAAAAGAAAATTCAAGTCACCCACACAAACACATTTACAGACTGCAGTCGGCAACTGCGAAAAGTGATGCATACACAAGCGAAAAAAGAAGAAAAATTCCGTTTACAGCAATTTGCACTCTTTTTCGGCAGTGTTCTTTTTTGTTTGCAAGACTTTAGTAAATTCATTCATGAAATTTGTAAAGTTCTCTTGATTTTTTTTTTGGCCTTTGGAGATTTATAAGACAAACTGTATGTTACTTTTTACTGTTAAGGGCTTTGCTCTTATTACTGCGCTGCGACAGCAGTTAACGGCTGTTGTTTGCACTGGCTATTGCATTTTCCGCGATTCAAATGTACAATTAACAAAAATATTTACAAGCGCCGTTTGGCGACGCGTCCGGCTGTGTTTGTGATTTCGCGGCGAATGAGCGTGTAGAGAGTACGGAGAGCGCAAGAACCGCAAGTGGCGGCGGCGGACCATCGGAAACGAAACGCAAAAGGGAGAGCGCGGGCGGCAGAGAAAGAGAGACAGAGAGGCCCGCTTTACATTTGTTGTTTCCACGCACCGTGGTTGATGATTGCACTTTTTTTTGTCTTTACATGTTTTCTTAGTTTTATTTTTAGGAGTTTTAGCTGTATTAATCACATTAATGAGCCAGCTTTTAGTTTGCCTAAGATGGGTCCACTGTGGCTCGCTAATAACAGTAATTGCGCACTCTTTTTCCTGCTTTCCTTCATTCGGTTTTATTTTTTTGTTGTCACGCATCGGCAGGCGACAGATGTGCGCTCTTACAATGGGGAGGGAGGTGGAAGCCCAGCAGAGAGAATGCGTGAGAGGAGTGGGGAGTCTGCGAAAGAGCTCTGGCAATTTATAACAAGCAAGAAATAAAACAAACGGACAGAACGACTTGGTACAGTGAAGCTTGCTTAATGACAGTCAGCAAAGCAAGCCGTTTTAAAGGAATCTGATTATGGAATTTTACGGGGTTTTTTGAAAATATGTTTTCTGTAATCAAGACGTGTCCATATAATTTCGCACATTTTAATTGGGTTACTATCAGTACTTAGAATAGATAGGCTTCCGCACATTTTAGCACATTTGATTTCATTGTTTAATCCCCTTTTTATTCACTTTCCCAGTTGCATTTGATGTTTAAGCGGAAGTAGCACTGTAAAACTTTGCCTTTTGTCTTCTTCTTCAACTTGGGCTTTTGCCTTTGGGTTCACCGCAAGTGGTATGGGGGCAGGGTGAAAGGTTGAAGGGAGGTAAAGTAGAGGGAGCAAGCAATATGATTCGGGCATTTGTCTTTGACACCGCCACACTTTGCTGCCTTTGCCGTTGCCTCTGTCGGCGTCGCGGGGGCGGCAGCGCAGCCGACGTCGACGCCGGCGATTGAGAAGGTGTGTGCGTTGTTCTTGTTGTTTCTATAT >tinman-early_139 CGGGCAGGCCACAGGACACGAGGGAAATATGCGTGAAGGATGAAAGATGATTTGGCAAATATCCTTGCCAGACAATGCGGCACCAGAAACTGGAAATGCCTAATGTGATTTGAGGATCTTGAGGGATGGCATCAAGATACTGTTATGGATTATGAATTATTAAATTTGTAATTCGACTTAGAATAGGTAGATTAAAAGATTGAACGAATATGTACATATATAAATTAAATTTACAGATACAAAATGATATCAGGTAACAAACAGACTTTTCTTATCGTTACAAAGAGTGATATCAAATTATATAAAATGTATTACAAGTAATCATACAATGCAGAGTGCATAATTCCCCATATCATTTTCATTTCCAGCACACATGTTGCTGCAATTCCGCTTTATAAATAGAAACTCAATAGTGCTTGGGTTGCTTCCGCTTTTAGTGCTTATCGGGCAGATTTTGGAGTCGAGGCAAGTGCCAAGCGGAAATTCTCCGCCGCCTCGTCTTCAGACCCCCAAAAGAAATGGAAATGGTAATAAAAATAATACGAACACGTGCCTCCCACTTCAGGGCGGCTCAAGTGCTCGAAAAACGAACGAATTGAAAACCCCCCTGGATGAAGAAGAACCCTTTTGCAATCGAAGCCCCGGAAAGTGTTATTTGAACTTAAGAAGCGGAAATGCCGCAGACAAAGCAGGGGAATCTCAAGTGAGCAGAGTGAAAAATGAAATTAAAGCCAGCTCAAAACATTAGTTATCTATTGCCGCAAATCATCAATCAAAGTGACCGCCCAGTAAAATTGGAGCAGGAAAATGTATGTCCTTCTTGCTGACCAGAGGAAATTTCCACAGTCCTGTGTCGGCCAAATTCAAAGTGAAAGTATTCGATTACCCAAAGCGATCGTTAAGTGAGAAAGGGAAAAGTATCGAAGAAATTATAAATTGCCGACAAAGCCGAGGGCCAAATACTTGAGATTTCGAGAGGGAAAGGACATAACAAGGAGAAAGTCAAACGAGGACAGTTAGCCGCACTAATTAACAAGCAGAATTGTTTATTTTTACAAGTGCCAAGTGGCCACTTTTTTTTCAGATCGACCGCCCTTTGTTTTCACAACTTGTGTTTTCCTTGGCTTTTGACATTTTGGTGACATACGGGACTGCCACCTAAAAAGACACACATGTAGATGCACAGACAAGGCCAACATATTAGGAACATAGGCTCTTTTTATGAAAGGAAGTGTAAACCTTTTTCTAGAATCAAAGTATGCACTCTACTTTTTAGAACCAGCGTTCTTGTGTCATAAAATTGTATTTAAAGTTATCCCAATCTATAATGCTATCGTCCGTTTTGCTTTTGAATTTGTTAATGCTGATAAAACATTTTATAGCTATCAACTTAACTGCAATTGTAACCAGAACACACACGACGCATCCCTCCCCGTCCACTTTCACCATTTCTCCCAGGGCGAAAGTGTCAAAGTGACATGCCCACTTTGTCGCTACCCGACTTAAAAAATGTCTCAAATTACAAAAATCACACAACAAGACGGTTTGTTCCTCGCACAGGATAAGCGGGATTAGTCAGCTTGTTAGTTAGTTTGGATCTCCGCGTTCCGTTTTTCAGGATCGCAGGATTTCCGTCATGGCAATTGGCAATCCGAGCAATACGTAATGAATGTGACGAACTGTCAGTTGTTTCGAGTTTCCTTGTCGCAGCCGCATTTTCTTTGCATTTTTTAGCGGGTTAATTGGCACTGACAAAGGTCTTGACTTTTGCCTGGGAAACATAACAATTTGTTGACATCTGTAAATATTATGGGGGAATTTTCCAGAGGACTAGCTTCTATCATACAGCGTGTGGAATTGTATATAATTACTAACGGAAAAATATCCCTGTTAACTTTTTGAATCGCCATAATCTAAATTTCATGATCATATCCTCTTGAATATAAAAAAATATAGCTTTACAAATAAAAATGTCTTGACTAATGCTTATAGATATACTATGTTTTCAAATGGACTTCTATAATATGTACTAAGCGAATTGTTTAAAATATGTTCTTCCCTTTACCGAAATAAATTCAAATTGCATATACATTTTAAATAAACTTGTAGTTATTTTCGCACTCTTTATTCATGTTTTTAGAGCACAGAAAAATTTCTCGAGCTCAAACTAATAAATTCCCAACGTCCCATTGCAATAACCTTGACTCAAGGTCCATGTCCTTGCAGATAAATAAGAAAGGAAAAACAACGGGATGCAACTGCAGAAAAAAAACAGAGACATAAAACTCACTCAAAACAGAATTGAGTGAGACACGGAACGTGTTGGAAACGAAATGGAACAGACGGAAGATGGAAGAAAACGTCAGAAGAGGAGATAACAGGAAATGGAATGCGACGACAACGGCGAAAAGTTGTTTAAAAAAAAAGACCAAGAAAAACCGCAAGCAAAACGTTGGCACTTCATAAATGGGCGAGAAAGAGAGGCAAAAATAAAAAGAAAGAGACCTCGCTATGGAGAAATAGAGAGGTGCGAGGGAGAAGGCCAAAAGCGCCGACATTTTTAATTGTAAATGAGTTATGACGCGTTCGTCAGAGAGAGAGGTGAGGCGAAGAAAAGAGAGGGGCGCAAGTTGCTGACAGCGCCGCCTTTCTCTCTGTCGCACGTTCACTCCCCCGCCGATTTTCCAATTGGAAAACGAGAGAGCGAGTGGCGTTTTCACTCAGAGTGTGCGGCTATAGGGCTATCCCGCTTGCACTCGTACTGGCGCGGCTCGCTGTAGGGTGGTCTCGCTTGCACACATAGGGACCATAAGAGGAATAACTGGTTTCCCGAGTTTTGCGCCTGCCGTCGTTTTTGGCTTCTCAGCGGCAACATTTTCCTGACACAATTCAGTTCGTCGTCGCCTGCCTTCCGTTGCGCACACACTCAGTCCGCGAGATTCTCAGTTCGCGTGCGCCAAAGATAAGCAAAAGAAAGCAAGAGAGATATAAAGATACAAGATACTTACACCTTGAAACATACACATACCCCTCGAGTTACCAAAGCTAAAGATGCATCTAGCTTTATAAGTGCCAAGTGTTGAAAAGTGACCAGATACAATACCCCAATACCTTTCCAAGGTATTCCCTAGTTAAAGCCCCATTAAGAACAACATCATACCCTTCAAAATGCTGGTTGGATCTCATCCGTATTTGTGCAACGGGGTGCCCGCCGCCCCTGCAGCACCAAATGCCAGTGGACAGACAACAACAACGGCTAGCAAAAGTGCCGCCGGCTCAGCGACGGACTTTTCCATCGCCGCCATCATGGCTCGCGAAGATGCCTCCAGTCGGGAGTCCTCCATTCGCAGTGCAAGTAAGTTCAGATACATATTGAAAATATCCTTAATAAGAAAGGAATTCTGTGGGAAGCGGCAGAATCATTAATCGGTTGATAGGAACAATTTGTTCTTAAAAATATATTTTTATGCATTAGAAATTTAATGCCTTGAATATTAAGCATTTCGATTATATAGTGATTTTTAAACAAGATTTTTTTAGGCTTAACAGACTTACAGCAAGATTCATTGATGAGAT >tinman-early_2098 AGCCGTTTCCGCTTCCGATTTTGAGCGGCACTTCCAGCGAATTCCTGGCCCATAAAATATGAGCTAGAATTGTAACCTAATCGGAGTTGCACCCGTGAAGTCGCCTGGCAAATTGCAGCCACATTAGCATCGAGAAAAAGGCATTCAACCCCCAGCCTTGATACGAGTATATGTATACCTCGTTTGGAGGCCAGAATCGGGAATTCTGGATTGGGAAGTGCTAGGGCGTCGCCAGGCACTCAGACATGTCTTATCGGGCTCGTGCTGTGAAAGGAGTCTCGGGCCAGGCCGAAGTTCCAGACTCACTGCTCACAGCTAATGGGCACAAAACATTTCGGCTACCTGGACACCTCACCTAATGGGCCTCGCATCGGACTCGGAATCGAAAACTGAATGGGTATGGAAATGGAAACGGATATTAATGCGTGTGCAGTGCATTTCTTCGGGCAGGCTAATAGACACATTTAGCATTTAGCACGCGCCCAAATGCATCGGCAAATGTCGACACTGGGAGAAAATGTGGCAAAGAGATTGAAGTGGGAAGACATTTCCTAGCTTGAAATATAATCATAACAACCACAAAAGAAGCAACAGTTTTTTAATTTCACCATATTTTGAAACATCAAAAAGAATATCTAAAAAGAATATGATAATATTGAAGCACCGATTTCTCCGGATTTTTGTAACTTTAGATGCTTGTTTTTTTCGACCTATGGCACTTGCATTGGGCGCCAATTGGCGTTGGAATCGAAAATGGAAGGGAAATAAACACAGGTAAGCTGTCCGCGTATCTCAGCCCCTGAAAGTCTCTGAATATCAGTGGATTGCGATTGCGGGCGGCCCACAACAGTTTCTCAGTTTCTCCAGATGCCGCTGTGTTGGTATTTATGCCCATGTATCCGTGGGTAAAAACCCCGATGAGTCATCGCCGTCATCCATTTCAGAGTGGCGATGCGTGCCGCGCCACTTTGGCATACATAACACACAAATCGGATTCAAAAGCAACAAAATCCAACATGTGCCCGCTCTCTTGAGTGTTTGCTTTTTGGTTGGGGAGGGAGGAGGGGAGATATGTATCGAGAGAGAATGGAGAAACCAAAACAAAAACGTATCTTCAAGTGGAAAATGTCAACAAACGAGCTGCGCGCCAAATTGCCACTTTGAGTGCATACACATTGAGTTGAGTGGAAAGTGAATCGCACTCGAAATCGAGCCACTATCTATCGGAATTAAAAATCGCATGCGATAACCGCACGACAGCCAATTTGTATCTGTATCTGTGAGATTGTTGCCTCTCTCGCGCTCTCTCATTGGCTCTCCCCCTTTTGCCGCATCATTCTGACAGTTATTGTGTGGTCTCCGCTTCACACCCACATTCGCCAGGGCCGCGAGAATCATGCGGAGCATACGAAAATCGCTCAGCTGGGATTTTCCATGTGGTTTTGTGCGGTTTTATGGACTTTTCGGACTACCGATTGTTTTTCATTGCTTTTGCGCTGGTCAAGGAGATATTTTGGGATGTGTGAAATTCCGTGACGGCTAACGGAAGTGTGGAATTACTTAAAAAGTCAAGGTGCAAATTGGCATTATTAAAACGAAATTAAGTCAAGGGAGTTGCATGTGAAATGAATTCAATGAAGCAGGAAGTTAACATATCTAAGGAATAACAAAAAAATGTAATAAAATAAAAGATTGGATAAAAGCTTAAATTAAGACAATAACATATAATCAATTAGTATTATTAGATAAAAGTGGGCAAAATATTGAAAATCTTTTAATCCATACCTTTCAACAACTTATCAATGATACTATTCTAGTTATAAAGAGTAAGACTTTAAATATCTCTGGCAACTGGTTCCGATTATTTCAATATCTTCTGCATCCCTGGCATCTACGCAACTTGAATTTCCAGGCTTGCCGCTTGTGTGCGTGTATCTGTGTGGGCCGCTCGCAGCTCGCGAGCGTGTGTGTGCGTGTGTATGCGAGGCAATGGCCAAGACGTCACCCGTCTGTTCGCTGCAGTGGGAAAGAGAGAGGAGTGGATAGCGGGAGAGACAGAG PWMEnrich/inst/NEWS0000644000175100017510000000531514614305422015047 0ustar00biocbuildbiocbuildPWMEnrich package Version 4.5.1: o Convert log(P-values) back to P-values for human using a chi-sq distribution Version 4: o New algorithm for human backgrounds o New function: toPWM() that takes both PFMs and PPMs Version 3.5: o After further testing revert back to PWMEnrich 2.x group P-value algorithm o Introduced group sorting by top motifs Version 3.1.4 (devel branch only): o New way of estimating P-value for groups of sequences. Note this will produce different P-values for groups of sequences than PWMEnrich 2.x ! Version 2.4.4: o Vignette update and fix naming of columns in the motif enrichment report Version 2.4.2: o Improve promoter selection for human and mouse genomes (duplicates are now disregarded) Version 2.4.0: o Major update with more functions and small bugfixes o Added sequenceReport() and groupReport() for easier report generation o Visualise motif scores along a sequence with plotMotifScores() o Creation of empirical CDFs for motif scores o Almost complete rewrite of the vignette to emphasize the main use cases o Converted documenation to roxygen2 Version 2.3.2: o Subsetting functions for backgrounds from Diego Diez Version 2.3.1: o Fix a bug with plotTopMotifsSequence() with calling an unknown function o Implement group.only for all background, not only pval in motifEnrichment() o New default to plotMultipleMotifs() so the margins are better Version 2.2: o Bioconductor 2.12 release version (same as 2.0.0) Version 2.0.0: o General cleanup of the code with various small optimisations o A FASTA file name is now also taken as input to motifEnrichment() o The output of motifEnrichment() is now wrapped into a class MotifEnrichmentResults that provides a number of convenience methods for common tasks like ranking and plotting motifs o Functions makeBackground() and getBackgroundFrequencies() can now take BSgenome objects as input. Thanks to Diego Diez for suggesting this and providing the code. o Another version of motifScores() has been implemented that requires large amounts of memory, but is at least 2 times faster than the old motifScores() implementation. Use a new option useBigMemoryPWMEnrich() to switch to this implementation. o PFMtoPWM now accepts a new parameter seq.count so that MotifDb motifs that are expressed as probabilities instead of frequencies can be easily used. Version 1.3.0: o Bioconductor 2.11 release version Version 1.0.2: o Use core package parallel instead of doMC Version 1.0.1: o Fix a typo in test cases and remove doMC as build dependency Version 1.0.0: o Initial release of the package PWMEnrich/inst/tests/0000755000175100017510000000000014614305422015506 5ustar00biocbuildbiocbuildPWMEnrich/inst/tests/test_clover.R0000644000175100017510000000100214614305422020153 0ustar00biocbuildbiocbuildlibrary(PWMEnrich) library(testthat) library(gtools) # test cloverScore scores = cbind(c(1, 5, 10, 2), c(5, 4, 3, 1)) colnames(scores) = c("m1", "m2") clover = PWMEnrich:::cloverScore(scores, lr3=TRUE) clover.mean = colMeans(clover) # calculate manually using all subset combinations s = scores[,1] lr3 = rep(0, 4) for(i in 1:4){ comb = combinations(4, i) lr3[i] = mean(apply(comb, 1, function(x) prod(s[x]))) } # clover scores should match test_that("Clover score", { expect_equal(lr3, clover[,1]) }) PWMEnrich/inst/tests/test_invalidInput.R0000644000175100017510000000100014614305422021325 0ustar00biocbuildbiocbuildlibrary(PWMEnrich) library(testthat) # test for cases when the input is somehow invalid although it looks like a sequence motifs.denovo = readMotifs(system.file(package="PWMEnrich", dir="extdata", file="example.transfac"), remove.acc=TRUE) inseq = list(DNAString("AGTACCGATGACCGATGACC"), DNAString("NNNNNNNNNNNNNNNNNNN")) # this should produce an error on the second sequence test_that("N as input", { expect_error(motifScores(inseq, motifs.denovo)) expect_error(motifEnrichment(inseq, motifs.denovo)) }) PWMEnrich/inst/tests/test_motifEnrichment.R0000644000175100017510000001475314614305422022035 0ustar00biocbuildbiocbuildlibrary(PWMEnrich) library(testthat) # check if two things are numerically equal, e.g. to precision of 1e-8 numEqual = function(x, y, prec=1e-8){ all(abs(x-y) < prec) } motifs.denovo = readMotifs(system.file(package="PWMEnrich", dir="extdata", file="example.transfac"), remove.acc=TRUE) # test that motif reading in works properly test_that("readMotifs", { expect_equal(length(motifs.denovo), 2) expect_equal(names(motifs.denovo), c("tin_like_motif", "gata_like_motif")) }) # create a GATA motif ACGT gata = rbind("A"=c(0, 10, 0, 10), "C"= c(0, 0, 0, 0), "G"=c(10, 0, 0, 0), "T"=c(0, 0, 10, 0) ) gata = t(apply(gata, 1, as.integer)) # GATA PWM and example sequence gata.pwm = PFMtoPWM(gata) s = DNAString("AAAAGATAAA") s1 = DNAString("AAAAAAAAAGATA") sequences = list(s, s1) scores = motifScores(s, gata.pwm, raw.scores=T) scores.count = motifScores(s, gata.pwm, cutoff=log2(exp(5))) scores2 = motifScores(list(s,s1), gata.pwm) test_that("motifScores works properly", { expect_true(numEqual( scores[[1]][5,1], 2^(gata.pwm$pwm["G",1]*4))) # hand-calculate PWM score expect_equal(nrow(scores[[1]]), 20) # one significant motif hit expect_true( all( scores.count == matrix(1) ) ) expect_equal(dim(scores2), c(2,1)) }) # now do something else res.affinity = motifEnrichment(s, gata.pwm) res.cutoff = motifEnrichment(s, gata.pwm, cutoff=log2(exp(5)), score="cutoff") # check affinity and cutoff scores test_that("motifEnrichment for raw affinity and cutoff", { # check if the affinity is right expect_true( numEqual( mean(scores[[1]], na.rm=TRUE), res.affinity$group.nobg[1] )) expect_equal( res.cutoff$group.nobg[1], 1) }) ### # make backgrounds and check scores # load the testing bg.seq object load(system.file(package="PWMEnrich", dir="extdata", file="bg.seq-test.RData")) ## make diferent background distributions and use them to scan bg.logn = makePWMLognBackground(bg.seq, gata.pwm, bg.len=1000) bg.z5 = makePWMCutoffBackground(bg.seq, gata.pwm, cutoff=log2(exp(5))) # check if the Z-score calculation works, on background the z-score should be 0 for the group res.bg.z5 = motifEnrichment(bg.seq, bg.z5) res.z5 = motifEnrichment(sequences, bg.z5) test_that("motifEnrichment Z-score", { expect_true(numEqual(res.bg.z5$group.bg,0)) # both sequence have one binding site expect_equal(as.vector(res.z5$sequence.nobg), c(1,1)) # both have 1 binding site, but first sequence is longer expect_true(res.z5$sequence.bg[1,1] > res.z5$sequence.bg[2,1]) }) # check lognormal results res.logn = motifEnrichment(sequences, bg.logn) # calculate lognormal p-value by hand mx = bg.logn$bg.mean sx = bg.logn$bg.sd * sqrt(1000-3) / sqrt(10-3) # first sequence is 10-4+1 = 7bp long ml = 2*log(mx) - 0.5*log(mx^2+sx^2) sl = sqrt(-2*log(mx) + log(mx^2+sx^2)) # compare lognormal P-value test_that("motifEnrichment P-value", { expect_true(numEqual(res.logn$sequence.bg[1,1], plnorm(res.logn$sequence.nobg[1,], meanlog=ml, sdlog=sl, lower.tail=FALSE))) }) ## other backgrounds test_that("other background produce no error", { bg.p = makePWMEmpiricalBackground(bg.seq, gata.pwm) expect_equal(nrow(bg.p$bg.fwd), 10000) expect_equal(nrow(bg.p$bg.rev), 10000) bg.1e2 = makePWMPvalCutoffBackground(bg.p, 1e-2) expect_equal(length(bg.1e2$bg.cutoff), 1) expect_equal(length(bg.1e2$bg.P), 1) res.bg.1e2 = motifEnrichment(bg.seq, gata.pwm, cutoff=bg.1e2$bg.cutoff, score="cutoff") }) bg.gev = makePWMGEVBackground(bg.seq, gata.pwm, bg.len=seq(200, 1000, 200)) test_that("GEV", { expect_equal(length(bg.gev$bg.loc), 1) expect_equal(class(bg.gev$bg.loc[[1]]), "lm") expect_equal(class(bg.gev$bg.scale[[1]]), "lm") expect_equal(class(bg.gev$bg.shape[[1]]), "lm") }) #### ## Differential enrichment diff.logn = motifDiffEnrichment(s, s1, bg.logn) diff.z5 = motifDiffEnrichment(s, s1, bg.z5) test_that("motifDiffEnrichment", { expect_true(diff.logn$group.bg > 0) expect_equal(diff.z5$group.nobg, 0) expect_true(diff.z5$group.bg > 0) }) ### test priors calculation prior = makePriors(list(DNAString(c("AAAACCCCCC"))), 1) test_that("makePriors", { expect_equal(prior, c("A"=0.2, "C"=0.3, "G"=0.3, "T"=0.2)) }) #### Test MotifEnrichmentResults pfm.all = list(tin_like_motif=motifs.denovo[[1]], gata_like_motif=motifs.denovo[[2]], gata=gata) motifs.all = PFMtoPWM(pfm.all, name=c("tin", "GATA", "GATA")) test_that("PFMtoPWM names", { expect_equal(sapply(motifs.all, function(x) x$name), c(tin_like_motif="tin", gata_like_motif="GATA", gata="GATA")) }) # setup stuff for motifEnrichment bg.logn = makePWMLognBackground(bg.seq, motifs.all) bg.z5 = makePWMCutoffBackground(bg.seq, motifs.all, cutoff=log2(exp(4))) sequences = list(DNAString("AAATTTAAGATAAAATTGCGT"), DNAString("AAAAAGATAAAAAAAA")) res.logn = motifEnrichment(sequences, bg.logn) res.z5 = motifEnrichment(sequences, bg.z5) ### test new methods for motifEnrichmentResults test_that("MotifEnrichmentResults methods for logn", { expect_equal(names(motifRankingForGroup(res.logn)), c("GATA", "GATA", "tin")) expect_equal(names(motifRankingForGroup(res.logn, id=TRUE)), c("gata", "gata_like_motif", "tin_like_motif")) expect_equal(motifRankingForGroup(res.logn, rank=TRUE), c("tin"=3, "GATA"=2, "GATA"=1)) expect_equal(motifRankingForGroup(res.logn, order=TRUE, id=TRUE), c("gata"=3, "gata_like_motif"=2, "tin_like_motif"=1)) expect_equal(names(motifRankingForGroup(res.logn, unique=TRUE)), c("GATA", "tin")) expect_equal(motifRankingForGroup(res.logn, rank=TRUE, unique=TRUE), c("tin"=2, "GATA"=1)) expect_error(motifRankingForGroup(res.logn, order=TRUE, unique=TRUE)) }) test_that("MotifEnrichmentResults methods for z5", { expect_equal(names(motifRankingForGroup(res.z5)), c("GATA", "GATA", "tin")) expect_equal(names(motifRankingForGroup(res.z5, id=TRUE)), c("gata_like_motif", "gata", "tin_like_motif")) expect_equal(motifRankingForGroup(res.z5, rank=TRUE), c("tin"=3, "GATA"=1, "GATA"=2)) expect_equal(motifRankingForGroup(res.z5, order=TRUE, id=TRUE), c("gata_like_motif"=2, "gata"=3, "tin_like_motif"=1)) expect_equal(names(motifRankingForGroup(res.z5, unique=TRUE)), c("GATA", "tin")) expect_equal(motifRankingForGroup(res.z5, rank=TRUE, unique=TRUE), c("tin"=2, "GATA"=1)) expect_error(motifRankingForGroup(res.z5, order=TRUE, unique=TRUE)) }) ### test the new parameter to PWMUnscaled gatan = gata/10 gatan.pwm = PWMUnscaled(gatan, seq.count=10) gatan.pwm2 = PFMtoPWM(gatan, seq.count=10) test_that("seq.count parameter", { expect_equal(gata.pwm$pfm, gatan.pwm$pfm) expect_equal(gata.pwm$pwm, gatan.pwm$pwm) expect_equal(gata.pwm$pfm, gatan.pwm2$pfm) expect_equal(gata.pwm$pwm, gatan.pwm2$pwm) }) PWMEnrich/inst/tests/test_similarity.R0000644000175100017510000000076314614305422021064 0ustar00biocbuildbiocbuildlibrary(PWMEnrich) library(testthat) # test function for motif similarity motifs = readMotifs(system.file(package="PWMEnrich", dir="extdata", file="jaspar-insecta.jaspar"), remove.acc=TRUE) # check if two things are numerically equal, e.g. to precision of 1e-8 numEqual = function(x, y, prec=1e-8){ all(abs(x-y) < prec) } test_that("motifSimilarity", { expect_equal(motifSimilarity(motifs$tin, motifs$tin), 1) expect_true(numEqual(motifSimilarity(motifs$tin, motifs$vnd), 0.8785692, 1e-6)) }) PWMEnrich/man/0000755000175100017510000000000014614305422014142 5ustar00biocbuildbiocbuildPWMEnrich/man/affinitySequenceSet.Rd0000644000175100017510000000071214614305422020407 0ustar00biocbuildbiocbuild% Generated by roxygen2: do not edit by hand % Please edit documentation in R/pwm.R \name{affinitySequenceSet} \alias{affinitySequenceSet} \title{Calculate total affinity over a set of sequences} \usage{ affinitySequenceSet(scores, seq.len, pwm.len) } \arguments{ \item{scores}{affinity scores for individual sequences} \item{seq.len}{lengths of sequences} \item{pwm.len}{lengths of PWMs} } \description{ Calculate total affinity over a set of sequences } PWMEnrich/man/as.data.frame-MotifEnrichmentReport-method.Rd0000644000175100017510000000113114614305422024574 0ustar00biocbuildbiocbuild% Generated by roxygen2: do not edit by hand % Please edit documentation in R/MotifEnrichmentReport-methods.R \name{as.data.frame,MotifEnrichmentReport-method} \alias{as.data.frame,MotifEnrichmentReport-method} \alias{as.data.frame} \title{Convert a MotifEnrichmentReport into a data.frame object} \usage{ \S4method{as.data.frame}{MotifEnrichmentReport}(x, row.names = NULL, optional = FALSE, ...) } \arguments{ \item{x}{the MotifEnrichmentReport object} \item{row.names}{unused} \item{optional}{unused} \item{...}{unused} } \description{ Convert a MotifEnrichmentReport into a data.frame object } PWMEnrich/man/cloverPvalue1seq.Rd0000644000175100017510000000156114614305422017675 0ustar00biocbuildbiocbuild% Generated by roxygen2: do not edit by hand % Please edit documentation in R/clover.R \name{cloverPvalue1seq} \alias{cloverPvalue1seq} \title{Calculate the Clover P-value as described in the Clover paper} \usage{ cloverPvalue1seq( scores, seq.len, pwm.len, bg.fwd, bg.rev, B = 1000, verbose = TRUE, clover = NULL ) } \arguments{ \item{scores}{the affinity scores for individual sequences} \item{seq.len}{lengths of sequences} \item{pwm.len}{lengths of PWMs} \item{bg.fwd}{the raw score of forward strand} \item{bg.rev}{the raw scores of reverse strand} \item{B}{the number of random replicates} \item{verbose}{if to give verbose progress reports} \item{clover}{the clover scores if already calculated} } \value{ P-value } \description{ This function only take one background sequence as input, it also just calculates the P-value so it is more efficient. } PWMEnrich/man/cloverScore.Rd0000644000175100017510000000130014614305422016711 0ustar00biocbuildbiocbuild% Generated by roxygen2: do not edit by hand % Please edit documentation in R/clover.R \name{cloverScore} \alias{cloverScore} \title{Calculate the Clover score using the recursive formula from Frith et al} \usage{ cloverScore(scores, lr3 = FALSE, verbose = FALSE) } \arguments{ \item{scores}{a matrix of average odds scores, where columns are motifs, and rows sequences} \item{lr3}{if to return a matrix of LR3 scores, where columns correpond to motifs, and rows to subset sizes} \item{verbose}{if to produce verbose output of progress} } \value{ the LR4 score, which is the mean of LR3 scores over subset sizes } \description{ Calculate the Clover score using the recursive formula from Frith et al } PWMEnrich/man/colMedians.Rd0000644000175100017510000000040514614305422016506 0ustar00biocbuildbiocbuild% Generated by roxygen2: do not edit by hand % Please edit documentation in R/misc.R \name{colMedians} \alias{colMedians} \title{Calculate medians of columns} \usage{ colMedians(x) } \arguments{ \item{x}{a matrix} } \description{ Calculate medians of columns } PWMEnrich/man/colSds.Rd0000644000175100017510000000042114614305422015655 0ustar00biocbuildbiocbuild% Generated by roxygen2: do not edit by hand % Please edit documentation in R/misc.R \name{colSds} \alias{colSds} \title{Calculate standard deviations of columns} \usage{ colSds(x) } \arguments{ \item{x}{a matrix} } \description{ Calculate standard deviations of columns } PWMEnrich/man/concatenateSequences.Rd0000644000175100017510000000067414614305422020600 0ustar00biocbuildbiocbuild% Generated by roxygen2: do not edit by hand % Please edit documentation in R/misc.R \name{concatenateSequences} \alias{concatenateSequences} \title{Concatenata DNA sequences into a single character object} \usage{ concatenateSequences(sequences) } \arguments{ \item{sequences}{either a list of DNAString objects, or a DNAStringSet} } \value{ a single character string } \description{ Concatenata DNA sequences into a single character object } PWMEnrich/man/cutoffZscore.Rd0000644000175100017510000000115014614305422017102 0ustar00biocbuildbiocbuild% Generated by roxygen2: do not edit by hand % Please edit documentation in R/pwm.R \name{cutoffZscore} \alias{cutoffZscore} \title{Z-score calculation for cutoff hits} \usage{ cutoffZscore(scores, seq.len, pwm.len, bg.P) } \arguments{ \item{scores}{the hit counts for the sequences} \item{seq.len}{the length distribution of sequences} \item{pwm.len}{the length distribution of the PWMs} \item{bg.P}{background probabilities of observing a motif hit at nucleotide resolution (scaled to sequence length, not 2 * length)} } \value{ Z-score } \description{ The Z-score is calculated separately for each sequence } PWMEnrich/man/cutoffZscoreSequenceSet.Rd0000644000175100017510000000121114614305422021245 0ustar00biocbuildbiocbuild% Generated by roxygen2: do not edit by hand % Please edit documentation in R/pwm.R \name{cutoffZscoreSequenceSet} \alias{cutoffZscoreSequenceSet} \title{Z-score calculation for cutoff hits for group of sequences} \usage{ cutoffZscoreSequenceSet(scores, seq.len, pwm.len, bg.P) } \arguments{ \item{scores}{the hit counts for the sequences} \item{seq.len}{the length distribution of sequences} \item{pwm.len}{the length distribution of the PWMs} \item{bg.P}{background probabilities of observing a motif hit at nucleotide resolution} } \value{ Z-score } \description{ The Z-score is calculated as if the sequence came for one very long sequence } PWMEnrich/man/divideRows.Rd0000644000175100017510000000053014614305422016546 0ustar00biocbuildbiocbuild% Generated by roxygen2: do not edit by hand % Please edit documentation in R/misc.R \name{divideRows} \alias{divideRows} \title{Divide each row of a matrix with a vector} \usage{ divideRows(m, v) } \arguments{ \item{m}{matrix to be divided} \item{v}{the vector to use for division} } \description{ Divide each row of a matrix with a vector } PWMEnrich/man/DNAStringSetToList.Rd0000644000175100017510000000060314614305422020034 0ustar00biocbuildbiocbuild% Generated by roxygen2: do not edit by hand % Please edit documentation in R/misc.R \name{DNAStringSetToList} \alias{DNAStringSetToList} \title{Convert DNAStringSet to list of DNAString objects} \usage{ DNAStringSetToList(x) } \arguments{ \item{x}{an object of class DNAStringSet} } \description{ as.list doesn't seem to always work for DNAStringSets, so implementing this ourselves. } PWMEnrich/man/dot-inputParamMotifs.Rd0000644000175100017510000000075214614305422020523 0ustar00biocbuildbiocbuild% Generated by roxygen2: do not edit by hand % Please edit documentation in R/pwm.R \name{.inputParamMotifs} \alias{.inputParamMotifs} \title{Normalizes the motifs input argument for multiple functions} \usage{ .inputParamMotifs(motifs) } \arguments{ \item{motifs}{a list of motifs either as frequency matrices (PFM) or as PWM objects. If PFMs are specified they are converted to PWMs using uniform background.} } \description{ Normalizes the motifs input argument for multiple functions } PWMEnrich/man/dot-inputParamSequences.Rd0000644000175100017510000000061614614305422021214 0ustar00biocbuildbiocbuild% Generated by roxygen2: do not edit by hand % Please edit documentation in R/pwm.R \name{.inputParamSequences} \alias{.inputParamSequences} \title{Normalize the sequences input argument} \usage{ .inputParamSequences(sequences) } \arguments{ \item{sequences}{a set of sequences to be scanned, a list of DNAString or other scannable objects} } \description{ Normalize the sequences input argument } PWMEnrich/man/dot-inputPFMfromMatrixOrPWM.Rd0000644000175100017510000000065414614305422021662 0ustar00biocbuildbiocbuild% Generated by roxygen2: do not edit by hand % Please edit documentation in R/similarity.R \name{.inputPFMfromMatrixOrPWM} \alias{.inputPFMfromMatrixOrPWM} \title{Check the frequency matrix input parameter for motifSimilarity} \usage{ .inputPFMfromMatrixOrPWM(m) } \arguments{ \item{m}{either a PWM object or a matrix} } \value{ corresponding PFM } \description{ Check the frequency matrix input parameter for motifSimilarity } PWMEnrich/man/dot-normalize.bg.seq.Rd0000644000175100017510000000062714614305422020400 0ustar00biocbuildbiocbuild% Generated by roxygen2: do not edit by hand % Please edit documentation in R/background.R \name{.normalize.bg.seq} \alias{.normalize.bg.seq} \title{check consistency of bg.seq input parameter} \usage{ .normalize.bg.seq(bg.seq) } \arguments{ \item{bg.seq}{a set of background sequences, either a list of DNAString object or DNAStringSet object} } \description{ check consistency of bg.seq input parameter } PWMEnrich/man/dot-normargPfm.Rd0000644000175100017510000000073514614305422017332 0ustar00biocbuildbiocbuild% Generated by roxygen2: do not edit by hand % Please edit documentation in R/pwm.R \name{.normargPfm} \alias{.normargPfm} \title{Input parameter normalization for PWMUnscaled} \usage{ .normargPfm(x) } \arguments{ \item{x}{a frequency matrix} } \description{ This function is from Biostrings package. A Position Frequency Matrix (PFM) is also represented as an ordinary matrix. Unlike a PWM, it must be of type integer (it will typically be the result of consensusMatrix()). } PWMEnrich/man/dot-normargPriorParams.Rd0000644000175100017510000000062314614305422021043 0ustar00biocbuildbiocbuild% Generated by roxygen2: do not edit by hand % Please edit documentation in R/pwm.R \name{.normargPriorParams} \alias{.normargPriorParams} \title{Input parameter normalization function for PWMUnscaled} \usage{ .normargPriorParams(prior.params) } \arguments{ \item{prior.params}{Typical 'prior.params' vector: c(A=0.25, C=0.25, G=0.25, T=0.25)} } \description{ This function is from Biostrings package } PWMEnrich/man/empiricalPvalue.Rd0000644000175100017510000000254714614305422017563 0ustar00biocbuildbiocbuild% Generated by roxygen2: do not edit by hand % Please edit documentation in R/pwm.R \name{empiricalPvalue} \alias{empiricalPvalue} \title{Calculate the empirical P-value by affinity of cutoff.} \usage{ empiricalPvalue( scores, seq.len, pwm.len, bg.fwd, bg.rev, cutoff = NULL, B = 10000, verbose = FALSE, exact.length = FALSE ) } \arguments{ \item{scores}{the scores obtained for the sequence} \item{seq.len}{the length of the sequence, if a single value will take a single sequence of given length. If a vector of values, will take sequences of given lengths and joint them together} \item{pwm.len}{the lengths of PWMs} \item{bg.fwd}{raw odds scores for the forward strand of background} \item{bg.rev}{raw odds scores for the reverse strand of background} \item{cutoff}{if not NULL, will use hit count above this cutoff. The cutoff should be specified in log2.} \item{B}{the number of random replicates} \item{verbose}{if to give verbose progress reports} \item{exact.length}{if to take into consideration that the actual sequence lengths differ for different PWMs. For very long sequences (i.e. seq.len >> pwm.len) this make very little difference, however the run time with exact.length is much longer.} } \description{ This is the new backend function for empirical P-values for either affinity or cutoff. The function only works on single sequences. } PWMEnrich/man/empiricalPvalueSequenceSet.Rd0000644000175100017510000000201714614305422021720 0ustar00biocbuildbiocbuild% Generated by roxygen2: do not edit by hand % Please edit documentation in R/pwm.R \name{empiricalPvalueSequenceSet} \alias{empiricalPvalueSequenceSet} \title{Empirical P-value for a set of sequences} \usage{ empiricalPvalueSequenceSet( scores, seq.len, pwm.len, bg.fwd, bg.rev, cutoff = NULL, B = 10000, verbose = FALSE ) } \arguments{ \item{scores}{a matrix of scores, rows for sequences, columns for PWMs} \item{seq.len}{the lengths of sequences} \item{pwm.len}{the lengths of PWMs} \item{bg.fwd}{raw odds scores for the forward strand of background} \item{bg.rev}{raw odds scores for the reverse strand of background} \item{cutoff}{if not NULL, will use hit count above this cutoff. The cutoff should be specified in log2.} \item{B}{the number of random replicates} \item{verbose}{if to give verbose progress reports} } \description{ Calculate empirical P-value for a set of sequences, using either affinity or cutoff. When cutoff is used, the score is a number of motif hits above a certain log-odds cutoff. } PWMEnrich/man/getBackgroundFrequencies.Rd0000644000175100017510000000205014614305422021377 0ustar00biocbuildbiocbuild% Generated by roxygen2: do not edit by hand % Please edit documentation in R/background.R \name{getBackgroundFrequencies} \alias{getBackgroundFrequencies} \title{Get the four nucleotides background frequencies} \usage{ getBackgroundFrequencies(organism = "dm3", pseudo.count = 1, quick = FALSE) } \arguments{ \item{organism}{either a name of the organisms for which the background should be compiled (supported names are "dm3", "mm9" and "hg19"), a \code{BSgenome} object, \code{DNAStringSet}, or list of \code{DNAString} objects} \item{pseudo.count}{the number to which the frequencies sum up to, by default 1} \item{quick}{if to preform fitting on a reduced set of 100 promoters. This will not give as good results but is much quicker than fitting to all the promoters (~10k). Usage of this parameter is recommended only for testing and rough estimates.} } \description{ Estimate the background frequencies of A,C,G,T on a set of promoters from an organism } \examples{ \dontrun{ getBackgroundFrequencies("dm3") } } \author{ Robert Stojnic, Diego Diez } PWMEnrich/man/getPromoters.Rd0000644000175100017510000000111714614305422017123 0ustar00biocbuildbiocbuild% Generated by roxygen2: do not edit by hand % Please edit documentation in R/background.R \name{getPromoters} \alias{getPromoters} \title{Get the promoter sequences either for a named organism such as "dm3" or a BSgenome object} \usage{ getPromoters(organismOrGenome) } \arguments{ \item{organismOrGenome}{either organism name, e.g. "dm3", or BSgenome object} } \value{ a list of: promoters - DNAStringSet of (unique) promoters; organism - name of species; version - genome version } \description{ Get the promoter sequences either for a named organism such as "dm3" or a BSgenome object } PWMEnrich/man/gevPerSequence.Rd0000644000175100017510000000141214614305422017350 0ustar00biocbuildbiocbuild% Generated by roxygen2: do not edit by hand % Please edit documentation in R/pwm.R \name{gevPerSequence} \alias{gevPerSequence} \title{Apply GEV background normalization per every sequence} \usage{ gevPerSequence(scores, seq.len, pwm.len, bg.loc, bg.scale, bg.shape) } \arguments{ \item{scores}{affinity scores for the PWMs, can contain scores for more than one sequence (as rows), P-values are extracted separately} \item{seq.len}{the length distribution of the sequences} \item{pwm.len}{the lengths of PWMs} \item{bg.loc}{list of linear regression for location parameter} \item{bg.scale}{list of linear regression for scale parameter} \item{bg.shape}{list of linear regression for shape parameter} } \description{ Apply GEV background normalization per every sequence } PWMEnrich/man/groupReport-MotifEnrichmentResults-method.Rd0000644000175100017510000000434314614305422024716 0ustar00biocbuildbiocbuild% Generated by roxygen2: do not edit by hand % Please edit documentation in R/MotifEnrichmentResults-methods.R \name{groupReport,MotifEnrichmentResults-method} \alias{groupReport,MotifEnrichmentResults-method} \alias{groupReport} \title{Generate a motif enrichment report for the whole group of sequences together} \usage{ \S4method{groupReport}{MotifEnrichmentResults}(obj, top = 0.05, bg = TRUE, by.top.motifs = FALSE, ...) } \arguments{ \item{obj}{a MotifEnrichmentResults object} \item{top}{what proportion of top motifs should be examined in each individual sequence (by default 0.05, i.e. 5\%)} \item{bg}{if to use background corrected P-values to do the ranking (if available)} \item{by.top.motifs}{if to rank by the proportion of sequences where the motif is within 'top' percentage of motifs} \item{...}{unused} } \value{ a MotifEnrichmentReport object containing a table with the following columns: \itemize{ \item 'rank' - The rank of the PWM's enrichment in the whole group of sequences together \item 'target' - The name of the PWM's target gene, transcript or protein complex. \item 'id' - The unique identifier of the PWM (if set during PWM creation). \item 'raw.score' - The raw score before P-value calculation \item 'p.value' - The P-value of motif enrichment (if available) \item 'top.motif.prop' - The proportion (between 0 and 1) of sequences where the motif is within \code{top} proportion of enrichment motifs. } } \description{ Generate a motif enrichment report for the whole group of sequences together } \examples{ if(requireNamespace("PWMEnrich.Dmelanogaster.background")){ ### # load the pre-compiled lognormal background data(PWMLogn.dm3.MotifDb.Dmel, package = "PWMEnrich.Dmelanogaster.background") # scan two sequences for motif enrichment sequences = list(DNAString("GAAGTATCAAGTGACCAGTAAGTCCCAGATGA"), DNAString("AGGTAGATAGAACAGTAGGCAATGAAGCCGATG")) res = motifEnrichment(sequences, PWMLogn.dm3.MotifDb.Dmel) # produce a report for all sequences taken together r.default = groupReport(res) # produce a report where the last column takes top 1\% motifs r = groupReport(res, top=0.01) # view the results r # plot the top 10 most enriched motifs plot(r[1:10]) } } PWMEnrich/man/keepFinite.Rd0000644000175100017510000000042614614305422016516 0ustar00biocbuildbiocbuild% Generated by roxygen2: do not edit by hand % Please edit documentation in R/pwm.R \name{keepFinite} \alias{keepFinite} \title{Replace all infinite values by 0} \usage{ keepFinite(x) } \arguments{ \item{x}{a vector of values} } \description{ Replace all infinite values by 0 } PWMEnrich/man/logNormPval.Rd0000644000175100017510000000157614614305422016702 0ustar00biocbuildbiocbuild% Generated by roxygen2: do not edit by hand % Please edit documentation in R/pwm.R \name{logNormPval} \alias{logNormPval} \title{Calculate the P-value from lognormal distribution with background of equal length} \usage{ logNormPval(scores, seq.len, pwm.len, bg.mean, bg.sd, bg.len, log = FALSE) } \arguments{ \item{scores}{affinity scores for the PWMs, can contain scores for more than one sequence (as rows), P-values are extracted separately} \item{seq.len}{the length distribution of the sequences} \item{pwm.len}{the leggths of PWMs} \item{bg.mean}{the mean values from the background for PWMs} \item{bg.sd}{the sd values from the background} \item{bg.len}{the length distribution of the background (we currently support only constant length)} \item{log}{if to produce log p-values} } \description{ Calculate the P-value from lognormal distribution with background of equal length } PWMEnrich/man/logNormPvalSequenceSet.Rd0000644000175100017510000000124614614305422021041 0ustar00biocbuildbiocbuild% Generated by roxygen2: do not edit by hand % Please edit documentation in R/pwm.R \name{logNormPvalSequenceSet} \alias{logNormPvalSequenceSet} \title{Lognormal P-value for a set of sequences} \usage{ logNormPvalSequenceSet(scores, seq.len, pwm.len, bg.mean, bg.sd, bg.len) } \arguments{ \item{scores}{a matrix of per-sequence affinity scores} \item{seq.len}{lengths of sequences} \item{pwm.len}{lengths of pwms} \item{bg.mean}{mean background at length of bg.len} \item{bg.sd}{standard deviation of background at length of bg.len} \item{bg.len}{the length for which mean and sd are calculated} } \value{ P-value } \description{ Lognormal P-value for a set of sequences } PWMEnrich/man/makeBackground.Rd0000644000175100017510000000573214614305422017355 0ustar00biocbuildbiocbuild% Generated by roxygen2: do not edit by hand % Please edit documentation in R/background.R \name{makeBackground} \alias{makeBackground} \title{Make a background for a set of position frequency matrices} \usage{ makeBackground( motifs, organism = "dm3", type = "logn", quick = FALSE, bg.seq = NULL, ... ) } \arguments{ \item{motifs}{a list of position frequency matrices (4xL matrices)} \item{organism}{either a name of the organisms for which the background should be compiled (currently supported names are "dm3", "mm9" and "hg19"), or a \code{BSgenome} object (see \code{BSgenome} package).} \item{type}{the type of background to be compiled. Possible types are: \itemize{ \item "logn" - estimate a lognormal background \item "cutoff" - estimate a Z-score background with fixed log-odds cutoff (in log2) \item "pval" - estimate a Z-score background with a fixed P-value cutoff. Note that this may require a lot of memory since the P-value of motif hits is first estimated from the empirical distribution. \item "empirical" - create an empirical P-value background. Note that this may require a lot of memory (up to 10GB in default "slow" mode (quick=FALSE) for 126 JASPAR motifs and 1000 D. melanogaster promoters). \item "GEV" - estimate a generalized extreme value (GEV) distribution background by fitting linear regression to distribution parameters in log space }} \item{quick}{if to preform fitting on a reduced set of 100 promoters. This will not give as good results but is much quicker than fitting to all the promoters (~10k). Usage of this parameter is recommended only for testing and rough estimates.} \item{bg.seq}{a set of background sequences to use. This parameter overrides the "organism" and "quick" parameters.} \item{...}{other named parameters that backend function makePWM***Background functions take.} } \description{ This is a convenience front-end function to compile new backgrounds for a set of PFMs. Currently only supports D. melanogaster, but in the future should support other common organisms as well. } \examples{ # load in the two example de-novo motifs motifs = readMotifs(system.file(package = "PWMEnrich", dir = "extdata", file = "example.transfac"), remove.acc = TRUE) \dontrun{ # construct lognormal background bg.logn = makeBackground(motifs, organism="dm3", type="logn") # alternatively, any BSgenome object can also be used if(requireNamespace("BSgenome.Dmelanogaster.UCSC.dm3")) bg.logn = makeBackground(motifs, organism=Dmelanogaster, type="logn") # construct a Z-score of hits with P-value background bg.pval = makeBackground(motifs, organism="dm3", type="pval", p.value=1e-3) # now we can use them to scan for enrichment in sequences (in this case there is a consensus # Tin binding site). motifEnrichment(DNAString("TGCATCAAGTGTGTAGTG"), bg.logn) motifEnrichment(DNAString("TGCATCAAGTGTGTAGTG"), bg.pval) } } \author{ Robert Stojnic, Diego Diez } PWMEnrich/man/makePriors.Rd0000644000175100017510000000122214614305422016542 0ustar00biocbuildbiocbuild% Generated by roxygen2: do not edit by hand % Please edit documentation in R/background.R \name{makePriors} \alias{makePriors} \title{Make priors from background sequences} \usage{ makePriors(bg.seq, bg.pseudo.count) } \arguments{ \item{bg.seq}{a set of background sequences} \item{bg.pseudo.count}{the total pseudocount shared between nucleotides} } \description{ These priors serve both as background nucleotide frequencies and pseudo-counts for PWMs. } \examples{ # some example sequences sequences = list(DNAString("AAAGAGAGTGACCGATGAC"), DNAString("ACGATGAGGATGAC")) # make priors with pseudo-count of 1 shared between them makePriors(sequences, 1) } PWMEnrich/man/makePWMCutoffBackground.Rd0000644000175100017510000000275314614305422021110 0ustar00biocbuildbiocbuild% Generated by roxygen2: do not edit by hand % Please edit documentation in R/background.R \name{makePWMCutoffBackground} \alias{makePWMCutoffBackground} \title{Make a cutoff background} \usage{ makePWMCutoffBackground( bg.seq, motifs, cutoff = log2(exp(4)), bg.pseudo.count = 1, bg.source = "", verbose = TRUE ) } \arguments{ \item{bg.seq}{a set of background sequences, either a list of DNAString object or DNAStringSet object} \item{motifs}{a set of motifs, either a list of frequency matrices, or a list of PWM objects. If frequency matrices are given, the background distribution is fitted from bg.seq.} \item{cutoff}{the cutoff at which the background should be made, i.e. at which a motif hit is called significant} \item{bg.pseudo.count}{the pseudo count which is shared between nucleotides when frequency matrices are given} \item{bg.source}{a free-form textual description of how the background was generated} \item{verbose}{if to produce verbose output} } \description{ Make a background based on number of motifs hits above a certain threshold. } \examples{ \dontrun{ if(requireNamespace("PWMEnrich.Dmelanogaster.background")){ data(MotifDb.Dmel.PFM, package = "PWMEnrich.Dmelanogaster.background") # make background for MotifDb motifs using 2Kb promoters of all D. melanogaster transcripts # using a cutoff of 5 if(requireNamespace("BSgenome.Dmelanogaster.UCSC.dm3")) makePWMCutoffBackground(Dmelanogaster$upstream2000, MotifDb.Dmel.PFM, cutoff=log2(exp(5))) } } } PWMEnrich/man/makePWMEmpiricalBackground.Rd0000644000175100017510000000411714614305422021563 0ustar00biocbuildbiocbuild% Generated by roxygen2: do not edit by hand % Please edit documentation in R/background.R \name{makePWMEmpiricalBackground} \alias{makePWMEmpiricalBackground} \title{Make an empirical P-value background} \usage{ makePWMEmpiricalBackground( bg.seq, motifs, bg.pseudo.count = 1, bg.source = "", verbose = TRUE, ... ) } \arguments{ \item{bg.seq}{a set of background sequences, either a list of DNAString object or DNAStringSet object} \item{motifs}{a set of motifs, either a list of frequency matrices, or a list of PWM objects. If frequency matrices are given, the background distribution is fitted from bg.seq.} \item{bg.pseudo.count}{the pseudo count which is shared between nucleotides when frequency matrices are given} \item{bg.source}{a free-form textual description of how the background was generated} \item{verbose}{if to produce verbose output} \item{...}{currently unused (this is for convenience for makeBackground function)} } \description{ Make a background appropriate for empirical P-value calculation. The provided set of background sequences is contcatenated into a single long sequence which is then scanned with the motifs and raw scores are saved. This object can be very large. } \details{ For reliable P-value calculation the size of the background set needs to be at least seq.len / min.P.value. For instance, to get P-values at a resolution of 0.001 for a single sequence of 500bp, we would need a background of at least 500/0.001 = 50kb. This ensures that we can make 1000 independent 500bp samples from this background to properly estimate the P-value. For a group of sequences, we would take seq.len to be the total length of all sequences in a group. } \examples{ \dontrun{ if(requireNamespace("PWMEnrich.Dmelanogaster.background")){ data(MotifDb.Dmel.PFM, package = "PWMEnrich.Dmelanogaster.background") # make empirical background by saving raw scores for each bp in the sequence. This can be # very large in memory! if(requireNamespace("BSgenome.Dmelanogaster.UCSC.dm3")) makePWMEmpiricalBackground(Dmelanogaster$upstream2000[1:100], MotifDb.Dmel.PFM) } } } PWMEnrich/man/makePWMGEVBackground.Rd0000644000175100017510000000305114614305422020273 0ustar00biocbuildbiocbuild% Generated by roxygen2: do not edit by hand % Please edit documentation in R/background.R \name{makePWMGEVBackground} \alias{makePWMGEVBackground} \title{Make a GEV background distribution} \usage{ makePWMGEVBackground( bg.seq, motifs, bg.pseudo.count = 1, bg.len = seq(200, 2000, 200), bg.source = "", verbose = TRUE, fit.log = TRUE ) } \arguments{ \item{bg.seq}{a set of background sequences, either a list of DNAString object or DNAStringSet object} \item{motifs}{a set of motifs, either a list of frequency matrices, or a list of PWM objects. If frequency matrices are given, the background distribution is fitted from bg.seq.} \item{bg.pseudo.count}{the pseudo count which is shared between nucleotides when frequency matrices are given} \item{bg.len}{the length range of background chunks} \item{bg.source}{a free-form textual description of how the background was generated} \item{verbose}{if to produce verbose output} \item{fit.log}{if to fit log odds (instead of odds)} } \description{ Construct a lognormal background distribution for a set of sequences. Sequences concatenated are binned in 'bg.len' chunks and lognormal distribution fitted to them. } \examples{ \dontrun{ if(requireNamespace("PWMEnrich.Dmelanogaster.background")){ data(MotifDb.Dmel.PFM, package = "PWMEnrich.Dmelanogaster.background") # make background for MotifDb motifs using 2kb promoters of all D. melanogaster transcripts if(requireNamespace("BSgenome.Dmelanogaster.UCSC.dm3")) makePWMGEVBackground(Dmelanogaster$upstream2000, MotifDb.Dmel.PFM) } } } PWMEnrich/man/makePWMLognBackground.Rd0000644000175100017510000000364114614305422020556 0ustar00biocbuildbiocbuild% Generated by roxygen2: do not edit by hand % Please edit documentation in R/background.R \name{makePWMLognBackground} \alias{makePWMLognBackground} \title{Make a lognormal background distribution} \usage{ makePWMLognBackground( bg.seq, motifs, bg.pseudo.count = 1, bg.len = 250, bg.len.sizes = 2^(0:4), bg.source = "", verbose = TRUE, algorithm = "default" ) } \arguments{ \item{bg.seq}{a set of background sequences, either a list of DNAString object or DNAStringSet object} \item{motifs}{a set of motifs, either a list of frequency matrices, or a list of PWM objects. If frequency matrices are given, the background distribution is fitted from bg.seq.} \item{bg.pseudo.count}{the pseudo count which is shared between nucleotides when frequency matrices are given} \item{bg.len}{background sequences will be split into tiles of this length (default: 250bp)} \item{bg.len.sizes}{background tiles will be joined into bigger tiles containing this much smaller tiles. The default is \code{2^(0:4)}, which with \code{bg.len} translates into 250bp, 500bp, 1000bp, 1500bp, 2000bp, 4000bp. Note this is only used in the "human" algorithm.} \item{bg.source}{a free-form textual description of how the background was generated} \item{verbose}{if to produce verbose output} \item{algorithm}{type of algorithm to use, valid values are: "default" and "human".} } \description{ Construct a lognormal background distribution for a set of sequences. Sequences concatenated are binned in 'bg.len' chunks and lognormal distribution fitted to them. } \examples{ \dontrun{ if(requireNamespace("PWMEnrich.Dmelanogaster.background")){ data(MotifDb.Dmel.PFM, package = "PWMEnrich.Dmelanogaster.background") # make background for MotifDb motifs using 2kb promoters of all D. melanogaster transcripts if(requireNamespace("BSgenome.Dmelanogaster.UCSC.dm3")) makePWMLognBackground(Dmelanogaster$upstream2000, MotifDb.Dmel.PFM) } } } PWMEnrich/man/makePWMPvalCutoffBackground.Rd0000644000175100017510000000234514614305422021730 0ustar00biocbuildbiocbuild% Generated by roxygen2: do not edit by hand % Please edit documentation in R/background.R \name{makePWMPvalCutoffBackground} \alias{makePWMPvalCutoffBackground} \title{Construct a cutoff background from empirical background} \usage{ makePWMPvalCutoffBackground(bg.p, p.value = 0.001, bg.source = "") } \arguments{ \item{bg.p}{an object of class PWMEmpiricalBackground} \item{p.value}{the P-value used to find cuttoffs for each of the motifs} \item{bg.source}{textual description of background source} } \value{ an object of type PWMCutoffBackground } \description{ This function takes already calculated empirical background distribution and chooses cutoff for each motif based on P-value cutoff for individual sites. } \examples{ \dontrun{ if(requireNamespace("PWMEnrich.Dmelanogaster.background")){ data(MotifDb.Dmel.PFM, package = "PWMEnrich.Dmelanogaster.background") # make empirical background - here we use only 100 sequences for illustrative purposes if(requireNamespace("BSgenome.Dmelanogaster.UCSC.dm3")) bg.p = makePWMEmpiricalBackground(Dmelanogaster$upstream2000[1:100], MotifDb.Dmel.PFM) # use the empirical background to pick a threshold and make cutoff background makePWMPvalCutoffBackground(bg.p, 0.001) } } } PWMEnrich/man/makePWMPvalCutoffBackgroundFromSeq.Rd0000644000175100017510000000261714614305422023227 0ustar00biocbuildbiocbuild% Generated by roxygen2: do not edit by hand % Please edit documentation in R/background.R \name{makePWMPvalCutoffBackgroundFromSeq} \alias{makePWMPvalCutoffBackgroundFromSeq} \title{Construct a P-value cutoff background from a set of sequences} \usage{ makePWMPvalCutoffBackgroundFromSeq( bg.seq, motifs, p.value = 0.001, bg.pseudo.count = 1, bg.source = "", verbose = TRUE ) } \arguments{ \item{bg.seq}{a set of background sequences, either a list of DNAString object or DNAStringSet object} \item{motifs}{a set of motifs, either a list of frequency matrices, or a list of PWM objects. If frequency matrices are given, the background distribution is fitted from bg.seq.} \item{p.value}{the P-value used to find cuttoffs for each of the motifs} \item{bg.pseudo.count}{the pseudo count which is shared between nucleotides when frequency matrices are given} \item{bg.source}{textual description of background source} \item{verbose}{if to print verbose output} } \value{ an object of type PWMCutoffBackground } \description{ This function creates a P-value cutoff background for motif enrichment. } \examples{ \dontrun{ if(requireNamespace("PWMEnrich.Dmelanogaster.background")){ data(MotifDb.Dmel.PFM, package = "PWMEnrich.Dmelanogaster.background") # use the empirical background to pick a threshold and make cutoff background makePWMPvalCutoffBackground(Dmelanogaster$upstream2000, 0.001) } } } PWMEnrich/man/makeStartEndPos.Rd0000644000175100017510000000103414614305422017473 0ustar00biocbuildbiocbuild% Generated by roxygen2: do not edit by hand % Please edit documentation in R/background.R \name{makeStartEndPos} \alias{makeStartEndPos} \title{Divide total.len into fragments of length len by providing start,end positions} \usage{ makeStartEndPos(total.len, len) } \arguments{ \item{total.len}{total available length to be subdivided} \item{len}{size of the individual chunk} } \value{ a data.frame containing paired up start,end positions } \description{ Divide total.len into fragments of length len by providing start,end positions } PWMEnrich/man/matrixShuffleZscorePerSequence.Rd0000644000175100017510000000162314614305422022602 0ustar00biocbuildbiocbuild% Generated by roxygen2: do not edit by hand % Please edit documentation in R/pwm.R \name{matrixShuffleZscorePerSequence} \alias{matrixShuffleZscorePerSequence} \title{Obtain z-score for motif column shuffling} \usage{ matrixShuffleZscorePerSequence(scores, sequences, pwms, cutoff = NULL, B = 30) } \arguments{ \item{scores}{a set of already calculated scores} \item{sequences}{either one sequence or a list/set of sequences (objects of type DNAString or DNAStringSet)} \item{pwms}{a list of PWMs} \item{cutoff}{if NULL, will use affinity, otherwise will use number of hits over this log2 odds cutoff} \item{B}{number of replicates, i.e. PWM column shuffles} } \description{ All PWMs are shuffled at the same time. This function would be too slow to produce empirical P-values, thus we return a z-score from a small number of shuffles. } \details{ The z-scores are calculated for each sequence individually. } PWMEnrich/man/maxAligned.Rd0000644000175100017510000000122114614305422016476 0ustar00biocbuildbiocbuild% Generated by roxygen2: do not edit by hand % Please edit documentation in R/similarity.R \name{maxAligned} \alias{maxAligned} \title{Returned the aligned motif parts} \usage{ maxAligned(m1, m2, offset) } \arguments{ \item{m1}{frequency matrix of first motif} \item{m2}{frequency matrix of second motif} \item{offset}{a number of nucleotides by which the first motif is offsetted compared to the second} } \value{ a list of column-trimmed motifs m1, m2 } \description{ This function takes the offset of first motif relative to second and chops off the end of both motifs that are not aligned. It returns a list containing only the columns that align. } PWMEnrich/man/motifDiffEnrichment.Rd0000644000175100017510000001002114614305422020347 0ustar00biocbuildbiocbuild% Generated by roxygen2: do not edit by hand % Please edit documentation in R/diff.R \name{motifDiffEnrichment} \alias{motifDiffEnrichment} \title{Differential motif enrichment} \usage{ motifDiffEnrichment( sequences1, sequences2, pwms, score = "autodetect", bg = "autodetect", cutoff = log2(exp(4)), verbose = TRUE, res1 = NULL, res2 = NULL ) } \arguments{ \item{sequences1}{First set of sequences. Can be either a single sequence (an object of class DNAString), or a list of DNAString objects, or a DNAStringSet object.} \item{sequences2}{Second set of sequences. Can be either a single sequence (an object of class DNAString), or a list of DNAString objects, or a DNAStringSet object.} \item{pwms}{this parameter can take multiple values depending on the scoring scheme and background correction used. When the \code{method} parameter is set to "autodetect", the following default algorithms are going to be used: \itemize{ \item if \code{pwms} is a list containing either frequency matrices or a list of PWM objects then the "affinity" algorithm is selected. If frequency matrices are given, they are converted to PWMs using uniform background. For best performance, convert frequency matrices to PWMs before calling this function using realistic genomic background. \item Otherwise, appropriate scoring scheme and background correction are selected based on the class of the object (see below). }} \item{score}{this parameter determines which scoring scheme to use. Following scheme as available: \itemize{ \item "autodetect" - default value. Scoring method is determined based on the type of \code{pwms} parameter. \item "affinity" - use threshold-free affinity scores without a background. The \code{pwms} parameter can either be a list of frequency matrices, \code{PWM} objects, or a \code{PWMLognBackground} object. \item "cutoff" - use number of motif hits above a score cutoff as a measure of enrichment. No background correction is performed. The \code{pwms} parameter can either be a list of frequency matrices, \code{PWM} objects, or a \code{PWMCutoffBackground} object. }} \item{bg}{this parameter determines which background correction to use, if any. \itemize{ \item "autodetect" - default value. Background correction is determined based on the type of the \code{pwms} parameter. \item "logn" - use a lognormal distribution background pre-computed for a set of PWMs. This requires \code{pwms} to be of class \code{PWMLognBackground}. \item "z" - use a z-score for the number of significant motif hits compared to background number of hits. This requires \code{pwms} to be of class \code{PWMCutoffBackground}. \item "none" - no background correction }} \item{cutoff}{the score cutoff for a significant motif hit if scoring scheme "cutoff" is selected.} \item{verbose}{if to produce verbose output} \item{res1}{the output of \code{motifEnrichment} if already calculated for \code{sequences1}} \item{res2}{the output of \code{motifEnrichment} if already calculated for \code{sequences2}} } \description{ Test for differential enrichment between two groups of sequences } \details{ This function calls \code{motifEnrichment} on two groups of sequences and calculates the difference statistics when possible. } \examples{ if(requireNamespace("PWMEnrich.Dmelanogaster.background")){ # load the background file for drosophila and lognormal correction data(PWMLogn.dm3.MotifDb.Dmel, package = "PWMEnrich.Dmelanogaster.background") # get the differential enrichment diff = motifDiffEnrichment(DNAString("TGCATCAAGTGTGTAGTGTGAGATTAGT"), DNAString("TGAACGAGTAGGACGATGAGAGATTGATG"), PWMLogn.dm3.MotifDb.Dmel, verbose=FALSE) # motifs differentially enriched in the first sequence (with lognormal background correction) head(sort(diff$group.bg, decreasing=TRUE)) # motifs differentially enriched in the second sequence (with lognormal background correction) head(sort(diff$group.bg)) } } PWMEnrich/man/motifEcdf.Rd0000644000175100017510000000237314614305422016336 0ustar00biocbuildbiocbuild% Generated by roxygen2: do not edit by hand % Please edit documentation in R/background.R \name{motifEcdf} \alias{motifEcdf} \title{Calculate the empirical distribution score distribution for a set of motifs} \usage{ motifEcdf( motifs, organism = NULL, bg.seq = NULL, quick = FALSE, pseudo.count = 1 ) } \arguments{ \item{motifs}{a set of motifs, either a list of frequency matrices, or a list of PWM objects. If frequency matrices are given, the background distribution is fitted from bg.seq.} \item{organism}{either a name of the organisms for which the background should be compiled (supported names are "dm3", "mm9" and "hg19"), or a \code{BSgenome} object (see \code{BSgenome} package).} \item{bg.seq}{a set of background sequence (either this or organism needs to be specified!). Can be a DNAString or DNAStringSet object.} \item{quick}{if to do the fitting only on a small subset of the data (only in combination with \code{organism}). Useful only for code testing!} \item{pseudo.count}{the pseudo count which is shared between nucleotides when frequency matrices are given} } \value{ a list of \code{ecdf} objects (see help page for \code{ecdf} for usage). } \description{ Calculate the empirical distribution score distribution for a set of motifs } PWMEnrich/man/motifEnrichment.Rd0000644000175100017510000002107614614305422017572 0ustar00biocbuildbiocbuild% Generated by roxygen2: do not edit by hand % Please edit documentation in R/pwm.R \name{motifEnrichment} \alias{motifEnrichment} \title{Motif enrichment} \usage{ motifEnrichment( sequences, pwms, score = "autodetect", bg = "autodetect", cutoff = NULL, verbose = TRUE, motif.shuffles = 30, B = 1000, group.only = FALSE ) } \arguments{ \item{sequences}{the sequences to be scanned for enrichment. Can be either a single sequence (an object of class DNAString), or a list of DNAString objects, or a DNAStringSet object.} \item{pwms}{this parameter can take multiple values depending on the scoring scheme and background correction used. When the \code{method} parameter is set to "autodetect", the following default algorithms are going to be used: \itemize{ \item if \code{pwms} is a list containing either frequency matrices or a list of PWM objects then the "affinity" algorithm is selected. If frequency matrices are given, they are converted to PWMs using uniform background. For best performance, convert frequency matrices to PWMs before calling this function using realistic genomic background. \item Otherwise, appropriate scoring scheme and background correction are selected based on the class of the object (see below). }} \item{score}{this parameter determines which scoring scheme to use. Following scheme as available: \itemize{ \item "autodetect" - default value. Scoring method is determined based on the type of \code{pwms} parameter. \item "affinity" - use threshold-free affinity score. The \code{pwms} parameter can either be a list of frequency matrices, \code{PWM} objects, or a \code{PWMLognBackground} object. \item "cutoff" - use number of motif hits above a score cutoff. The \code{pwms} parameter can either be a list of frequency matrices, \code{PWM} objects, or a \code{PWMCutoffBackground} object. \item "clover" - use the Clover algorithm (Frith et al, 2004). The Clover score of a single sequence is identical to the affinity score, while for a group of sequences is an average of products of affinities over all sequence subsets. }} \item{bg}{this parameter determines how the raw score is compared to the background distribution. \itemize{ \item "autodetect" - default value. Background correction is determined based on the type of the \code{pwms} parameter. \item "logn" - use a lognormal distribution background pre-computed for a set of PWMs. This requires \code{pwms} to be of class \code{PWMLognBackground}. \item "z" - use a z-score for the number of significant motif hits compared to background number of hits. This requires \code{pwms} to be of class \code{PWMCutoffBackground}. \item "pval" - use empirical P-value based on a set of background sequences. This requires \code{pwms} to be of class \code{PWMEmpiricalBackground}. Note that PWMEmpiricalBackground objects tend to be very large so that the empirical P-value can be calculated in reasonable time. \item "ms" - shuffle columns of motif matrices and use that as basis for P-value calculation. Note that since the sequences need to rescanned with all of the new shuffled motifs this can be very slow. Also, this also works only no *individual* sequences, not groups. \item "none" - no background correction }} \item{cutoff}{the score cutoff for a significant motif hit if scoring scheme "cutoff" is selected.} \item{verbose}{if to print verbose output} \item{motif.shuffles}{number of times to shuffle motifs if using "ms" background correction} \item{B}{number of replicates when calculating empirical P-value} \item{group.only}{if to return statistics only for the group of sequences, not individual sequences. In the case of empirical background the P-values for individual sequences are not calculated (thus saving time), for other backgrounds they are calculated but not returned.} } \value{ a MotifEnrichmentResults object containing a subset following elements: \itemize{ \item "score" - scoring scheme used \item "bg" - background correction used \item "params" - any additional parameters \item "sequences" - the set of sequences used \item "pwms" - the set of pwms used \item "sequence.nobg" - per-sequence scores without any background correction. For "affinity" and "clover" a matrix of mean affinity scores; for "cutoff" number of significant hits above a cutoff \item "sequence.bg" - per-sequence scores after background correction. For "logn" and "pval" the P-value (smaller is better); for "z" and "ms" background corrections the z-scores (bigger is better). \item "group.nobg" - aggregate scores for the whole group of sequences without background correction. For "affinity" and "clover" the mean affinity over all sequences in the set; for "cutoff" the total number of hits in all sequences. \item "group.bg" - aggregate scores for the whole group of sequences with background correction. For "logn" and "pval", the P-value for the whole group (smaller is better); for "z" and "ms" the z-score for the whole set (bigger is better). \item "sequence.norm" - (only for "logn") the length-normalized scores for each of the sequences. Currently only implemented for "logn", where it returns the values normalized from LogN(0,1) distribution \item "group.norm" - (only for "logn") similar to sequence.norm, but for the whole group of sequences } } \description{ Calculate motif enrichment using one of available scoring algorithms and background corrections. } \details{ This function provides and interface to all algorithms available in PWMEnrich to find motif enrichment in a single or a group of sequences with/without background correction. Since for all algorithms the first step involves calculating raw scores without background correction, the output always contains the scores without background correction together with (optional) background-corrected scores. Unless otherwise specified the scores are returned both separately for each sequence (without/with background) and for the whole group of sequences (without/with background). To use a background correction you need to supply a set of PWMs with precompiled background distribution parameters (see function \code{\link{makeBackground}}). When such an object is supplied as the \code{pwm} parameter, the scoring scheme and background correction are automatically determined. There are additional packages with already pre-computed background (e.g. see package \code{PWMEnrich.Dmelanogaster.background}). Please refer to (Stojnic & Adryan, 2012) for more details on the algorithms. } \examples{ if(requireNamespace("PWMEnrich.Dmelanogaster.background")){ ### # load the pre-compiled lognormal background data(PWMLogn.dm3.MotifDb.Dmel, package = "PWMEnrich.Dmelanogaster.background") # scan two sequences for motif enrichment sequences = list(DNAString("GAAGTATCAAGTGACCAGTAGATTGAAGTAGACCAGTC"), DNAString("AGGTAGATAGAACAGTAGGCAATGGGGGAAATTGAGAGTC")) res = motifEnrichment(sequences, PWMLogn.dm3.MotifDb.Dmel) # most enriched in both sequences (lognormal background P-value) head(motifRankingForGroup(res)) # most enriched in both sequences (raw affinity, no background) head(motifRankingForGroup(res, bg=FALSE)) # most enriched in the first sequence (lognormal background P-value) head(motifRankingForSequence(res, 1)) # most enriched in the first sequence (raw affinity, no background) head(motifRankingForSequence(res, 1, bg=FALSE)) ### # Load the pre-compiled background for hit-based motif counts with cutoff of P-value = 0.001 data(PWMPvalueCutoff1e3.dm3.MotifDb.Dmel, package = "PWMEnrich.Dmelanogaster.background") res.count = motifEnrichment(sequences, PWMPvalueCutoff1e3.dm3.MotifDb.Dmel) # Enrichment in the whole group, z-score for the number of motif hits head(motifRankingForGroup(res)) # First sequence, sorted by number of motif hits with P-value < 0.001 head(motifRankingForSequence(res, 1, bg=FALSE)) } } \references{ \itemize{ \item R. Stojnic & B. Adryan: Identification of functional DNA motifs using a binding affinity lognormal background distribution, submitted. \item MC Frith et al: Detection of functional DNA motifs via statistical over-representation, Nucleid Acid Research (2004). } } PWMEnrich/man/MotifEnrichmentReport-class.Rd0000644000175100017510000000113114614305422022017 0ustar00biocbuildbiocbuild% Generated by roxygen2: do not edit by hand % Please edit documentation in R/AllDataClasses.R \docType{class} \name{MotifEnrichmentReport-class} \alias{MotifEnrichmentReport-class} \title{A report class with formatted results of motif enrichment} \description{ The columns stored in this object will depend on the type of the report (either for group of sequences, or individual sequences). } \section{Slots}{ \describe{ \item{\code{d}:}{a DataFrame object that contains the main tabular report data} \item{\code{pwms}:}{a list of \code{PWM} objects corresponding to rows of \code{d}} } } PWMEnrich/man/MotifEnrichmentResults-class.Rd0000644000175100017510000000123014614305422022205 0ustar00biocbuildbiocbuild% Generated by roxygen2: do not edit by hand % Please edit documentation in R/AllDataClasses.R \docType{class} \name{MotifEnrichmentResults-class} \alias{MotifEnrichmentResults-class} \title{A wrapper class for results of motifEnrichment() that should make it easier to access the results.} \description{ Note that this is only a wrapper around a list which is the return value in PWMEnrich 1.3 and as such it provides the same interface as a list (for backward compatibility), with some additional methods. } \section{Slots}{ \describe{ \item{\code{res}:}{a list of old results with elements such as: sequence.bg, sequence.nobg, group.bg, group.nobg} } } PWMEnrich/man/motifIC.Rd0000644000175100017510000000211614614305422015763 0ustar00biocbuildbiocbuild% Generated by roxygen2: do not edit by hand % Please edit documentation in R/pwm.R \name{motifIC} \alias{motifIC} \title{Information content for a PWM or PFM} \usage{ motifIC( motif, prior.params = c(A = 0.25, C = 0.25, G = 0.25, T = 0.25), bycol = FALSE ) } \arguments{ \item{motif}{a matrix of frequencies, or a PWM object} \item{prior.params}{the prior parameters to use when a matrix is given (ignored if motif is already a PWM)} \item{bycol}{if to return values separately for each column} } \value{ information content in bits (i.e. log2) } \description{ Information content for a PWM or PFM } \examples{ if(requireNamespace("PWMEnrich.Dmelanogaster.background")){ data(MotifDb.Dmel, package = "PWMEnrich.Dmelanogaster.background") data(MotifDb.Dmel.PFM, package = "PWMEnrich.Dmelanogaster.background") # the nucleotide distribution is taken from the PWM (in this case genomic background) motifIC(MotifDb.Dmel[["ttk"]]) # information content with default uniform background because the input is a matrix, # not PWM object motifIC(MotifDb.Dmel.PFM[["ttk"]]) } } PWMEnrich/man/motifPrAUC.Rd0000644000175100017510000000065614614305422016411 0ustar00biocbuildbiocbuild% Generated by roxygen2: do not edit by hand % Please edit documentation in R/pwm.R \name{motifPrAUC} \alias{motifPrAUC} \title{Calculate PR-AUC for motifs ranked according to some scoring scheme} \usage{ motifPrAUC(seq.res) } \arguments{ \item{seq.res}{a matrix where each column represents a PWM and each row a result for a different sequence.} } \description{ Note that this function asssumes that smaller values are better! } PWMEnrich/man/motifRankingForGroup-MotifEnrichmentResults-method.Rd0000644000175100017510000000410314614305422026474 0ustar00biocbuildbiocbuild% Generated by roxygen2: do not edit by hand % Please edit documentation in R/MotifEnrichmentResults-methods.R \name{motifRankingForGroup,MotifEnrichmentResults-method} \alias{motifRankingForGroup,MotifEnrichmentResults-method} \alias{motifRankingForGroup} \title{Get a ranking of motifs by their enrichment in the whole set of sequences} \usage{ \S4method{motifRankingForGroup}{MotifEnrichmentResults}( obj, bg = TRUE, id = FALSE, order = FALSE, rank = FALSE, unique = FALSE, ... ) } \arguments{ \item{obj}{a MotifEnrichmentResults object} \item{bg}{if to use background corrected P-values to do the ranking (if available)} \item{id}{if to show PWM IDs instead of target TF names} \item{order}{if to output the ordering of PWMs instead of actual P-values or raw values} \item{rank}{if the output should be rank of a PWM instead of actual P-values or raw values} \item{unique}{if TRUE, only the best rank is taken for each TF (only when id = FALSE, order = FALSE)} \item{...}{currently unused} } \value{ a vector of P-values or raw enrichments sorted such that the first motif is most enriched } \description{ Get a ranking of motifs by their enrichment in the whole set of sequences } \examples{ if(requireNamespace("PWMEnrich.Dmelanogaster.background")){ ### # load the pre-compiled lognormal background data(PWMLogn.dm3.MotifDb.Dmel, package = "PWMEnrich.Dmelanogaster.background") # scan two sequences for motif enrichment sequences = list(DNAString("GAAGTATCAAGTGACCAGTAAGTCCCAGATGA"), DNAString("AGGTAGATAGAACAGTAGGCAATGAAGCCGATG")) res = motifEnrichment(sequences, PWMLogn.dm3.MotifDb.Dmel) # most enriched in both sequences (sorted by lognormal background P-value) head(motifRankingForGroup(res)) # Return a non-redundant set of TFs head(motifRankingForGroup(res, unique=TRUE)) # sorted by raw affinity instead of P-value head(motifRankingForGroup(res, bg=FALSE)) # show IDs instead of target TF names head(motifRankingForGroup(res, id=TRUE)) # output the rank instead of P-value head(motifRankingForGroup(res, rank=TRUE)) } } PWMEnrich/man/motifRankingForSequence-MotifEnrichmentResults-method.Rd0000644000175100017510000000426614614305422027162 0ustar00biocbuildbiocbuild% Generated by roxygen2: do not edit by hand % Please edit documentation in R/MotifEnrichmentResults-methods.R \name{motifRankingForSequence,MotifEnrichmentResults-method} \alias{motifRankingForSequence,MotifEnrichmentResults-method} \alias{motifRankingForSequence} \title{Get a ranking of motifs by their enrichment in one specific sequence} \usage{ \S4method{motifRankingForSequence}{MotifEnrichmentResults}( obj, seq.id, bg = TRUE, id = FALSE, order = FALSE, rank = FALSE, unique = FALSE, ... ) } \arguments{ \item{obj}{a MotifEnrichmentResults object} \item{seq.id}{either the sequence number or sequence name} \item{bg}{if to use background corrected P-values to do the ranking (if available)} \item{id}{if to show PWM IDs instead of target TF names} \item{order}{if to output the ordering of PWMs instead of actual P-values or raw values} \item{rank}{if the output should be rank of a PWM instead of actual P-values or raw values} \item{unique}{if TRUE, only the best rank is taken for each TF (only when id = FALSE, order = FALSE)} \item{...}{currently unused} } \value{ a vector of P-values or raw enrichments sorted such that the first motif is most enriched } \description{ Get a ranking of motifs by their enrichment in one specific sequence } \examples{ if(requireNamespace("PWMEnrich.Dmelanogaster.background")){ ### # load the pre-compiled lognormal background data(PWMLogn.dm3.MotifDb.Dmel, package = "PWMEnrich.Dmelanogaster.background") # scan two sequences for motif enrichment sequences = list(DNAString("GAAGTATCAAGTGACCAGTAAGTCCCAGATGA"), DNAString("AGGTAGATAGAACAGTAGGCAATGAAGCCGATG")) res = motifEnrichment(sequences, PWMLogn.dm3.MotifDb.Dmel) # most enriched in the second sequences (sorted by lognormal background P-value) head(motifRankingForSequence(res, 2)) # return unique TFs enriched in sequence 2 head(motifRankingForSequence(res, 2, unique=TRUE)) # sorted by raw affinity instead of P-value head(motifRankingForSequence(res, 2, bg=FALSE)) # show IDs instead of target TF names head(motifRankingForSequence(res, 2, id=TRUE)) # output the rank instead of P-value head(motifRankingForSequence(res, 2, rank=TRUE)) } } PWMEnrich/man/motifRecoveryAUC.Rd0000644000175100017510000000070614614305422017622 0ustar00biocbuildbiocbuild% Generated by roxygen2: do not edit by hand % Please edit documentation in R/pwm.R \name{motifRecoveryAUC} \alias{motifRecoveryAUC} \title{Calculate Recovery-AUC for motifs ranked according to some scoring scheme} \usage{ motifRecoveryAUC(seq.res) } \arguments{ \item{seq.res}{a matrix where each column represents a PWM and each row a result for a different sequence.} } \description{ Note that this function asssumes that smaller values are better! } PWMEnrich/man/motifScores.Rd0000644000175100017510000000414314614305422016730 0ustar00biocbuildbiocbuild% Generated by roxygen2: do not edit by hand % Please edit documentation in R/pwm.R \name{motifScores} \alias{motifScores} \title{Motif affinity or number of hits over a threshold} \usage{ motifScores( sequences, motifs, raw.scores = FALSE, verbose = TRUE, cutoff = NULL ) } \arguments{ \item{sequences}{a set of sequences to be scanned, a list of DNAString or other scannable objects} \item{motifs}{a list of motifs either as frequency matrices (PFM) or as PWM objects. If PFMs are specified they are converted to PWMs using uniform background.} \item{raw.scores}{if to return raw scores (odds) for each position in the sequence. Note that scores for forward and reverse strand are concatenated into a single long vector of scores (twice the length of the sequence)} \item{verbose}{if to print verbose output} \item{cutoff}{if not NULL, will count number of matches with score above value specified (instead of returning the average affinity). Can either be one value, or a vector of values for each of the motifs.} } \value{ if raw.scores=FALSE, returns a matrix of mean scores (after cutoff if any), where columns are motifs. The returned values are either mean odd scores (not log-odd), or number of hits above a threshold; otherwise if raw.scores=TRUE, returns a list of raw score values (before cutoff) } \description{ Scan a number of sequences either to find overall affinity, or a number of hits over a score threshold. } \examples{ if(requireNamespace("PWMEnrich.Dmelanogaster.background")){ data(MotifDb.Dmel, package = "PWMEnrich.Dmelanogaster.background") # affinity scores affinity = motifScores(DNAString("CGTAGGATAAAGTAACTAGTTGATGATGAAAG"), MotifDb.Dmel) # motif hit count with Patser score of 4 counts = motifScores(DNAString("CGTAGGATAAAGTAACTAGTTGATGATGAAAG"), MotifDb.Dmel, cutoff=log2(exp(4))) print(affinity) print(counts) # scanning multiple sequences sequences = list(DNAString("CGTAGGATAAAGTAACTAGTTGATGATGAAAG"), DNAString("TGAGACGAAGGGGATGAGATGCGGAAGAGTGAAA")) affinity2 = motifScores(sequences, MotifDb.Dmel) print(affinity2) } } PWMEnrich/man/motifScoresBigMemory.Rd0000644000175100017510000000212514614305422020541 0ustar00biocbuildbiocbuild% Generated by roxygen2: do not edit by hand % Please edit documentation in R/pwm.R \name{motifScoresBigMemory} \alias{motifScoresBigMemory} \title{This is a memory intensive version of motifScore() which is about 2 times faster} \usage{ motifScoresBigMemory( sequences, motifs, raw.scores = FALSE, verbose = TRUE, cutoff = NULL, seq.all = NULL ) } \arguments{ \item{sequences}{set of input sequences} \item{motifs}{set of input PWMs or PFMs} \item{raw.scores}{if to return scores for each base-pair} \item{verbose}{if to produce verbose output} \item{cutoff}{the cutoff for calling binding sites (in base 2 log).} \item{seq.all}{already concatenated sequences if already available (used to internally speed up things)} } \description{ The parameters and functionality are the same as \code{\link{motifScores}}. Please refer to documentation of this function for detailed explanation of functionality. } \details{ This function is not meant to be called directly, but is indirectly called by motifScores() once a global parameters useBigMemory is set. } \seealso{ \code{\link{motifScores}} } PWMEnrich/man/motifSimilarity.Rd0000644000175100017510000000402514614305422017617 0ustar00biocbuildbiocbuild% Generated by roxygen2: do not edit by hand % Please edit documentation in R/similarity.R \name{motifSimilarity} \alias{motifSimilarity} \title{Calculates similarity between two PFMs.} \usage{ motifSimilarity(m1, m2, trim = 0.4, self.sim = FALSE) } \arguments{ \item{m1}{matrix with four rows representing the frequency matrix of first motif} \item{m2}{matrix with four rows representing the frequency matrix of second motif} \item{trim}{bases with information content smaller than this value will be trimmed off both motif ends} \item{self.sim}{if to calculate self similarity (i.e. without including offset=0 in alignment)} } \description{ This function calculates the normalized motif correlation as a measure of motif frequency matrix similarity. } \details{ This score is essentially a normalized version of the sum of column correlations as proposed by Pietrokovski (1996). The sum is normalized by the average motif length of m1 and m2, i.e. (ncol(m1)+ncol(m2))/2. Thus, for two idential motifs this score is going to be 1. For unrelated motifs the score is going to be typically around 0. Motifs need to aligned for this score to be calculated. The current implementation tries all possible ungapped alignment with a minimal of two basepair matching, and the maximal score over all alignments is returned. Motif 1 is aligned both to Motif 2 and its reverse complement. Thus, the motif similarities are the same if the reverse complement of any of the two motifs is given. } \examples{ if(requireNamespace("PWMEnrich.Dmelanogaster.background")){ data(MotifDb.Dmel.PFM, package = "PWMEnrich.Dmelanogaster.background") # calculate the similarity of tin and vnd motifs (which are almost identical) motifSimilarity(MotifDb.Dmel.PFM[["tin"]], MotifDb.Dmel.PFM[["vnd"]]) # similarity of two unrelated motifs motifSimilarity(MotifDb.Dmel.PFM[["tin"]], MotifDb.Dmel.PFM[["ttk"]]) } } \references{ Pietrokovski S. Searching databases of conserved sequence regions by aligning protein multiple-alignments. Nucleic Acids Res 1996;24:3836-3845. } PWMEnrich/man/operators-MotifEnrichmentReport.Rd0000644000175100017510000000141614614305422022736 0ustar00biocbuildbiocbuild% Generated by roxygen2: do not edit by hand % Please edit documentation in R/MotifEnrichmentReport-methods.R \name{names,MotifEnrichmentReport} \alias{names,MotifEnrichmentReport} \alias{names,MotifEnrichmentReport-method} \alias{$,MotifEnrichmentReport-method} \alias{[,MotifEnrichmentReport-method} \title{Names of variables} \usage{ \S4method{names}{MotifEnrichmentReport}(x) \S4method{$}{MotifEnrichmentReport}(x, name) \S4method{[}{MotifEnrichmentReport}(x, i, j, ..., drop = TRUE) } \arguments{ \item{x}{the MotifEnrichmentReport object} \item{name}{the variable name} \item{i}{the row selector} \item{j}{unused} \item{...}{unused} \item{drop}{unused (always FALSE)} } \value{ the names of the variables } \description{ Columns stored in the motif enrichment report } PWMEnrich/man/operators-MotifEnrichmentResults.Rd0000644000175100017510000000115214614305422023121 0ustar00biocbuildbiocbuild% Generated by roxygen2: do not edit by hand % Please edit documentation in R/MotifEnrichmentResults-methods.R \name{names,MotifEnrichmentResults} \alias{names,MotifEnrichmentResults} \alias{names,MotifEnrichmentResults-method} \alias{$,MotifEnrichmentResults-method} \title{Names of variables} \usage{ \S4method{names}{MotifEnrichmentResults}(x) \S4method{$}{MotifEnrichmentResults}(x, name) } \arguments{ \item{x}{the MotifEnrichmentResults object} \item{name}{the variable name} } \value{ the names of the variables } \description{ Name of different pieces of information associated with MotifEnrichmentResults } PWMEnrich/man/operators-PWM.Rd0000644000175100017510000000106614614305422017113 0ustar00biocbuildbiocbuild% Generated by roxygen2: do not edit by hand % Please edit documentation in R/PWM-methods.R \name{names,PWM} \alias{names,PWM} \alias{names,PWM-method} \alias{$,PWM-method} \alias{length,PWM-method} \title{Names of variables} \usage{ \S4method{names}{PWM}(x) \S4method{$}{PWM}(x, name) \S4method{length}{PWM}(x) } \arguments{ \item{x}{the PWM object} \item{name}{the variable name} } \value{ the names of the variables } \description{ Name of different pieces of information associated with PWM Returns the motif length, i.e. the number of columns in the PWM. } PWMEnrich/man/operators-PWMCutoffBackground.Rd0000644000175100017510000000111114614305422022251 0ustar00biocbuildbiocbuild% Generated by roxygen2: do not edit by hand % Please edit documentation in R/PWMBackground-methods.R \name{names,PWMCutoffBackground} \alias{names,PWMCutoffBackground} \alias{names,PWMCutoffBackground-method} \alias{$,PWMCutoffBackground-method} \title{Names of variables} \usage{ \S4method{names}{PWMCutoffBackground}(x) \S4method{$}{PWMCutoffBackground}(x, name) } \arguments{ \item{x}{the PWMCutoffBackground object} \item{name}{the variable name} } \value{ the names of the variables } \description{ Name of different pieces of information associated with PWMCutoffBackground } PWMEnrich/man/operators-PWMEmpiricalBackground.Rd0000644000175100017510000000114114614305422022733 0ustar00biocbuildbiocbuild% Generated by roxygen2: do not edit by hand % Please edit documentation in R/PWMBackground-methods.R \name{names,PWMEmpiricalBackground} \alias{names,PWMEmpiricalBackground} \alias{names,PWMEmpiricalBackground-method} \alias{$,PWMEmpiricalBackground-method} \title{Names of variables} \usage{ \S4method{names}{PWMEmpiricalBackground}(x) \S4method{$}{PWMEmpiricalBackground}(x, name) } \arguments{ \item{x}{the PWMEmpiricalBackground object} \item{name}{the variable name} } \value{ the names of the variables } \description{ Name of different pieces of information associated with PWMEmpiricalBackground } PWMEnrich/man/operators-PWMGEVBackground.Rd0000644000175100017510000000106114614305422021450 0ustar00biocbuildbiocbuild% Generated by roxygen2: do not edit by hand % Please edit documentation in R/PWMBackground-methods.R \name{names,PWMGEVBackground} \alias{names,PWMGEVBackground} \alias{names,PWMGEVBackground-method} \alias{$,PWMGEVBackground-method} \title{Names of variables} \usage{ \S4method{names}{PWMGEVBackground}(x) \S4method{$}{PWMGEVBackground}(x, name) } \arguments{ \item{x}{the PWMGEVBackground object} \item{name}{the variable name} } \value{ the names of the variables } \description{ Name of different pieces of information associated with PWMGEVBackground } PWMEnrich/man/operators-PWMLognBackground.Rd0000644000175100017510000000107114614305422021727 0ustar00biocbuildbiocbuild% Generated by roxygen2: do not edit by hand % Please edit documentation in R/PWMBackground-methods.R \name{names,PWMLognBackground} \alias{names,PWMLognBackground} \alias{names,PWMLognBackground-method} \alias{$,PWMLognBackground-method} \title{Names of variables} \usage{ \S4method{names}{PWMLognBackground}(x) \S4method{$}{PWMLognBackground}(x, name) } \arguments{ \item{x}{the PWMLognBackground object} \item{name}{the variable name} } \value{ the names of the variables } \description{ Name of different pieces of information associated with PWMLognBackground } PWMEnrich/man/PFMtoPWM.Rd0000644000175100017510000000234314614305422016004 0ustar00biocbuildbiocbuild% Generated by roxygen2: do not edit by hand % Please edit documentation in R/pwm.R \name{PFMtoPWM} \alias{PFMtoPWM} \title{Convert frequencies into motifs using PWMUnscaled} \usage{ PFMtoPWM( motifs, id = names(motifs), name = names(motifs), seq.count = NULL, ... ) } \arguments{ \item{motifs}{a list of motifs represented as matrices of frequencies (PFM)} \item{id}{the set of IDs for the motifs (defaults to names of the 'motifs' list)} \item{name}{the set of names for the motifs (defaults to names of the 'motifs' list)} \item{seq.count}{if frequencies in the motifs are normalized to 1, provides a vector of sequence counts (e.g. for MotifDb motifs)} \item{...}{other parameters to PWMUnscaled} } \description{ Note that this function is deprecated and replaced by \code{toPWM()}. } \examples{ \dontrun{ if (requireNamespace("PWMEnrich.Dmelanogaster.background")) { data(MotifDb.Dmel.PFM, package = "PWMEnrich.Dmelanogaster.background") # convert to PWM with uniform background PFMtoPWM(MotifDb.Dmel.PFM) # get background for drosophila (quick mode on a reduced dataset) prior = getBackgroundFrequencies("dm3", quick=TRUE) # convert with genomic background PFMtoPWM(MotifDb.Dmel.PFM, prior.params=prior) } } } PWMEnrich/man/plot-MotifEnrichmentReport-missing-method.Rd0000644000175100017510000000264314614305422024626 0ustar00biocbuildbiocbuild% Generated by roxygen2: do not edit by hand % Please edit documentation in R/plot.R \name{plot,MotifEnrichmentReport,missing-method} \alias{plot,MotifEnrichmentReport,missing-method} \title{Plot the motif enrichment report} \usage{ \S4method{plot}{MotifEnrichmentReport,missing}( x, y, fontsize = 14, id.fontsize = fontsize, header.fontsize = fontsize, widths = NULL, ... ) } \arguments{ \item{x}{a MotifEnrichmentReport object} \item{y}{unused} \item{fontsize}{font size to use in the plot} \item{id.fontsize}{font size to use for the motif IDs} \item{header.fontsize}{font size of the header} \item{widths}{the relative widths of columns} \item{...}{unused if(requireNamespace("PWMEnrich.Dmelanogaster.background")){ ### # load the pre-compiled lognormal background data(PWMLogn.dm3.MotifDb.Dmel, package = "PWMEnrich.Dmelanogaster.background") # scan two sequences for motif enrichment sequences = list(DNAString("GAAGTATCAAGTGACCAGTAAGTCCCAGATGA"), DNAString("AGGTAGATAGAACAGTAGGCAATGAAGCCGATG")) res = motifEnrichment(sequences, PWMLogn.dm3.MotifDb.Dmel) # produce a report for all sequences taken together r = groupReport(res) # plot the top 10 most enriched motifs plot(r[1:10]) }} } \description{ Plots a graphical version of the motif enrichment report. Note that all values are plotted, if you want to plot only a subset of a report, first select this subset (see examples). } PWMEnrich/man/plot-PWM-missing-method.Rd0000644000175100017510000000121614614305422020775 0ustar00biocbuildbiocbuild% Generated by roxygen2: do not edit by hand % Please edit documentation in R/plot.R \name{plot,PWM,missing-method} \alias{plot,PWM,missing-method} \title{Plotting for the PWM class} \usage{ \S4method{plot}{PWM,missing}(x, y, ...) } \arguments{ \item{x}{the PWM object} \item{y}{unused} \item{...}{other parameters to pass to seqLogo's \code{plot} function} } \description{ This function produces a sequence logo (via package seqLogo). } \examples{ if(requireNamespace("PWMEnrich.Dmelanogaster.background")){ data(MotifDb.Dmel, package = "PWMEnrich.Dmelanogaster.background") # plot the tinman motif from MotifDb plot(MotifDb.Dmel[["tin"]]) } } PWMEnrich/man/plotMotifScores.Rd0000644000175100017510000000774614614305422017603 0ustar00biocbuildbiocbuild% Generated by roxygen2: do not edit by hand % Please edit documentation in R/plot.R \name{plotMotifScores} \alias{plotMotifScores} \title{Plot the raw motifs scores as returned by motifScores()} \usage{ plotMotifScores( scores, sel.motifs = NULL, seq.names = NULL, cols = NULL, cutoff = NULL, log.fun = log2, main = "", legend.space = 0.3, max.score = NULL, trans = 0.5, text.cex = 0.9, legend.cex = 0.9, motif.names = NULL, seq.len.spacing = 8, shape = "rectangle" ) } \arguments{ \item{scores}{the list of motifs scores. Each element of the list is a matrix of scores for one sequences. The columns in the matrix correspond to different motifs. Each column contains the odds (not log-odds!) scores over both strands. For example, for a sequence of length 5, scores for a 3 bp motifs could be: \code{c(0.1, 1, 4, NA, NA, 1, 0.3, 2, NA, NA)}. The first 3 numbers are odds scores starting at first three bases, and the second lot of 3 numbers is the scores starting at the same positions but with the reverse complement of the motif. The last two values are NA on both strands because we do not support partial motif hits.} \item{sel.motifs}{a vector of motif names. Use this parameter to show the motif hits to only a subset of motifs for which the scores are available.} \item{seq.names}{a vector of sequence names to show in the graph. If none specified, the sequences will be named Sequence 1, Sequence 2, ...} \item{cols}{a vector of colours to use to colour code motif hits. If none are specified, the current palette will be used.} \item{cutoff}{either a single value, or a vector of values. The values are PWM cutoffs after \code{log.fun} (see below). Only motif scores above these cutoffs will be shown. If a single values is specified, it will be used for all PWMs, otherwise the vector needs to specify one cutoff per PWM.} \item{log.fun}{the logarithm function to use to calculate log-odds. By default log2 is used for consistency with Biostrings.} \item{main}{the main title} \item{legend.space}{the proportion of horizontal space to reserve for the legend. The default is 30\%.} \item{max.score}{the maximal log-odds score used to scale all other scores. By default this values is automatically determined, but it can also be set manually to make multiple plots comparable.} \item{trans}{the level of transparency. By default 50\% transparency to be able to see overlapping binding sites} \item{text.cex}{the scaling factor for sequence names} \item{legend.cex}{the scaling factor for the legend} \item{motif.names}{optional vector of motif names to show instead of those present as column names in \code{scores}} \item{seq.len.spacing}{the spacing (in bp units) between the end of the sequence line and the text showing the length in bp} \item{shape}{the shape to use to draw motif occurances, valid values are "rectangle" (default), "line" and "triangle"} } \description{ This function visualises the motif scores for one or more sequences. Sequences are drawn as lines, and scores are plotted as triangles at both sides of the line (corresponding to the two strands). The width of the base of the triangle corresponds to motif width and the height to the motif \code{log(score)} that is positive and greater than the \code{cutoff} parameter (if specified). All scores have the same y-axis, so the heights of bars are comparable between sequences and motifs. } \examples{ if(requireNamespace("PWMEnrich.Dmelanogaster.background")){ ### # Load Drosophila PWMs data(MotifDb.Dmel, package = "PWMEnrich.Dmelanogaster.background") # two sequences of interest sequences = list(DNAString("GAAGTATCAAGTGACCAGGTGAAGTCCCAGATGA"), DNAString("AGGTAGATAGAACAGTAGGCAATGAAGCCGATG")) # select the tinman and snail motifs pwms = MotifDb.Dmel[c("tin", "sna")] # get the raw score that will be plotted scores = motifScores(sequences, pwms, raw.scores=TRUE) # plot the scores in both sequences, green for tin and blue for sna plotMotifScores(scores, cols=c("green", "blue")) } } PWMEnrich/man/plotMultipleMotifs.Rd0000644000175100017510000000231214614305422020303 0ustar00biocbuildbiocbuild% Generated by roxygen2: do not edit by hand % Please edit documentation in R/plot.R \name{plotMultipleMotifs} \alias{plotMultipleMotifs} \title{Plot mulitple motifs in a single plot} \usage{ plotMultipleMotifs( pwms, titles = names(pwms), rows = ceiling(sqrt(length(pwms))), cols = ceiling(sqrt(length(pwms))), xmargin.scale = 0.4, ymargin.scale = 0.4, ... ) } \arguments{ \item{pwms}{a list of PWM objects or frequency matrices} \item{titles}{a characater vector of titles for each of the plots} \item{rows}{number of rows in the grid} \item{cols}{number or cols in the grid} \item{xmargin.scale}{the scaling parameter for the X-axis margin. Useful when plotting more than one logo on a page} \item{ymargin.scale}{the scaling parameter for the Y-axis margin. Useful when plotting more than one logo on a page} \item{...}{other parameters passed to seqLogoGrid()} } \description{ Individual motif logos are plotted on a rows x cols grid. This function is a convenience interface for the \code{seqLogoGrid} function that deals with viewpoint placement in a matrix-like grid layout. } \details{ By default will try to make a square grid plot that would fit all the motifs and use list names as captions. } PWMEnrich/man/plotPFM.Rd0000644000175100017510000000054314614305422015754 0ustar00biocbuildbiocbuild% Generated by roxygen2: do not edit by hand % Please edit documentation in R/plot.R \name{plotPFM} \alias{plotPFM} \title{Plot a PFM (not PWM) using seqLogo} \usage{ plotPFM(pfm, ...) } \arguments{ \item{pfm}{a matrix where rows are the four nucleotides} \item{...}{additional parameters for plot()} } \description{ Plot a PFM (not PWM) using seqLogo } PWMEnrich/man/plotTopMotifsGroup-MotifEnrichmentResults-method.Rd0000644000175100017510000000260014614305422026220 0ustar00biocbuildbiocbuild% Generated by roxygen2: do not edit by hand % Please edit documentation in R/MotifEnrichmentResults-methods.R \name{plotTopMotifsGroup,MotifEnrichmentResults-method} \alias{plotTopMotifsGroup,MotifEnrichmentResults-method} \alias{plotTopMotifsGroup} \title{Plot the top N enrichment motifs in a group of sequences} \usage{ \S4method{plotTopMotifsGroup}{MotifEnrichmentResults}(obj, n, bg = TRUE, id = FALSE, ...) } \arguments{ \item{obj}{a MotifEnrichmentResults object} \item{n}{the number of top ranked motifs to plot} \item{bg}{if to use background corrected P-values to do the ranking (if available)} \item{id}{if to show PWM IDs instead of target TF names} \item{...}{other parameters passed to \code{plotMultipleMotifs()}} } \description{ Plot the top N enrichment motifs in a group of sequences } \examples{ if(requireNamespace("PWMEnrich.Dmelanogaster.background")){ ### # load the pre-compiled lognormal background data(PWMLogn.dm3.MotifDb.Dmel, package = "PWMEnrich.Dmelanogaster.background") # scan two sequences for motif enrichment sequences = list(DNAString("GAAGTATCAAGTGACCAGTAAGTCCCAGATGA"), DNAString("AGGTAGATAGAACAGTAGGCAATGAAGCCGATG")) res = motifEnrichment(sequences, PWMLogn.dm3.MotifDb.Dmel) # plot the top 4 motifs in a 2x2 grid plotTopMotifsGroup(res, 4) # plot top 3 motifs in a single row plotTopMotifsGroup(res, 3, row=1, cols=3) } } PWMEnrich/man/plotTopMotifsSequence-MotifEnrichmentResults-method.Rd0000644000175100017510000000272614614305422026705 0ustar00biocbuildbiocbuild% Generated by roxygen2: do not edit by hand % Please edit documentation in R/MotifEnrichmentResults-methods.R \name{plotTopMotifsSequence,MotifEnrichmentResults-method} \alias{plotTopMotifsSequence,MotifEnrichmentResults-method} \alias{plotTopMotifsSequence} \title{Plot the top N enrichment motifs in a single sequence} \usage{ \S4method{plotTopMotifsSequence}{MotifEnrichmentResults}(obj, seq.id, n, bg = TRUE, id = FALSE, ...) } \arguments{ \item{obj}{a MotifEnrichmentResults object} \item{seq.id}{either the sequence number or sequence name} \item{n}{the number of top ranked motifs to plot} \item{bg}{if to use background corrected P-values to do the ranking (if available)} \item{id}{if to show PWM IDs instead of target TF names} \item{...}{other parameters passed to \code{plotMultipleMotifs()}} } \description{ Plot the top N enrichment motifs in a single sequence } \examples{ if(requireNamespace("PWMEnrich.Dmelanogaster.background")){ ### # load the pre-compiled lognormal background data(PWMLogn.dm3.MotifDb.Dmel, package = "PWMEnrich.Dmelanogaster.background") # scan two sequences for motif enrichment sequences = list(DNAString("GAAGTATCAAGTGACCAGTAAGTCCCAGATGA"), DNAString("AGGTAGATAGAACAGTAGGCAATGAAGCCGATG")) res = motifEnrichment(sequences, PWMLogn.dm3.MotifDb.Dmel) # plot the top 4 motifs in a 2x2 grid plotTopMotifsSequence(res, 1, 4) # plot top 3 motifs in a single row plotTopMotifsSequence(res, 1, 3, row=1, cols=3) } } PWMEnrich/man/PWM-class.Rd0000644000175100017510000000162514614305422016203 0ustar00biocbuildbiocbuild% Generated by roxygen2: do not edit by hand % Please edit documentation in R/AllDataClasses.R \docType{class} \name{PWM-class} \alias{PWM-class} \title{A class that represents a Position Weight Matrix (PWM)} \description{ A class that represents a Position Weight Matrix (PWM) } \section{Slots}{ \describe{ \item{\code{id}:}{a systematic ID given to this PWM, could include the source, version, etc} \item{\code{name}:}{the name of the transcription factor (TF) to which the PWM corresponds to} \item{\code{pfm}:}{Position Frequency Matrix (PFM) from which the PWM is derived} \item{\code{prior.params}:}{Defines prior frequencies of the four bases (A,C,G,T), a named vector. These will be added to individual values for the PFM and at the same time used as background probabilities} \item{\code{pwm}:}{Final Position Weight Matrix (PWM) constructed using prior.params with logarithm base 2} } } PWMEnrich/man/PWMCutoffBackground-class.Rd0000644000175100017510000000136214614305422021350 0ustar00biocbuildbiocbuild% Generated by roxygen2: do not edit by hand % Please edit documentation in R/AllDataClasses.R \docType{class} \name{PWMCutoffBackground-class} \alias{PWMCutoffBackground-class} \title{Hit count background distribution for a set of PWMs} \description{ Hit count background distribution for a set of PWMs } \section{Slots}{ \describe{ \item{\code{bg.source}:}{textual description of where the background distribution is derived from} \item{\code{bg.cutoff}:}{the cutoff score used to find significant motif hits (in log2 odds), either a single value or a vector of values} \item{\code{bg.P}:}{the density of significant motif hits per nucleotide in background} \item{\code{pwms}:}{the pwms for which the background has been compiled} } } PWMEnrich/man/PWMEmpiricalBackground-class.Rd0000644000175100017510000000133614614305422022030 0ustar00biocbuildbiocbuild% Generated by roxygen2: do not edit by hand % Please edit documentation in R/AllDataClasses.R \docType{class} \name{PWMEmpiricalBackground-class} \alias{PWMEmpiricalBackground-class} \title{Background for calculating empirical P-values} \description{ This object contains raw scores for one very long sequence, thus it can be very large. } \section{Slots}{ \describe{ \item{\code{bg.source}:}{textual description of where the background distribution is derived from} \item{\code{bg.fwd}:}{affinity scores (odds) for the forward strand. PWMs as columns} \item{\code{bg.rev}:}{affinity scores (odds) for the reverse strand. PWMs as columns} \item{\code{pwms}:}{the pwms for which the background has been compiled} } } PWMEnrich/man/PWMEnrich-package.Rd0000644000175100017510000000145414614305422017622 0ustar00biocbuildbiocbuild% Generated by roxygen2: do not edit by hand % Please edit documentation in R/PWMEnrich-package.R \docType{package} \name{PWMEnrich-package} \alias{PWMEnrich} \alias{PWMEnrich-package} \title{PWMEnrich: PWM enrichment analysis} \description{ A toolkit of high-level functions for DNA motif scanning and enrichment analysis built upon Biostrings. The main functionality is PWM enrichment analysis of already known PWMs (e.g. from databases such as MotifDb), but the package also implements high-level functions for PWM scanning and visualisation. The package does not perform "de novo" motif discovery, but is instead focused on using motifs that are either experimentally derived or computationally constructed by other tools. } \keyword{internal} PWMEnrich/man/PWMGEVBackground-class.Rd0000644000175100017510000000171414614305422020544 0ustar00biocbuildbiocbuild% Generated by roxygen2: do not edit by hand % Please edit documentation in R/AllDataClasses.R \docType{class} \name{PWMGEVBackground-class} \alias{PWMGEVBackground-class} \title{Generalized Extreme Values (GEV) background for P-values} \description{ The three parameters of the GEV distribution are fitted by doing linear regression on log of sequence length. } \section{Slots}{ \describe{ \item{\code{bg.source}:}{textual description of where the background distribution is derived from} \item{\code{bg.loc}:}{linear regression model for estimating the location parameter based on log(L), list of lm objects of PWMs} \item{\code{bg.scale}:}{linear regression model for estimating the scale parameter based on log(L), list of lm objects of PWMs} \item{\code{bg.shape}:}{linear regression model for estimating the shape parameter based on log(L), list of lm objects of PWMs} \item{\code{pwms}:}{the pwms for which the background has been compiled} } } PWMEnrich/man/PWMLognBackground-class.Rd0000644000175100017510000000150514614305422021020 0ustar00biocbuildbiocbuild% Generated by roxygen2: do not edit by hand % Please edit documentation in R/AllDataClasses.R \docType{class} \name{PWMLognBackground-class} \alias{PWMLognBackground-class} \title{Lognormal background distribution for a set of PWMs} \description{ Lognormal background distribution for a set of PWMs } \section{Slots}{ \describe{ \item{\code{bg.source}:}{textual description of where the background distribution is derived from} \item{\code{bg.len}:}{the length to which the background is normalized to. This is a vector of values, can have a different value for each motif.} \item{\code{bg.mean}:}{the mean value of the lognormal distribution at bg.len} \item{\code{bg.sd}:}{the standard deviation of the lognormal distribution at bg.len} \item{\code{pwms}:}{the pwms for which the background has been compiled} } } PWMEnrich/man/PWMUnscaled.Rd0000644000175100017510000000423314614305422016555 0ustar00biocbuildbiocbuild% Generated by roxygen2: do not edit by hand % Please edit documentation in R/pwm.R \name{PWMUnscaled} \alias{PWMUnscaled} \title{Create a PWM from PFM} \usage{ PWMUnscaled( x, id = "", name = "", type = c("log2probratio", "prob"), prior.params = c(A = 0.25, C = 0.25, G = 0.25, T = 0.25), pseudo.count = prior.params, unit.scale = FALSE, seq.count = NULL ) } \arguments{ \item{x}{the integer count matrix representing the motif, rows as nucleotides} \item{id}{a systematic ID given to this PWM, could include the source, version, etc} \item{name}{the name of the transcription factor (TF) to which the PWM corresponds to} \item{type}{the type of PWM calculation, either as log2-odds, or posterior probability (frequency matrix)} \item{prior.params}{the pseudocounts for each of the nucleotides} \item{pseudo.count}{the pseudo-count values if different from priors} \item{unit.scale}{if to unit.scale the pwm (default is no unit scaling)} \item{seq.count}{if x is a normalised PFM (i.e. with probabilities instead of sequence counts), then this sequence count will be used to convert \code{x} into a count matrix} } \value{ a new PWM object representing the PWM } \description{ The PWM function from Biostrings without unit scaling } \details{ By default the Biostrings package scales the log-odds score so it is within 0 and 1. In this function we take a more traditional approach with no unit scaling and offer unit scaling as an additional parameter. See ?PWM from Biostrings for more information on input arguments. } \examples{ if(requireNamespace("PWMEnrich.Dmelanogaster.background")){ data(MotifDb.Dmel.PFM, package = "PWMEnrich.Dmelanogaster.background") ttk = MotifDb.Dmel.PFM[["ttk"]] # make a PWM with uniform background PWMUnscaled(ttk, id="ttk-JASPAR", name="ttk") # custom background PWMUnscaled(ttk, id="ttk-JASPAR", name="ttk", prior.params=c("A"= 0.2, "C" = 0.3, "G" = 0.3, "T" = 0.2)) # get background for drosophila (quick mode on a reduced dataset) prior = getBackgroundFrequencies("dm3", quick=TRUE) # convert using genomic background PWMUnscaled(ttk, id="ttk-JASPAR", name="ttk", prior.params=prior) } } PWMEnrich/man/rankingProcessAndReturn.Rd0000644000175100017510000000150514614305422021245 0ustar00biocbuildbiocbuild% Generated by roxygen2: do not edit by hand % Please edit documentation in R/MotifEnrichmentResults-methods.R \name{rankingProcessAndReturn} \alias{rankingProcessAndReturn} \title{A helper function for motifRankingForGroup and motifRankingForSequence with the common code} \usage{ rankingProcessAndReturn(res, r, id, order, rank, unique, decreasing) } \arguments{ \item{res}{the list of results from MotifEnrichmentResults object} \item{r}{the vector of raw results that needs to be processed} \item{id}{if to return IDs instead of names} \item{order}{if to return the ordering of motifs} \item{rank}{if to return the rank of motifs} \item{unique}{if to remove duplicates} \item{decreasing}{specifies the sorting order} } \description{ A helper function for motifRankingForGroup and motifRankingForSequence with the common code } PWMEnrich/man/readJASPAR.Rd0000644000175100017510000000074214614305422016250 0ustar00biocbuildbiocbuild% Generated by roxygen2: do not edit by hand % Please edit documentation in R/readData.R \name{readJASPAR} \alias{readJASPAR} \title{Read motifs in JASPAR format} \usage{ readJASPAR(file, remove.ids = FALSE) } \arguments{ \item{file}{the filename} \item{remove.ids}{if to strip JASPAR ID's from motif names, e.g. "MA0211.1 bap" would become just "bap"} } \value{ a list of matrices representing motifs (with four nucleotides as rows) } \description{ Read motifs in JASPAR format } PWMEnrich/man/readMotifs.Rd0000644000175100017510000000222614614305422016530 0ustar00biocbuildbiocbuild% Generated by roxygen2: do not edit by hand % Please edit documentation in R/readData.R \name{readMotifs} \alias{readMotifs} \title{Read in motifs in JASPAR or TRANSFAC format} \usage{ readMotifs(file, remove.acc = FALSE) } \arguments{ \item{file}{the filename} \item{remove.acc}{if to remove accession numbers. If TRUE, the AC entry in TRANSFAC files is ignored, and the accession is stripped from JASPAR, e.g. motif with name "MA0211.1 bap" would become just "bap". If FALSE, botht he AC and ID are used to generate the TRANSFAC name and the original motif names are preserved in JASPAR files.} } \value{ a list of 4xL matrices representing motifs (four nucleotides as rows) } \description{ The format is autodetected based on file format. If the autodetection fail then the file cannot be read. } \examples{ # read in example TRANSFAC motifs without accession codes (just IDs) readMotifs(system.file(package = "PWMEnrich", dir = "extdata", file = "example.transfac"), remove.acc = TRUE) # read in the JASPAR insects motifs provided as example readMotifs(system.file(package = "PWMEnrich", dir = "extdata", file = "jaspar-insecta.jaspar"), remove.acc = TRUE) } PWMEnrich/man/readTRANSFAC.Rd0000644000175100017510000000070314614305422016466 0ustar00biocbuildbiocbuild% Generated by roxygen2: do not edit by hand % Please edit documentation in R/readData.R \name{readTRANSFAC} \alias{readTRANSFAC} \title{Read in motifs in TRANSFAC format} \usage{ readTRANSFAC(file, remove.acc = TRUE) } \arguments{ \item{file}{the filename} \item{remove.acc}{if to ignore transfac accession numbers} } \value{ a list of matrices representing motifs (with four nucleotides as rows) } \description{ Read in motifs in TRANSFAC format } PWMEnrich/man/registerCoresPWMEnrich.Rd0000644000175100017510000000177714614305422021002 0ustar00biocbuildbiocbuild% Generated by roxygen2: do not edit by hand % Please edit documentation in R/options.R \name{registerCoresPWMEnrich} \alias{registerCoresPWMEnrich} \title{Register than PWMEnrich can use parallel CPU cores} \usage{ registerCoresPWMEnrich(numCores = NA) } \arguments{ \item{numCores}{number of cores to use (default to take all cores), or NULL if no parallel execution is to be used} } \description{ Certain functions (like motif scanning) can be parallelized in PWMEnrich. This function registers a number of parallel cores (via core package parallel) to be used in code that can be parallelized. After this function is called, all further PWMEnrich function calls will run in parallel if possible. } \details{ By default parallel execution is turned off. To turn it off after using it, call this function by passing NULL. } \examples{ \dontrun{ registerCoresPWMEnrich(4) # use 4 CPU cores in PWMEnrich registerCoresPWMEnrich() # use maximal number of CPUs registerCoresPWMEnrich(NULL) # do not use parallel execution } } PWMEnrich/man/reverseComplement-PWM-method.Rd0000644000175100017510000000125514614305422022052 0ustar00biocbuildbiocbuild% Generated by roxygen2: do not edit by hand % Please edit documentation in R/PWM-methods.R \name{reverseComplement,PWM-method} \alias{reverseComplement,PWM-method} \title{Reverse complement for the PWM object} \usage{ \S4method{reverseComplement}{PWM}(x, ...) } \arguments{ \item{x}{an object of type PWM} \item{...}{unused} } \value{ an object of type PWM that is reverse complement of x } \description{ Finds the reverse complement of the PWM } \examples{ if(requireNamespace("PWMEnrich.Dmelanogaster.background")){ data(MotifDb.Dmel.PFM, package = "PWMEnrich.Dmelanogaster.background") reverseComplement(MotifDb.Dmel.PFM[["ttk"]]) # reverse complement of the ttk PWM } } PWMEnrich/man/scanWithPWM.Rd0000644000175100017510000000315214614305422016576 0ustar00biocbuildbiocbuild% Generated by roxygen2: do not edit by hand % Please edit documentation in R/pwm.R \name{scanWithPWM} \alias{scanWithPWM} \title{Scan the whole sequence on both strands} \usage{ scanWithPWM( pwm, dna, pwm.rev = NULL, odds.score = FALSE, both.strands = FALSE, strand.fun = "mean" ) } \arguments{ \item{pwm}{PWM object} \item{dna}{a DNAString or other sequence from Biostrings} \item{pwm.rev}{the reverse complement for a pwm (if it is already pre-computed)} \item{odds.score}{if to return raw scores in odds (not logodds) space} \item{both.strands}{if to return results on both strands} \item{strand.fun}{which function to use to summarise values over two strands (default is "mean")} } \value{ a vector representing scores starting at each position, or a matrix with score in the two strands } \description{ The whole sequence is scanned with a PWM and scores returned beginning at each position. Partial motif matches are not done, thus the last #[length of motif]-1 scores are NA. } \details{ The function returns either an odds average (*not* log-odds average), maximal score on each strand, or scores on both strands. The function by default returns the score in log2 following the package \code{Biostrings}. } \examples{ if(requireNamespace("PWMEnrich.Dmelanogaster.background")){ data(MotifDb.Dmel, package = "PWMEnrich.Dmelanogaster.background") ttk = MotifDb.Dmel[["ttk"]] # odds average over the two strands expressed as log2-odds scanWithPWM(ttk, DNAString("CGTAGGATAAAGTAACT")) # log2-odds scores on both strands scanWithPWM(ttk, DNAString("CGTAGGATAAAGTAACT"), both.strands=TRUE) } } PWMEnrich/man/seqLogoGrid.Rd0000644000175100017510000000274414614305422016657 0ustar00biocbuildbiocbuild% Generated by roxygen2: do not edit by hand % Please edit documentation in R/plot.R \name{seqLogoGrid} \alias{seqLogoGrid} \title{Draw a motif logo on an existing viewport} \usage{ seqLogoGrid( pwm, ic.scale = TRUE, xaxis = TRUE, yaxis = TRUE, xfontsize = 10, yfontsize = 10, xmargin.scale = 1, ymargin.scale = 1, title = "", titlefontsize = 15 ) } \arguments{ \item{pwm}{numeric The 4xW position weight matrix.} \item{ic.scale}{logical If TRUE, the height of each column is proportional to its information content. Otherwise, all columns have the same height.} \item{xaxis}{logical If TRUE, an X-axis will be plotted.} \item{yaxis}{logical If TRUE, a Y-axis will be plotted.} \item{xfontsize}{numeric Font size to be used for the X-axis.} \item{yfontsize}{numeric Font size to be used for the Y-axis.} \item{xmargin.scale}{the scaling parameter for the X-axis margin. Useful when plotting more than one logo on a page} \item{ymargin.scale}{the scaling parameter for the Y-axis margin. Useful when plotting more than one logo on a page} \item{title}{to be shown on the top} \item{titlefontsize}{the fontsize of the title} } \description{ This function comes from the seqLogo package. It has been modified to remove some unneccessary code as suggested by W Huber (https://stat.ethz.ch/pipermail/bioconductor/2010-September/035267.html). } \details{ Use this function for more advanced plotting where the viewports are directly set up and maintained (see package \code{grid}). } PWMEnrich/man/sequenceReport-MotifEnrichmentResults-method.Rd0000644000175100017510000000345014614305422025370 0ustar00biocbuildbiocbuild% Generated by roxygen2: do not edit by hand % Please edit documentation in R/MotifEnrichmentResults-methods.R \name{sequenceReport,MotifEnrichmentResults-method} \alias{sequenceReport,MotifEnrichmentResults-method} \alias{sequenceReport} \title{Generate a motif enrichment report for a single sequence} \usage{ \S4method{sequenceReport}{MotifEnrichmentResults}(obj, seq.id, bg = TRUE, ...) } \arguments{ \item{obj}{a MotifEnrichmentResults object} \item{seq.id}{the sequence index or name} \item{bg}{if to use background corrected P-values to do the ranking (if available)} \item{...}{unused} } \value{ a MotifEnrichmentReport object containing a table with the following columns: \itemize{ \item 'rank' - The rank of the PWM's enrichment in the sequence \item 'target' - The name of the PWM's target gene, transcript or protein complex. \item 'id' - The unique identifier of the PWM (if set during PWM creation). \item 'raw.score' - The raw score before P-value calculation \item 'p.value' - The P-value of motif enrichment (if available) } } \description{ Generate a motif enrichment report for a single sequence } \examples{ if(requireNamespace("PWMEnrich.Dmelanogaster.background")){ ### # load the pre-compiled lognormal background data(PWMLogn.dm3.MotifDb.Dmel, package = "PWMEnrich.Dmelanogaster.background") # scan two sequences for motif enrichment sequences = list(DNAString("GAAGTATCAAGTGACCAGTAAGTCCCAGATGA"), DNAString("AGGTAGATAGAACAGTAGGCAATGAAGCCGATG")) res = motifEnrichment(sequences, PWMLogn.dm3.MotifDb.Dmel) # reports for the two sequences r1 = sequenceReport(res, 1) r2 = sequenceReport(res, 2) # view the results r1 r2 # plot the top 10 most enriched motifs in the first, and then second sequence plot(r1[1:10]) plot(r2[1:10]) } } PWMEnrich/man/show-MotifEnrichmentReport-method.Rd0000644000175100017510000000063414614305422023157 0ustar00biocbuildbiocbuild% Generated by roxygen2: do not edit by hand % Please edit documentation in R/MotifEnrichmentReport-methods.R \name{show,MotifEnrichmentReport-method} \alias{show,MotifEnrichmentReport-method} \title{show method for MotifEnrichmentReport} \usage{ \S4method{show}{MotifEnrichmentReport}(object) } \arguments{ \item{object}{the MotifEnrichmentReport object} } \description{ show method for MotifEnrichmentReport } PWMEnrich/man/show-MotifEnrichmentResults-method.Rd0000644000175100017510000000064314614305422023345 0ustar00biocbuildbiocbuild% Generated by roxygen2: do not edit by hand % Please edit documentation in R/MotifEnrichmentResults-methods.R \name{show,MotifEnrichmentResults-method} \alias{show,MotifEnrichmentResults-method} \title{show method for MotifEnrichmentResults} \usage{ \S4method{show}{MotifEnrichmentResults}(object) } \arguments{ \item{object}{the MotifEnrichmentResults object} } \description{ show method for MotifEnrichmentResults } PWMEnrich/man/show-PWM-method.Rd0000644000175100017510000000043614614305422017333 0ustar00biocbuildbiocbuild% Generated by roxygen2: do not edit by hand % Please edit documentation in R/PWM-methods.R \name{show,PWM-method} \alias{show,PWM-method} \title{show method for PWM} \usage{ \S4method{show}{PWM}(object) } \arguments{ \item{object}{the PWM object} } \description{ show method for PWM } PWMEnrich/man/show-PWMCutoffBackground-method.Rd0000644000175100017510000000061014614305422022474 0ustar00biocbuildbiocbuild% Generated by roxygen2: do not edit by hand % Please edit documentation in R/PWMBackground-methods.R \name{show,PWMCutoffBackground-method} \alias{show,PWMCutoffBackground-method} \title{show method for PWMCutoffBackground} \usage{ \S4method{show}{PWMCutoffBackground}(object) } \arguments{ \item{object}{the PWMCutoffBackground object} } \description{ show method for PWMCutoffBackground } PWMEnrich/man/show-PWMEmpiricalBackground-method.Rd0000644000175100017510000000063214614305422023157 0ustar00biocbuildbiocbuild% Generated by roxygen2: do not edit by hand % Please edit documentation in R/PWMBackground-methods.R \name{show,PWMEmpiricalBackground-method} \alias{show,PWMEmpiricalBackground-method} \title{show method for PWMEmpiricalBackground} \usage{ \S4method{show}{PWMEmpiricalBackground}(object) } \arguments{ \item{object}{the PWMEmpiricalBackground object} } \description{ show method for PWMEmpiricalBackground } PWMEnrich/man/show-PWMGEVBackground-method.Rd0000644000175100017510000000056614614305422021701 0ustar00biocbuildbiocbuild% Generated by roxygen2: do not edit by hand % Please edit documentation in R/PWMBackground-methods.R \name{show,PWMGEVBackground-method} \alias{show,PWMGEVBackground-method} \title{show method for PWMGEVBackground} \usage{ \S4method{show}{PWMGEVBackground}(object) } \arguments{ \item{object}{the PWMGEVBackground object} } \description{ show method for PWMGEVBackground } PWMEnrich/man/show-PWMLognBackground-method.Rd0000644000175100017510000000057414614305422022156 0ustar00biocbuildbiocbuild% Generated by roxygen2: do not edit by hand % Please edit documentation in R/PWMBackground-methods.R \name{show,PWMLognBackground-method} \alias{show,PWMLognBackground-method} \title{show method for PWMLognBackground} \usage{ \S4method{show}{PWMLognBackground}(object) } \arguments{ \item{object}{the PWMLognBackground object} } \description{ show method for PWMLognBackground } PWMEnrich/man/subsetting-PWMCutoffBackground.Rd0000644000175100017510000000076014614305422022433 0ustar00biocbuildbiocbuild% Generated by roxygen2: do not edit by hand % Please edit documentation in R/PWMBackground-methods.R \name{[,PWMCutoffBackground-method} \alias{[,PWMCutoffBackground-method} \title{Get the background for a subset of PWMs} \usage{ \S4method{[}{PWMCutoffBackground}(x, i, j, ..., drop = TRUE) } \arguments{ \item{x}{the PWMCutoffBackground object} \item{i}{the indicies of PWMs} \item{j}{unused} \item{...}{unused} \item{drop}{unused} } \description{ Get the background for a subset of PWMs } PWMEnrich/man/subsetting-PWMEmpiricalBackground.Rd0000644000175100017510000000077414614305422023117 0ustar00biocbuildbiocbuild% Generated by roxygen2: do not edit by hand % Please edit documentation in R/PWMBackground-methods.R \name{[,PWMEmpiricalBackground-method} \alias{[,PWMEmpiricalBackground-method} \title{Get the background for a subset of PWMs} \usage{ \S4method{[}{PWMEmpiricalBackground}(x, i, j, ..., drop = TRUE) } \arguments{ \item{x}{the PWMEmpiricalBackground object} \item{i}{the indicies of PWMs} \item{j}{unused} \item{...}{unused} \item{drop}{unused} } \description{ Get the background for a subset of PWMs } PWMEnrich/man/subsetting-PWMGEVBackground.Rd0000644000175100017510000000074414614305422021630 0ustar00biocbuildbiocbuild% Generated by roxygen2: do not edit by hand % Please edit documentation in R/PWMBackground-methods.R \name{[,PWMGEVBackground-method} \alias{[,PWMGEVBackground-method} \title{Get the background for a subset of PWMs} \usage{ \S4method{[}{PWMGEVBackground}(x, i, j, ..., drop = TRUE) } \arguments{ \item{x}{the PWMGEVBackground object} \item{i}{the indicies of PWMs} \item{j}{unused} \item{...}{unused} \item{drop}{unused} } \description{ Get the background for a subset of PWMs } PWMEnrich/man/subsetting-PWMLognBackground.Rd0000644000175100017510000000075014614305422022103 0ustar00biocbuildbiocbuild% Generated by roxygen2: do not edit by hand % Please edit documentation in R/PWMBackground-methods.R \name{[,PWMLognBackground-method} \alias{[,PWMLognBackground-method} \title{Get the background for a subset of PWMs} \usage{ \S4method{[}{PWMLognBackground}(x, i, j, ..., drop = TRUE) } \arguments{ \item{x}{the PWMLognBackground object} \item{i}{the indicies of PWMs} \item{j}{unused} \item{...}{unused} \item{drop}{unused} } \description{ Get the background for a subset of PWMs } PWMEnrich/man/toPWM.Rd0000644000175100017510000000245014614305422015440 0ustar00biocbuildbiocbuild% Generated by roxygen2: do not edit by hand % Please edit documentation in R/pwm.R \name{toPWM} \alias{toPWM} \title{Convert motifs into PWMs} \usage{ toPWM( motifs, ids = names(motifs), targets = names(motifs), seq.count = 50, prior = c(A = 0.25, C = 0.25, G = 0.25, T = 0.25), ... ) } \arguments{ \item{motifs}{a list of motifs either as position probability matrices (PPM) or frequency matirces (PFMs)} \item{ids}{the set of IDs for the motifs (defaults to names of the 'motifs' list)} \item{targets}{the set of target TF names for the motifs (defaults to names of the 'motifs' list)} \item{seq.count}{provides a vector of sequence counts for probability matrices (PPMs). Default it 50.} \item{prior}{frequencies of the four letters in the genome. Default is uniform background.} \item{...}{other parameters to PWMUnscaled} } \description{ Convert motifs into PWMs } \examples{ \dontrun{ if (requireNamespace("PWMEnrich.Dmelanogaster.background")) { data(MotifDb.Dmel.PFM, package = "PWMEnrich.Dmelanogaster.background") toPWM(MotifDb.Dmel.PFM) # convert to PWM with uniform background # get background for drosophila (quick mode on a reduced dataset) prior = getBackgroundFrequencies("dm3", quick=TRUE) toPWM(MotifDb.Dmel.PFM, prior=prior) # convert with genomic background } } } PWMEnrich/man/tryAllMotifAlignments.Rd0000644000175100017510000000173614614305422020730 0ustar00biocbuildbiocbuild% Generated by roxygen2: do not edit by hand % Please edit documentation in R/similarity.R \name{tryAllMotifAlignments} \alias{tryAllMotifAlignments} \title{Try all motif alignments and return max score} \usage{ tryAllMotifAlignments(m1, m2, min.align = 2, exclude.zero = FALSE) } \arguments{ \item{m1}{frequency matrix of motif 1} \item{m2}{frequency matrix of motif 2} \item{min.align}{minimal number of basepairs that need to align} \item{exclude.zero}{if to exclude offset=0, useful for calculating self-similarity} } \value{ single maximal score } \description{ This function tries all offsets of motif1 compared to motif2 and returns the maximal (unnormalized) correlation score. } \details{ The correlation score is essentially the sum of correlations of individual aligned columns as described in Pietrokovski (1996). } \references{ Pietrokovski S. Searching databases of conserved sequence regions by aligning protein multiple-alignments. Nucleic Acids Res 1996;24:3836-3845. } PWMEnrich/man/useBigMemoryPWMEnrich.Rd0000644000175100017510000000126014614305422020554 0ustar00biocbuildbiocbuild% Generated by roxygen2: do not edit by hand % Please edit documentation in R/options.R \name{useBigMemoryPWMEnrich} \alias{useBigMemoryPWMEnrich} \title{If to use a faster implementation of motif scanning that requires abount 5 to 10 times more memory} \usage{ useBigMemoryPWMEnrich(useBigMemory = FALSE) } \arguments{ \item{useBigMemory}{a boolean value denoting if to use big memory implementation} } \description{ If to use a faster implementation of motif scanning that requires abount 5 to 10 times more memory } \examples{ \dontrun{ useBigMemoryPWMEnrich(TRUE) # switch to big memory implementation globally useBigMemoryPWMEnrich(FALSE) # switch back to default implementation } } PWMEnrich/NAMESPACE0000644000175100017510000000437714614305422014621 0ustar00biocbuildbiocbuild# Generated by roxygen2: do not edit by hand export(PFMtoPWM) export(PWMUnscaled) export(getBackgroundFrequencies) export(makeBackground) export(makePWMCutoffBackground) export(makePWMEmpiricalBackground) export(makePWMGEVBackground) export(makePWMLognBackground) export(makePWMPvalCutoffBackground) export(makePWMPvalCutoffBackgroundFromSeq) export(makePriors) export(motifDiffEnrichment) export(motifEcdf) export(motifEnrichment) export(motifIC) export(motifScores) export(motifSimilarity) export(plotMotifScores) export(plotMultipleMotifs) export(readMotifs) export(registerCoresPWMEnrich) export(scanWithPWM) export(seqLogoGrid) export(toPWM) export(useBigMemoryPWMEnrich) exportClasses(MotifEnrichmentReport) exportClasses(MotifEnrichmentResults) exportClasses(PWM) exportClasses(PWMCutoffBackground) exportClasses(PWMEmpiricalBackground) exportClasses(PWMGEVBackground) exportClasses(PWMLognBackground) exportMethods(as.data.frame) exportMethods(groupReport) exportMethods(motifRankingForGroup) exportMethods(motifRankingForSequence) exportMethods(plot) exportMethods(plotTopMotifsGroup) exportMethods(plotTopMotifsSequence) exportMethods(reverseComplement) exportMethods(sequenceReport) import(BiocGenerics) import(Biostrings) import(methods) importFrom(S4Vectors,metadata) importFrom(evd,fgev) importFrom(evd,pgev) importFrom(gdata,trim) importFrom(grDevices,col2rgb) importFrom(grDevices,palette) importFrom(grDevices,rgb) importFrom(graphics,lines) importFrom(graphics,par) importFrom(graphics,polygon) importFrom(graphics,rect) importFrom(graphics,text) importFrom(grid,dataViewport) importFrom(grid,gpar) importFrom(grid,grid.layout) importFrom(grid,grid.newpage) importFrom(grid,grid.polygon) importFrom(grid,grid.text) importFrom(grid,grid.xaxis) importFrom(grid,grid.yaxis) importFrom(grid,plotViewport) importFrom(grid,popViewport) importFrom(grid,pushViewport) importFrom(grid,unit) importFrom(grid,viewport) importFrom(seqLogo,makePWM) importFrom(seqLogo,plot) importFrom(stats,cor) importFrom(stats,dlnorm) importFrom(stats,ecdf) importFrom(stats,lm) importFrom(stats,median) importFrom(stats,na.omit) importFrom(stats,optimize) importFrom(stats,pchisq) importFrom(stats,plnorm) importFrom(stats,predict.lm) importFrom(stats,qlnorm) importFrom(stats,quantile) importFrom(utils,data) PWMEnrich/R/0000755000175100017510000000000014614305422013570 5ustar00biocbuildbiocbuildPWMEnrich/R/AllDataClasses.R0000644000175100017510000001270614614305422016541 0ustar00biocbuildbiocbuild setClassUnion("NumericOrMatrix", c("numeric", "matrix")) #' A class that represents a Position Weight Matrix (PWM) #' #' @aliases PWM-class #' @section Slots: #' \describe{ #' \item{\code{id}:}{a systematic ID given to this PWM, could include the source, version, etc} #' \item{\code{name}:}{the name of the transcription factor (TF) to which the PWM corresponds to} #' \item{\code{pfm}:}{Position Frequency Matrix (PFM) from which the PWM is derived} #' \item{\code{prior.params}:}{Defines prior frequencies of the four bases (A,C,G,T), a named vector. These will be added to individual values for the PFM and at the same time used as background probabilities} #' \item{\code{pwm}:}{Final Position Weight Matrix (PWM) constructed using prior.params with logarithm base 2} #' } #' @export setClass("PWM", representation = representation( id = "character", name = "character", pfm = "matrix", prior.params = "vector", pwm = "matrix" ) ) #' Lognormal background distribution for a set of PWMs #' #' @aliases PWMLognBackground-class #' @section Slots: #' \describe{ #' \item{\code{bg.source}:}{textual description of where the background distribution is derived from} #' \item{\code{bg.len}:}{the length to which the background is normalized to. This is a vector of values, can have a different value for each motif.} #' \item{\code{bg.mean}:}{the mean value of the lognormal distribution at bg.len} #' \item{\code{bg.sd}:}{the standard deviation of the lognormal distribution at bg.len} #' \item{\code{pwms}:}{the pwms for which the background has been compiled} #' } #' @export setClass("PWMLognBackground", representation = representation( bg.source = "character", bg.len = "NumericOrMatrix", bg.mean = "NumericOrMatrix", bg.sd = "NumericOrMatrix", pwms = "list" ) ) #' Hit count background distribution for a set of PWMs #' #' @aliases PWMCutoffBackground-class #' @section Slots: #' \describe{ #' \item{\code{bg.source}:}{textual description of where the background distribution is derived from} #' \item{\code{bg.cutoff}:}{the cutoff score used to find significant motif hits (in log2 odds), either a single value or a vector of values} #' \item{\code{bg.P}:}{the density of significant motif hits per nucleotide in background} #' \item{\code{pwms}:}{the pwms for which the background has been compiled} #' } #' @export setClass("PWMCutoffBackground", representation = representation( bg.source = "character", bg.cutoff = "numeric", bg.P = "numeric", pwms = "list" ) ) #' Background for calculating empirical P-values #' #' This object contains raw scores for one very long sequence, thus it can be very large. #' #' @aliases PWMEmpiricalBackground-class #' @section Slots: #' \describe{ #' \item{\code{bg.source}:}{textual description of where the background distribution is derived from} #' \item{\code{bg.fwd}:}{affinity scores (odds) for the forward strand. PWMs as columns} #' \item{\code{bg.rev}:}{affinity scores (odds) for the reverse strand. PWMs as columns} #' \item{\code{pwms}:}{the pwms for which the background has been compiled} #' } #' @export setClass("PWMEmpiricalBackground", representation = representation( bg.source = "character", bg.fwd = "matrix", bg.rev = "matrix", pwms = "list" ) ) #' Generalized Extreme Values (GEV) background for P-values #' #' The three parameters of the GEV distribution are fitted by doing linear regression #' on log of sequence length. #' #' @aliases PWMGEVBackground-class #' @section Slots: #' \describe{ #' \item{\code{bg.source}:}{textual description of where the background distribution is derived from} #' \item{\code{bg.loc}:}{linear regression model for estimating the location parameter based on log(L), list of lm objects of PWMs} #' \item{\code{bg.scale}:}{linear regression model for estimating the scale parameter based on log(L), list of lm objects of PWMs} #' \item{\code{bg.shape}:}{linear regression model for estimating the shape parameter based on log(L), list of lm objects of PWMs} #' \item{\code{pwms}:}{the pwms for which the background has been compiled} #' } #' @export setClass("PWMGEVBackground", representation = representation( bg.source = "character", bg.loc = "list", bg.scale = "list", bg.shape = "list", pwms = "list" ) ) #' A wrapper class for results of motifEnrichment() that should make it easier to access the results. #' #' Note that this is only a wrapper around a list which is the return value in PWMEnrich 1.3 and as such it #' provides the same interface as a list (for backward compatibility), with some additional methods. #' #' @aliases MotifEnrichmentResults-class #' @section Slots: #' \describe{ #' \item{\code{res}:}{a list of old results with elements such as: sequence.bg, sequence.nobg, group.bg, group.nobg} #' } #' @export setClass("MotifEnrichmentResults", representation = representation( res = "list" ) ) #' A report class with formatted results of motif enrichment #' #' The columns stored in this object will depend on the type of the report (either for group of sequences, or individual sequences). #' #' @aliases MotifEnrichmentReport-class #' @section Slots: #' \describe{ #' \item{\code{d}:}{a DataFrame object that contains the main tabular report data} #' \item{\code{pwms}:}{a list of \code{PWM} objects corresponding to rows of \code{d}} #' } #' @export setClass("MotifEnrichmentReport", representation = representation( d = "data.frame", pwms = "list" ) ) PWMEnrich/R/AllGenerics.R0000644000175100017510000000113214614305422016100 0ustar00biocbuildbiocbuild### define generics, mostly for MotifEnrichmentResults setGeneric("motifRankingForGroup", function(obj, ...) standardGeneric("motifRankingForGroup")) setGeneric("motifRankingForSequence", function(obj, ...) standardGeneric("motifRankingForSequence")) setGeneric("plotTopMotifsGroup", function(obj, ...) standardGeneric("plotTopMotifsGroup")) setGeneric("plotTopMotifsSequence", function(obj, ...) standardGeneric("plotTopMotifsSequence")) setGeneric("groupReport", function(obj, ...) standardGeneric("groupReport")) setGeneric("sequenceReport", function(obj, ...) standardGeneric("sequenceReport")) PWMEnrich/R/background.R0000644000175100017510000010213014614305422016027 0ustar00biocbuildbiocbuild# Functions to create background distributions #' check consistency of bg.seq input parameter #' #' @param bg.seq a set of background sequences, either a list of DNAString object or DNAStringSet object .normalize.bg.seq = function(bg.seq){ # check if the sequences are in the right format if(!inherits(bg.seq, "DNAStringSet")) { if(is.list(bg.seq)) { if(length(bg.seq) > 0 && ! inherits(bg.seq[[1]], "DNAString")) stop("bg.seq needs to be a list of DNAString objects or a DNAStringSet object") } else { stop("bg.seq needs to be a list of DNAString objects or a DNAStringSet object") } } #else { #bg.seq = DNAStringSetToList(bg.seq) #} return(bg.seq) } #' Make priors from background sequences #' #' These priors serve both as background nucleotide frequencies and pseudo-counts #' for PWMs. #' #' @param bg.seq a set of background sequences #' @param bg.pseudo.count the total pseudocount shared between nucleotides #' @export #' @examples #' # some example sequences #' sequences = list(DNAString("AAAGAGAGTGACCGATGAC"), DNAString("ACGATGAGGATGAC")) #' # make priors with pseudo-count of 1 shared between them #' makePriors(sequences, 1) makePriors = function(bg.seq, bg.pseudo.count){ bg.seq = .normalize.bg.seq(bg.seq) # start with the count of the first sequence acgt.count = alphabetFrequency(bg.seq[[1]])[c("A", "C", "G", "T")] acgt.count = acgt.count[c("A", "C", "G", "T")] + acgt.count[c("T", "G", "C", "A")] # add ACGT counts for the rest if(length(bg.seq) > 1 ){ for(i in 2:length(bg.seq)){ cont = alphabetFrequency(bg.seq[[i]])[c("A", "C", "G", "T")] # add the counts on the other strand cont[c("A", "C", "G", "T")] = cont[c("A", "C", "G", "T")] + cont[c("T", "G", "C", "A")] # sum up acgt.count = acgt.count + cont } } # scale to bg.pseudo.count prior = (acgt.count / sum(acgt.count)) * bg.pseudo.count return(prior) } #' Make a lognormal background distribution #' #' Construct a lognormal background distribution for a set of sequences. #' Sequences concatenated are binned in 'bg.len' chunks and lognormal distribution #' fitted to them. #' #' @param bg.seq a set of background sequences, either a list of DNAString object or DNAStringSet object #' @param motifs a set of motifs, either a list of frequency matrices, or a list of PWM objects. If #' frequency matrices are given, the background distribution is fitted from bg.seq. #' @param bg.pseudo.count the pseudo count which is shared between nucleotides when frequency matrices are given #' @param bg.len background sequences will be split into tiles of this length (default: 250bp) #' @param bg.len.sizes background tiles will be joined into bigger tiles containing this much smaller tiles. #' The default is \code{2^(0:4)}, which with \code{bg.len} translates into #' 250bp, 500bp, 1000bp, 1500bp, 2000bp, 4000bp. Note this is only used in the "human" algorithm. #' @param bg.source a free-form textual description of how the background was generated #' @param verbose if to produce verbose output #' @param algorithm type of algorithm to use, valid values are: "default" and "human". #' @export #' @examples #' \dontrun{ #' if(requireNamespace("PWMEnrich.Dmelanogaster.background")){ #' data(MotifDb.Dmel.PFM, package = "PWMEnrich.Dmelanogaster.background") #' #' # make background for MotifDb motifs using 2kb promoters of all D. melanogaster transcripts #' if(requireNamespace("BSgenome.Dmelanogaster.UCSC.dm3")) #' makePWMLognBackground(Dmelanogaster$upstream2000, MotifDb.Dmel.PFM) #' } #' } makePWMLognBackground = function(bg.seq, motifs, bg.pseudo.count=1, bg.len=250, bg.len.sizes=2^(0:4), bg.source="", verbose=TRUE, algorithm="default"){ # check if the sequences are in the right format bg.seq = .normalize.bg.seq(bg.seq) # convert to list if a single motif is given if(!is.list(motifs)) motifs = list(motifs) if(bg.len.sizes[1] != 1) stop("First value of 'bg.len.sizes' needs to be 1") if(!(algorithm %in% c("default", "human"))) stop("Parameter 'algorithm' needs to be one of: ['default', 'human']") cat("NOTE: Using the '", algorithm, "' algorithm to infer background parameters,\n ", sep="") if(algorithm == "default"){ cat("appropriate for all organisms except human.\n") } else { cat("appropriate only for human data.\n") } # concatenate all the background sequences into a single long sequence bg.seq.all = concatenateSequences(bg.seq) # generate start and end positions bg.seq.start = seq(1, nchar(bg.seq.all)+1, bg.len) bg.seq.end = bg.seq.start - 1 bg.seq.start = bg.seq.start[1:(length(bg.seq.start)-1)] bg.seq.end = bg.seq.end[2:length(bg.seq.end)] # split into bg.len sequences bg = DNAStringSet(bg.seq.all, start=bg.seq.start, end=bg.seq.end) if(length(bg)<1000) warnings(paste("The number of chunks of size", bg.len, "is smaller than 1000. This might lead to an non-robust estimate of lognormal distribution parameters.")) # convert motifs to PWM format if neccessary if(!inherits(motifs[[1]], "PWM")) { prior = makePriors(list(DNAString(bg.seq.all)), bg.pseudo.count) pwms = PFMtoPWM(motifs, prior.params = prior) } else { pwms = motifs } # finally, do motif scanning and calculate mean and sd bg.res = motifScores(bg, pwms, verbose=verbose) # pwm lengths and base lengths pwm.len = sapply(pwms, length) bg.len.real = bg.len - pwm.len + 1 # do a robust estimate of parameters # dnorm with left-censored data and known meanlog # # x value for which dnorm is needed # cen logical vector if the value is censored # meanlog meanlog parameter # sdlog sdlog parameter dlnorm.lcen = function(x, cen, meanlog, sdlog){ if(sdlog <= 0) -Inf xn = x[!cen] xc = x[cen] sum(dlnorm(xn, meanlog, sdlog, log=TRUE)) + sum(plnorm(xc, meanlog, sdlog, lower.tail=TRUE, log.p=TRUE)) } # Negative log-likelihood of left-censored data # # NOTE: this function assumes xc, cen and meanlog and in environment # # p set of parameters (in this case only sdlog) ll.lcen = function(p){ -dlnorm.lcen(xc, cen, meanlog, p) } if(algorithm == "default"){ # the simple default algorithm bg.mean = colMeans(bg.res) bg.sd = apply(bg.res, 2, sd) } else { # a matrix of inferred values that will be averaged over bg.mean.mat = bg.sd.mat = matrix(0, nrow=length(bg.len.sizes), ncol=ncol(bg.res)) colnames(bg.mean.mat) = colnames(bg.sd.mat) = colnames(bg.res) if(verbose){ cat("Parameter estimation...\n") } # do estimation for different sizes of background for(size.inx in 1:length(bg.len.sizes)){ # current size multiplier cur.mul = bg.len.sizes[size.inx] if(verbose){ cat("Recording distribution properties for", bg.len * cur.mul, "bp tiles (this may take a while...)\n") } # summarise bg.res for the current size max.len = nrow(bg.res) %/% cur.mul * cur.mul group = (0:(max.len-1)) %/% cur.mul # group by the groups and do an average if(cur.mul == 1){ bg.res.size = bg.res } else { # new implementation for faster grouping! out = matrix(0, nrow=length(unique(group)), ncol=ncol(bg.res)) for(i in 1:nrow(out)){ row.inx = (i-1)*cur.mul+1 r = bg.res[row.inx,] for(j in 1:(cur.mul-1)){ r = r + bg.res[row.inx+j,] } out[i,] = r / cur.mul } bg.res.size = out # double-check just in case ! #bg.res.size = by(bg.res[1:max.len,], group, colMeans) #bg.res.size = do.call("rbind", as.list(bg.res.size)) } # consistency check stopifnot(nrow(bg.res.size) == max.len/cur.mul) # do robust estimate for each motif for(i in 1:ncol(bg.res)){ if(verbose){ cat("Estimating parameters for motif", i, "/", ncol(bg.res), "\n") } x = bg.res.size[,i] # censor everything below the 75% quantile b = quantile(x, 0.75) # left-censor data xc = x xc[x<=b] = b cen = rep(FALSE, length(x)) cen[x<=b] = TRUE # meanlog based on median meanlog = median(log(x)) # optimize sdlog p = optimize(ll.lcen, c(1e-8, 1e5)) # transform and save ml = meanlog sl = p$minimum mx = exp(ml + (sl^2)/2) sx = sqrt((exp(sl^2)-1)*exp(2*ml+sl^2)) # save bg.mean.mat[size.inx, i] = mx bg.sd.mat[size.inx, i] = sx } } # record the values bg.mean = bg.mean.mat bg.sd = bg.sd.mat # record the sizes bg.len.real = outer(bg.len.sizes, bg.len.real) rownames(bg.len.real) = NULL } new("PWMLognBackground", bg.source=bg.source, bg.len=bg.len.real, bg.mean=bg.mean, bg.sd=bg.sd, pwms=pwms) } #' Make a cutoff background #' #' Make a background based on number of motifs hits above a certain threshold. #' #' @param bg.seq a set of background sequences, either a list of DNAString object or DNAStringSet object #' @param motifs a set of motifs, either a list of frequency matrices, or a list of PWM objects. If #' frequency matrices are given, the background distribution is fitted from bg.seq. #' @param cutoff the cutoff at which the background should be made, i.e. at which a motif hit is called significant #' @param bg.pseudo.count the pseudo count which is shared between nucleotides when frequency matrices are given #' @param bg.source a free-form textual description of how the background was generated #' @param verbose if to produce verbose output #' @export #' @examples #' \dontrun{ #' if(requireNamespace("PWMEnrich.Dmelanogaster.background")){ #' data(MotifDb.Dmel.PFM, package = "PWMEnrich.Dmelanogaster.background") #' #' # make background for MotifDb motifs using 2Kb promoters of all D. melanogaster transcripts #' # using a cutoff of 5 #' if(requireNamespace("BSgenome.Dmelanogaster.UCSC.dm3")) #' makePWMCutoffBackground(Dmelanogaster$upstream2000, MotifDb.Dmel.PFM, cutoff=log2(exp(5))) #' } #' } makePWMCutoffBackground = function(bg.seq, motifs, cutoff=log2(exp(4)), bg.pseudo.count=1, bg.source="", verbose=TRUE){ # check if the sequences are in the right format bg.seq = .normalize.bg.seq(bg.seq) # convert to list if a single motif is given if(!is.list(motifs)) motifs = list(motifs) # make priors and PWMs if(!inherits(motifs[[1]], "PWM")) { prior = makePriors(bg.seq, bg.pseudo.count) pwms = PFMtoPWM(motifs, prior.params = prior) } else { pwms = motifs } # scan with cutoff - this gives motif *hit counts* bg.res = motifScores(bg.seq, pwms, verbose=verbose, cutoff=cutoff) seq.len = sapply(bg.seq, length) pwm.len = sapply(pwms, length) # total length of the scanned sequences for each sequence total.len = sum(seq.len) - length(seq.len)*(pwm.len-1) # total number of hits total.hits = colSums(bg.res) # density of binding sites bg.P = total.hits / total.len new("PWMCutoffBackground", bg.source=bg.source, bg.cutoff=cutoff, bg.P=bg.P, pwms=pwms) } #' Make an empirical P-value background #' #' Make a background appropriate for empirical P-value calculation. The provided set of background #' sequences is contcatenated into a single long sequence which is then scanned with the motifs #' and raw scores are saved. This object can be very large. #' #' For reliable P-value calculation the size of the background set needs to be at least seq.len / min.P.value. #' For instance, to get P-values at a resolution of 0.001 for a single sequence of 500bp, we would need #' a background of at least 500/0.001 = 50kb. This ensures that we can make 1000 independent 500bp samples from #' this background to properly estimate the P-value. For a group of sequences, we would take seq.len to be the #' total length of all sequences in a group. #' #' @param bg.seq a set of background sequences, either a list of DNAString object or DNAStringSet object #' @param motifs a set of motifs, either a list of frequency matrices, or a list of PWM objects. If #' frequency matrices are given, the background distribution is fitted from bg.seq. #' @param bg.pseudo.count the pseudo count which is shared between nucleotides when frequency matrices are given #' @param bg.source a free-form textual description of how the background was generated #' @param verbose if to produce verbose output #' @param ... currently unused (this is for convenience for makeBackground function) #' @export #' @examples #' \dontrun{ #' if(requireNamespace("PWMEnrich.Dmelanogaster.background")){ #' data(MotifDb.Dmel.PFM, package = "PWMEnrich.Dmelanogaster.background") #' #' # make empirical background by saving raw scores for each bp in the sequence. This can be #' # very large in memory! #' if(requireNamespace("BSgenome.Dmelanogaster.UCSC.dm3")) #' makePWMEmpiricalBackground(Dmelanogaster$upstream2000[1:100], MotifDb.Dmel.PFM) #' } #' } makePWMEmpiricalBackground = function(bg.seq, motifs, bg.pseudo.count=1, bg.source="", verbose=TRUE, ...){ # check if the sequences are in the right format bg.seq = .normalize.bg.seq(bg.seq) # convert to list if a single motif is given if(!is.list(motifs)) motifs = list(motifs) # make priors and PWMs if(!inherits(motifs[[1]], "PWM")) { prior = makePriors(bg.seq, bg.pseudo.count) pwms = PFMtoPWM(motifs, prior.params = prior) } else { pwms = motifs } # scan one very long sequence that is formed by concatenating all sequences bg.seq.all = DNAString(concatenateSequences(bg.seq)) bg.res = motifScores(bg.seq.all, pwms, raw.scores=TRUE, verbose=verbose) bg.len = length(bg.seq.all) bg.fwd = bg.res[[1]][1:bg.len,] bg.rev = bg.res[[1]][(bg.len+1):(bg.len*2),] if(length(motifs) == 1){ bg.fwd = matrix(bg.fwd, ncol=1, dimnames=list(NULL, names(motifs))) bg.rev = matrix(bg.rev, ncol=1, dimnames=list(NULL, names(motifs))) } new("PWMEmpiricalBackground", bg.source=bg.source, bg.fwd=bg.fwd, bg.rev=bg.rev, pwms=pwms) } #' Construct a cutoff background from empirical background #' #' This function takes already calculated empirical background distribution and chooses #' cutoff for each motif based on P-value cutoff for individual sites. #' #' @param bg.p an object of class PWMEmpiricalBackground #' @param p.value the P-value used to find cuttoffs for each of the motifs #' @param bg.source textual description of background source #' @return an object of type PWMCutoffBackground #' @export #' @examples #' \dontrun{ #' if(requireNamespace("PWMEnrich.Dmelanogaster.background")){ #' data(MotifDb.Dmel.PFM, package = "PWMEnrich.Dmelanogaster.background") #' #' # make empirical background - here we use only 100 sequences for illustrative purposes #' if(requireNamespace("BSgenome.Dmelanogaster.UCSC.dm3")) #' bg.p = makePWMEmpiricalBackground(Dmelanogaster$upstream2000[1:100], MotifDb.Dmel.PFM) #' #' # use the empirical background to pick a threshold and make cutoff background #' makePWMPvalCutoffBackground(bg.p, 0.001) #' } #' } makePWMPvalCutoffBackground = function(bg.p, p.value=1e-3, bg.source=""){ # extract stuffs bg.fwd = bg.p@bg.fwd bg.rev = bg.p@bg.rev pwms = bg.p@pwms bg.len = nrow(bg.fwd) if(bg.len * p.value < 1){ stop("The P-value is too small for background of this size. Should have at least 1/p.value nucleotides in the background.") } # get cutoffs cutoff = structure(rep(0, length(pwms)), names=names(pwms)) P = structure(rep(0, length(pwms)), names=names(pwms)) for(i in 1:length(pwms)){ # join both strands all.data = log2(c(bg.fwd[!is.na(bg.fwd[,i]),i], bg.rev[!is.na(bg.rev[,i]),i])) # stop if not all data is finite, because then there is an error! stopifnot(length(is.finite(all.data)) == length(all.data)) # find out the cutoff for 1-p.value cutoff[i] = quantile(ecdf(all.data), 1 - p.value) # work out the actual P value # NOTE: since the distribution is decrete, this won't be exactly the same as 2*P.value but will be quite close P[i] = 2 * (sum(all.data >= cutoff[i]) / length(all.data)) } # final object new("PWMCutoffBackground", bg.source=bg.source, bg.cutoff=cutoff, bg.P=P, pwms=pwms) } #' Construct a P-value cutoff background from a set of sequences #' #' This function creates a P-value cutoff background for motif enrichment. #' #' @param bg.seq a set of background sequences, either a list of DNAString object or DNAStringSet object #' @param motifs a set of motifs, either a list of frequency matrices, or a list of PWM objects. If #' frequency matrices are given, the background distribution is fitted from bg.seq. #' @param p.value the P-value used to find cuttoffs for each of the motifs #' @param bg.pseudo.count the pseudo count which is shared between nucleotides when frequency matrices are given #' @param bg.source textual description of background source #' @param verbose if to print verbose output #' @return an object of type PWMCutoffBackground #' @export #' @examples #' \dontrun{ #' if(requireNamespace("PWMEnrich.Dmelanogaster.background")){ #' data(MotifDb.Dmel.PFM, package = "PWMEnrich.Dmelanogaster.background") #' #' # use the empirical background to pick a threshold and make cutoff background #' makePWMPvalCutoffBackground(Dmelanogaster$upstream2000, 0.001) #' } #' } makePWMPvalCutoffBackgroundFromSeq = function(bg.seq, motifs, p.value=1e-3, bg.pseudo.count=1, bg.source="", verbose=TRUE){ # check if the sequences are in the right format bg.seq = .normalize.bg.seq(bg.seq) # convert to list if a single motif is given if(!is.list(motifs)) motifs = list(motifs) # make priors and PWMs if(!inherits(motifs[[1]], "PWM")) { prior = makePriors(bg.seq, bg.pseudo.count) pwms = PFMtoPWM(motifs, prior.params = prior) } else { pwms = motifs } # pre-calculate the big sequence motifScoresBigMemory() is faster seq.input = .inputParamSequences(bg.seq) seq.all = DNAString(concatenateSequences(seq.input)) # get cutoffs cutoff = structure(rep(0, length(pwms)), names=names(pwms)) P = structure(rep(0, length(pwms)), names=names(pwms)) for(i in 1:length(pwms)){ # get the raw values for this motif only! bg.res = motifScoresBigMemory(bg.seq, pwms[i], verbose=verbose, raw.scores=TRUE, seq.all=seq.all) bg.res = na.omit(unlist(bg.res)) # transform to log2 to get the final data all.data = log2(bg.res) # find out the cutoff for 1-p.value cutoff[i] = quantile(ecdf(all.data), 1 - p.value) # work out the actual P value # NOTE: since the distribution is decrete, this won't be exactly the same as 2*P.value but will be quite close P[i] = 2 * (sum(all.data >= cutoff[i]) / length(all.data)) } # final object new("PWMCutoffBackground", bg.source=bg.source, bg.cutoff=cutoff, bg.P=P, pwms=pwms) } #' Divide total.len into fragments of length len by providing start,end positions #' #' @param total.len total available length to be subdivided #' @param len size of the individual chunk #' @return a data.frame containing paired up start,end positions makeStartEndPos = function(total.len, len){ # make start positions start = seq(1, total.len+1, len) # make end positions end = start - 1 # pair them up start = start[1:(length(start)-1)] end = end[2:length(end)] data.frame(start, end) } #' Make a GEV background distribution #' #' Construct a lognormal background distribution for a set of sequences. #' Sequences concatenated are binned in 'bg.len' chunks and lognormal distribution #' fitted to them. #' #' @param bg.seq a set of background sequences, either a list of DNAString object or DNAStringSet object #' @param motifs a set of motifs, either a list of frequency matrices, or a list of PWM objects. If #' frequency matrices are given, the background distribution is fitted from bg.seq. #' @param bg.pseudo.count the pseudo count which is shared between nucleotides when frequency matrices are given #' @param bg.len the length range of background chunks #' @param bg.source a free-form textual description of how the background was generated #' @param verbose if to produce verbose output #' @param fit.log if to fit log odds (instead of odds) #' @export #' @examples #' \dontrun{ #' if(requireNamespace("PWMEnrich.Dmelanogaster.background")){ #' data(MotifDb.Dmel.PFM, package = "PWMEnrich.Dmelanogaster.background") #' #' # make background for MotifDb motifs using 2kb promoters of all D. melanogaster transcripts #' if(requireNamespace("BSgenome.Dmelanogaster.UCSC.dm3")) #' makePWMGEVBackground(Dmelanogaster$upstream2000, MotifDb.Dmel.PFM) #' } #' } makePWMGEVBackground = function(bg.seq, motifs, bg.pseudo.count=1, bg.len=seq(200,2000,200), bg.source="", verbose=TRUE, fit.log=TRUE){ # check if the sequences are in the right format bg.seq = .normalize.bg.seq(bg.seq) # convert to list if a single motif is given if(!is.list(motifs)) motifs = list(motifs) # give automatic names if(is.null(names(motifs))){ names(motifs) = paste("Motif", 1:length(motifs), sep="") } # concatenate all the background sequences into a single long sequence bg.seq.all = concatenateSequences(bg.seq) # convert motifs to PWM format if neccessary if(!inherits(motifs[[1]], "PWM")) { prior = makePriors(list(DNAString(bg.seq.all)), bg.pseudo.count) pwms = PFMtoPWM(motifs, prior.params = prior) } else { pwms = motifs } # GEV parameters that need to be fitted in linear regression params = array(0, dim=c(length(bg.len), 3, length(pwms)), dimnames=list(bg.len, c("loc", "scale", "shape"), names(pwms))) # iterate over background lengths and fit GEV distributions for(i in 1:length(bg.len)){ if(verbose){ message("Scanning background of size ", bg.len[i]) } bg.pos = makeStartEndPos(nchar(bg.seq.all), bg.len[i]) bg = DNAStringSet(bg.seq.all, start=bg.pos$start, end=bg.pos$end) # scan bg.res = motifScores(bg, pwms, verbose=verbose) # fit GEV for each PWM params[i,,] = apply(bg.res, 2, function(x) { if(fit.log) x = log(x) fgev(x, std.err=FALSE)$param[c("loc", "scale", "shape")] }) } ## now fit linear regression for each PWM bg.loc = lapply(names(pwms), function(pwm.name){ loc = params[,"loc",pwm.name] log.len = log(bg.len) lm(loc~log.len) }) names(bg.loc) = names(pwms) bg.scale = lapply(names(pwms), function(pwm.name){ scale = params[,"scale",pwm.name] log.len = log(bg.len) lm(scale~log.len) }) names(bg.scale) = names(pwms) bg.shape = lapply(names(pwms), function(pwm.name){ shape = params[,"shape",pwm.name] log.len = log(bg.len) lm(shape~log.len) }) names(bg.shape) = names(pwms) # return the object new("PWMGEVBackground", bg.source=bg.source, bg.loc=bg.loc, bg.scale=bg.scale, bg.shape=bg.shape, pwms=pwms) } #' Get the promoter sequences either for a named organism such as "dm3" or a BSgenome object #' #' @param organismOrGenome either organism name, e.g. "dm3", or BSgenome object #' @return a list of: promoters - DNAStringSet of (unique) promoters; organism - name of species; version - genome version getPromoters = function(organismOrGenome){ org = organismOrGenome sek = NULL org.valid = c("dm3", "mm9", "hg19") # check if it's a valid UCSC name if(is.character(org)){ if(org %in% org.valid){ sel = org } else { stop(paste("Unrecognised organism name, valid values are:", paste(org.valid, collapse=", "))) } # check for a BSgenome object } else if(is(org, "BSgenome")){ genome <- metadata(org)[["genome"]] if(genome %in% org.valid){ sel = genome } else { stop(paste("Promoter sequences cannot be retrieved automatically for ", genome, ", please provide a set of background sequences explicitely.", sep="")) } } else { stop("The input parameter needs to be a valid genome name ('dm3', 'mm9' or 'hg19') or a set of background sequences.") } e = new.env() # get the promoter sequences from the saved object if(sel == "dm3"){ if(requireNamespace("PWMEnrich.Dmelanogaster.background")){ data("dm3.upstream2000", package = "PWMEnrich.Dmelanogaster.background", envir=e) promoters = e$dm3.upstream2000 organism = "D. melanogaster" version = "dm3" } else { stop("This function requires the 'PWMEnrich.Dmelanogaster.background' package, please install it.") } } else if(sel == "mm9"){ if(requireNamespace("PWMEnrich.Mmusculus.background")){ data("mm9.upstream2000", package = "PWMEnrich.Mmusculus.background", envir=e) promoters = e$mm9.upstream2000 organism = "M. musculus" version = "mm9" } else { stop("This function requires the 'PWMEnrich.Mmusculus.background' package, please install it.") } } else if(sel == "hg19"){ if(requireNamespace("PWMEnrich.Hsapiens.background")){ data("hg19.upstream2000", package = "PWMEnrich.Hsapiens.background", envir=e) promoters = e$hg19.upstream2000 organism = "H. sapiens" version = "hg19" } else { stop("This function requires the 'PWMEnrich.Hsapiens.background' package, please install it.") } } else { stop("Internal error, should never reach this point!") } list(promoters=promoters, organism=organism, version=version) } #' Make a background for a set of position frequency matrices #' #' This is a convenience front-end function to compile new backgrounds for a set of PFMs. #' Currently only supports D. melanogaster, but in the future should support other common organisms as well. #' #' @param motifs a list of position frequency matrices (4xL matrices) #' @param organism either a name of the organisms for which the background should be compiled #' (currently supported names are "dm3", "mm9" and "hg19"), or a \code{BSgenome} object (see \code{BSgenome} package). #' @param type the type of background to be compiled. Possible types are: #' \itemize{ #' \item "logn" - estimate a lognormal background #' \item "cutoff" - estimate a Z-score background with fixed log-odds cutoff (in log2) #' \item "pval" - estimate a Z-score background with a fixed P-value cutoff. Note that this may require a lot of memory #' since the P-value of motif hits is first estimated from the empirical distribution. #' \item "empirical" - create an empirical P-value background. Note that this may require a lot of memory (up to 10GB in #' default "slow" mode (quick=FALSE) for 126 JASPAR motifs and 1000 D. melanogaster promoters). #' \item "GEV" - estimate a generalized extreme value (GEV) distribution background by fitting linear regression to distribution #' parameters in log space #' } #' @param quick if to preform fitting on a reduced set of 100 promoters. This will not give as good results but is much quicker than fitting to all the promoters (~10k). #' Usage of this parameter is recommended only for testing and rough estimates. #' @param bg.seq a set of background sequences to use. This parameter overrides the "organism" and "quick" parameters. #' @param ... other named parameters that backend function makePWM***Background functions take. #' @export #' @author Robert Stojnic, Diego Diez #' @examples #' #' # load in the two example de-novo motifs #' motifs = readMotifs(system.file(package = "PWMEnrich", dir = "extdata", file = "example.transfac"), #' remove.acc = TRUE) #' #' \dontrun{ #' # construct lognormal background #' bg.logn = makeBackground(motifs, organism="dm3", type="logn") #' #' # alternatively, any BSgenome object can also be used #' if(requireNamespace("BSgenome.Dmelanogaster.UCSC.dm3")) #' bg.logn = makeBackground(motifs, organism=Dmelanogaster, type="logn") #' #' # construct a Z-score of hits with P-value background #' bg.pval = makeBackground(motifs, organism="dm3", type="pval", p.value=1e-3) #' #' # now we can use them to scan for enrichment in sequences (in this case there is a consensus #' # Tin binding site). #' motifEnrichment(DNAString("TGCATCAAGTGTGTAGTG"), bg.logn) #' motifEnrichment(DNAString("TGCATCAAGTGTGTAGTG"), bg.pval) #' } #' makeBackground = function(motifs, organism="dm3", type="logn", quick=FALSE, bg.seq=NULL, ...){ # check input parameters valid.types = c("logn", "cutoff", "pval", "empirical", "GEV") if(!(type %in% valid.types)){ stop(paste("Invalid type, please choose from:", paste(valid.types, collapse=", "))) } # make sure we have this defined.... if(!hasArg("bg.source")) bg.source = NULL else bg.source = list(...)$bg.source # use the explicitely set sequences if(!is.null(bg.seq)){ bg.seq = .normalize.bg.seq(bg.seq) } else { # get the promoters for the organism promoters.all = getPromoters(organism) promoters = promoters.all$promoters if(quick){ bg.seq = promoters[seq(1, length(promoters), length.out=100)] if(is.null(bg.source)) bg.source = paste(promoters.all$organism, " (", promoters.all$version, ") 100 unique 2kb promoters", sep="") } else { if(type %in% c("pval")){ bg.seq = promoters[seq(1, length(promoters), length.out=500)] if(is.null(bg.source)) bg.source = paste(promoters.all$organism, " (", promoters.all$version, ") 500 unique 2kb promoters", sep="") } else { bg.seq = promoters if(is.null(bg.source)) bg.source = paste(promoters.all$organism, " (", promoters.all$version, ") ", length(promoters), " unique 2kb promoters", sep="") } } } #bg.seq = DNAStringSetToList(bg.seq) # capture ... as parameters so we can remove bg.source if present # and set the other parameters params = list(...) params$bg.seq = bg.seq params$motifs = motifs params$bg.source = bg.source # select the human algorithm if(type == "logn" && is.character(organism) && organism == "hg19" && !("algorithm" %in% names(params))) params$algorithm = "human" ## now run the appropriate backend function if(type == "logn"){ bg = do.call("makePWMLognBackground", params) } else if(type == "cutoff"){ bg = do.call("makePWMCutoffBackground", params) } else if(type == "pval"){ bg = do.call("makePWMPvalCutoffBackgroundFromSeq", params) } else if(type == "empirical"){ bg = do.call("makePWMEmpiricalBackground", params) } else if(type == "GEV"){ bg = do.call("makePWMGEVBackground", params) } return(bg) } #' Get the four nucleotides background frequencies #' #' Estimate the background frequencies of A,C,G,T on a set of promoters from an organism #' #' @param organism either a name of the organisms for which the background should be compiled #' (supported names are "dm3", "mm9" and "hg19"), a \code{BSgenome} object, #' \code{DNAStringSet}, or list of \code{DNAString} objects #' @param pseudo.count the number to which the frequencies sum up to, by default 1 #' @param quick if to preform fitting on a reduced set of 100 promoters. This will not give as good results but is much quicker than fitting to all the promoters (~10k). #' Usage of this parameter is recommended only for testing and rough estimates. #' @export #' @author Robert Stojnic, Diego Diez #' @examples #' \dontrun{ #' getBackgroundFrequencies("dm3") #' } getBackgroundFrequencies = function(organism="dm3", pseudo.count=1, quick=FALSE){ # bug reported on support.bioconductor.org if (inherits(organism, "DNAStringSet")) { bg.seq = organism } else if (is.list(organism) && length(organism) > 0 && inherits(organism[[1]], "DNAString")) { bg.seq = organism } else { # pick the set of background sequences promoters = getPromoters(organism)$promoters if(quick){ bg.seq = promoters[seq(1, length(promoters), length.out=100)] } else { bg.seq = promoters } } #bg.seq = DNAStringSetToList(bg.seq) makePriors(bg.seq, pseudo.count) } #' Calculate the empirical distribution score distribution for a set of motifs #' #' @param motifs a set of motifs, either a list of frequency matrices, or a list of PWM objects. If #' frequency matrices are given, the background distribution is fitted from bg.seq. #' @param organism either a name of the organisms for which the background should be compiled #' (supported names are "dm3", "mm9" and "hg19"), or a \code{BSgenome} object (see \code{BSgenome} package). #' @param bg.seq a set of background sequence (either this or organism needs to be specified!). Can be a DNAString or DNAStringSet object. #' @param quick if to do the fitting only on a small subset of the data (only in combination with \code{organism}). Useful only for code testing! #' @param pseudo.count the pseudo count which is shared between nucleotides when frequency matrices are given #' @return a list of \code{ecdf} objects (see help page for \code{ecdf} for usage). #' @export motifEcdf = function(motifs, organism=NULL, bg.seq=NULL, quick=FALSE, pseudo.count=1){ if(is.null(organism) && is.null(bg.seq)){ stop("Either the 'organism' or 'bg.seq' parameter need to be specified!") } if(!is.list(motifs)) motifs = list(motifs) if(!is.null(bg.seq)){ # check if the sequences are in the right format bg.seq = .normalize.bg.seq(bg.seq) } else { # take only a single promoter from each of the genes promoters = getPromoters(organism)$promoters if(quick){ bg.seq = promoters[seq(1, length(promoters), length.out=100)] } else { bg.seq = promoters } } # make priors and PWMs if(! inherits(motifs[[1]], "PWM")) { prior = makePriors(bg.seq, pseudo.count) pwms = PFMtoPWM(motifs, prior.params = prior) } else { pwms = motifs } # always use the big memory implementation as it is much faster in typical usage! s = motifScoresBigMemory(bg.seq, pwms, raw.scores=TRUE) # group together distributions for motifs s = lapply(1:length(pwms), function(i) na.omit(unlist(lapply(s, function(x) x[,i])))) # create empirical CDFs and return e = lapply(s, ecdf) names(e) = names(pwms) e } PWMEnrich/R/clover.R0000644000175100017510000000651714614305422015216 0ustar00biocbuildbiocbuild# Calculate the clover score #' Calculate the Clover score using the recursive formula from Frith et al #' #' @param scores a matrix of average odds scores, where columns are motifs, and rows sequences #' @param lr3 if to return a matrix of LR3 scores, where columns correpond to motifs, and rows to subset sizes #' @param verbose if to produce verbose output of progress #' @return the LR4 score, which is the mean of LR3 scores over subset sizes cloverScore = function(scores, lr3=FALSE, verbose=FALSE){ if(is.vector(scores)) return(scores) # LR3 scores clover = matrix(0, nrow=nrow(scores), ncol=ncol(scores)) colnames(clover) = colnames(scores) rownames(clover) = rownames(scores) # iterate over motifs for(m.inx in 1:ncol(scores)){ if(verbose) message(paste("Calculating Clover score for motif", m.inx, "/", ncol(scores))) s = scores[,m.inx] N = length(s) A = matrix(NA, ncol=N+1, nrow=N+1) # load initial values A[1,] = 1 for(i in 2:ncol(A)){ A[i,i-1] = 0 } # calculate the A matrix from the paper (Frith et al, NAR, 2004) for(i in 2:(N+1)){ for(j in 2:(N+1)){ if(j >= i){ # i,j in their formula di = i-1 dj = j-1 # i,j is still used to subscript A A[i,j] = ( di * s[dj] * A[i-1,j-1] + (dj - di) * A[i,j-1] ) / dj } } } clover[,m.inx] = A[2:(N+1),N+1] } # final score (LR4) is the mean of LR3 scores if(lr3) clover else colMeans(clover) } #' Calculate the Clover P-value as described in the Clover paper #' #' This function only take one background sequence as input, it also just calculates the P-value #' so it is more efficient. #' #' @param scores the affinity scores for individual sequences #' @param seq.len lengths of sequences #' @param pwm.len lengths of PWMs #' @param bg.fwd the raw score of forward strand #' @param bg.rev the raw scores of reverse strand #' @param B the number of random replicates #' @param verbose if to give verbose progress reports #' @param clover the clover scores if already calculated #' @return P-value cloverPvalue1seq = function(scores, seq.len, pwm.len, bg.fwd, bg.rev, B=1000, verbose=TRUE, clover=NULL){ if(is.vector(scores)) scores = matrix(scores, nrow=1, dimnames=list(NULL, names(scores))) # length of sequences bg.len = nrow(bg.fwd) if(max(seq.len) > bg.len){ stop("The maximal string length in 'sequences' is greater than the background length") } # original scores for the set of sequences if(is.null(clover)) original.clover = cloverScore(scores) else original.clover = clover # the empricial p-value clover.p = rep(0, ncol(scores)) names(clover.p) = colnames(scores) # do average over strands bg.res = (bg.fwd + bg.rev) / 2 for(i in 1:B){ if(verbose) message("Random sample ", i, " / ", B) # sample background sequences with the same length distribution new.scores = matrix(0, nrow=nrow(scores), ncol=ncol(scores)) colnames(new.scores) = colnames(scores) for(j in 1:nrow(scores)){ # choose a subsequence with matching length start.range = 1:(bg.len - seq.len[j]) start = sample(start.range, 1) # fetch the scores new.scores[j,] = colMeans(bg.res[start:(start+seq.len[j]-1),], na.rm=TRUE) } # calculate scores and clover new.clover = cloverScore(new.scores) clover.p = clover.p + ((new.clover > original.clover) + 0) } return(clover.p / B) } PWMEnrich/R/diff.R0000644000175100017510000001320314614305422014622 0ustar00biocbuildbiocbuild### Functions for differential enrichment #' Test for differential enrichment between two groups of sequences #' #' This function calls \code{motifEnrichment} on two groups of sequences and calculates #' the difference statistics when possible. #' #' @title Differential motif enrichment #' @param sequences1 First set of sequences. Can be either a single sequence #' (an object of class DNAString), or a list of DNAString objects, or a DNAStringSet object. #' @param sequences2 Second set of sequences. Can be either a single sequence #' (an object of class DNAString), or a list of DNAString objects, or a DNAStringSet object. #' @param pwms this parameter can take multiple values depending on the scoring scheme and background correction used. #' When the \code{method} parameter is set to "autodetect", the following default algorithms #' are going to be used: #' \itemize{ #' \item if \code{pwms} is a list containing either frequency matrices or a list of PWM objects then #' the "affinity" algorithm is selected. If frequency matrices are given, they are converted #' to PWMs using uniform background. For best performance, convert frequency matrices to PWMs #' before calling this function using realistic genomic background. #' \item Otherwise, appropriate scoring scheme and background correction are selected based on the #' class of the object (see below). #' } #' @param score this parameter determines which scoring scheme to use. Following scheme as available: #' \itemize{ #' \item "autodetect" - default value. Scoring method is determined based #' on the type of \code{pwms} parameter. #' \item "affinity" - use threshold-free affinity scores without a background. The \code{pwms} #' parameter can either be a list of frequency matrices, \code{PWM} objects, or a #' \code{PWMLognBackground} object. #' \item "cutoff" - use number of motif hits above a score cutoff as a measure of enrichment. #' No background correction is performed. The \code{pwms} #' parameter can either be a list of frequency matrices, \code{PWM} objects, or a #' \code{PWMCutoffBackground} object. #' } #' #' @param bg this parameter determines which background correction to use, if any. #' \itemize{ #' \item "autodetect" - default value. Background correction is determined based on the type #' of the \code{pwms} parameter. #' \item "logn" - use a lognormal distribution background pre-computed for a set of PWMs. #' This requires \code{pwms} to be of class \code{PWMLognBackground}. #' \item "z" - use a z-score for the number of significant motif hits compared to background number of hits. #' This requires \code{pwms} to be of class \code{PWMCutoffBackground}. #' \item "none" - no background correction #' } #' @param cutoff the score cutoff for a significant motif hit if scoring scheme "cutoff" is selected. #' @param res1 the output of \code{motifEnrichment} if already calculated for \code{sequences1} #' @param res2 the output of \code{motifEnrichment} if already calculated for \code{sequences2} #' @param verbose if to produce verbose output #' @export #' @examples #' #' if(requireNamespace("PWMEnrich.Dmelanogaster.background")){ #' # load the background file for drosophila and lognormal correction #' data(PWMLogn.dm3.MotifDb.Dmel, package = "PWMEnrich.Dmelanogaster.background") #' #' # get the differential enrichment #' diff = motifDiffEnrichment(DNAString("TGCATCAAGTGTGTAGTGTGAGATTAGT"), #' DNAString("TGAACGAGTAGGACGATGAGAGATTGATG"), PWMLogn.dm3.MotifDb.Dmel, verbose=FALSE) #' #' # motifs differentially enriched in the first sequence (with lognormal background correction) #' head(sort(diff$group.bg, decreasing=TRUE)) #' #' # motifs differentially enriched in the second sequence (with lognormal background correction) #' head(sort(diff$group.bg)) #' } motifDiffEnrichment = function(sequences1, sequences2, pwms, score="autodetect", bg="autodetect", cutoff=log2(exp(4)), verbose=TRUE, res1=NULL, res2=NULL){ # check the input param constrains if(!inherits(pwms, c("NULL", "list", "PWMLognBackground", "PWMCutoffBackground"))) { stop("pwms needs to be either a list of frequency matrices, list of PWM objects, or an object of class PWMLognBackground or PWMCutoffBackground.") } if(!(score %in% c("autodetect", "affinity", "cutoff"))){ stop("score needs to be one of: autodetect, affinity, cutoff") } if(!(bg %in% c("autodetect", "logn", "z", "none"))){ stop("score needs to be one of: autodetect, logn, z, none") } # results for both groups if(is.null(res1)) res1 = motifEnrichment(sequences1, pwms, score=score, bg=bg, cutoff=cutoff, verbose=verbose) if(is.null(res2)) res2 = motifEnrichment(sequences2, pwms, score=score, bg=bg, cutoff=cutoff, verbose=verbose) if((res1$score != res2$score) | (res1$bg != res2$bg)) stop("Supplied motif enrichments for sequences1 and sequences2 use different scoring schemes and/or background corrections") # we use the same rules to decided on score and bg.. if(score == "autodetect") score = res1$score if(bg == "autodetect") bg = res1$bg # calculated differential enrichment res = list() res$group.nobg = res1$group.nobg - res2$group.nobg if(bg == "none") res$group.bg = NULL else if(bg == "logn") res$group.bg = res1$group.norm - res2$group.norm else res$group.bg = res1$group.bg - res2$group.bg return(res) } PWMEnrich/R/misc.R0000644000175100017510000000206314614305422014647 0ustar00biocbuildbiocbuild#' Calculate medians of columns #' @param x a matrix colMedians = function(x){ apply(x, 2, median, na.rm=T) } #' Calculate standard deviations of columns #' @param x a matrix colSds = function(x){ apply(x, 2, sd, na.rm=T) } #' Divide each row of a matrix with a vector #' #' @param m matrix to be divided #' @param v the vector to use for division divideRows = function(m, v){ t(apply(m, 1, function(x) x/v)) } #' Convert DNAStringSet to list of DNAString objects #' #' as.list doesn't seem to always work for DNAStringSets, so #' implementing this ourselves. #' #' @param x an object of class DNAStringSet DNAStringSetToList = function(x){ res = list() for(i in 1:length(x)){ res[[i]] = x[[i]] } return(res) } #' Concatenata DNA sequences into a single character object #' #' @param sequences either a list of DNAString objects, or a DNAStringSet #' @return a single character string concatenateSequences = function(sequences){ if(is.list(sequences)){ paste(unlist(sapply(sequences, toString)), collapse="") } else{ paste(sequences, collapse="") } } PWMEnrich/R/MotifEnrichmentReport-methods.R0000644000175100017510000000410214614305422021640 0ustar00biocbuildbiocbuild# methods to extract data from, and subset MotifEnrichmentReport #' Columns stored in the motif enrichment report #' #' @title Names of variables #' @name names,MotifEnrichmentReport #' @aliases names,MotifEnrichmentReport-method #' @param x the MotifEnrichmentReport object #' @return the names of the variables #' @rdname operators-MotifEnrichmentReport setMethod("names", signature=signature(x="MotifEnrichmentReport"), function(x){ c(names(x@d), "pwms") }) #' Access a column by name #' #' @aliases $,MotifEnrichmentReport-method #' @param x the MotifEnrichmentReport object #' @param name the variable name #' @rdname operators-MotifEnrichmentReport setMethod("$", signature=signature(x="MotifEnrichmentReport"), function(x, name){ if(name == "pwms") return(x@pwms) else return(x@d[[name]]) }) #' Subset the report #' #' @aliases [,MotifEnrichmentReport-method #' @param x the MotifEnrichmentReport object #' @param i the row selector #' @param j unused #' @param ... unused #' @param drop unused (always FALSE) #' @rdname operators-MotifEnrichmentReport setMethod("[", signature=signature(x="MotifEnrichmentReport"), function (x, i, j, ..., drop=TRUE){ d = x@d[i, , drop=FALSE] pwms = x@pwms[i] new("MotifEnrichmentReport", d=d, pwms=pwms) }) #' show method for MotifEnrichmentReport #' @param object the MotifEnrichmentReport object setMethod("show", signature=signature(object="MotifEnrichmentReport"), function(object){ d = object@d pwms = object@pwms cat("An object of class 'MotifEnrichmentReport':\n") if(nrow(d) > 20){ # show only the first 10 and the last one dd = rbind(d[1:10, ], "..."=rep("...", ncol(d)), d[nrow(d),]) print(dd) } else { print(d) } }) #' Convert a MotifEnrichmentReport into a data.frame object #' #' @name as.data.frame,MotifEnrichmentReport-method #' @aliases as.data.frame #' @param x the MotifEnrichmentReport object #' @param row.names unused #' @param optional unused #' @param ... unused #' @export setMethod("as.data.frame", signature=signature(x="MotifEnrichmentReport"), function (x, row.names = NULL, optional = FALSE, ...){ x@d }) PWMEnrich/R/MotifEnrichmentResults-methods.R0000644000175100017510000004304114614305422022033 0ustar00biocbuildbiocbuild #' Name of different pieces of information associated with MotifEnrichmentResults #' #' @title Names of variables #' @name names,MotifEnrichmentResults #' @aliases names,MotifEnrichmentResults-method #' @param x the MotifEnrichmentResults object #' @return the names of the variables #' @rdname operators-MotifEnrichmentResults setMethod("names", signature=signature(x="MotifEnrichmentResults"), function(x) names(x@res)) #' Access a property by name #' #' @aliases $,MotifEnrichmentResults-method #' @param x the MotifEnrichmentResults object #' @param name the variable name #' @rdname operators-MotifEnrichmentResults setMethod("$", signature=signature(x="MotifEnrichmentResults"), function(x, name){ x@res[[name]] }) #' show method for MotifEnrichmentResults #' @param object the MotifEnrichmentResults object setMethod("show", signature=signature(object="MotifEnrichmentResults"), function(object){ res = object@res is.group.only = length(grep("sequence[.]", names(res))) == 0 cat("An object of class 'MotifEnrichmentResults':\n") cat("* created with '", res$score, "' scoring function with '", res$bg, "' background correction\n", sep="") cat("* on a set of ", length(res$sequences), " sequence(s) and ", length(res$pwms), " PWMs\n", sep="") cat("Result sets for the group:",paste("$", names(res)[grep("group[.]", names(res))], sep="", collapse=", "), "\n") if(!is.group.only){ cat("Result sets for individual sequences:", paste("$", names(res)[grep("sequence[.]", names(res))], sep="", collapse=", "), "\n") cat("Report methods: groupReport(), sequenceReport()\n") #cat("Methods to extract data: motifRankingForGroup(), motifRankingForSequence()\n") #cat("Methods to plot data: plotTopMotifsGroup(), plotTopMotifsSequence()\n") } else { cat("Report method: sequenceReport()\n") #cat("Methods to extract data: motifRankingForGroup()\n") #cat("Methods to plot data: plotTopMotifsGroup()\n") } }) #' A helper function for motifRankingForGroup and motifRankingForSequence with the common code #' #' @param res the list of results from MotifEnrichmentResults object #' @param r the vector of raw results that needs to be processed #' @param id if to return IDs instead of names #' @param order if to return the ordering of motifs #' @param rank if to return the rank of motifs #' @param unique if to remove duplicates #' @param decreasing specifies the sorting order rankingProcessAndReturn = function(res, r, id, order, rank, unique, decreasing){ if((unique & id) || (unique & order)){ stop("Parameter 'unique' can be set to TRUE only if 'id' and 'order' are FALSE.") } # set either the names or IDs all.ids = sapply(res$pwms, function(x) x@id) all.names = sapply(res$pwms, function(x) x@name) if(id){ names(r) = all.ids } else { if(!all(all.names == "")) names(r) = all.names } # do the required transformation on the results ret = NULL if(rank){ x = base::rank(r) if(decreasing) x = length(x) - x + 1 names(x) = names(r) ret = x } else if(order){ x = base::order(r, decreasing=decreasing) names(x) = names(r)[x] ret = x } else { ret = sort(r, decreasing=decreasing) } # remove duplicates if applicable if(unique){ original.names = names(ret) if(decreasing & !rank) ret = tapply(ret, names(ret), max) else ret = tapply(ret, names(ret), min) if(rank){ # re-rank the ranks... ret = rank(ret) } # unique names with correct ordering uniq.names = c() for(i in 1:length(original.names)){ if(!(original.names[i] %in% uniq.names)) uniq.names[length(uniq.names)+1] = original.names[i] } # re-order in the original ordering ret = ret[uniq.names] } ret } #' Get a ranking of motifs by their enrichment in the whole set of sequences #' #' @name motifRankingForGroup,MotifEnrichmentResults-method #' @aliases motifRankingForGroup #' @param obj a MotifEnrichmentResults object #' @param bg if to use background corrected P-values to do the ranking (if available) #' @param id if to show PWM IDs instead of target TF names #' @param order if to output the ordering of PWMs instead of actual P-values or raw values #' @param rank if the output should be rank of a PWM instead of actual P-values or raw values #' @param unique if TRUE, only the best rank is taken for each TF (only when id = FALSE, order = FALSE) #' @param ... currently unused #' #' @return a vector of P-values or raw enrichments sorted such that the first motif is most enriched #' @export #' @examples #' if(requireNamespace("PWMEnrich.Dmelanogaster.background")){ #' ### #' # load the pre-compiled lognormal background #' data(PWMLogn.dm3.MotifDb.Dmel, package = "PWMEnrich.Dmelanogaster.background") #' #' # scan two sequences for motif enrichment #' sequences = list(DNAString("GAAGTATCAAGTGACCAGTAAGTCCCAGATGA"), #' DNAString("AGGTAGATAGAACAGTAGGCAATGAAGCCGATG")) #' res = motifEnrichment(sequences, PWMLogn.dm3.MotifDb.Dmel) #' #' # most enriched in both sequences (sorted by lognormal background P-value) #' head(motifRankingForGroup(res)) #' #' # Return a non-redundant set of TFs #' head(motifRankingForGroup(res, unique=TRUE)) #' #' # sorted by raw affinity instead of P-value #' head(motifRankingForGroup(res, bg=FALSE)) #' #' # show IDs instead of target TF names #' head(motifRankingForGroup(res, id=TRUE)) #' #' # output the rank instead of P-value #' head(motifRankingForGroup(res, rank=TRUE)) #' } setMethod("motifRankingForGroup", signature=signature(obj="MotifEnrichmentResults"), function(obj, bg=TRUE, id=FALSE, order=FALSE, rank=FALSE, unique=FALSE, ...){ res = obj@res # vector of scores if(bg && "group.bg" %in% names(res)){ r = res$group.bg if(res$score == "cutoff" | res$bg == "ms") decreasing = TRUE else decreasing = FALSE } else { #if(bg) # warning("Parameter 'bg' is TRUE but this MotifEnrichmentResults object has no background correction, ignoring parameter.") r = res$group.nobg decreasing = TRUE } rankingProcessAndReturn(res, r, id, order, rank, unique, decreasing) }) #' Get a ranking of motifs by their enrichment in one specific sequence #' #' @name motifRankingForSequence,MotifEnrichmentResults-method #' @aliases motifRankingForSequence #' @param obj a MotifEnrichmentResults object #' @param seq.id either the sequence number or sequence name #' @param bg if to use background corrected P-values to do the ranking (if available) #' @param id if to show PWM IDs instead of target TF names #' @param order if to output the ordering of PWMs instead of actual P-values or raw values #' @param rank if the output should be rank of a PWM instead of actual P-values or raw values #' @param unique if TRUE, only the best rank is taken for each TF (only when id = FALSE, order = FALSE) #' @param ... currently unused #' #' @export #' @return a vector of P-values or raw enrichments sorted such that the first motif is most enriched #' #' @examples #' if(requireNamespace("PWMEnrich.Dmelanogaster.background")){ #' ### #' # load the pre-compiled lognormal background #' data(PWMLogn.dm3.MotifDb.Dmel, package = "PWMEnrich.Dmelanogaster.background") #' #' # scan two sequences for motif enrichment #' sequences = list(DNAString("GAAGTATCAAGTGACCAGTAAGTCCCAGATGA"), #' DNAString("AGGTAGATAGAACAGTAGGCAATGAAGCCGATG")) #' res = motifEnrichment(sequences, PWMLogn.dm3.MotifDb.Dmel) #' #' # most enriched in the second sequences (sorted by lognormal background P-value) #' head(motifRankingForSequence(res, 2)) #' #' # return unique TFs enriched in sequence 2 #' head(motifRankingForSequence(res, 2, unique=TRUE)) #' #' # sorted by raw affinity instead of P-value #' head(motifRankingForSequence(res, 2, bg=FALSE)) #' #' # show IDs instead of target TF names #' head(motifRankingForSequence(res, 2, id=TRUE)) #' #' # output the rank instead of P-value #' head(motifRankingForSequence(res, 2, rank=TRUE)) #' } setMethod("motifRankingForSequence", signature=signature(obj="MotifEnrichmentResults"), function(obj, seq.id, bg=TRUE, id=FALSE, order=FALSE, rank=FALSE, unique=FALSE, ...){ res = obj@res if(missing(seq.id)){ stop("Please specify the sequence number with 'seq.id'") } # vector of scores if(bg && "sequence.bg" %in% names(res)){ r = res$sequence.bg[seq.id,] if(res$score == "cutoff" | res$bg == "ms") decreasing = TRUE else decreasing = FALSE } else { #if(bg) # warning("Parameter 'bg' is TRUE but this MotifEnrichmentResults object has no background correction, ignoring parameter.") r = res$sequence.nobg[seq.id,] decreasing = TRUE } rankingProcessAndReturn(res, r, id, order, rank, unique, decreasing) }) #' Plot the top N enrichment motifs in a group of sequences #' #' @name plotTopMotifsGroup,MotifEnrichmentResults-method #' @aliases plotTopMotifsGroup #' @param obj a MotifEnrichmentResults object #' @param n the number of top ranked motifs to plot #' @param bg if to use background corrected P-values to do the ranking (if available) #' @param id if to show PWM IDs instead of target TF names #' @param ... other parameters passed to \code{plotMultipleMotifs()} #' @export #' @examples #' if(requireNamespace("PWMEnrich.Dmelanogaster.background")){ #' ### #' # load the pre-compiled lognormal background #' data(PWMLogn.dm3.MotifDb.Dmel, package = "PWMEnrich.Dmelanogaster.background") #' #' # scan two sequences for motif enrichment #' sequences = list(DNAString("GAAGTATCAAGTGACCAGTAAGTCCCAGATGA"), #' DNAString("AGGTAGATAGAACAGTAGGCAATGAAGCCGATG")) #' #' res = motifEnrichment(sequences, PWMLogn.dm3.MotifDb.Dmel) #' #' # plot the top 4 motifs in a 2x2 grid #' plotTopMotifsGroup(res, 4) #' #' # plot top 3 motifs in a single row #' plotTopMotifsGroup(res, 3, row=1, cols=3) #' } setMethod("plotTopMotifsGroup", signature=signature(obj="MotifEnrichmentResults"), function(obj, n, bg=TRUE, id=FALSE, ...){ o = motifRankingForGroup(obj, bg, id, order=TRUE) plotMultipleMotifs(obj@res$pwms[o[1:n]], names(o)[1:n], ...) }) #' Plot the top N enrichment motifs in a single sequence #' #' @name plotTopMotifsSequence,MotifEnrichmentResults-method #' @aliases plotTopMotifsSequence #' @param obj a MotifEnrichmentResults object #' @param seq.id either the sequence number or sequence name #' @param n the number of top ranked motifs to plot #' @param bg if to use background corrected P-values to do the ranking (if available) #' @param id if to show PWM IDs instead of target TF names #' @param ... other parameters passed to \code{plotMultipleMotifs()} #' @export #' @examples #' if(requireNamespace("PWMEnrich.Dmelanogaster.background")){ #' ### #' # load the pre-compiled lognormal background #' data(PWMLogn.dm3.MotifDb.Dmel, package = "PWMEnrich.Dmelanogaster.background") #' #' # scan two sequences for motif enrichment #' sequences = list(DNAString("GAAGTATCAAGTGACCAGTAAGTCCCAGATGA"), #' DNAString("AGGTAGATAGAACAGTAGGCAATGAAGCCGATG")) #' #' res = motifEnrichment(sequences, PWMLogn.dm3.MotifDb.Dmel) #' #' # plot the top 4 motifs in a 2x2 grid #' plotTopMotifsSequence(res, 1, 4) #' #' # plot top 3 motifs in a single row #' plotTopMotifsSequence(res, 1, 3, row=1, cols=3) #' } setMethod("plotTopMotifsSequence", signature=signature(obj="MotifEnrichmentResults"), function(obj, seq.id, n, bg=TRUE, id=FALSE, ...){ o = motifRankingForSequence(obj, seq.id, bg, id, order=TRUE) plotMultipleMotifs(obj@res$pwms[o[1:n]], names(o)[1:n], ...) }) #' Generate a motif enrichment report for the whole group of sequences together #' #' @name groupReport,MotifEnrichmentResults-method #' @aliases groupReport #' @param obj a MotifEnrichmentResults object #' @param top what proportion of top motifs should be examined in each individual sequence (by default 0.05, i.e. 5\%) #' @param bg if to use background corrected P-values to do the ranking (if available) #' @param by.top.motifs if to rank by the proportion of sequences where the motif is within 'top' percentage of motifs #' @param ... unused #' @return a MotifEnrichmentReport object containing a table with the following columns: #' \itemize{ #' \item 'rank' - The rank of the PWM's enrichment in the whole group of sequences together #' \item 'target' - The name of the PWM's target gene, transcript or protein complex. #' \item 'id' - The unique identifier of the PWM (if set during PWM creation). #' \item 'raw.score' - The raw score before P-value calculation #' \item 'p.value' - The P-value of motif enrichment (if available) #' \item 'top.motif.prop' - The proportion (between 0 and 1) of sequences where the motif is within \code{top} proportion of enrichment motifs. #' } #' @export #' @examples #' if(requireNamespace("PWMEnrich.Dmelanogaster.background")){ #' ### #' # load the pre-compiled lognormal background #' data(PWMLogn.dm3.MotifDb.Dmel, package = "PWMEnrich.Dmelanogaster.background") #' #' # scan two sequences for motif enrichment #' sequences = list(DNAString("GAAGTATCAAGTGACCAGTAAGTCCCAGATGA"), #' DNAString("AGGTAGATAGAACAGTAGGCAATGAAGCCGATG")) #' #' res = motifEnrichment(sequences, PWMLogn.dm3.MotifDb.Dmel) #' #' # produce a report for all sequences taken together #' r.default = groupReport(res) #' #' # produce a report where the last column takes top 1% motifs #' r = groupReport(res, top=0.01) #' #' # view the results #' r #' #' # plot the top 10 most enriched motifs #' plot(r[1:10]) #' #' } setMethod("groupReport", signature=signature(obj="MotifEnrichmentResults"), function(obj, top=0.05, bg=TRUE, by.top.motifs=FALSE, ...){ pwms = obj$pwms res = obj@res # correct ordering of motifs o = motifRankingForGroup(obj, rank=TRUE, bg=bg) targets = sapply(obj$pwms, function(x) x@name) ids = sapply(obj$pwms, function(x) x@id) # construct the initial data frame object df = data.frame(rank=o, target=targets, id=ids, raw.score=obj$group.nobg, stringsAsFactors=FALSE) if(bg && "group.bg" %in% names(obj)){ p.value = obj$group.bg } else { p.value = as.numeric(NA) } if(res$score == "cutoff" | res$bg == "ms"){ df = data.frame(df, z.score=p.value, stringsAsFactors=FALSE) } else { df = data.frame(df, p.value=p.value, stringsAsFactors=FALSE) } # calculate in how many top sequences is the motif present if("sequence.nobg" %in% names(obj)){ pct.top = rep(0, nrow(df)) pct.cutoff = nrow(df) * top num.seq = nrow(obj$sequence.nobg) for(i in 1:num.seq){ os = motifRankingForSequence(obj, i, rank=TRUE, bg=bg) pct.top = pct.top + (os <= pct.cutoff) } pct.top = pct.top / num.seq top.motif = pct.top } else { top.motif = as.numeric(NA) } df = data.frame(df, "top.motif.prop"=top.motif, stringsAsFactors=FALSE) if(by.top.motifs){ if(length(pwms) < 20) warning("You are sorting by top motifs, but this can be unreliable when number of PWMs is small.") df$rank = base::rank(-top.motif) } # sort and return correct.order = order(df$rank) df = df[correct.order,] rownames(df) = NULL pwms = pwms[correct.order] # return a MotifEnrichmentReport object new("MotifEnrichmentReport", d=df, pwms=pwms) }) #' Generate a motif enrichment report for a single sequence #' #' @name sequenceReport,MotifEnrichmentResults-method #' @aliases sequenceReport #' @param obj a MotifEnrichmentResults object #' @param seq.id the sequence index or name #' @param bg if to use background corrected P-values to do the ranking (if available) #' @param ... unused #' @return a MotifEnrichmentReport object containing a table with the following columns: #' \itemize{ #' \item 'rank' - The rank of the PWM's enrichment in the sequence #' \item 'target' - The name of the PWM's target gene, transcript or protein complex. #' \item 'id' - The unique identifier of the PWM (if set during PWM creation). #' \item 'raw.score' - The raw score before P-value calculation #' \item 'p.value' - The P-value of motif enrichment (if available) #' } #' @export #' @examples #' if(requireNamespace("PWMEnrich.Dmelanogaster.background")){ #' ### #' # load the pre-compiled lognormal background #' data(PWMLogn.dm3.MotifDb.Dmel, package = "PWMEnrich.Dmelanogaster.background") #' #' # scan two sequences for motif enrichment #' sequences = list(DNAString("GAAGTATCAAGTGACCAGTAAGTCCCAGATGA"), #' DNAString("AGGTAGATAGAACAGTAGGCAATGAAGCCGATG")) #' #' res = motifEnrichment(sequences, PWMLogn.dm3.MotifDb.Dmel) #' #' # reports for the two sequences #' r1 = sequenceReport(res, 1) #' r2 = sequenceReport(res, 2) #' #' # view the results #' r1 #' r2 #' #' # plot the top 10 most enriched motifs in the first, and then second sequence #' plot(r1[1:10]) #' plot(r2[1:10]) #' #' } setMethod("sequenceReport", signature=signature(obj="MotifEnrichmentResults"), function(obj, seq.id, bg=TRUE, ...){ if(missing(seq.id)){ stop("Please specify the sequence number with 'seq.id'") } res = obj@res pwms = obj$pwms # correct ordering of motifs o = motifRankingForSequence(obj, seq.id, rank=TRUE, bg=bg) targets = sapply(obj$pwms, function(x) x@name) ids = sapply(obj$pwms, function(x) x@id) # construct the initial data frame object df = data.frame(rank=o, target=targets, id=ids, raw.score=obj$sequence.nobg[seq.id,], stringsAsFactors=FALSE) if(bg && "sequence.bg" %in% names(obj)){ p.value = obj$sequence.bg[seq.id,] } else { p.value = as.numeric(NA) } if(res$score == "cutoff" | res$bg == "ms"){ df = data.frame(df, z.score=p.value, stringsAsFactors=FALSE) } else { df = data.frame(df, p.value=p.value, stringsAsFactors=FALSE) } # sort and return correct.order = order(df$rank) df = df[correct.order,] rownames(df) = NULL pwms = pwms[correct.order] # return a MotifEnrichmentReport object new("MotifEnrichmentReport", d=df, pwms=pwms) }) PWMEnrich/R/options.R0000644000175100017510000000336514614305422015415 0ustar00biocbuildbiocbuild# managing parallel execution of code # this global variable records options, e.g. for parallel execution etc .PWMEnrich.Options = new.env(parent=emptyenv()) #' Register than PWMEnrich can use parallel CPU cores #' #' Certain functions (like motif scanning) can be parallelized in PWMEnrich. This function #' registers a number of parallel cores (via core package parallel) to be used in #' code that can be parallelized. After this function is called, all further PWMEnrich #' function calls will run in parallel if possible. #' #' By default parallel execution is turned off. To turn it off after using it, call this #' function by passing NULL. #' #' @param numCores number of cores to use (default to take all cores), or NULL if no parallel execution is to be used #' @export #' @examples #' \dontrun{ #' registerCoresPWMEnrich(4) # use 4 CPU cores in PWMEnrich #' registerCoresPWMEnrich() # use maximal number of CPUs #' registerCoresPWMEnrich(NULL) # do not use parallel execution #' } registerCoresPWMEnrich = function(numCores=NA){ if (!requireNamespace("parallel")) stop("Parallel execution requires package parallel") if(!is.null(numCores) && is.na(numCores)) numCores = parallel::detectCores() assign("numCores", numCores, pos=.PWMEnrich.Options) } #' If to use a faster implementation of motif scanning that requires abount 5 to 10 times more memory #' #' @param useBigMemory a boolean value denoting if to use big memory implementation #' #' @export #' @examples #' \dontrun{ #' useBigMemoryPWMEnrich(TRUE) # switch to big memory implementation globally #' useBigMemoryPWMEnrich(FALSE) # switch back to default implementation #' } useBigMemoryPWMEnrich = function(useBigMemory=FALSE){ assign("useBigMemory", useBigMemory, pos=.PWMEnrich.Options) } PWMEnrich/R/plot.R0000644000175100017510000004205214614305422014674 0ustar00biocbuildbiocbuild #' Plot a PFM (not PWM) using seqLogo #' #' @param pfm a matrix where rows are the four nucleotides #' @param ... additional parameters for plot() plotPFM = function(pfm, ...){ plot(makePWM(divideRows(pfm,colSums(pfm))), ...) } #' Plotting for the PWM class #' #' This function produces a sequence logo (via package seqLogo). #' #' @aliases plot,PWM,missing-method #' @param x the PWM object #' @param y unused #' @param ... other parameters to pass to seqLogo's \code{plot} function #' @export #' @examples #' if(requireNamespace("PWMEnrich.Dmelanogaster.background")){ #' data(MotifDb.Dmel, package = "PWMEnrich.Dmelanogaster.background") #' #' # plot the tinman motif from MotifDb #' plot(MotifDb.Dmel[["tin"]]) #' } setMethod("plot", signature=signature(x="PWM", y="missing"), function(x, y, ...){ pfm = x$pfm plot(makePWM(divideRows(pfm,colSums(pfm))), ...) }) #' Plot mulitple motifs in a single plot #' #' Individual motif logos are plotted on a rows x cols grid. This function is a convenience #' interface for the \code{seqLogoGrid} function that deals with viewpoint placement in a #' matrix-like grid layout. #' #' By default will try to make a square grid plot that would fit all the motifs and use #' list names as captions. #' #' @param pwms a list of PWM objects or frequency matrices #' @param titles a characater vector of titles for each of the plots #' @param rows number of rows in the grid #' @param cols number or cols in the grid #' @param xmargin.scale the scaling parameter for the X-axis margin. Useful when plotting more than one logo on a page #' @param ymargin.scale the scaling parameter for the Y-axis margin. Useful when plotting more than one logo on a page #' @param ... other parameters passed to seqLogoGrid() #' @export plotMultipleMotifs = function(pwms, titles=names(pwms), rows=ceiling(sqrt(length(pwms))), cols=ceiling(sqrt(length(pwms))), xmargin.scale=0.4, ymargin.scale=0.4, ...){ if(!is.list(pwms)) pwms = list(pwms) if(length(pwms) != length(titles)) stop("Number of titles in the 'titles' parameter does not match the number of input motifs") # start a new viewport page grid.newpage() pushViewport(viewport(layout = grid.layout(rows,cols))) # use the grid layout for(i in 1:rows){ for(j in 1:cols){ pushViewport(viewport(layout.pos.row = i, layout.pos.col = j)) # work out which PWM to plot inx = (i-1)*cols + j # only plot if available if( inx <= length(pwms) ){ pwm = pwms[[inx]] if(inherits(pwm, "PWM")) pwm = pwm$pfm # use the backend functions to plot seqLogoGrid(divideRows(pwm,colSums(pwm)), xmargin.scale=xmargin.scale, ymargin.scale=ymargin.scale, title=titles[inx], ...) } popViewport() } } popViewport() } #' Plot the motif enrichment report #' #' Plots a graphical version of the motif enrichment report. Note that all values are plotted, if you want to plot only a subset of #' a report, first select this subset (see examples). #' #' @aliases plot,MotifEnrichmentReport,missing-method #' @param x a MotifEnrichmentReport object #' @param y unused #' @param fontsize font size to use in the plot #' @param id.fontsize font size to use for the motif IDs #' @param header.fontsize font size of the header #' @param widths the relative widths of columns #' @param ... unused #' if(requireNamespace("PWMEnrich.Dmelanogaster.background")){ #' ### #' # load the pre-compiled lognormal background #' data(PWMLogn.dm3.MotifDb.Dmel, package = "PWMEnrich.Dmelanogaster.background") #' #' # scan two sequences for motif enrichment #' sequences = list(DNAString("GAAGTATCAAGTGACCAGTAAGTCCCAGATGA"), DNAString("AGGTAGATAGAACAGTAGGCAATGAAGCCGATG")) #' #' res = motifEnrichment(sequences, PWMLogn.dm3.MotifDb.Dmel) #' #' # produce a report for all sequences taken together #' r = groupReport(res) #' #' # plot the top 10 most enriched motifs #' plot(r[1:10]) #' #' } setMethod("plot", signature=signature(x="MotifEnrichmentReport", y="missing"), function(x, y, fontsize=14, id.fontsize=fontsize, header.fontsize=fontsize, widths=NULL, ...){ d = x@d pwms = x@pwms rows = nrow(d)+1 cols = ncol(d)+1 # some default widths if(is.null(widths)) widths = c(0.05, 0.1, 0.2, 0.36, 0.08, 0.08, 0.08)[1:(ncol(d)+1)] widths = widths / sum(widths) if("z.score" %in% colnames(d)){ names(d) = c("Rank", "Target", "Motif ID", "Raw score", "Z score", "In top\nmotifs")[1:ncol(d)] } else { names(d) = c("Rank", "Target", "Motif ID", "Raw score", "P-value", "In top\nmotifs")[1:ncol(d)] } # start a new viewport page grid.newpage() pushViewport(viewport(layout = grid.layout(rows, cols, widths=widths))) # use the grid layout for(ii in 1:rows){ for(j in 1:cols){ pushViewport(viewport(layout.pos.row = ii, layout.pos.col = j)) # figure out which column to plot if(j <= 2){ inx = j } else { inx = j - 1 } if(ii == 1){ # header if(j == 3) grid.text("PWM", gp=gpar(fontsize=header.fontsize, fontface="bold")) else grid.text(names(d)[inx], gp=gpar(fontsize=header.fontsize, fontface="bold")) } else { # rest of the table i = ii - 1 # 3rd column is PWM if(j == 3){ pwm = pwms[[i]]$pfm # use the backend functions to plot seqLogoGrid(divideRows(pwm,colSums(pwm)), xmargin.scale=0.01, ymargin.scale=0.01, xaxis=FALSE, yaxis=FALSE, ...) } else { if(inx == 6){ grid.text(paste(round(d[i,inx]*100), "%"), gp=gpar(fontsize=fontsize)) } else if(inx == 3){ grid.text(d[i,inx], gp=gpar(fontsize=id.fontsize)) } else if(inx > 3){ grid.text(signif(d[i,inx],3), gp=gpar(fontsize=fontsize)) } else { grid.text(d[i,inx], gp=gpar(fontsize=fontsize)) } } } popViewport() } } popViewport() }) #' Draw a motif logo on an existing viewport #' #' This function comes from the seqLogo package. It has been modified to remove #' some unneccessary code as suggested by W Huber (https://stat.ethz.ch/pipermail/bioconductor/2010-September/035267.html). #' #' Use this function for more advanced plotting where the viewports are directly set up and maintained (see package \code{grid}). #' #' @param pwm numeric The 4xW position weight matrix. #' @param ic.scale logical If TRUE, the height of each column is proportional to its information #' content. Otherwise, all columns have the same height. #' @param xaxis logical If TRUE, an X-axis will be plotted. #' @param yaxis logical If TRUE, a Y-axis will be plotted. #' @param xfontsize numeric Font size to be used for the X-axis. #' @param yfontsize numeric Font size to be used for the Y-axis. #' @param xmargin.scale the scaling parameter for the X-axis margin. Useful when plotting more than one logo on a page #' @param ymargin.scale the scaling parameter for the Y-axis margin. Useful when plotting more than one logo on a page #' @param title to be shown on the top #' @param titlefontsize the fontsize of the title #' @export seqLogoGrid <- function(pwm, ic.scale=TRUE, xaxis=TRUE, yaxis=TRUE, xfontsize=10, yfontsize=10, xmargin.scale=1, ymargin.scale=1, title="", titlefontsize=15){ if (inherits(pwm, "pwm")){ pwm <- pwm@pwm }else if (is.data.frame(pwm)){ pwm <- as.matrix(pwm) }else if (!is.matrix(pwm)){ stop("pwm must be of class matrix or data.frame") } if (any(abs(1 - apply(pwm,2,sum)) > 0.01)) stop("Columns of PWM must add up to 1.0") chars <- c("A","C","G","T") letters <- list(x=NULL,y=NULL,id=NULL,fill=NULL) npos <- ncol(pwm) if (ic.scale){ ylim <- 2 ylab <- "Information content" facs <- pwm2ic(pwm) }else{ ylim <- 1 ylab <- "Probability" facs <- rep(1, npos) } wt <- 1 x.pos <- 0 for (j in 1:npos){ column <- pwm[,j] hts <- 0.95*column*facs[j] letterOrder <- order(hts) y.pos <- 0 for (i in 1:4){ letter <- chars[letterOrder[i]] ht <- hts[letterOrder[i]] if (ht>0) letters <- addLetter(letters,letter,x.pos,y.pos,ht,wt) y.pos <- y.pos + ht + 0.01 } x.pos <- x.pos + wt } bottomMargin = ifelse(xaxis, 2 + xfontsize/3.5, 2) * ymargin.scale leftMargin = ifelse(yaxis, 2 + yfontsize/3.5, 2) * xmargin.scale rightMargin = 2 * xmargin.scale topMargin = 2 * ymargin.scale pushViewport(plotViewport(c(bottomMargin,leftMargin,topMargin,rightMargin))) pushViewport(dataViewport(0:ncol(pwm),0:ylim,name="vp1")) grid.polygon(x=unit(letters$x,"native"), y=unit(letters$y,"native"), id=letters$id, gp=gpar(fill=letters$fill,col="transparent")) # put in the title grid.text(title, y=1.1, vjust=0, gp=gpar(fontsize=titlefontsize)) if (xaxis){ grid.xaxis(at=seq(0.5,ncol(pwm)-0.5),label=1:ncol(pwm), gp=gpar(fontsize=xfontsize)) grid.text("Position",y=unit(-3,"lines"), gp=gpar(fontsize=xfontsize)) } if (yaxis){ grid.yaxis(gp=gpar(fontsize=yfontsize)) grid.text(ylab,x=unit(-3,"lines"),rot=90, gp=gpar(fontsize=yfontsize)) } popViewport() popViewport() } #' Plot the raw motifs scores as returned by motifScores() #' #' This function visualises the motif scores for one or more sequences. Sequences are drawn as lines, and scores are plotted #' as triangles at both sides of the line (corresponding to the two strands). The width of the base of the triangle corresponds to motif width and #' the height to the motif \code{log(score)} that is positive and greater than the \code{cutoff} parameter (if specified). All scores #' have the same y-axis, so the heights of bars are comparable between sequences and motifs. #' #' @param scores the list of motifs scores. Each element of the list is a matrix of scores for one sequences. The columns in the matrix #' correspond to different motifs. Each column contains the odds (not log-odds!) scores over both strands. For example, #' for a sequence of length 5, scores for a 3 bp motifs could be: \code{c(0.1, 1, 4, NA, NA, 1, 0.3, 2, NA, NA)}. The first #' 3 numbers are odds scores starting at first three bases, and the second lot of 3 numbers is the scores starting at the #' same positions but with the reverse complement of the motif. The last two values are NA on both strands because we do not #' support partial motif hits. #' @param sel.motifs a vector of motif names. Use this parameter to show the motif hits to only a subset of motifs for which the scores are available. #' @param seq.names a vector of sequence names to show in the graph. If none specified, the sequences will be named Sequence 1, Sequence 2, ... #' @param cols a vector of colours to use to colour code motif hits. If none are specified, the current palette will be used. #' @param cutoff either a single value, or a vector of values. The values are PWM cutoffs after \code{log.fun} (see below). Only motif scores above these cutoffs will be shown. #' If a single values is specified, it will be used for all PWMs, otherwise the vector needs to specify one cutoff per PWM. #' @param log.fun the logarithm function to use to calculate log-odds. By default log2 is used for consistency with Biostrings. #' @param main the main title #' @param legend.space the proportion of horizontal space to reserve for the legend. The default is 30\%. #' @param max.score the maximal log-odds score used to scale all other scores. By default this values is automatically determined, but it can #' also be set manually to make multiple plots comparable. #' @param trans the level of transparency. By default 50\% transparency to be able to see overlapping binding sites #' @param text.cex the scaling factor for sequence names #' @param legend.cex the scaling factor for the legend #' @param motif.names optional vector of motif names to show instead of those present as column names in \code{scores} #' @param seq.len.spacing the spacing (in bp units) between the end of the sequence line and the text showing the length in bp #' @param shape the shape to use to draw motif occurances, valid values are "rectangle" (default), "line" and "triangle" #' @export #' @examples #' if(requireNamespace("PWMEnrich.Dmelanogaster.background")){ #' ### #' # Load Drosophila PWMs #' data(MotifDb.Dmel, package = "PWMEnrich.Dmelanogaster.background") #' #' # two sequences of interest #' sequences = list(DNAString("GAAGTATCAAGTGACCAGGTGAAGTCCCAGATGA"), #' DNAString("AGGTAGATAGAACAGTAGGCAATGAAGCCGATG")) #' #' # select the tinman and snail motifs #' pwms = MotifDb.Dmel[c("tin", "sna")] #' #' # get the raw score that will be plotted #' scores = motifScores(sequences, pwms, raw.scores=TRUE) #' #' # plot the scores in both sequences, green for tin and blue for sna #' plotMotifScores(scores, cols=c("green", "blue")) #' #' } plotMotifScores = function(scores, sel.motifs=NULL, seq.names=NULL, cols=NULL, cutoff=NULL, log.fun=log2, main="", legend.space=0.30, max.score=NULL, trans=0.5, text.cex=0.9, legend.cex=0.9, motif.names=NULL, seq.len.spacing=8, shape="rectangle"){ # subset motifs if(!is.null(sel.motifs)){ scores = lapply(scores, function(x) x[, sel.motifs, drop=FALSE]) } if(length(unique(sapply(scores, ncol)))!=1){ stop("All elements of the 'scores' list need to have matrices with the same number of columns.") } if(!(shape %in% c("line", "triangle", "rectangle"))){ stop("'shape' parameter needs to be 'rectangle', 'line' or 'triangle'") } # reverse the scores for plotting order! scores = rev(scores) # number of sequences num.seq = length(scores) # maximal sequence length seq.len = sapply(scores, nrow)/2 max.seq.len = max(seq.len) # find out the length of each motif by the number of NAs motif.len = apply(scores[[1]], 2, function(x) sum(is.na(x))/2 + 1) num.motifs = length(motif.len) # threshold the signal if(is.null(cutoff)){ cutoff = rep(0, num.motifs) } else if(length(cutoff) == 1) { cutoff = rep(cutoff, num.motifs) } else if(length(cutoff) != num.motifs){ stop("The length of 'cutoff' does not match the number of shown motifs") } # do the log of scores scores = lapply(scores, log.fun) # apply the cutoff to the scores scores = lapply(scores, function(s){ for(i in 1:ncol(s)){ sel = which(s[,i] <= cutoff[i]) if(length(sel) > 0) s[sel,i] = 0 } s }) # the largest score to use to scale all scores if(is.null(max.score)) max.score = max(sapply(scores, function(s) max(s, na.rm=TRUE))) ############# PLOTING ############# if(is.null(cols)){ pal = palette() cols = pal[ (1:num.motifs) %% length(pal) ] } # add transparency cols.rgb = col2rgb(cols) cols.rgb = rbind(cols.rgb, "alpha"=(1-trans)*255) / 255 for(i in 1:length(cols)){ cols[i] = rgb(cols.rgb[1, i], cols.rgb[2, i], cols.rgb[3, i], cols.rgb[4, i]) } # set up the empty plotting area ylim = c(0, 2*num.seq) xlim = c(0, max.seq.len/(1-legend.space)) # allow for extra space for the legend par(mar=c(0,0,2,1)) plot(NULL, xlim=xlim, ylim=ylim, xaxt="n", yaxt="n", ylab="", xlab="", bty="n", main=main) # plot the lines corresponding to the sequences for(i in 1:num.seq){ y = 1+(i-1)*2 lines(c(1, seq.len[i]+1), c(y, y)) # ticks at the dn lines(c(1,1), c(y-0.03, y+0.03)) lines(c(seq.len[i]+1, seq.len[i]+1), c(y-0.03, y+0.03)) } # now plot the signal for(i in 1:num.seq){ s = scores[[i]] # the basic y axis y = 1+(i-1)*2 # iterate over motifs for(j in 1:ncol(s)){ by.strand = matrix(s[,j], ncol=2) for(k in 1:2){ x.start = which(by.strand[,k] > 0) x.end = x.start + motif.len[j] y.start = rep(y, length(x.start)) if(k == 1){ # multiply by 0.8 to leave some space between sequences! y.end = y.start + by.strand[x.start,k] / max.score * 0.8 } else { y.end = y.start - by.strand[x.start,k] / max.score * 0.8 } for(kk in 1:length(x.start)){ if(shape == "triangle"){ polygon(c(x.start[kk], x.end[kk], (x.start[kk]+x.end[kk])/2), c(y.start[kk], y.start[kk], y.end[kk]), col=cols[j], border=cols[j]) } else if(shape == "rectangle"){ rect(x.start[kk], y.start[kk], x.end[kk], y.end[kk], col=cols[j], border=cols[j]) } else { xmid = (x.start[kk]+x.end[kk])/2 rect(xmid-0.5, y.start[kk], xmid+0.5, y.end[kk], col=cols[j], border=cols[j]) } } } } } # find out max scores for each motif separately max.score.motif = sapply(1:num.motifs, function(i){ max(sapply(scores, function(s) max(s[,i], na.rm=TRUE))) }) # set up the legend if(is.null(motif.names)) motif.names = colnames(scores[[1]]) legend = paste(motif.names, " (", round(max.score.motif, 2), " max)", sep="") legend("topright", pch=rep(15, num.motifs), col=cols, legend=legend, cex=legend.cex) # plot sequence names if(is.null(seq.names)){ if(is.null(names(scores))) seq.names = paste("Sequence", 1:length(scores)) else seq.names = names(scores) } seq.names = rev(seq.names) for(i in 1:length(seq.names)){ y = 1+(i-1)*2 + 1 text(1, y, seq.names[i], adj=0, cex=text.cex) } # plot the lengths of sequences for(i in 1:length(seq.len)){ y = 1+(i-1)*2 x = seq.len[i] + seq.len.spacing text(x, y, paste(seq.len[i], "bp"), adj=0, cex=text.cex*0.8) } } PWMEnrich/R/PWM-methods.R0000644000175100017510000000403714614305422016023 0ustar00biocbuildbiocbuild#' Name of different pieces of information associated with PWM #' #' @title Names of variables #' @name names,PWM #' @aliases names,PWM-method #' @param x the PWM object #' @return the names of the variables #' @rdname operators-PWM setMethod("names", signature=signature(x="PWM"), function(x) slotNames(x)) #' Access a property by name #' #' @aliases $,PWM-method #' @param x the PWM object #' @param name the variable name #' @rdname operators-PWM setMethod("$", signature=signature(x="PWM"), function(x, name){ slot(x, name) }) #' Length of the motif #' #' Returns the motif length, i.e. the number of columns in the PWM. #' #' @param x the PWM object #' @aliases length,PWM-method #' @rdname operators-PWM setMethod("length", signature=signature(x="PWM"), function(x){ ncol(x@pwm) }) #' Reverse complement for the PWM object #' #' Finds the reverse complement of the PWM #' #' @aliases reverseComplement,PWM-method #' @param x an object of type PWM #' @param ... unused #' @return an object of type PWM that is reverse complement of x #' @export #' @examples #' #' if(requireNamespace("PWMEnrich.Dmelanogaster.background")){ #' data(MotifDb.Dmel.PFM, package = "PWMEnrich.Dmelanogaster.background") #' #' reverseComplement(MotifDb.Dmel.PFM[["ttk"]]) # reverse complement of the ttk PWM #' } #' setMethod("reverseComplement", signature=signature(x="PWM"), function (x, ...) { pfm = reverseComplement(x@pfm) pwm = reverseComplement(x@pwm) new("PWM", id=paste(x@id, "-- reverse complement"), name=x@name, pfm=pfm, prior.params=x@prior.params, pwm=pwm) }) #' show method for PWM #' @param object the PWM object setMethod("show", signature=signature(object="PWM"), function(object){ cat("An object of class 'PWM'\n") cat("ID:", object$id, "\n") cat("Target name:", object$name, "\n") cat("Frequency matrix:\n") cat("$pfm\n") print(object$pfm) cat("Position weight matrix (PWM):\n") cat("$pwm\n") print(object$pwm) cat("With background nucleotide frequencies which also serve as pseudo-count:\n") cat("$prior.params\n") print(object$prior.params) }) PWMEnrich/R/pwm.R0000644000175100017510000016435214614305422014531 0ustar00biocbuildbiocbuild # Globals from Biostrings DNA_BASES = c("A", "C", "G", "T") DNA_ALPHABET = c("A", "C", "G", "T", "M", "R", "W", "S", "Y", "K", "V", "H", "D", "B", "N", "-", "+") #' Input parameter normalization function for PWMUnscaled #' #' This function is from Biostrings package #' #' @param prior.params Typical 'prior.params' vector: c(A=0.25, C=0.25, G=0.25, T=0.25) .normargPriorParams <- function(prior.params) { if (!is.numeric(prior.params)) stop("'prior.params' must be a numeric vector") if (length(prior.params) != length(DNA_BASES) || !setequal(names(prior.params), DNA_BASES)) stop("'prior.params' elements must be named A, C, G and T") ## Re-order the elements. prior.params <- prior.params[DNA_BASES] if (any(is.na(prior.params)) || any(prior.params < 0)) stop("'prior.params' contains NAs and/or negative values") prior.params } #' Input parameter normalization for PWMUnscaled #' #' This function is from Biostrings package. #' A Position Frequency Matrix (PFM) is also represented as an ordinary #' matrix. Unlike a PWM, it must be of type integer (it will typically be #' the result of consensusMatrix()). #' #' @param x a frequency matrix .normargPfm <- function(x) { if (!is.matrix(x) || !is.integer(x)) stop("invalid PFM 'x': not an integer matrix") ## Check the row names. if (is.null(rownames(x))) stop("invalid PFM 'x': no row names") if (!all(rownames(x) %in% DNA_ALPHABET)) stop("invalid PFM 'x': row names must be in 'DNA_ALPHABET'") if (!all(DNA_BASES %in% rownames(x))) stop("invalid PFM 'x': row names must contain A, C, G and T") if (any(duplicated(rownames(x)))) stop("invalid PFM 'x': duplicated row names") ## Check the nb of cols. if (ncol(x) == 0L) stop("invalid PFM 'x': no columns") ## Check the values. if (any(is.na(x)) || any(x < 0L)) stop("invalid PFM 'x': values cannot be NA or negative") if (any(x[!(rownames(x) %in% DNA_BASES), ] != 0L)) stop("invalid PFM 'x': IUPAC ambiguity letters are represented") x <- x[DNA_BASES, , drop=FALSE] x } #' The PWM function from Biostrings without unit scaling #' #' By default the Biostrings package scales the log-odds score so it is within 0 and 1. In this function #' we take a more traditional approach with no unit scaling and offer unit scaling as an additional parameter. #' #' See ?PWM from Biostrings for more information on input arguments. #' #' @title Create a PWM from PFM #' @param x the integer count matrix representing the motif, rows as nucleotides #' @param id a systematic ID given to this PWM, could include the source, version, etc #' @param name the name of the transcription factor (TF) to which the PWM corresponds to #' @param type the type of PWM calculation, either as log2-odds, or posterior probability (frequency matrix) #' @param prior.params the pseudocounts for each of the nucleotides #' @param pseudo.count the pseudo-count values if different from priors #' @param unit.scale if to unit.scale the pwm (default is no unit scaling) #' @param seq.count if x is a normalised PFM (i.e. with probabilities instead of sequence counts), then this sequence count #' will be used to convert \code{x} into a count matrix #' @return a new PWM object representing the PWM #' @export #' @examples #' #' if(requireNamespace("PWMEnrich.Dmelanogaster.background")){ #' data(MotifDb.Dmel.PFM, package = "PWMEnrich.Dmelanogaster.background") #' #' ttk = MotifDb.Dmel.PFM[["ttk"]] #' #' # make a PWM with uniform background #' PWMUnscaled(ttk, id="ttk-JASPAR", name="ttk") #' #' # custom background #' PWMUnscaled(ttk, id="ttk-JASPAR", name="ttk", #' prior.params=c("A"= 0.2, "C" = 0.3, "G" = 0.3, "T" = 0.2)) #' #' # get background for drosophila (quick mode on a reduced dataset) #' prior = getBackgroundFrequencies("dm3", quick=TRUE) #' #' # convert using genomic background #' PWMUnscaled(ttk, id="ttk-JASPAR", name="ttk", prior.params=prior) #' } #' PWMUnscaled = function(x, id="", name="", type=c("log2probratio", "prob"), prior.params=c(A=0.25, C=0.25, G=0.25, T=0.25), pseudo.count=prior.params, unit.scale=FALSE, seq.count=NULL){ # convert to PFM if needed if(!is.null(seq.count)){ x = apply(round(x * seq.count), 1:2, as.integer) } # match input params x <- .normargPfm(x) nseq <- colSums(x) type <- match.arg(type) prior.params <- .normargPriorParams(prior.params) # total prior added priorN <- sum(prior.params) # posterior probabilities with unequal number of counts per row postProbs <- divideRows(x + prior.params, nseq + priorN) rownames(postProbs) = rownames(x) colnames(postProbs) = colnames(x) # calculate logodds, or return probabilities if (type == "log2probratio") { if (any(prior.params == 0)) stop("infinite values in PWM due to 0's in 'prior.params'") prior.probs <- prior.params/priorN ans <- log2(postProbs/prior.probs) } else { ans <- postProbs } # if to apply unit.scale if(unit.scale) ans = unitScale(ans) # return an object new("PWM", id=id, name=name, pfm=x, prior.params=prior.params, pwm=ans) } #' Scan the whole sequence on both strands #' #' The whole sequence is scanned with a PWM and scores returned beginning at each position. Partial motif #' matches are not done, thus the last #[length of motif]-1 scores are NA. #' #' The function returns either an odds average (*not* log-odds average), maximal score on each strand, #' or scores on both strands. #' #' The function by default returns the score in log2 following the package \code{Biostrings}. #' #' @param pwm PWM object #' @param dna a DNAString or other sequence from Biostrings #' @param pwm.rev the reverse complement for a pwm (if it is already pre-computed) #' @param odds.score if to return raw scores in odds (not logodds) space #' @param both.strands if to return results on both strands #' @param strand.fun which function to use to summarise values over two strands (default is "mean") #' @return a vector representing scores starting at each position, or a matrix with score in the two strands #' @export #' @examples #' #' if(requireNamespace("PWMEnrich.Dmelanogaster.background")){ #' data(MotifDb.Dmel, package = "PWMEnrich.Dmelanogaster.background") #' #' ttk = MotifDb.Dmel[["ttk"]] #' #' # odds average over the two strands expressed as log2-odds #' scanWithPWM(ttk, DNAString("CGTAGGATAAAGTAACT")) #' #' # log2-odds scores on both strands #' scanWithPWM(ttk, DNAString("CGTAGGATAAAGTAACT"), both.strands=TRUE) #' } #' scanWithPWM = function(pwm, dna, pwm.rev=NULL, odds.score=FALSE, both.strands=FALSE, strand.fun="mean"){ if(is.character(dna)) dna = DNAString(dna) if(!inherits(dna, c("DNAString", "DNAStringSet"))) stop("The input sequence needs to be either of type DNAString or DNAStringSet") if(is.null(pwm.rev)) pwm.rev = reverseComplement(pwm) # extract only the PWM matrices pwm = pwm$pwm pwm.rev = pwm.rev$pwm if(length(dna) < ncol(pwm)){ stop("DNA sequence needs to be at least as long as the PWM") } fwd.motif = PWMscoreStartingAt(pwm, dna, starting.at=1:(length(dna)-ncol(pwm)+1)) back.motif = PWMscoreStartingAt(pwm.rev, dna, starting.at=1:(length(dna)-ncol(pwm)+1)) if(both.strands){ res = cbind(fwd.motif, back.motif) colnames(res) = c("fwd", "rev") res = rbind(res, matrix(NA, nrow=(ncol(pwm)-1), ncol=2)) } else { if(strand.fun == "max"){ res = apply(cbind(fwd.motif, back.motif), 1, max) } else if(strand.fun == "mean"){ res = 2^cbind(fwd.motif, back.motif) res = log2(rowMeans(res)) } else { stop("Unknown strand function", strand.fun, ". Valid values are: mean, max") } # pad out the result so it is of same length as input sequence res = c(res, rep(NA, ncol(pwm)-1)) } if(odds.score) return(2^res) else return(res) } #' Convert frequencies into motifs using PWMUnscaled #' #' Note that this function is deprecated and replaced by \code{toPWM()}. #' #' @param motifs a list of motifs represented as matrices of frequencies (PFM) #' @param id the set of IDs for the motifs (defaults to names of the 'motifs' list) #' @param name the set of names for the motifs (defaults to names of the 'motifs' list) #' @param seq.count if frequencies in the motifs are normalized to 1, provides a vector of sequence counts (e.g. for MotifDb motifs) #' @param ... other parameters to PWMUnscaled #' #' @export #' @examples #' \dontrun{ #' if (requireNamespace("PWMEnrich.Dmelanogaster.background")) { #' data(MotifDb.Dmel.PFM, package = "PWMEnrich.Dmelanogaster.background") #' #' # convert to PWM with uniform background #' PFMtoPWM(MotifDb.Dmel.PFM) #' #' # get background for drosophila (quick mode on a reduced dataset) #' prior = getBackgroundFrequencies("dm3", quick=TRUE) #' #' # convert with genomic background #' PFMtoPWM(MotifDb.Dmel.PFM, prior.params=prior) #' } #' } PFMtoPWM = function(motifs, id=names(motifs), name=names(motifs), seq.count=NULL, ...){ if(!is.list(motifs)){ was.list = FALSE motifs = list(motifs) } else { was.list = TRUE } if(is.null(id)) id = rep("", length(motifs)) if(is.null(name)) name = rep("", length(motifs)) if(length(id) != length(motifs)) stop("The number of IDs (parameter 'id') need to be the same as number of motifs (parameter 'motifs')") if(length(name) != length(motifs)) stop("The number of names (parameter 'name') need to be the same as number of motifs (parameter 'motifs')") if(!is.null(seq.count) && length(seq.count) != length(motifs)){ stop("The 'seq.count' vector needs to be of the same length as the list of motifs (parameters 'motifs')") } # call PWMUnscaled res = list() for(i in 1:length(motifs)){ if(is.null(seq.count)){ res[[i]] = PWMUnscaled(motifs[[i]], id=id[i], name=name[i], ...) } else { res[[i]] = PWMUnscaled(motifs[[i]], id=id[i], name=name[i], seq.count=seq.count[i], ...) } } names(res) = names(motifs) # convert back to single object if that's how the input was if(!was.list){ return(res[[1]]) } else { return(res) } } #' Convert motifs into PWMs #' #' @param motifs a list of motifs either as position probability matrices (PPM) or frequency matirces (PFMs) #' @param ids the set of IDs for the motifs (defaults to names of the 'motifs' list) #' @param targets the set of target TF names for the motifs (defaults to names of the 'motifs' list) #' @param seq.count provides a vector of sequence counts for probability matrices (PPMs). Default it 50. #' @param prior frequencies of the four letters in the genome. Default is uniform background. #' @param ... other parameters to PWMUnscaled #' #' @export #' @examples #' \dontrun{ #' if (requireNamespace("PWMEnrich.Dmelanogaster.background")) { #' data(MotifDb.Dmel.PFM, package = "PWMEnrich.Dmelanogaster.background") #' #' toPWM(MotifDb.Dmel.PFM) # convert to PWM with uniform background #' #' # get background for drosophila (quick mode on a reduced dataset) #' prior = getBackgroundFrequencies("dm3", quick=TRUE) #' toPWM(MotifDb.Dmel.PFM, prior=prior) # convert with genomic background #' } #' } toPWM = function(motifs, ids=names(motifs), targets=names(motifs), seq.count=50, prior=c(A=0.25, C=0.25, G=0.25, T=0.25), ...){ if(!is.list(motifs)){ was.list = FALSE motifs = list(motifs) } else { was.list = TRUE } # extend seq.count if needed if(length(seq.count) == 1 && length(seq.count) != length(motifs)) seq.count = rep(seq.count, length(motifs)) # convert each individual motif into integers or multiply with seq.count for(i in 1:length(motifs)){ m = motifs[[i]] mi = apply(m, 1:2, as.integer) if(all(m == mi)){ # can be coersced to an integer matrix, use that motifs[[i]] = mi } else { # if the values is NA use 50 as default if(is.na(seq.count[i])){ motifs[[i]] = apply(round(m * 50), 1:2, as.integer) } else { motifs[[i]] = apply(round(m * seq.count[i]), 1:2, as.integer) } } } if(is.null(ids)) ids = rep("", length(motifs)) if(is.null(targets)) targets = rep("", length(motifs)) if(length(ids) != length(motifs)) stop("The number of IDs (parameter 'ids') need to be the same as number of motifs (parameter 'motifs')") if(length(targets) != length(motifs)) stop("The number of TF target names (parameter 'targets') need to be the same as number of motifs (parameter 'motifs')") # call PWMUnscaled res = list() for(i in 1:length(motifs)){ res[[i]] = PWMUnscaled(motifs[[i]], id=ids[i], name=targets[i], prior.params=prior, ...) } names(res) = names(motifs) # convert back to single object if that's how the input was if(!was.list){ return(res[[1]]) } else { return(res) } } #' Normalizes the motifs input argument for multiple functions #' #' @param motifs a list of motifs either as frequency matrices (PFM) or as PWM objects. If PFMs are specified #' they are converted to PWMs using uniform background. .inputParamMotifs = function(motifs){ # check motifs format and convert to PWM if(!is.list(motifs)) motifs = list(motifs) if(is.matrix(motifs[[1]])) pwms = PFMtoPWM(motifs) else if(inherits(motifs[[1]], "PWM")) pwms = motifs else stop("motifs need to be either frequency matrices or PWM objects") return(pwms) } #' Normalize the sequences input argument #' #' @param sequences a set of sequences to be scanned, a list of DNAString or other scannable objects .inputParamSequences = function(sequences){ if(is.character(sequences)){ sequences = readDNAStringSet(sequences) } # make sure sequences are in the right format if(!is.list(sequences) & !inherits(sequences, "DNAStringSet")) sequences = list(sequences) if(is.list(sequences) & length(sequences)>0){ sequences = lapply(sequences, function(s){ if(is.character(s)) DNAString(s) else s }) } #if(class(sequences) == "DNAStringSet") # sequences = DNAStringSetToList(sequences) return(sequences) } #' Motif affinity or number of hits over a threshold #' #' Scan a number of sequences either to find overall affinity, or a number of hits over a score threshold. #' #' @param sequences a set of sequences to be scanned, a list of DNAString or other scannable objects #' @param motifs a list of motifs either as frequency matrices (PFM) or as PWM objects. If PFMs are specified #' they are converted to PWMs using uniform background. #' @param raw.scores if to return raw scores (odds) for each position in the sequence. Note that scores for forward and reverse #' strand are concatenated into a single long vector of scores (twice the length of the sequence) #' @param verbose if to print verbose output #' @param cutoff if not NULL, will count number of matches with score above value specified (instead of returning the average affinity). #' Can either be one value, or a vector of values for each of the motifs. #' @return if raw.scores=FALSE, returns a matrix of mean scores (after cutoff if any), where columns are motifs. #' The returned values are either mean odd scores (not log-odd), or number of hits above a threshold; #' otherwise if raw.scores=TRUE, returns a list of raw score values (before cutoff) #' @export #' @examples #' #' if(requireNamespace("PWMEnrich.Dmelanogaster.background")){ #' data(MotifDb.Dmel, package = "PWMEnrich.Dmelanogaster.background") #' #' # affinity scores #' affinity = motifScores(DNAString("CGTAGGATAAAGTAACTAGTTGATGATGAAAG"), MotifDb.Dmel) #' #' # motif hit count with Patser score of 4 #' counts = motifScores(DNAString("CGTAGGATAAAGTAACTAGTTGATGATGAAAG"), MotifDb.Dmel, #' cutoff=log2(exp(4))) #' #' print(affinity) #' print(counts) #' #' # scanning multiple sequences #' sequences = list(DNAString("CGTAGGATAAAGTAACTAGTTGATGATGAAAG"), #' DNAString("TGAGACGAAGGGGATGAGATGCGGAAGAGTGAAA")) #' affinity2 = motifScores(sequences, MotifDb.Dmel) #' print(affinity2) #' } motifScores = function(sequences, motifs, raw.scores=FALSE, verbose=TRUE, cutoff=NULL){ if(!is.null(.PWMEnrich.Options[["useBigMemory"]]) && .PWMEnrich.Options[["useBigMemory"]]) return(motifScoresBigMemory(sequences, motifs, raw.scores, verbose, cutoff)) # check motifs format and convert to PWM pwms = .inputParamMotifs(motifs) sequences = .inputParamSequences(sequences) # check if any of the sequences is all Ns, if so, produce an error if(is(sequences, "DNAStringSet")){ al = alphabetFrequency(sequences) } else { al = do.call("rbind", lapply(sequences, alphabetFrequency)) } # check if any of the sequences is all Ns all.n = which(al[,"N"] == rowSums(al)) if(length(all.n) >0){ stop(paste("Sequence with index(es):", paste(all.n, collapse=","), "only contain N's. Please remove this sequence as the PWM score cannot be computed for any position.")) } pwms.rev = lapply(pwms, reverseComplement) # always unfold cutoff into a vector of values if(!is.null(cutoff)){ if(length(cutoff) == 1){ cutoff = rep(cutoff, length(pwms)) } else if(length(cutoff) != length(pwms)) { stop("The lengths of cutoff and pwms do not match. Either provide one values for cutoff, or a vector of values, one for each PWM") } } # inner loop function to use for parallel processing motifScoresLoop = function(i){ if(verbose) message(paste("Scanning sequence", i, "/", length(sequences))) s = sequences[[i]] # if using raw scores, record matrix of values; # otherwise a single final value for each pwm if(raw.scores){ r = matrix(0, nrow=length(s)*2, ncol=length(pwms)) colnames(r) = names(pwms) } else { r = rep(0, length(pwms)) names(r) = names(pwms) } for(j in 1:length(pwms)){ # if we are interested in counts, use max over two strands if(raw.scores){ # record raw scores that are averages over strands r[,j] = as.vector(scanWithPWM(pwms[[j]], s, pwms.rev[[j]], odds.score=TRUE, both.strands=TRUE)) } else if(!is.null(cutoff)){ # count number of hits on both strands r.pwm = scanWithPWM(pwms[[j]], s, pwms.rev[[j]], odds.score=FALSE, both.strands=TRUE) r[j] = sum(r.pwm >= cutoff[j], na.rm=TRUE) } else { # do average over both strands and also over all values r[j] = mean(scanWithPWM(pwms[[j]], s, pwms.rev[[j]], odds.score=TRUE), na.rm=TRUE) } } r } ####################################### END OF INNER LOOP ################ # either do it parallel or serial if(!is.null(.PWMEnrich.Options[["numCores"]])){ cat("Parallel scanning with", .PWMEnrich.Options[["numCores"]], "cores\n") # do it in parallel res = parallel::mclapply(1:length(sequences), motifScoresLoop, mc.cores = .PWMEnrich.Options[["numCores"]]) if(is.list(res)){ if( any(sapply(res, is.null)) ){ stop("Parallel scanning failed for some sequences. This could be due to a number of reasons including not enough memory.") } } } else { # do it serial res = lapply(1:length(sequences), motifScoresLoop) } ## return either raw scores, or counts or means if(raw.scores){ names(res) = names(sequences) return(res) } else { if(length(pwms) == 1){ return( matrix(sapply(res, identity), ncol=1, dimnames=list(NULL, names(pwms))) ) } else { r = t(sapply(res, identity)) rownames(r) = names(sequences) colnames(r) = names(pwms) return( r ) } } } #' This is a memory intensive version of motifScore() which is about 2 times faster #' #' The parameters and functionality are the same as \code{\link{motifScores}}. Please refer to documentation of this function #' for detailed explanation of functionality. #' #' This function is not meant to be called directly, but is indirectly called by motifScores() once a global parameters useBigMemory is set. #' #' @param sequences set of input sequences #' @param motifs set of input PWMs or PFMs #' @param raw.scores if to return scores for each base-pair #' @param verbose if to produce verbose output #' @param cutoff the cutoff for calling binding sites (in base 2 log). #' @param seq.all already concatenated sequences if already available (used to internally speed up things) #' #' @seealso \code{\link{motifScores}} motifScoresBigMemory = function(sequences, motifs, raw.scores=FALSE, verbose=TRUE, cutoff=NULL, seq.all=NULL){ # check motifs format and convert to PWM pwms = .inputParamMotifs(motifs) sequences = .inputParamSequences(sequences) pwms.rev = lapply(pwms, reverseComplement) pwms.len = sapply(pwms, length) min.pwm.len = min(pwms.len) # always unfold cutoff into a vector of values if(!is.null(cutoff)){ if(length(cutoff) == 1){ cutoff = rep(cutoff, length(pwms)) } else if(length(cutoff) != length(pwms)) { stop("The lengths of 'cutoff' and 'pwms' do not match. Either provide one values for 'cutoff' or a vector of values, one for each PWM") } # cutoff in odds space cutoff.2 = 2^cutoff } # work on a single sequence that is all concatenated together because it's faster if(is.null(seq.all)) seq.all = DNAString(concatenateSequences(sequences)) seq.len = sapply(sequences, length) # a common error with shorter sequences shorter.seq = which(seq.len < min.pwm.len) if(length(shorter.seq)>0){ stop(paste(length(shorter.seq), "sequence(s) have length shorter than the shortest PWM (", min.pwm.len, "), please identify and remove these sequences.")) } # a grouping data frame seq.len.sum = cumsum(seq.len) seq.group = data.frame("from"=rep(0, length(sequences)), "to"=0) seq.group$from = c(1, 1+seq.len.sum[-length(seq.len.sum)]) seq.group$to = seq.len.sum # inner loop function to use for parallel processing # # @param motif.start the index of the first motif # @param motif.end the index of the last motif motifScoresLoop = function(input.params){ motif.start = input.params[[1]] motif.end = input.params[[2]] if(verbose){ if(motif.start == motif.end){ message(paste("Starting scanning with motif", names(pwms)[motif.start])) } else { message(paste("Starting scanning with motifs from", motif.start, "to", motif.end)) } } # local copy of the sequences and motifs used in the function s = seq.all s.group = seq.group pwms.l = pwms[motif.start:motif.end] pwms.rev.l = pwms.rev[motif.start:motif.end] pwms.len.l = pwms.len[motif.start:motif.end] if(!is.null(cutoff)) cutoff.2.l = cutoff.2[motif.start:motif.end] # create the output structure res = list() for(i in 1:nrow(s.group)){ if(raw.scores){ r = matrix(0, nrow=seq.len[i]*2, ncol=length(pwms.l)) colnames(r) = names(pwms.l) res[[i]] = r } else { r = rep(0, length(pwms.l)) names(r) = names(pwms.l) res[[i]] = r } } ### do the scanning, convert back to individual sequences and do the post-processing for(j in 1:length(pwms.l)){ if(verbose){ message(paste("Scanning all sequences with motif", j, "/", length(pwms.l))) } raw = as.vector(scanWithPWM(pwms.l[[j]], s, pwms.rev.l[[j]], odds.score=TRUE, both.strands=TRUE)) ### split the raw results by sequence for(i in 1:nrow(s.group)){ # selector for this sequence from = seq.group$from[i] to = seq.group$to[i] from.rev = length(raw)/2 + seq.group$from[i] to.rev = length(raw)/2 + seq.group$to[i] sel = c(from:to, from.rev:to.rev) r = raw[sel] # fwd strand r[((seq.len[i]-pwms.len.l[j])+2):seq.len[i]] = NA # rev strand r[((2*seq.len[i]-pwms.len.l[j])+2):(2*seq.len[i])] = NA # save the values if(raw.scores){ res[[i]][,j] = r } else if(!is.null(cutoff)){ # count number of hits on both strands res[[i]][j] = sum(r >= cutoff.2.l[j], na.rm=TRUE) } else { # MeanAffinity res[[i]][j] = mean(r, na.rm=TRUE) } } } res } ######################################## END OF INNER LOOP ###################################### # either do it parallel or serial if(!is.null(.PWMEnrich.Options[["numCores"]]) && length(pwms) >= .PWMEnrich.Options[["numCores"]]){ cores = .PWMEnrich.Options[["numCores"]] sel = round(seq(0, length(pwms), length.out=cores+1)) start = sel[1:cores]+1 end = sel[2:(cores+1)] input = lapply(1:cores, function(i) list(start[i], end[i])) # do it in parallel res.parallel = parallel::mclapply(input, motifScoresLoop, mc.cores = cores) if(is.list(res.parallel)){ if( any(sapply(res.parallel, is.null)) ){ stop("Parallel scanning failed for some sequences. This could be due to a number of reasons including not enough memory.") } } # concatenate results for different motifs if(raw.scores) res = lapply(1:length(sequences), function(i) do.call("cbind", lapply(res.parallel, function(x) x[[i]]))) else res = lapply(1:length(sequences), function(i) do.call("c", lapply(res.parallel, function(x) x[[i]]))) } else { # do it serial res = motifScoresLoop(list(1, length(pwms))) } ## return either raw scores, or counts or means if(raw.scores){ names(res) = names(sequences) return(res) } else { if(length(pwms) == 1){ return( matrix(sapply(res, identity), ncol=1, dimnames=list(names(sequences), names(pwms))) ) } else { r = t(sapply(res, identity)) rownames(r) = names(sequences) colnames(r) = names(pwms) return( r ) } } } #' Information content for a PWM or PFM #' #' @param motif a matrix of frequencies, or a PWM object #' @param prior.params the prior parameters to use when a matrix is given (ignored if motif is already a PWM) #' @param bycol if to return values separately for each column #' @return information content in bits (i.e. log2) #' @export #' @examples #' #' if(requireNamespace("PWMEnrich.Dmelanogaster.background")){ #' data(MotifDb.Dmel, package = "PWMEnrich.Dmelanogaster.background") #' data(MotifDb.Dmel.PFM, package = "PWMEnrich.Dmelanogaster.background") #' #' # the nucleotide distribution is taken from the PWM (in this case genomic background) #' motifIC(MotifDb.Dmel[["ttk"]]) #' # information content with default uniform background because the input is a matrix, #' # not PWM object #' motifIC(MotifDb.Dmel.PFM[["ttk"]]) #' } motifIC = function(motif, prior.params=c(A=0.25, C=0.25, G=0.25, T=0.25), bycol=FALSE){ if(inherits(motif, "PWM")) { p = PWMUnscaled(motif$pfm, type="prob", prior.params=motif$prior.params) bg = motif$prior.params } else { p = PWMUnscaled(motif, type="prob", prior.params=prior.params) bg = prior.params } bg = bg/sum(bg) # normalize to 1 cols = p$pwm * log2(p$pwm/bg) if(bycol) return(colSums(cols)) else return(sum(cols)) } #' Calculate motif enrichment using one of available scoring algorithms and background corrections. #' #' This function provides and interface to all algorithms available in PWMEnrich #' to find motif enrichment in a single or a group of sequences with/without #' background correction. #' #' Since for all algorithms the first step involves calculating raw scores without background correction, the output #' always contains the scores without background correction together with (optional) background-corrected #' scores. #' #' Unless otherwise specified the scores are returned both separately for each sequence (without/with background) and #' for the whole group of sequences (without/with background). #' #' To use a background correction you need to supply a set of PWMs with precompiled background distribution parameters #' (see function \code{\link{makeBackground}}). When such an object is supplied as the \code{pwm} parameter, the scoring #' scheme and background correction are automatically determined. #' #' There are additional packages with already pre-computed background (e.g. see package \code{PWMEnrich.Dmelanogaster.background}). #' #' Please refer to (Stojnic & Adryan, 2012) for more details on the algorithms. #' #' @title Motif enrichment #' @param sequences the sequences to be scanned for enrichment. Can be either a single sequence #' (an object of class DNAString), or a list of DNAString objects, or a DNAStringSet object. #' @param pwms this parameter can take multiple values depending on the scoring scheme and background correction used. #' When the \code{method} parameter is set to "autodetect", the following default algorithms #' are going to be used: #' \itemize{ #' \item if \code{pwms} is a list containing either frequency matrices or a list of PWM objects then #' the "affinity" algorithm is selected. If frequency matrices are given, they are converted #' to PWMs using uniform background. For best performance, convert frequency matrices to PWMs #' before calling this function using realistic genomic background. #' \item Otherwise, appropriate scoring scheme and background correction are selected based on the #' class of the object (see below). #' } #' @param score this parameter determines which scoring scheme to use. Following scheme as available: #' \itemize{ #' \item "autodetect" - default value. Scoring method is determined based #' on the type of \code{pwms} parameter. #' \item "affinity" - use threshold-free affinity score. The \code{pwms} #' parameter can either be a list of frequency matrices, \code{PWM} objects, or a #' \code{PWMLognBackground} object. #' \item "cutoff" - use number of motif hits above a score cutoff. The \code{pwms} #' parameter can either be a list of frequency matrices, \code{PWM} objects, or a #' \code{PWMCutoffBackground} object. #' \item "clover" - use the Clover algorithm (Frith et al, 2004). The Clover score of a single #' sequence is identical to the affinity score, while for a group of sequences is an #' average of products of affinities over all sequence subsets. #' } #' #' @param bg this parameter determines how the raw score is compared to the background distribution. #' \itemize{ #' \item "autodetect" - default value. Background correction is determined based on the type #' of the \code{pwms} parameter. #' \item "logn" - use a lognormal distribution background pre-computed for a set of PWMs. #' This requires \code{pwms} to be of class \code{PWMLognBackground}. #' \item "z" - use a z-score for the number of significant motif hits compared to background number of hits. #' This requires \code{pwms} to be of class \code{PWMCutoffBackground}. #' \item "pval" - use empirical P-value based on a set of background sequences. This requires #' \code{pwms} to be of class \code{PWMEmpiricalBackground}. Note that PWMEmpiricalBackground #' objects tend to be very large so that the empirical P-value can be calculated in reasonable time. #' \item "ms" - shuffle columns of motif matrices and use that as basis for P-value calculation. Note that #' since the sequences need to rescanned with all of the new shuffled motifs this can be very slow. #' Also, this also works only no *individual* sequences, not groups. #' \item "none" - no background correction #' } #' @param cutoff the score cutoff for a significant motif hit if scoring scheme "cutoff" is selected. #' @param verbose if to print verbose output #' @param motif.shuffles number of times to shuffle motifs if using "ms" background correction #' @param B number of replicates when calculating empirical P-value #' @param group.only if to return statistics only for the group of sequences, not individual sequences. In the case of #' empirical background the P-values for individual sequences are not calculated (thus saving time), for other #' backgrounds they are calculated but not returned. #' @return a MotifEnrichmentResults object containing a subset following elements: #' \itemize{ #' \item "score" - scoring scheme used #' \item "bg" - background correction used #' \item "params" - any additional parameters #' \item "sequences" - the set of sequences used #' \item "pwms" - the set of pwms used #' \item "sequence.nobg" - per-sequence scores without any background correction. #' For "affinity" and "clover" a matrix of mean affinity scores; for #' "cutoff" number of significant hits above a cutoff #' \item "sequence.bg" - per-sequence scores after background correction. For "logn" and "pval" the P-value (smaller is better); #' for "z" and "ms" background corrections the z-scores (bigger is better). #' \item "group.nobg" - aggregate scores for the whole group of sequences without background correction. For "affinity" #' and "clover" the mean affinity over all sequences in the set; for "cutoff" the total number of hits in all #' sequences. #' \item "group.bg" - aggregate scores for the whole group of sequences with background correction. For "logn" and "pval", #' the P-value for the whole group (smaller is better); for "z" and "ms" the z-score for the whole set (bigger is better). #' \item "sequence.norm" - (only for "logn") the length-normalized scores for each of the sequences. Currently only implemented #' for "logn", where it returns the values normalized from LogN(0,1) distribution #' \item "group.norm" - (only for "logn") similar to sequence.norm, but for the whole group of sequences #' } #' @export #' @references \itemize{ #' \item R. Stojnic & B. Adryan: Identification of functional DNA motifs using a binding affinity lognormal background distribution, submitted. #' \item MC Frith et al: Detection of functional DNA motifs via statistical over-representation, Nucleid Acid Research (2004). #' } #' @examples #' #' if(requireNamespace("PWMEnrich.Dmelanogaster.background")){ #' ### #' # load the pre-compiled lognormal background #' data(PWMLogn.dm3.MotifDb.Dmel, package = "PWMEnrich.Dmelanogaster.background") #' #' # scan two sequences for motif enrichment #' sequences = list(DNAString("GAAGTATCAAGTGACCAGTAGATTGAAGTAGACCAGTC"), #' DNAString("AGGTAGATAGAACAGTAGGCAATGGGGGAAATTGAGAGTC")) #' res = motifEnrichment(sequences, PWMLogn.dm3.MotifDb.Dmel) #' #' # most enriched in both sequences (lognormal background P-value) #' head(motifRankingForGroup(res)) #' #' # most enriched in both sequences (raw affinity, no background) #' head(motifRankingForGroup(res, bg=FALSE)) #' #' # most enriched in the first sequence (lognormal background P-value) #' head(motifRankingForSequence(res, 1)) #' #' # most enriched in the first sequence (raw affinity, no background) #' head(motifRankingForSequence(res, 1, bg=FALSE)) #' #' ### #' # Load the pre-compiled background for hit-based motif counts with cutoff of P-value = 0.001 #' data(PWMPvalueCutoff1e3.dm3.MotifDb.Dmel, package = "PWMEnrich.Dmelanogaster.background") #' #' res.count = motifEnrichment(sequences, PWMPvalueCutoff1e3.dm3.MotifDb.Dmel) #' #' # Enrichment in the whole group, z-score for the number of motif hits #' head(motifRankingForGroup(res)) #' #' # First sequence, sorted by number of motif hits with P-value < 0.001 #' head(motifRankingForSequence(res, 1, bg=FALSE)) #' #' } motifEnrichment = function(sequences, pwms, score="autodetect", bg="autodetect", cutoff=NULL, verbose=TRUE, motif.shuffles=30, B=1000, group.only=FALSE){ # detect scoring method if(score == "autodetect"){ if(inherits(pwms, "PWMLognBackground")) { score = "affinity" } else if(inherits(pwms, "PWMCutoffBackground")) { score = "cutoff" } else { score = "affinity" } } if(score == "cutoff" & is.null(cutoff)) cutoff = pwms@bg.cutoff # the PWM* object pwmobj = NULL # detect background correction if(!(bg %in% c("none", "ms"))){ if(inherits(pwms, "PWMLognBackground")) { bg = "logn" pwmobj = pwms pwms = pwmobj@pwms } else if(inherits(pwms, "PWMCutoffBackground")) { bg = "z" pwmobj = pwms pwms = pwmobj@pwms } else if(inherits(pwms, "PWMEmpiricalBackground")) { bg = "pval" pwmobj = pwms pwms = pwmobj@pwms } else if(inherits(pwms, "PWMGEVBackground")) { bg = "gev" pwmobj = pwms pwms = pwmobj@pwms } else { bg = "none" } } if(!is.list(pwms)) pwms = list(pwms) sequences = .inputParamSequences(sequences) # return list res = list(score=score, bg=bg, pwms=pwms, sequences=sequences, params=list()) # apply no-bg scoring if(score == "affinity"){ # lengths of sequences and PWMs seq.len = sapply(sequences, length) pwm.len = sapply(pwms, length) res$sequence.nobg = motifScores(sequences, pwms, verbose=verbose) res$group.nobg = affinitySequenceSet(res$sequence.nobg, seq.len, pwm.len) } else if(score == "clover"){ res$sequence.nobg = motifScores(sequences, pwms, verbose=verbose) res$group.nobg = cloverScore(res$sequence.nobg, verbose=verbose) } else if(score == "cutoff"){ res$params = list(cutoff=cutoff) res$sequence.nobg = motifScores(sequences, pwms, cutoff=cutoff, verbose=verbose) res$group.nobg = colSums(res$sequence.nobg) } else { stop(paste("Unknown scoring algorithm: '", score, "'. Please select one of: 'affinity', 'cutoff', 'clover'", sep="")) } if(verbose){ cat("Calculating motif enrichment scores ...\n") } # apply background correction if needed if(bg == "none"){ res$sequence.bg = NULL res$group.bg = NULL } else if(bg == "logn"){ # lengths of sequences and PWMs seq.len = sapply(sequences, length) pwm.len = sapply(pwms, length) # do it per sequence res$sequence.bg = logNormPval(res$sequence.nobg, seq.len, pwm.len, pwmobj@bg.mean, pwmobj@bg.sd, pwmobj@bg.len) colnames(res$sequence.bg) = names(pwms) # convert logn P-values into normalized observations and run Clover on that res$sequence.norm = apply(res$sequence.bg, 1:2, qlnorm, lower.tail=FALSE) if(score == "affinity"){ # and for the group if(is.matrix(pwmobj@bg.mean)){ # convert back to P-values, using the chi-sq distribution res$group.bg = apply(res$sequence.bg, 2, function(x) { pchisq(-2*sum(log(x)), 2*length(x), lower.tail=FALSE) }) #res$group.bg = colMeans(log(res$sequence.bg)) res$group.norm = sapply(res$group.bg, qlnorm, lower.tail=FALSE) } else { res$group.bg = logNormPvalSequenceSet(res$sequence.nobg, seq.len, pwm.len, pwmobj@bg.mean, pwmobj@bg.sd, pwmobj@bg.len) res$group.norm = sapply(res$group.bg, qlnorm, lower.tail=FALSE) } } else if(score == "clover"){ # now run Clover on normalized scores res$group.bg = cloverScore(res$sequence.norm, verbose=verbose) } } else if(bg == "z"){ # lengths of sequences and PWMs seq.len = sapply(sequences, length) pwm.len = sapply(pwms, length) res$sequence.bg = cutoffZscore(res$sequence.nobg, seq.len, pwm.len, pwmobj@bg.P) res$group.bg = cutoffZscoreSequenceSet(res$sequence.nobg, seq.len, pwm.len, pwmobj@bg.P) } else if(bg == "pval"){ seq.len = sapply(sequences, length) pwm.len = sapply(pwms, length) # if to use cutoff usecutoff = NULL if(score == "cutoff") usecutoff = cutoff if(!group.only){ res$sequence.bg = t(sapply(1:length(seq.len), function(i) empiricalPvalue(res$sequence.nobg[i,], seq.len[i], pwm.len, pwmobj@bg.fwd, pwmobj@bg.rev, cutoff=usecutoff, B=B, verbose=verbose))) } # do empirical P-value if(score == "clover") res$group.bg = cloverPvalue1seq(res$sequence.nobg, seq.len, pwm.len, pwmobj@bg.fwd, pwmobj@bg.rev, B=B, verbose=verbose, clover=res$group.nobg) else res$group.bg = empiricalPvalueSequenceSet(res$sequence.nobg, seq.len, pwm.len, pwmobj@bg.fwd, pwmobj@bg.rev, cutoff=usecutoff, B=B, verbose=verbose) } else if(bg == "ms"){ # if to use cutoff usecutoff = NULL if(score == "cutoff") usecutoff = cutoff res$sequence.bg = matrixShuffleZscorePerSequence(res$sequence.nobg, sequences, pwms, cutoff=usecutoff, B=motif.shuffles) } else if(bg == "gev"){ seq.len = sapply(sequences, length) pwm.len = sapply(pwms, length) res$sequence.bg = gevPerSequence(res$sequence.nobg, seq.len, pwm.len, pwmobj@bg.loc, pwmobj@bg.scale, pwmobj@bg.shape) res$group.bg = NULL } else { stop(paste("Uknown background correction algorithm: '", bg, "', Please select one of: 'none', 'logn', 'z', 'pval', 'ms'", sep="")) } # only store the values for the group, not individual sequences! if(group.only){ seq.n = grep("^sequence[.]", names(res)) if(length(seq.n)>0){ res = res[-seq.n] } } # restore the sequence names if("sequence.nobg" %in% names(res)){ rownames(res$sequence.nobg) = names(sequences) } if("sequence.bg" %in% names(res)){ rownames(res$sequence.bg) = names(sequences) } return(new("MotifEnrichmentResults", res=res)) } #' Obtain z-score for motif column shuffling #' #' All PWMs are shuffled at the same time. This function would be too slow to produce #' empirical P-values, thus we return a z-score from a small number of shuffles. #' #' The z-scores are calculated for each sequence individually. #' #' @param scores a set of already calculated scores #' @param sequences either one sequence or a list/set of sequences (objects of type DNAString or DNAStringSet) #' @param pwms a list of PWMs #' @param cutoff if NULL, will use affinity, otherwise will use number of hits over this log2 odds cutoff #' @param B number of replicates, i.e. PWM column shuffles matrixShuffleZscorePerSequence = function(scores, sequences, pwms, cutoff=NULL, B=30){ # scores from different replicates for each of the sequences scores.matrix = array(0, dim=c(nrow(scores), ncol(scores), B)) dimnames(scores.matrix) = list(rownames(scores), colnames(scores), NULL) # perfom shuffles for(i in 1:B){ cat("Perfoming shuffle", i, "/", B, "\n") pwms.shuffled = pwms for(j in 1:length(pwms)){ p = pwms[[j]] pwms.shuffled[[j]]@pwm = p@pwm[, sample(ncol(p@pwm))] } scores.suffled = motifScores(sequences, pwms.shuffled, cutoff=cutoff, verbose=FALSE) scores.matrix[,,i] = scores.suffled } # work out mean and sd shuffled.mean = apply(scores.matrix, c(1,2), mean) shuffled.sd = apply(scores.matrix, c(1,2), sd) return( (scores - shuffled.mean) / shuffled.sd ) } #' Calculate the P-value from lognormal distribution with background of equal length #' #' @param scores affinity scores for the PWMs, can contain scores for more than one sequence (as rows), P-values are extracted separately #' @param seq.len the length distribution of the sequences #' @param pwm.len the leggths of PWMs #' @param bg.mean the mean values from the background for PWMs #' @param bg.sd the sd values from the background #' @param bg.len the length distribution of the background (we currently support only constant length) #' @param log if to produce log p-values logNormPval = function(scores, seq.len, pwm.len, bg.mean, bg.sd, bg.len, log=FALSE){ if(is.vector(scores)){ scores = matrix(scores, nrow=1, dimnames=list(NULL, names(scores))) } if( length(seq.len) != nrow(scores) ){ stop("The sequence length distribution and number of rows for scores doesn't match") } if( !all(colnames(scores) == names(bg.mean)) ){ stop("The motifs column names in 'scores' and 'bg.mean' do not match") } seq.mean = seq.sd = matrix(0, nrow=nrow(scores), ncol=ncol(scores)) slen = matrix(seq.len, nrow=nrow(scores), ncol=ncol(scores)) - matrix(pwm.len, nrow=nrow(scores), ncol=ncol(scores), byrow=TRUE) + 1 # rows (i) - sequences # cols (j) - motifs if(is.matrix(bg.mean)){ # human implementation ... bg.len.min = matrix(bg.len[1,], nrow=nrow(scores), ncol=ncol(scores), byrow=TRUE) bg.len.max = matrix(bg.len[nrow(bg.len),], nrow=nrow(scores), ncol=ncol(scores), byrow=TRUE) bg.len.all = lapply(1:nrow(bg.len), function(i) matrix(bg.len[i,], nrow=nrow(scores), ncol=ncol(scores), byrow=TRUE)) # split into three classes min.inx = slen <= bg.len.min max.inx = slen >= bg.len.max int.inx = !min.inx & !max.inx # bg.sd correction factor value bgsd = bg.sd * sqrt(bg.len/matrix(bg.len[1,], nrow=nrow(bg.len), ncol=ncol(bg.len), byrow=TRUE)) bgsd = bgsd / matrix(bgsd[1,], nrow=nrow(bgsd), ncol=ncol(bgsd), byrow=TRUE) bgsd.all = lapply(1:nrow(bgsd), function(i) matrix(bgsd[i,], nrow=nrow(scores), ncol=ncol(scores), byrow=TRUE)) # smaller if(any(min.inx)){ bg.mean.min = matrix(bg.mean[1,], nrow=nrow(scores), ncol=ncol(scores), byrow=TRUE) bg.sd.min = matrix(bg.sd[1,], nrow=nrow(scores), ncol=ncol(scores), byrow=TRUE) # keep mean, use quadratic for sd seq.mean[min.inx] = bg.mean.min[min.inx] seq.sd[min.inx] = bg.sd.min[min.inx] / sqrt(slen[min.inx]/bg.len.min[min.inx]) } if(any(max.inx)){ bg.mean.max = matrix(bg.mean[nrow(bg.mean),], nrow=nrow(scores), ncol=ncol(scores), byrow=TRUE) bg.sd.max = matrix(bg.sd[nrow(bg.sd),], nrow=nrow(scores), ncol=ncol(scores), byrow=TRUE) # keep mean, use quadratic for sd seq.mean[max.inx] = bg.mean.max[max.inx] seq.sd[max.inx] = bg.sd.max[max.inx] / sqrt(slen[max.inx]/bg.len.max[max.inx]) } if(any(int.inx)){ bg.mean.all = lapply(1:nrow(bg.mean), function(i) matrix(bg.mean[i,], nrow=nrow(scores), ncol=ncol(scores), byrow=TRUE)) bg.sd.all = lapply(1:nrow(bg.sd), function(i) matrix(bg.sd[i,], nrow=nrow(scores), ncol=ncol(scores), byrow=TRUE)) # interpolate! for(k in 1:(nrow(bg.mean)-1)){ a = bg.len.all[[k]] b = bg.len.all[[k+1]] inx = int.inx & slen >= a & slen < b if(any(inx)){ # direct linear interpolation for mean seq.mean[inx] = bg.mean.all[[k]][inx] + (bg.mean.all[[k+1]][inx] - bg.mean.all[[k]][inx])/(b[inx]-a[inx]) * (slen[inx]-a[inx]) # base value of seq.sd seq.sd[inx] = bg.sd.all[[1]][inx] / sqrt(slen[inx]/bg.len.all[[1]][inx]) # this correction factor would ideally be 1 seq.sd[inx] = seq.sd[inx] * (bgsd.all[[k]][inx] + (bgsd.all[[k+1]][inx]-bgsd.all[[k]][inx])/(b[inx]-a[inx]) * (slen[inx]-a[inx])) } } } } else { # default implementation seq.mean = matrix(bg.mean, nrow=nrow(scores), ncol=ncol(scores), byrow=TRUE) seq.sd = matrix(bg.sd, nrow=nrow(scores), ncol=ncol(scores), byrow=TRUE) / sqrt(slen / matrix(bg.len, nrow=nrow(scores), ncol=ncol(scores), byrow=TRUE)) } # calculate p-values seq.p = matrix(0, nrow=nrow(scores), ncol=ncol(scores)) colnames(seq.p) = colnames(scores) rownames(seq.p) = rownames(scores) # calculate the mean/sd parameters of the lognormal distribution seq.ml = 2*log(seq.mean) - 0.5*log(seq.mean^2+seq.sd^2) seq.sl = sqrt(-2*log(seq.mean) + log(seq.mean^2+seq.sd^2)) # convert to z-scores so we can streamline... z = (log(scores) - seq.ml) / seq.sl seq.p[,] = plnorm(exp(z), meanlog=0, sdlog=1, lower.tail=FALSE, log.p=log) # convert to log10 P-values if(log) seq.p = seq.p * log10(exp(1)) seq.p } #' Lognormal P-value for a set of sequences #' #' @param scores a matrix of per-sequence affinity scores #' @param seq.len lengths of sequences #' @param pwm.len lengths of pwms #' @param bg.mean mean background at length of bg.len #' @param bg.sd standard deviation of background at length of bg.len #' @param bg.len the length for which mean and sd are calculated #' @return P-value logNormPvalSequenceSet = function(scores, seq.len, pwm.len, bg.mean, bg.sd, bg.len){ # total score for all the sequences s = structure(rep(0, ncol(scores)), names=colnames(scores)) res = structure(rep(0, ncol(scores)), names=colnames(scores)) for(i in 1:ncol(scores)){ # lengths of sequences for this pwm, all of them are shorter by pwm.len-1 seq.len.pwm = seq.len - pwm.len[i] + 1 # sum over sequences then average by total length s[i] = sum(scores[,i] * seq.len.pwm) / sum(seq.len.pwm) if(is.matrix(bg.mean)){ res[i] = logNormPval(s[i], sum(seq.len.pwm)+pwm.len[i]-1, pwm.len[i], bg.mean[,i,drop=FALSE], bg.sd[,i,drop=FALSE], bg.len[,i,drop=FALSE]) } else { res[i] = logNormPval(s[i], sum(seq.len.pwm)+pwm.len[i]-1, pwm.len[i], bg.mean[i], bg.sd[i], bg.len[i]) } } return(res) } #' Replace all infinite values by 0 #' #' @param x a vector of values keepFinite = function(x) { if(any(!is.finite(x))) x[!is.finite(x)] = 0 x } #' Z-score calculation for cutoff hits #' #' The Z-score is calculated separately for each sequence #' #' @param scores the hit counts for the sequences #' @param seq.len the length distribution of sequences #' @param pwm.len the length distribution of the PWMs #' @param bg.P background probabilities of observing a motif hit at nucleotide resolution #' (scaled to sequence length, not 2 * length) #' @return Z-score cutoffZscore = function(scores, seq.len, pwm.len, bg.P){ if(is.vector(scores)) scores = matrix(scores, nrow=1) # actual probability is 1/2 because we have two strands bg.P = bg.P / 2 z = matrix(0, nrow=nrow(scores), ncol=ncol(scores)) rownames(z) = rownames(scores) colnames(z) = colnames(scores) for(i in 1:nrow(scores)){ # actualy length is 2x because we have two strands P.n = 2 * (seq.len[i] - pwm.len + 1) P.mean = bg.P * P.n P.sd = sqrt(P.n * bg.P * (1-bg.P)) # so this would be the poisson approximation # z[i,] = 1 / ppois(scores[i,], P.mean, lower.tail=FALSE) z[i,] = keepFinite( (scores[i,] - P.mean) / P.sd ) } return(z) } #' Z-score calculation for cutoff hits for group of sequences #' #' The Z-score is calculated as if the sequence came for one very long sequence #' #' @param scores the hit counts for the sequences #' @param seq.len the length distribution of sequences #' @param pwm.len the length distribution of the PWMs #' @param bg.P background probabilities of observing a motif hit at nucleotide resolution #' @return Z-score cutoffZscoreSequenceSet = function(scores, seq.len, pwm.len, bg.P){ total.hits = colSums(scores) total.len = sum(seq.len) - length(seq.len)*(pwm.len-1) + pwm.len - 1 # do it seperate for each PWM to make sure the lengths are right res = sapply(1:length(total.hits), function(i) cutoffZscore(total.hits[i], total.len[i], pwm.len[i], bg.P[i])) names(res) = names(total.hits) return(res) } #' Calculate the empirical P-value by affinity of cutoff. #' #' This is the new backend function for empirical P-values for either affinity or cutoff. #' The function only works on single sequences. #' #' @param scores the scores obtained for the sequence #' @param seq.len the length of the sequence, if a single value will take a single sequence #' of given length. If a vector of values, will take sequences of given lengths #' and joint them together #' @param pwm.len the lengths of PWMs #' @param bg.fwd raw odds scores for the forward strand of background #' @param bg.rev raw odds scores for the reverse strand of background #' @param cutoff if not NULL, will use hit count above this cutoff. The cutoff should be specified in log2. #' @param B the number of random replicates #' @param verbose if to give verbose progress reports #' @param exact.length if to take into consideration that the actual sequence lengths differ for different PWMs. #' For very long sequences (i.e. seq.len >> pwm.len) this make very little difference, however #' the run time with exact.length is much longer. empiricalPvalue = function(scores, seq.len, pwm.len, bg.fwd, bg.rev, cutoff=NULL, B=10000, verbose=FALSE, exact.length=FALSE){ # length of sequences bg.len = nrow(bg.fwd) if(max(seq.len) > bg.len){ stop("The length of sequence greater than background!") } if(max(seq.len) * B > bg.len){ warning("The length of the sequence multiplied by number of runs (B) is greater than background length. This might lead to unreliable P-value estimates.") } # the empricial p-value score.p = rep(0, length(scores)) for(i in 1:B){ if(verbose) message("Random sample ", i, " / ", B) # calculate the final score as sum over different seq.len sequences final.score = 0 for(k in 1:length(seq.len)){ # choose a subsequence with matching length start.range = 1:(bg.len - seq.len[k]) start = sample(start.range, 1) # fetch the scores from the two strands score.fwd = bg.fwd[start:(start+seq.len[k]-1),] score.rev = bg.rev[start:(start+seq.len[k]-1),] # simulate that we don't have scores for last pwm.len+1 nucleotides if(exact.length){ for(j in 1:length(pwm.len)){ score.fwd[(nrow(score.fwd)-pwm.len[j]+1):nrow(score.fwd),j] = NA score.rev[(nrow(score.rev)-pwm.len[j]+1):nrow(score.rev),j] = NA } } # append both strands to one long string score.both = rbind(score.fwd, score.rev) if(is.null(cutoff)){ # affinity algorithm new.score = colSums(score.both, na.rm=TRUE) } else { # number of hits above threshold new.score = colSums(score.both >= 2^cutoff, na.rm=TRUE) } final.score = final.score + new.score } # to obtain the mean we divide by twice the length (because it's both strands) final.score = final.score / (2 * sum(seq.len)) score.p = score.p + ((final.score > scores) + 0) } return(score.p / B) } #' Empirical P-value for a set of sequences #' #' Calculate empirical P-value for a set of sequences, using either affinity or cutoff. When cutoff is used, the score #' is a number of motif hits above a certain log-odds cutoff. #' #' @param scores a matrix of scores, rows for sequences, columns for PWMs #' @param seq.len the lengths of sequences #' @param pwm.len the lengths of PWMs #' @param bg.fwd raw odds scores for the forward strand of background #' @param bg.rev raw odds scores for the reverse strand of background #' @param cutoff if not NULL, will use hit count above this cutoff. The cutoff should be specified in log2. #' @param B the number of random replicates #' @param verbose if to give verbose progress reports empiricalPvalueSequenceSet = function(scores, seq.len, pwm.len, bg.fwd, bg.rev, cutoff=NULL, B=10000, verbose=FALSE){ # affinity if(is.null(cutoff)){ # total score for all the sequences s = structure(rep(0, ncol(scores)), names=colnames(scores)) res = structure(rep(0, ncol(scores)), names=colnames(scores)) exact.lengths = structure(rep(0, ncol(scores)), names=colnames(scores)) for(i in 1:ncol(scores)){ # lengths of sequences for this pwm, all of them are shorter by pwm.len-1 seq.len.pwm = seq.len - pwm.len[i] + 1 exact.lengths[i] = sum( seq.len.pwm ) # sum over sequences then average by total length s[i] = sum(scores[,i] * seq.len.pwm) / sum(seq.len.pwm) } # approximate sequence length without all the last positions being matched total.len = seq.len - round(mean(pwm.len)) # run it on mean sequence length res = empiricalPvalue(s, total.len, pwm.len, bg.fwd, bg.rev, cutoff, B, verbose, exact.length=FALSE) return(res) } else { total.hits = colSums(scores) total.len = seq.len - round(mean(pwm.len)) #sum(seq.len) - length(seq.len)*pwm.len # run ti on mean sequence lengths (with pwm.len) res = empiricalPvalue(total.hits, total.len, pwm.len, bg.fwd, bg.rev, cutoff, B, verbose, exact.length=FALSE) return(res) } } #' Apply GEV background normalization per every sequence #' #' @param scores affinity scores for the PWMs, can contain scores for more than one sequence (as rows), P-values are extracted separately #' @param seq.len the length distribution of the sequences #' @param pwm.len the lengths of PWMs #' @param bg.loc list of linear regression for location parameter #' @param bg.scale list of linear regression for scale parameter #' @param bg.shape list of linear regression for shape parameter gevPerSequence = function(scores, seq.len, pwm.len, bg.loc, bg.scale, bg.shape){ if(is.vector(scores)){ scores = matrix(scores, nrow=1, dimnames=list(NULL, names(scores))) } if( length(seq.len) != nrow(scores) ){ stop("The sequence length distribution and number of rows for scores doesn't match") } # calculate the expected sd values for the length distribution for each sequence seq.sd = matrix(0, nrow=nrow(scores), ncol=ncol(scores)) log.seqlen = data.frame("log.len"=log(seq.len)) # predict loc, scale, shape for each PWM loc = matrix(0, nrow=nrow(scores), ncol=ncol(scores)) scale = matrix(0, nrow=nrow(scores), ncol=ncol(scores)) shape = matrix(0, nrow=nrow(scores), ncol=ncol(scores)) for(i in 1:ncol(scores)){ loc[,i] = as.vector(predict.lm(bg.loc[[i]], newdata=log.seqlen)) scale[,i] = as.vector(predict.lm(bg.scale[[i]], newdata=log.seqlen)) shape[,i] = as.vector(predict.lm(bg.shape[[i]], newdata=log.seqlen)) } ## work out the P-values seq.p = matrix(0, nrow=nrow(scores), ncol=ncol(scores)) colnames(seq.p) = colnames(scores) rownames(seq.p) = rownames(scores) for(i in 1:nrow(seq.p)){ for(j in 1:ncol(seq.p)){ # if scale is <0 the GEV approximation is no longer valid if(scale[i,j] < 0) seq.p[i,j] = NA else seq.p[i,j] = pgev(log(scores[i,j]), loc=loc[i,j], scale=scale[i,j], shape=shape[i,j], lower.tail=FALSE) } } seq.p } #' Calculate total affinity over a set of sequences #' #' @param scores affinity scores for individual sequences #' @param seq.len lengths of sequences #' @param pwm.len lengths of PWMs affinitySequenceSet = function(scores, seq.len, pwm.len){ if(is.vector(scores)) scores = matrix(scores, nrow=1, dimnames=list(NULL, names(scores))) # final scores for the group final = structure(rep(0, ncol(scores)), names=colnames(scores)) for(i in 1:ncol(scores)){ # actual length of scanned sequence seq.len.pwm = seq.len - pwm.len[i] + 1 final[i] = sum(scores[,i] * seq.len.pwm) / sum(seq.len.pwm) } return(final) } #' Calculate Recovery-AUC for motifs ranked according to some scoring scheme #' #' Note that this function asssumes that smaller values are better! #' #' @param seq.res a matrix where each column represents a PWM and each row a result for a different sequence. motifRecoveryAUC = function(seq.res){ # calculate the ranks r = t(apply(seq.res, 1, rank)) # previous point needed to calculate AUC-ROC auc = rep(0, ncol(r)) names(auc) = colnames(r) # do it for all whole steps rec = t(sapply(1:ncol(r), function(i) colSums(r<=i))) list("rec"=rec, "auc"=colSums(rec)) } #' Calculate PR-AUC for motifs ranked according to some scoring scheme #' #' Note that this function asssumes that smaller values are better! #' #' @param seq.res a matrix where each column represents a PWM and each row a result for a different sequence. motifPrAUC = function(seq.res){ # calculate the ranks r = t(apply(seq.res, 1, rank)) # previous point needed to calculate AUC-ROC auc = rep(0, ncol(r)) names(auc) = colnames(r) # do it for all whole steps rec = t(sapply(1:ncol(r), function(i) colSums(r<=i))) recall = rec / nrow(r) prec = recall / 1:nrow(rec) list("prec"=prec, "recall"=recall) } PWMEnrich/R/PWMBackground-methods.R0000644000175100017510000001567014614305422020030 0ustar00biocbuildbiocbuild# Methods for the different PWMBackground objects #' show method for PWMLognBackground #' @param object the PWMLognBackground object setMethod("show", signature=signature(object="PWMLognBackground"), function(object){ cat("An object of class '", class(object), "'\n", sep="") cat("Background source:", object$bg.source, "\n") cat("Fitted on a mean sequence length of", round(mean(object$bg.len)), "for a set of", length(object$pwms), "PWMs\n") cat("Lognormal parameters: $bg.mean, $bg.sd\n") cat("PWMS: $pwms\n") }) #' show method for PWMCutoffBackground #' @param object the PWMCutoffBackground object setMethod("show", signature=signature(object="PWMCutoffBackground"), function(object){ cat("An object of class '", class(object), "'\n", sep="") cat("Background source:", object$bg.source, "\n") cat("Fitted for a set of", length(object$pwms), "PWMs\n") cat("Z-score parameters (cutoff is in log2): $bg.cutoff, $bg.P\n") cat("PWMS: $pwms\n") }) #' show method for PWMEmpiricalBackground #' @param object the PWMEmpiricalBackground object setMethod("show", signature=signature(object="PWMEmpiricalBackground"), function(object){ cat("An object of class '", class(object), "'\n", sep="") cat("Background source:", object$bg.source, "\n") cat("Raw scores for a ", nrow(object$bg.fwd), " bp sequence for ", length(object$pwms), " PWMs\n", sep="") cat("Raw scores (odds, not log-odds): $bg.fwd, $bg.rev\n") cat("PWMS: $pwms\n") }) #' show method for PWMGEVBackground #' @param object the PWMGEVBackground object setMethod("show", signature=signature(object="PWMGEVBackground"), function(object){ cat("An object of class '", class(object), "'\n", sep="") cat("Background source:", object$bg.source, "\n") cat("Generalized extreme value (GEV) distribution fitted for", length(object$pwms), "PWMs\n") cat("GEV parameters fitted with linear regressions: $bg.loc, $bg.scale, $bg.shape\n") cat("PWMS: $pwms\n") }) #' Name of different pieces of information associated with PWMLognBackground #' #' @title Names of variables #' @name names,PWMLognBackground #' @aliases names,PWMLognBackground-method #' @param x the PWMLognBackground object #' @return the names of the variables #' @rdname operators-PWMLognBackground setMethod("names", signature=signature(x="PWMLognBackground"), function(x) slotNames(x)) #' Access a property by name #' #' @aliases $,PWMLognBackground-method #' @param x the PWMLognBackground object #' @param name the variable name #' @rdname operators-PWMLognBackground setMethod("$", signature=signature(x="PWMLognBackground"), function(x, name){ slot(x, name) }) #' Name of different pieces of information associated with PWMCutoffBackground #' #' @title Names of variables #' @name names,PWMCutoffBackground #' @aliases names,PWMCutoffBackground-method #' @param x the PWMCutoffBackground object #' @return the names of the variables #' @rdname operators-PWMCutoffBackground setMethod("names", signature=signature(x="PWMCutoffBackground"), function(x) slotNames(x)) #' Access a property by name #' #' @aliases $,PWMCutoffBackground-method #' @param x the PWMCutoffBackground object #' @param name the variable name #' @rdname operators-PWMCutoffBackground setMethod("$", signature=signature(x="PWMCutoffBackground"), function(x, name){ slot(x, name) }) #' Name of different pieces of information associated with PWMEmpiricalBackground #' #' @title Names of variables #' @name names,PWMEmpiricalBackground #' @aliases names,PWMEmpiricalBackground-method #' @param x the PWMEmpiricalBackground object #' @return the names of the variables #' @rdname operators-PWMEmpiricalBackground setMethod("names", signature=signature(x="PWMEmpiricalBackground"), function(x) slotNames(x)) #' Access a property by name #' #' @aliases $,PWMEmpiricalBackground-method #' @param x the PWMEmpiricalBackground object #' @param name the variable name #' @rdname operators-PWMEmpiricalBackground setMethod("$", signature=signature(x="PWMEmpiricalBackground"), function(x, name){ slot(x, name) }) #' Name of different pieces of information associated with PWMGEVBackground #' #' @title Names of variables #' @name names,PWMGEVBackground #' @aliases names,PWMGEVBackground-method #' @param x the PWMGEVBackground object #' @return the names of the variables #' @rdname operators-PWMGEVBackground setMethod("names", signature=signature(x="PWMGEVBackground"), function(x) slotNames(x)) #' Access a property by name #' #' @aliases $,PWMGEVBackground-method #' @param x the PWMGEVBackground object #' @param name the variable name #' @rdname operators-PWMGEVBackground setMethod("$", signature=signature(x="PWMGEVBackground"), function(x, name){ slot(x, name) }) #' Get the background for a subset of PWMs #' #' @aliases [,PWMLognBackground-method #' @name [,PWMLognBackground-method #' @param x the PWMLognBackground object #' @param i the indicies of PWMs #' @param j unused #' @param ... unused #' @param drop unused #' @rdname subsetting-PWMLognBackground setMethod("[", "PWMLognBackground", function(x, i, j, ..., drop = TRUE){ if(is.matrix(x@bg.mean)){ new("PWMLognBackground", pwms=x@pwms[i], bg.mean=x@bg.mean[,i,drop=FALSE], bg.len=x@bg.len[,i,drop=FALSE], bg.sd=x@bg.sd[,i,drop=FALSE], bg.source=paste(x@bg.source, "--subset") ) } else { new("PWMLognBackground", pwms=x@pwms[i], bg.mean=x@bg.mean[i], bg.len=x@bg.len[i], bg.sd=x@bg.sd[i], bg.source=paste(x@bg.source, "--subset") ) } }) #' Get the background for a subset of PWMs #' #' @aliases [,PWMCutoffBackground-method #' @name [,PWMCutoffBackground-method #' @param x the PWMCutoffBackground object #' @param i the indicies of PWMs #' @param j unused #' @param ... unused #' @param drop unused #' @rdname subsetting-PWMCutoffBackground setMethod("[", "PWMCutoffBackground", function(x, i, j, ..., drop = TRUE){ new("PWMCutoffBackground", pwms=x@pwms[i], bg.cutoff=x@bg.cutoff[i], bg.P=x@bg.P[i], bg.source=paste(x@bg.source, "--subset") ) }) #' Get the background for a subset of PWMs #' #' @aliases [,PWMEmpiricalBackground-method #' @name [,PWMEmpiricalBackground-method #' @param x the PWMEmpiricalBackground object #' @param i the indicies of PWMs #' @param j unused #' @param ... unused #' @param drop unused #' @rdname subsetting-PWMEmpiricalBackground setMethod("[", "PWMEmpiricalBackground", function(x, i, j, ..., drop = TRUE) { new("PWMEmpiricalBackground", pwms=x@pwms[i], bg.fwd=x@bg.fwd[,i,drop=FALSE], bg.rev=x@bg.rev[,i,drop=FALSE], bg.source=paste(x@bg.source, "--subset") ) }) #' Get the background for a subset of PWMs #' #' @aliases [,PWMGEVBackground-method #' @name [,PWMGEVBackground-method #' @param x the PWMGEVBackground object #' @param i the indicies of PWMs #' @param j unused #' @param ... unused #' @param drop unused #' @rdname subsetting-PWMGEVBackground setMethod("[", "PWMGEVBackground", function(x, i, j, ..., drop = TRUE) { new("PWMGEVBackground", pwms=x@pwms[i], bg.loc=x@bg.loc[i], bg.scale=x@bg.scale[i], bg.shape=x@bg.shape[i], bg.source=paste(x@bg.source, "--subset") ) }) PWMEnrich/R/PWMEnrich-package.R0000644000175100017510000000136614614305422017106 0ustar00biocbuildbiocbuild#' @keywords internal "_PACKAGE" # The following block is used by usethis to automatically manage # roxygen namespace tags. Modify with care! ## usethis namespace: start #' @import BiocGenerics Biostrings methods #' @importFrom grid grid.newpage grid.text grid.polygon grid.xaxis grid.yaxis grid.layout gpar viewport plotViewport dataViewport pushViewport popViewport unit #' @importFrom grDevices col2rgb palette rgb #' @importFrom graphics lines par polygon rect text #' @importFrom stats cor dlnorm ecdf lm median na.omit optimize pchisq plnorm predict.lm qlnorm quantile #' @importFrom utils data #' @importFrom S4Vectors metadata #' @importFrom seqLogo makePWM plot #' @importFrom evd fgev pgev #' @importFrom gdata trim ## usethis namespace: end NULL PWMEnrich/R/readData.R0000644000175100017510000001121014614305422015413 0ustar00biocbuildbiocbuild## load in motifs #' Read in motifs in JASPAR or TRANSFAC format #' #' The format is autodetected based on file format. If the autodetection fail #' then the file cannot be read. #' #' @param file the filename #' @param remove.acc if to remove accession numbers. If TRUE, the AC entry in TRANSFAC files is ignored, #' and the accession is stripped from JASPAR, e.g. motif with name "MA0211.1 bap" would #' become just "bap". If FALSE, botht he AC and ID are used to generate the TRANSFAC name #' and the original motif names are preserved in JASPAR files. #' @return a list of 4xL matrices representing motifs (four nucleotides as rows) #' @export #' @examples #' #' # read in example TRANSFAC motifs without accession codes (just IDs) #' readMotifs(system.file(package = "PWMEnrich", dir = "extdata", file = "example.transfac"), #' remove.acc = TRUE) #' #' # read in the JASPAR insects motifs provided as example #' readMotifs(system.file(package = "PWMEnrich", dir = "extdata", file = "jaspar-insecta.jaspar"), #' remove.acc = TRUE) readMotifs = function(file, remove.acc=FALSE){ t = readLines(file) if(length(t) == 0){ stop(paste("Non-existing or empty file", file)) } is.transfac = length(grep("^XX", t)) > 0 is.jaspar = length(grep("^>", t)) > 0 if((is.transfac & is.jaspar) | (!is.transfac & !is.jaspar)){ stop("Cannot detect the file format of supplied file. Please look at the JASPAR and TRANSFAC websites for examples of well-formed motif files.") } if(is.jaspar){ return(readJASPAR(file, remove.acc)) } else { return(readTRANSFAC(file, remove.acc)) } } #' Read motifs in JASPAR format #' #' @param file the filename #' @param remove.ids if to strip JASPAR ID's from motif names, e.g. "MA0211.1 bap" would become just "bap" #' @return a list of matrices representing motifs (with four nucleotides as rows) readJASPAR = function(file, remove.ids=FALSE){ t = readLines(file) h = grep("^>", t) # ordering of nucleotides norder = c("A", "C", "G", "T") motifs = list() for(i in 1:length(h)){ # header and motif from jaspar format header = t[h[i]] h.inx = (h[i]+1) motif = t[h.inx:(h.inx+3)] # motif in clover format motif.matrix = t(sapply(strsplit(motif, "[ \t\\[]+"), function(x) as.integer(x[2:(length(x)-1)]))) nucleotides = sapply(strsplit(motif, "[ \t\\[]+"), function(x) x[1]) # in the new version of JASPAR format there is no ACGT... if(!all(nucleotides %in% norder)){ nucleotides = norder motif.matrix = t(sapply(strsplit(motif, "[ \t\\[]+"), function(x) as.integer(x))) } # if the ordering is not the same, re-order into ACGT order motif.matrix = motif.matrix[match(norder, nucleotides), ] rownames(motif.matrix) = norder motifs[[i]] = motif.matrix motif.name = trim(gsub(">", "", header)) if(remove.ids){ motif.parts = unlist(strsplit(motif.name, " +")) if(length(motif.parts)>1){ motif.name = paste(motif.parts[2:length(motif.parts)], collapse="_") } } names(motifs)[i] = motif.name } motifs } #' Read in motifs in TRANSFAC format #' #' @param file the filename #' @param remove.acc if to ignore transfac accession numbers #' @return a list of matrices representing motifs (with four nucleotides as rows) readTRANSFAC = function(file, remove.acc=TRUE){ t = readLines(file) # ordering of nucleotides norder = c("A", "C", "G", "T") # get indicies of different tags ac.inx = grep("^AC", t) id.inx = grep("^ID", t) po.inx = grep("^P[O0]", t) xx.inx = grep("^XX", t) if(length(ac.inx) != length(id.inx) || length(ac.inx) != length(po.inx) || !all(ac.inx < id.inx) || !all(id.inx < po.inx)){ stop("Inconsistent TRANSFAC format. Every motif needs to have exactly one AC, ID and PO/P0 entry (in that order).") } # extract individual motifs motifs = list() for(i in 1:length(ac.inx)){ ac = trim(gsub("^AC", "", t[ac.inx[i]])) # find the id entry that is just after the ac entry id.cur = min( id.inx[id.inx > ac.inx[i]] ) id = trim(gsub("^ID", "", t[id.cur])) # find boundaries of the motif po.cur = min( po.inx[po.inx > ac.inx[i]] ) xx.cur = min( xx.inx[xx.inx > po.cur] ) # check the ordering of nucleotides po = trim(gsub("^P[O0]", "", t[po.cur])) po = unlist(strsplit(po, "[ \t]+")) if(!identical(po, norder)){ stop(paste("In all motifs the nucleotides need to be ordered: A C G T. This is not the case for motif", id)) } # extract motif m = t[(po.cur+1) : (xx.cur-1)] m = strsplit(m, "[ \t]+") m = sapply(m, as.integer)[-1,] rownames(m) = norder # generate the name and save if(remove.acc) name = id else name = paste(ac, id, sep="_") motifs[[name]] = m } motifs } PWMEnrich/R/seqLogoSupp.R0000644000175100017510000000772714614305422016211 0ustar00biocbuildbiocbuild### Almost all of the functions from seqLogo which are not exported but we need to override seqLogo() function ## get information content profile from PWM pwm2ic<-function(pwm) { npos<-ncol(pwm) ic<-numeric(length=npos) for (i in 1:npos) { ic[i]<-2 + sum(sapply(pwm[, i], function(x) { if (x > 0) { x*log2(x) } else { 0 } })) } ic } ## get consensus sequence from PWM pwm2cons<-function(pwm) { if (!is.matrix(pwm)) {warning("pwm argument must be of class matrix")} letters <- c("A", "C", "G", "T") paste(apply(pwm, 2, function(x){letters[rev(order(x))[1]]}), collapse="") } letterA <- function(x.pos,y.pos,ht,wt,id=NULL){ x <- c(0,4,6,10,8,6.8,3.2,2,0,3.6,5,6.4,3.6) y <- c(0,10,10,0,0,3,3,0,0,4,7.5,4,4) x <- 0.1*x y <- 0.1*y x <- x.pos + wt*x y <- y.pos + ht*y if (is.null(id)){ id <- c(rep(1,9),rep(2,4)) }else{ id <- c(rep(id,9),rep(id+1,4)) } fill <- c("green","white") list(x=x,y=y,id=id,fill=fill) } ## T letterT <- function(x.pos,y.pos,ht,wt,id=NULL){ x <- c(0,10,10,6,6,4,4,0) y <- c(10,10,9,9,0,0,9,9) x <- 0.1*x y <- 0.1*y x <- x.pos + wt*x y <- y.pos + ht*y if (is.null(id)){ id <- rep(1,8) }else{ id <- rep(id,8) } fill <- "red" list(x=x,y=y,id=id,fill=fill) } ## C letterC <- function(x.pos,y.pos,ht,wt,id=NULL){ angle1 <- seq(0.3+pi/2,pi,length=100) angle2 <- seq(pi,1.5*pi,length=100) x.l1 <- 0.5 + 0.5*sin(angle1) y.l1 <- 0.5 + 0.5*cos(angle1) x.l2 <- 0.5 + 0.5*sin(angle2) y.l2 <- 0.5 + 0.5*cos(angle2) x.l <- c(x.l1,x.l2) y.l <- c(y.l1,y.l2) x <- c(x.l,rev(x.l)) y <- c(y.l,1-rev(y.l)) x.i1 <- 0.5 +0.35*sin(angle1) y.i1 <- 0.5 +0.35*cos(angle1) x.i1 <- x.i1[y.i1<=max(y.l1)] y.i1 <- y.i1[y.i1<=max(y.l1)] y.i1[1] <- max(y.l1) x.i2 <- 0.5 +0.35*sin(angle2) y.i2 <- 0.5 +0.35*cos(angle2) x.i <- c(x.i1,x.i2) y.i <- c(y.i1,y.i2) x1 <- c(x.i,rev(x.i)) y1 <- c(y.i,1-rev(y.i)) x <- c(x,rev(x1)) y <- c(y,rev(y1)) x <- x.pos + wt*x y <- y.pos + ht*y if (is.null(id)){ id <- rep(1,length(x)) }else{ id <- rep(id,length(x)) } fill <- "blue" list(x=x,y=y,id=id,fill=fill) } ## G letterG <- function(x.pos,y.pos,ht,wt,id=NULL){ angle1 <- seq(0.3+pi/2,pi,length=100) angle2 <- seq(pi,1.5*pi,length=100) x.l1 <- 0.5 + 0.5*sin(angle1) y.l1 <- 0.5 + 0.5*cos(angle1) x.l2 <- 0.5 + 0.5*sin(angle2) y.l2 <- 0.5 + 0.5*cos(angle2) x.l <- c(x.l1,x.l2) y.l <- c(y.l1,y.l2) x <- c(x.l,rev(x.l)) y <- c(y.l,1-rev(y.l)) x.i1 <- 0.5 +0.35*sin(angle1) y.i1 <- 0.5 +0.35*cos(angle1) x.i1 <- x.i1[y.i1<=max(y.l1)] y.i1 <- y.i1[y.i1<=max(y.l1)] y.i1[1] <- max(y.l1) x.i2 <- 0.5 +0.35*sin(angle2) y.i2 <- 0.5 +0.35*cos(angle2) x.i <- c(x.i1,x.i2) y.i <- c(y.i1,y.i2) x1 <- c(x.i,rev(x.i)) y1 <- c(y.i,1-rev(y.i)) x <- c(x,rev(x1)) y <- c(y,rev(y1)) h1 <- max(y.l1) r1 <- max(x.l1) h1 <- 0.4 x.add <- c(r1,0.5,0.5,r1-0.2,r1-0.2,r1,r1) y.add <- c(h1,h1,h1-0.1,h1-0.1,0,0,h1) if (is.null(id)){ id <- c(rep(1,length(x)),rep(2,length(x.add))) }else{ id <- c(rep(id,length(x)),rep(id+1,length(x.add))) } x <- c(rev(x),x.add) y <- c(rev(y),y.add) x <- x.pos + wt*x y <- y.pos + ht*y fill <- c("orange","orange") list(x=x,y=y,id=id,fill=fill) } addLetter <- function(letters,which,x.pos,y.pos,ht,wt){ if (which == "A"){ letter <- letterA(x.pos,y.pos,ht,wt) }else if (which == "C"){ letter <- letterC(x.pos,y.pos,ht,wt) }else if (which == "G"){ letter <- letterG(x.pos,y.pos,ht,wt) }else if (which == "T"){ letter <- letterT(x.pos,y.pos,ht,wt) }else{ stop("which must be one of A,C,G,T") } letters$x <- c(letters$x,letter$x) letters$y <- c(letters$y,letter$y) lastID <- ifelse(is.null(letters$id),0,max(letters$id)) letters$id <- c(letters$id,lastID+letter$id) letters$fill <- c(letters$fill,letter$fill) letters } PWMEnrich/R/similarity.R0000644000175100017510000001312014614305422016076 0ustar00biocbuildbiocbuild## functions to calculate motif similarity metrics #' Returned the aligned motif parts #' #' This function takes the offset of first motif relative to second and #' chops off the end of both motifs that are not aligned. It returns a list #' containing only the columns that align. #' #' @param m1 frequency matrix of first motif #' @param m2 frequency matrix of second motif #' @param offset a number of nucleotides by which the first motif is offsetted compared to the second #' @return a list of column-trimmed motifs m1, m2 maxAligned = function(m1, m2, offset){ if(offset > 0){ # need to cut off beginning of m2 m2 = m2[,(offset+1):ncol(m2)] } else if(offset < 0){ # net to cut off beginning of m1 m1 = m1[,(abs(offset)+1):ncol(m1)] } # make sure we keep matrix if(is.vector(m1)) m1 = matrix(m1, ncol=1) if(is.vector(m2)) m2 = matrix(m2, ncol=1) # maximal number of columns that align max.col = min(ncol(m1),ncol(m2)) m1 = m1[,1:max.col] m2 = m2[,1:max.col] # make sure we keep it in matrix if(is.vector(m1)) m1 = matrix(m1, ncol=1) if(is.vector(m2)) m2 = matrix(m2, ncol=1) list(m1, m2) } #' Try all motif alignments and return max score #' #' This function tries all offsets of motif1 compared to motif2 and returns #' the maximal (unnormalized) correlation score. #' #' The correlation score is essentially the sum of correlations of individual aligned #' columns as described in Pietrokovski (1996). #' #' @param m1 frequency matrix of motif 1 #' @param m2 frequency matrix of motif 2 #' @param min.align minimal number of basepairs that need to align #' @param exclude.zero if to exclude offset=0, useful for calculating self-similarity #' @references Pietrokovski S. Searching databases of conserved sequence regions by aligning protein multiple-alignments. Nucleic Acids Res 1996;24:3836-3845. #' @return single maximal score tryAllMotifAlignments = function(m1, m2, min.align=2, exclude.zero=FALSE){ offset = (-ncol(m1)+min.align):(ncol(m2)-min.align) if(exclude.zero) offset = setdiff(offset, 0) scores = rep(0, length(offset)) for(i in 1:length(offset)){ # get aligned matrices aligned = maxAligned(m1, m2, offset[i]) # convert to probabilities aligned.norm = lapply(aligned, function(x) divideRows(x, colSums(x))) # scores for first alignment (sum of column-wise correlations) scores[i] = sum(sapply(1:ncol(aligned.norm[[1]]), function(i) cor(aligned.norm[[1]][,i], aligned.norm[[2]][,i]))) } max(scores) } #' Check the frequency matrix input parameter for motifSimilarity #' #' @param m either a PWM object or a matrix #' @return corresponding PFM .inputPFMfromMatrixOrPWM = function(m){ if(inherits(m, "PWM")) return(m@pfm) else if(is.matrix(m)) return(m) else stop("Input motif needs to be either of class PWM, or a frequency matrix") } #' Calculates similarity between two PFMs. #' #' This function calculates the normalized motif correlation as a measure of motif #' frequency matrix similarity. #' #' This score is essentially a normalized version of the sum of column correlations #' as proposed by Pietrokovski (1996). The sum is normalized by the average motif #' length of m1 and m2, i.e. (ncol(m1)+ncol(m2))/2. Thus, for two idential motifs #' this score is going to be 1. For unrelated motifs the score is going to be typically #' around 0. #' #' Motifs need to aligned for this score to be calculated. The current implementation #' tries all possible ungapped alignment with a minimal of two basepair matching, and #' the maximal score over all alignments is returned. #' #' Motif 1 is aligned both to Motif 2 and its reverse complement. Thus, the motif #' similarities are the same if the reverse complement of any of the two motifs #' is given. #' #' @param m1 matrix with four rows representing the frequency matrix of first motif #' @param m2 matrix with four rows representing the frequency matrix of second motif #' @param trim bases with information content smaller than this value will be trimmed off both motif ends #' @param self.sim if to calculate self similarity (i.e. without including offset=0 in alignment) #' @references Pietrokovski S. Searching databases of conserved sequence regions by aligning protein multiple-alignments. Nucleic Acids Res 1996;24:3836-3845. #' @export #' @examples #' #' if(requireNamespace("PWMEnrich.Dmelanogaster.background")){ #' data(MotifDb.Dmel.PFM, package = "PWMEnrich.Dmelanogaster.background") #' #' # calculate the similarity of tin and vnd motifs (which are almost identical) #' motifSimilarity(MotifDb.Dmel.PFM[["tin"]], MotifDb.Dmel.PFM[["vnd"]]) #' #' # similarity of two unrelated motifs #' motifSimilarity(MotifDb.Dmel.PFM[["tin"]], MotifDb.Dmel.PFM[["ttk"]]) #' } motifSimilarity = function(m1, m2, trim=0.4, self.sim=FALSE){ # check input types m1 = .inputPFMfromMatrixOrPWM(m1) m2 = .inputPFMfromMatrixOrPWM(m2) # remove any zero-variance columns colSd1 = apply(m1, 2, sd) colSd2 = apply(m2, 2, sd) if(any(colSd1 == 0)) m1 = m1[,colSd1!=0] if(any(colSd2 == 0)) m2 = m2[,colSd2!=0] # trim bases with small motif content ic1 = motifIC(m1, bycol=TRUE) ic2 = motifIC(m2, bycol=TRUE) # find which columns to keep r1 = range(which(ic1 >= trim)) r2 = range(which(ic2 >= trim)) m1 = m1[,r1[1]:r1[2]] m2 = m2[,r2[1]:r2[2]] # get max score if(self.sim){ # we don't want offset=0 because then the score would always be 1 score = tryAllMotifAlignments(m1, m2, exclude.zero=TRUE) } else { score = max(tryAllMotifAlignments(m1,m2), tryAllMotifAlignments(m1,reverseComplement(m2))) } # normalize the max score by mean motif size return(score / mean(c(ncol(m1), ncol(m2)))) } PWMEnrich/tests/0000755000175100017510000000000014614305422014531 5ustar00biocbuildbiocbuildPWMEnrich/tests/test-all.R0000644000175100017510000000010114614305422016371 0ustar00biocbuildbiocbuildlibrary(testthat) library(PWMEnrich) test_package("PWMEnrich") PWMEnrich/vignettes/0000755000175100017510000000000014614351234015401 5ustar00biocbuildbiocbuildPWMEnrich/vignettes/PWMEnrich.Rnw0000644000175100017510000005173414614305422017675 0ustar00biocbuildbiocbuild%\VignetteEngine{knitr::knitr} %\VignetteIndexEntry{Overview of the 'PWMEnrich' package} %\VignetteKeywords{Motif enrichment, PWM} %\VignettePackage{PWMEnrich} \documentclass{article} \usepackage{float} \usepackage{a4wide} <>= library(knitr) opts_chunk$set( echo=TRUE,eval=TRUE,cache=FALSE,tidy=FALSE ) @ <>= library(knitr) opts_chunk$set( tidy=FALSE,dev='pdf', message=FALSE, warning=FALSE ) @ <>= BiocStyle::latex() @ %% colors \usepackage{color} \definecolor{Red}{rgb}{0.7,0,0} \definecolor{Blue}{rgb}{0,0,0.8} \hypersetup{% hyperindex = {true}, colorlinks = {true}, linktocpage = {true}, plainpages = {false}, linkcolor = {Blue}, citecolor = {Blue}, urlcolor = {Blue}, pdfstartview = {Fit}, pdfpagemode = {UseOutlines}, pdfview = {XYZ null null null} } \author{Robert Stojni\'{c}\footnote{ e-mail: \email{robert.stojnic@gmail.com}, Cambridge Systems Biology Institute, University of Cambridge, UK} } \begin{document} \title{Overview of the \Biocpkg{PWMEnrich} package} \maketitle \tableofcontents \section{Introduction}\label{sec:intro} The main functionality of the package is Position Weight Matrix (PWM)\footnote{In this vignette we use "PWM", "DNA motif" and "motif" interchangeably.} enrichment analysis in a single sequence (e.g. enhancer of interest) or a set of sequences (e.g. set of ChIP-chip/seq peaks). Note that this is not the same as \textit{de-novo} motif finding which discovers novel motifs, nor motif comparison which aligns motifs. The package is built upon \Robject{Biostrings} and offers high-level functions to scan for DNA motif occurrences and compare them against a genomic background. There are multiple packages with pre-compiled genomic backgrounds such as \Robject{PWMEnrich.Dmelanogaster.background}, \Robject{PWMEnrich.Hsapiens.background} and \Robject{PWMEnrich.Mmusculus.background}. In these packages the genomic distribution is calculated for motifs from the \Robject{MotifDb} database. The \Robject{PWMEnrich} package contains all the functions used to create these packages, so you can calculate your own background distributions for your own set of motifs. In this vignette we will use the \textit{Drosophila} package, but the other background packages are used in the same way (see Section \ref{sec:human} for minor human-specific differences). \subsection{Implemented algorithms} \Robject{PWMEnrich} uses the PWM scanning algorithm implemented by the package \Robject{Biostrings}. This package returns PWM scores at each position on one strand of a sequence. \Robject{PWMEnrich} extends this with a higher-level functions which automatically scans both strands for multiple motifs and sequences. The main goal of the package is to assess the enrichment of motif hits in a sequence (or group of sequences) compared to a genomic background. The traditional way of doing this is to use a threshold for the PWM score and count the number of motif hits in the sequence(s) of interest. Since this converts the sequence into a binary bound/not-bound string, the enrichment of binding events can be assessed using a binomial formula. The \Robject{PWMEnrich} package implements this algorithm, but by default uses a lognormal threshold-free approach \cite{stojnic_2012} which is related to the score used in Clover \cite{frith_detection_2004}. In the lognormal threshold-free approach average affinity is calculated over the whole sequence (or set of sequences) and compared to the average affinity of length-matched sequences from the genomic background. This approach performs better or same as the best threshold approach \cite{stojnic_2012}, with the added benefit of not having to choose a threshold or compare the results for multiple thresholds. We will use this threshold-free approach in all of our examples. Please consult the reference manual on how to use the fixed-threshold algorithms. \subsection{S4 class structure and accessors} As the \Biocpkg{PWMEnrich} package builds upon the \Biocpkg{Biostrings} package it uses the classes from this package to represent DNA sequences (\Robject{DNAString} and \Robject{DNAStringSet}). FASTA files can be loaded using functions from \Biocpkg{Biostrings} such as \Robject{readDNAStringSet}. The package introduces a new class \Robject{PWM} to represent a PWM together with the frequency matrix and other parameters (background nucleotide frequencies and pseudo-counts). All motif scoring is performed by the \Biocpkg{Biostrings} package which is why the \Biocpkg{PWMEnrich} package also returns log2 scores instead of more common log base \textit{e} scores. The results of motif scanning are stored in objects of class \Robject{MotifEnrichmentResults} and \Robject{MotifEnrichmentReport}. The package also introduces a number of classes that represent different background distributions: \Robject{PWMLognBackground}, \Robject{PWMCutoffBackground}, \Robject{PWMEmpiricalBackground}, \Robject{PWMGEVBackground}. In all cases, the classes are implemented with a list-like interface, that is, individual pieces of information within the objects are accessibly using \Rfunction{names(obj)} and \Rfunction{obj\$prop}. \section{Use case 1: Finding enrichment motifs in a single sequence} One of the most well-known example of combinatorial control by transcription factors in \textit{Drosophila} is the \textit{even skipped (eve)} stripe 2 enhancer. This well-studied enhancer has a number of annotated binding sites for TFs \textit{Kr}, \textit{vfl}, \textit{bcd}, \textit{hb} and \textit{gt}. We will use this enhancer as an example as we already know its functional structure. In order to predict which TFs are likely to functionally bind to the stripe 2 enhancer, we will calculate motif enrichment for a set of experimentally derived motifs from the \Biocpkg{MotifDb} database. We will do this by comparing the average affinity of each motif in the stripe 2 enhancers to the affinity over all \textit{D. melanogaster} promoters\footnote{For more information see \cite{stojnic_2012}}. These background distributions are already pre-calculated in the \Robject{PWMEnrich.Dmelanogaster.background} package which we will simply load and use. See the last section of this vignette for using your own motifs and background sequences. <>= library(PWMEnrich) library(PWMEnrich.Dmelanogaster.background) # load the pre-compiled lognormal background data(PWMLogn.dm3.MotifDb.Dmel) # load the stripe2 sequences from a FASTA file for motif enrichment sequence = readDNAStringSet(system.file(package="PWMEnrich", dir="extdata", file="stripe2.fa")) sequence # perform motif enrichment! res = motifEnrichment(sequence, PWMLogn.dm3.MotifDb.Dmel) report = sequenceReport(res, 1) report # plot the motif with P-value < 0.05 plot(report[report$p.value < 0.05], fontsize=7, id.fontsize=6) @ The main function we used is \Robject{motifEnrichment} which took our sequence and calculated motif enrichment using the lognormal affinity background distribution (fitted on a set of 10031 \textit{D. melanogaster} 2kb promoters). This function returns a set of scores and P-values for our sequence. We then used the \Robject{sequenceReport} function that create a ranked list of motifs, which we then plot using \Robject{plot}. The first column is the rank, the second shows the target name, which is either a gene name, an isoform name (such as ttk-PF), or a dimer name (such as tgo\_sim not present in this list). The next column in the plot is the PWM logo, and after that the motif ID. This ID comes from the \Robject{MotifDb} package and can be used to look up further information about the motif (such as the motif source). The next-to-last column is the raw affinity score, and the last column is the P-value of motif enrichment. As we can see, the top of the list is dominated by motifs similar to bcd. By further examining the list, we find we recovered the Kr, bcd and gt motifs, but not the vfl and hb motifs. These two TFs (vfl and hb) have the smallest number of annotated binding sites out of the five TFs in the stripe 2 enhancer. As a result, this affinity is not large enough to be picked up by motif enrichment. However, the other three motifs were picked up. We find this to be the typical case for many enhancers. \section{Use case 2: Examining the binding sites} We continue with our example of the eve stripe 2 enhancer from the previous section. We now want to visualise the binding sites for Kr, bcd and gt. <>= # extract the 3 PWMs for the TFs we are interested in ids = c("bcd_FlyReg_FBgn0000166", "gt_FlyReg_FBgn0001150", "Kr") sel.pwms = PWMLogn.dm3.MotifDb.Dmel$pwms[ids] names(sel.pwms) = c("bcd", "gt", "Kr") # scan and get the raw scores scores = motifScores(sequence, sel.pwms, raw.scores=TRUE) # raw scores for the first (and only) input sequence dim(scores[[1]]) head(scores[[1]]) # score starting at position 1 of forward strand scores[[1]][1, "bcd"] # score for the reverse complement of the motif, starting at the same position scores[[1]][485, "bcd"] # plot plotMotifScores(scores, cols=c("green", "red", "blue")) @ Here we used the \Robject{motifScores} function to obtain the raw scores at each position in the sequence. The result of this function is a list of matrices, each element of the list corresponding to an input sequence. In this case we had only one input sequence, and as a result we get a list of length 1. The matrix of scores is a 968 x 3 matrix, where the rows correspond to the two strands (2 x 484) and the columns correspond to motifs. It is important to remember that the scores are in real and not log space. In other words, a conventional PWM log2 score of 3 is represented as number 8 ($2^3$). The scores for the two strands are concatenated one after the other. Therefore, row 1 has the scores for the motif starting at position 1, and row 485 has the score at the same position, but with the reverse complement of the motif (i.e. motif score on the reverse strand). Note that there will be some NA values at the end of the sequence (e.g. position 484) because we do not support partial motif matches. Finally we use the \Robject{plotMotifScores} function to plot the log2 scores over the sequence. We colour-code the motifs with green, red and blue. The motif hits are shown as rectangles with the base being the length of the motif, and the hight being the log2 score of the motif hit. By default we show all motif hits with log2 scores larger then 0. The forward strand hits are shown on the top, and the reverse strand hits are shown on the bottom. We next might be interested in finding the P-value for individual motif hits so we can get an idea which sites are the most important. To do this we need to calculate the empirical PWM score distribution for single sites. We did not provide these values precalculated because they take up a very large amount of memory. To calculate it based on a set of promoter, we will need the \textit{D. melanogaster} genome sequence. Because the objects are so large, in this example we will determine the P-value only for the hits of the bcd motif, using only a small subset of promoters (controlled by the parameter \Robject{quick=TRUE}). <>= library(BSgenome.Dmelanogaster.UCSC.dm3) # empirical distribution for the bcd motif bcd.ecdf = motifEcdf(sel.pwms$bcd, Dmelanogaster, quick=TRUE)[[1]] # find the score that is equivalent to the P-value of 1e-3 threshold.1e3 = log2(quantile(bcd.ecdf, 1 - 1e-3)) threshold.1e3 # replot only the bcd motif hits with the P-value cutoff of 1e-3 (0.001) plotMotifScores(scores, cols="green", sel.motifs="bcd", cutoff=threshold.1e3) # Convert scores into P-values pvals = 1 - bcd.ecdf(scores[[1]][,"bcd"]) head(pvals) @ Here we have used the \Robject{motifEcdf} function to create an empirical cumulative distribution function (ECDF) for the bcd motif score on Drosophila promoters. This function returns an \Robject{ecdf} object which is part of base R. We can then use the quantile function to find which scores correspond to a P-value of 0.001, or we can use it to convert all the scores into P-values (not shown above). To plot the individual motif hits with P-values smaller than 0.001 we again use the \Robject{plotMotifScores} function, but now we apply the threshold so that only those motif hits above the threshold are drawn. In the last line we find out the positions of those motif hits where the P-value is smaller then 1e-3. Note that the values larger than the sequence length (484) indicate the reverse strand. Therefore, we find the four strong motif hits at positions 90 on the forward strand and 110, 354 and 475 on the reverse strand. Note that \Robject{plotMotifScores} can also plot multiple sequences on a single plot, and that the \Robject{cutoff} parameter can contain a vector of values if we wish to apply different cutoff to different motifs. \section{Use case 3: Finding enriched motifs in multiple sequences} So far we have only looked at motif enrichment in a single sequence, which was able to recover some but not all of the truly functional motifs. The power of the motif enrichment approach can be significantly boosted by performing it jointly on multiple sequences. For this example we are going to use the top 20 ChIP-chip peaks for transcription factor Tinman in \textit{Drosophila} \cite{jin_genome-wide_2013}. We are going to scan these 20 ChIP-chip peaks with all the motifs and then compare their enrichment to genomic background. Running on the whole set of peaks (i.e. thousands) is also possible but can take a long time (i.e. tens of minutes). The speed can be improved by using multiple CPU cores (see next section). <>= library(PWMEnrich.Dmelanogaster.background) # load the pre-compiled lognormal background data(PWMLogn.dm3.MotifDb.Dmel) sequences = readDNAStringSet(system.file(package="PWMEnrich", dir="extdata", file="tinman-early-top20.fa")) res = motifEnrichment(sequences, PWMLogn.dm3.MotifDb.Dmel) report = groupReport(res) report plot(report[1:10], fontsize=7, id.fontsize=5) @ As in Use case 1, the main function is \Robject{motifEnrichment} which took our sequences and calculated motif enrichment using the lognormal affinity background distribution (fitted on a set of 10031 \textit{D. melanogaster} 2kb promoters). We then applied the \Robject{groupReport} function to calculate the enrichment over the whole group of sequences. This produced a ranked list of motifs according to the estimated P-values. Then we used \Robject{plot} to plot the top 10 enriched motifs. The first three motifs are very similar and correspond to the tinman, which is the transcription factor for which the ChIP-chip experiment was performed. The first five columns are the same as before (see Use case 1). The sixth column gives the estimate P-value. The last column indicates the breadth of enrichment using a 5\% ranking threshold. This column helps to differentiate cases where the motif enrichment is strongly focused to a small subset of sequences (in which case breadth is small), versus being more widespread but weaker (in which case breadth is bigger). We can also sort by this column: <>= report.top = groupReport(res, by.top.motifs=TRUE) report.top @ This ranks motifs by breadth of enrichment, which is calculated by comparing enrichment \textit{between motifs} in individual sequences. This measure only makes sense when applied to a large number of sequence and when scanning with a large number of motifs (>20). The object returned by \Robject{motifEnrichment} has more information in it, as can be seen below: <>= res # raw scores res$sequence.nobg[1:5, 1:2] # P-values res$sequence.bg[1:5, 1:2] @ In these two matrices the rows correspond to the different input sequences and the columns correspond to motifs. The first matrix (sequence.nobg) contains the raw affinity scores, while the second (sequence.bg) contains the corresponding P-values. If you are using a fixed threshold background (e.g. scanning with \Robject{PWMPvalueCutoff1e3.dm3.MotifDb.Dmel}) the first matrix will contain the number of motif hits, and the second the corresponding Z-scores. \section{Using PWMEnrich on human sequences}\label{sec:human} Starting from PWMEnrich version 4.0 (October 2014) a new algorithm is used to better fit the background distributions in human sequences. The only difference from the usage perspective is in creating new background files - please make sure to set the parameter \Robject{algorithm="human"} in \Robject{makeBackground()} (and other related functions for creating backgrounds). This will instruct the function to fit separate parameters for different sequence lengths. The sequence lengths are obtained by multiplying the parameters \Robject{bg.len} and \Robject{bg.len.sizes}. The defaults are \Robject{bg.len=250bp} and \Robject{bg.len.size=c(1, 2, 4, 8, 16)}. This means that the P-values are the most accurate for sequences that are in the range of 250bp - 4000bp and the closest in size to 250bp, 500bp, 1000bp, 2000bp and 4000bp. \section{Speeding up execution} \subsection{Parallel execution} Motif scanning is the most time consuming operation. Because of this, the package has a support for parallel motif scanning using the \R{parallel} core package. Note that parallel execution is currently not supported on Windows. To turn on parallel scanning, simply register a number of cores available to the package: <>= registerCoresPWMEnrich(4) @ After this command is executed, all further calls to \Biocpkg{PWMEnrich} functions are going to be run in parallel using 4 cores (if possible). To turn off parallel execution call the function with parameter NULL: <>= registerCoresPWMEnrich(NULL) @ \subsection{Large memory backend} Motif scanning can be further speeded up by using large amount of memory. If you have an access to a machine with a lot of RAM, you can switch to the "big memory" backend: <>= useBigMemoryPWMEnrich(TRUE) @ From this point on, all motif scanning will be done using the optimised big memory backend. The memory requirement depends on the number of sequences scanned, and might require tens of GB of RAM. To turn it off: <>= useBigMemoryPWMEnrich(FALSE) @ \section{Customisation} \subsection{Using a custom set of PWMs}\label{sec:custom-pwm} Background motif distributions for a custom set of PWMs can be easily calculated for all model organisms. We will illustrate this by creating a new lognormal background for two \textit{de-novo} motifs in Drosophila. To load in the motifs the package provides functions to read standard JASPAR and TRANSFAC formats. <>= library(PWMEnrich.Dmelanogaster.background) motifs.denovo = readMotifs(system.file(package="PWMEnrich", dir="extdata", file="example.transfac"), remove.acc=TRUE) motifs.denovo # convert count matrices into PWMs genomic.acgt = getBackgroundFrequencies("dm3") pwms.denovo = toPWM(motifs.denovo, prior=genomic.acgt) bg.denovo = makeBackground(pwms.denovo, organism="dm3", type="logn", quick=TRUE) # use new motifs for motif enrichment res.denovo = motifEnrichment(sequences[1:5], bg.denovo) groupReport(res.denovo) @ We load in the count matrices and then convert them into PWMs using the genomic distributions of the A, C, G, T nucleotides. Next we use these PWMs to calculate the properties of the affinity distribution on the set of \textit{D. melanogaster} promoters. In this example we used \Robject{quick=TRUE} for illustrative purposes. This fits the parameters quickly on a reduced set of 100 promoters. We strongly discourage the users to use this parameter in their research, and instead only use it to obtain rough estimates and for testing. The resulting object \Robject{bg.denovo} can be used same as before to perform motif enrichment. The background object \Robject{bg.denovo} contains the two PWMs and their background distribution parameters. All of these can be accessed with the \$ operator. <>= bg.denovo bg.denovo$bg.mean @ %$ \subsection{Using a custom set of background sequences}\label{sec:custom-bg} Low-level functions are available for constructing custom backgrounds. We start with the two de-novo motifs from previous section and fit the background to first 20 \textit{D. melanogaster} promoters. <>= library(PWMEnrich.Dmelanogaster.background) data(dm3.upstream2000) # make a lognormal background for the two motifs using only first 20 promoters bg.seq = dm3.upstream2000[1:20] # the sequences are split into 100bp chunks and fitted bg.custom = makeBackground(pwms.denovo, bg.seq=bg.seq, type="logn", bg.len=100, bg.source="20 promoters split into 100bp chunks") bg.custom @ The resulting \Robject{bg.custom} object can be used as before for motif enrichment with the \Robject{motifEnrichment} function (as described before). \section{Session information} <>= toLatex(sessionInfo()) @ %\bibliographystyle{apalike} %\bibliography{references} \bibliography{references} \end{document} PWMEnrich/vignettes/references.bib0000644000175100017510000000171114614305422020176 0ustar00biocbuildbiocbuild@article{frith_detection_2004, title = {Detection of functional {DNA} motifs via statistical over-representation}, volume = {32}, number = {4}, journal = {Nucl. Acids Res.}, author = {Frith, Martin C. and Fu, Yutao and Yu, Liqun and Chen, {Jiang-Fan} and Hansen, Ulla and Weng, Zhiping}, year = {2004}, pages = {1372--1381} }, @article{stojnic_2012, title = {Affinity based {DNA} motif enrichment analysis with {R/Bioconductor} package {PWMEnrich}}, journal = {in preparation}, author = {Stojnic, Robert and Adryan, Boris}, year = {2014} }, @article{jin_genome-wide_2013, title = {Genome-Wide Screens for In Vivo {Tinman} Binding Sites Identify Cardiac Enhancers with Diverse Functional Architectures}, volume = {9}, journal = {{PLoS} Genet}, author = {Jin, Hong and Stojnic, Robert and Adryan, Boris and Ozdemir, Anil and Stathopoulos, Angelike and Frasch, Manfred}, year = {2013}, pages = {e1003195}, }