Mercurial > repos > lparsons > rseqc
changeset 0:306127635959 draft
planemo upload for repository https://github.com/lparsons/galaxy_tools/tree/master/tools/rseqc commit aeb25d807817746dd6957f30ce2070662cc10e91
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--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/README.txt Tue Jul 07 10:39:47 2015 -0400 @@ -0,0 +1,24 @@ +== RSeQC Galaxy Wrapper == + +This is a Galaxy wrapper for the RSeQC RNA-Seq QC package. + +** Installation ** + +Installation from a tool shed provides the necessary tool dependencies, R, numpy, and RSeQC. + +Otherwise, make sure that R and the RSeQC scripts are in the path and run under the Galaxy environment. +Move the xml files to a subdirectory of your tools directory and add lines in tool_conf.xml to point to them. +Restart the Galaxy server. + +Requires Python 2.7 + +** Attribution ** + +The RSeQC package and associated documentation can be found at: http://rseqc.sourceforge.net/ + +The galaxy wrapper code was written by + Nilesh Kavthekar, School of Engineering and Applied Sciences, University of Pennsylvania, Class of 2016 +Modified by + Lance Parsons, Lewis-Sigler Institute for Integrative Genomics, Princeton University, + Bjorn Gruning, University of Freiburg, bjoern.gruening@gmail.com +The development of the wrapper code is housed on BitBucket at: https://bitbucket.org/lance_parsons/rseqc_galaxy_wrapper
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/RPKM_count.xml Tue Jul 07 10:39:47 2015 -0400 @@ -0,0 +1,158 @@ +<tool id="rseqc_RPKM_count" name="RPKM Count" version="2.4galaxy1"> + <description>calculates raw count and RPKM values for transcript at exon, intron, and mRNA level</description> + + <macros> + <import>rseqc_macros.xml</import> + </macros> + + <requirements> + <expand macro="requirement_package_numpy" /> + <expand macro="requirement_package_rseqc" /> + </requirements> + + <expand macro="stdio" /> + + <version_command><![CDATA[RPKM_count.py --version]]></version_command> + + <command><![CDATA[ + ln -s "${input}" "local_input.bam" && + ln -s "${input.metadata.bam_index}" "local_input.bam.bai" && + RPKM_count.py -i "local_input.bam" -o output -r $refgene + + #if str($strand_type.strand_specific) == "pair" + -d + #if str($strand_type.pair_type) == "sd" + '1++,1--,2+-,2-+' + #else + '1+-,1-+,2++,2--' + #end if + #end if + + #if str($strand_type.strand_specific) == "single" + -d + #if str($strand_type.single_type) == "s" + '++,--' + #else + '+-,-+' + #end if + #end if + + #if $multihits.skipmultihits + --skip-multi-hits + --mapq=$multihits.mapq + #end if + + $onlyexonic + ]]> + </command> + + <inputs> + <param name="input" type="data" label="Input .bam File" format="bam" help="(--input-file)"/> + <param name="refgene" type="data" format="bed" label="reference gene model" help="(--refgene)"/> + <conditional name="strand_type"> + <param name="strand_specific" type="select" label="Strand-specific?" value="None"> + <option value="none">None</option> + <option value="pair">Pair-End RNA-seq</option> + <option value="single">Single-End RNA-seq</option> + </param> + <when value="pair"> + <param name="pair_type" type="select" display="radio" label="Pair-End Read Type (format: mapped --> parent)" value="sd" help="(--strand)"> + <option value="sd"> read1 (positive --> positive; negative --> negative), read2 (positive --> negative; negative --> positive)</option> + <option value="ds">read1 (positive --> negative; negative --> positive), read2 (positive --> positive; negative --> negative)</option> + </param> + </when> + <when value="single"> + <param name="single_type" type="select" display="radio" label="Single-End Read Type (format: mapped --> parent)" value="s" help="(--strand)"> + <option value="s">positive --> positive; negative --> negative</option> + <option value="d">positive --> negative; negative --> positive</option> + </param> + </when> + <when value="none"></when> + </conditional> + + <conditional name="multihits"> + <param name="skipmultihits" type="boolean" label="Skip Multiple Hit Reads/Only Use Uniquely Mapped Reads" value="false" help="(--skip-multi-hits)" /> + <when value="true"> + <param name="mapq" value="30" type="integer" label="Minimum mapping quality for an alignment to be called 'uniquly mapped'" help="(--mapq)" /> + </when> + <when value="false" /> + </conditional> + + <param name="onlyexonic" type="boolean" value="false" truevalue="--only-exonic" falsevalue="" label="Only use exonic (UTR exons and CDS exons) reads, otherwise use all reads" help="(--only-exonic)"/> + </inputs> + + <outputs> + <data format="xls" name="outputxls" from_work_dir="output_read_count.xls"/> + </outputs> + + <tests> + <test> + <param name="input" value="pairend_strandspecific_51mer_hg19_chr1_1-100000.bam"/> + <param name="refgene" value="hg19_RefSeq_chr1_1-100000.bed"/> + <output name="outputxls" file="output_read_count.xls"/> + </test> + </tests> + + <help><![CDATA[ +RPKM_count.py ++++++++++++++ + +Given a BAM file and reference gene model, this program will calculate the raw count and RPKM +values for transcript at exon, intron and mRNA level. For strand specific RNA-seq data, +program will assign read to its parental gene according to strand rule, if you don't know the +strand rule, run infer_experiment.py. Please note that chromosome ID, genome cooridinates +should be concordant between BAM and BED files. + +Inputs +++++++++++++++ + +Input BAM/SAM file + Alignment file in BAM/SAM format. + +Reference gene model + Gene model in BED format. + +Strand sequencing type (default=none) + See Infer Experiment tool if uncertain. + +Options +++++++++++++++ + +Skip Multiple Hit Reads + Use Multiple hit reads or use only uniquely mapped reads. + +Only use exonic reads + Renders program only used exonic (UTR exons and CDS exons) reads, otherwise use all reads. + +Sample Output +++++++++++++++ + +===== ======== ======== ===================== ===== =========== ============= ============= ======== ========= +chrom start end accession score gene strand tag count (+) tag count (-) RPKM (+) RPKM (-) +===== ======== ======== ===================== ===== =========== ============= ============= ======== ========= +chr1 29213722 29313959 NM_001166007_intron_1 0 '+' 431 4329 0.086 0.863 +chr1 29314417 29319841 NM_001166007_intron_2 0 '+' 31 1 0.114 0.004 +chr1 29320054 29323726 NM_001166007_intron_3 0 '+' 32 0 0.174 0.000 +chr1 29213602 29213722 NM_001166007_exon_1 0 '+' 164 0 27.321 0.000 +chr1 29313959 29314417 NM_001166007_exon_2 0 '+' 1699 4 74.158 0.175 +chr1 29319841 29320054 NM_001166007_exon_3 0 '+' 528 1 49.554 0.094 +===== ======== ======== ===================== ===== =========== ============= ============= ======== ========= + +----- + +About RSeQC ++++++++++++ + +The RSeQC_ package provides a number of useful modules that can comprehensively evaluate high throughput sequence data especially RNA-seq data. "Basic modules" quickly inspect sequence quality, nucleotide composition bias, PCR bias and GC bias, while "RNA-seq specific modules" investigate sequencing saturation status of both splicing junction detection and expression estimation, mapped reads clipping profile, mapped reads distribution, coverage uniformity over gene body, reproducibility, strand specificity and splice junction annotation. + +The RSeQC package is licensed under the GNU GPL v3 license. + +.. image:: http://rseqc.sourceforge.net/_static/logo.png + +.. _RSeQC: http://rseqc.sourceforge.net/ +]]> + </help> + + <expand macro="citations" /> + +</tool>
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/RPKM_saturation.xml Tue Jul 07 10:39:47 2015 -0400 @@ -0,0 +1,185 @@ +<tool id="rseqc_RPKM_saturation" name="RPKM Saturation" version="2.4galaxy1"> + <description>calculates raw count and RPKM values for transcript at exon, intron, and mRNA level</description> + + <macros> + <import>rseqc_macros.xml</import> + </macros> + + <requirements> + <expand macro="requirement_package_r" /> + <expand macro="requirement_package_numpy" /> + <expand macro="requirement_package_rseqc" /> + </requirements> + + <expand macro="stdio" /> + + <version_command><![CDATA[RPKM_saturation.py --version]]></version_command> + + <command><![CDATA[ + RPKM_saturation.py -i $input -o output -r $refgene + + #if str($strand_type.strand_specific) == "pair" + -d + #if str($strand_type.pair_type) == "sd" + '1++,1--,2+-,2-+' + #else + '1+-,1-+,2++,2--' + #end if + #end if + + #if str($strand_type.strand_specific) == "single" + -d + #if str($strand_type.single_type) == "s" + '++,--' + #else + '+-,-+' + #end if + #end if + + -l $percentileFloor -u $percentileCeiling -s $percentileStep -c $rpkmCutoff + ]]> + </command> + + <inputs> + <param name="input" type="data" label="Input .bam File" format="bam" help="(--input-file)"/> + <param name="refgene" type="data" format="bed" label="reference gene model" help="(--refgene)"/> + <conditional name="strand_type"> + <param name="strand_specific" type="select" label="Strand-specific?" value="None"> + <option value="none">None</option> + <option value="pair">Pair-End RNA-seq</option> + <option value="single">Single-End RNA-seq</option> + </param> + <when value="pair"> + <param name="pair_type" type="select" display="radio" label="Pair-End Read Type (format: mapped --> parent)" value="sd" help="(--strand)"> + <option value="sd"> read1 (positive --> positive; negative --> negative), read2 (positive --> negative; negative --> positive)</option> + <option value="ds">read1 (positive --> negative; negative --> positive), read2 (positive --> positive; negative --> negative)</option> + </param> + </when> + <when value="single"> + <param name="single_type" type="select" display="radio" label="Single-End Read Type (format: mapped --> parent)" value="s" help="(--strand)"> + <option value="s">positive --> positive; negative --> negative</option> + <option value="d">positive --> negative; negative --> positive</option> + </param> + </when> + <when value="none"></when> + </conditional> + <param name="percentileFloor" type="integer" value="5" label="Begin sampling from this percentile (default=5)" help="(--percentile-floor)"/> + <param name="percentileCeiling" type="integer" value="100" label="End sampling at this percentile (default=100)" help="(--percentile-ceiling)" /> + <param name="percentileStep" type="integer" value="5" label="Sampling step size (default=5)" help="(--percentile-step)" /> + <param name="rpkmCutoff" type="text" value="0.01" label="Ignore transcripts with RPKM smaller than this number (default=0.01)" help="(--rpkm-cutoff)" /> + <param name="mapq" value="30" type="integer" label="Minimum mapping quality for an alignment to be called 'uniquly mapped'" help="(--mapq)" /> + </inputs> + + <outputs> + <data format="xls" name="outputxls" from_work_dir="output.eRPKM.xls" label="${tool.name} on ${on_string} (RPKM XLS)"/> + <data format="xls" name="outputrawxls" from_work_dir="output.rawCount.xls" label="${tool.name} on ${on_string} (Raw Count XLS)"/> + <data format="txt" name="outputr" from_work_dir="output.saturation.r" label="${tool.name} on ${on_string} (R Script)"/> + <data format="pdf" name="outputpdf" from_work_dir="output.saturation.pdf" label="${tool.name} on ${on_string} (PDF)"/> + </outputs> + + <!-- Unable to succefully run this script with test data + <tests> + <test> + <param name="input" value="pairend_strandspecific_51mer_hg19_chr1_1-100000.bam"/> + <param name="refgene" value="hg19_RefSeq_chr1_1-100000.bed"/> + <output name="outputxls" file="output.eRPKM.xls"/> + <output name="outputrawxls" file="output.rawCount.xls"/> + <output name="outputr" file="output.saturation.r"/> + </test> + </tests> + --> + + <help><![CDATA[ +RPKM_saturation.py +++++++++++++++++++ + +The precision of any sample statitics (RPKM) is affected by sample size (sequencing depth); +\'resampling\' or \'jackknifing\' is a method to estimate the precision of sample statistics by +using subsets of available data. This module will resample a series of subsets from total RNA +reads and then calculate RPKM value using each subset. By doing this we are able to check if +the current sequencing depth was saturated or not (or if the RPKM values were stable or not) +in terms of genes' expression estimation. If sequencing depth was saturated, the estimated +RPKM value will be stationary or reproducible. By default, this module will calculate 20 +RPKM values (using 5%, 10%, ... , 95%,100% of total reads) for each transcripts. + +In the output figure, Y axis is "Percent Relative Error" or "Percent Error" which is used +to measures how the RPKM estimated from subset of reads (i.e. RPKMobs) deviates from real +expression level (i.e. RPKMreal). However, in practice one cannot know the RPKMreal. As a +proxy, we use the RPKM estimated from total reads to approximate RPKMreal. + +.. image:: http://rseqc.sourceforge.net/_images/RelativeError.png + :height: 80 px + :width: 400 px + :scale: 100 % + +Inputs +++++++++++++++ + +Input BAM/SAM file + Alignment file in BAM/SAM format. + +Reference gene model + Gene model in BED format. + +Strand sequencing type (default=none) + See Infer Experiment tool if uncertain. + +Options +++++++++++++++ + +Skip Multiple Hit Reads + Use Multiple hit reads or use only uniquely mapped reads. + +Only use exonic reads + Renders program only used exonic (UTR exons and CDS exons) reads, otherwise use all reads. + +Output +++++++++++++++ + +1. output..eRPKM.xls: RPKM values for each transcript +2. output.rawCount.xls: Raw count for each transcript +3. output.saturation.r: R script to generate plot +4. output.saturation.pdf: + +.. image:: http://rseqc.sourceforge.net/_images/saturation.png + :height: 600 px + :width: 600 px + :scale: 80 % + +- All transcripts were sorted in ascending order according to expression level (RPKM). Then they are divided into 4 groups: + 1. Q1 (0-25%): Transcripts with expression level ranked below 25 percentile. + 2. Q2 (25-50%): Transcripts with expression level ranked between 25 percentile and 50 percentile. + 3. Q3 (50-75%): Transcripts with expression level ranked between 50 percentile and 75 percentile. + 4. Q4 (75-100%): Transcripts with expression level ranked above 75 percentile. +- BAM/SAM file containing more than 100 million alignments will make module very slow. +- Follow example below to visualize a particular transcript (using R console):: + + pdf("xxx.pdf") #starts the graphics device driver for producing PDF graphics + x <- seq(5,100,5) #resampling percentage (5,10,15,...,100) + rpkm <- c(32.95,35.43,35.15,36.04,36.41,37.76,38.96,38.62,37.81,38.14,37.97,38.58,38.59,38.54,38.67, 38.67,38.87,38.68, 38.42, 38.23) #Paste RPKM values calculated from each subsets + scatter.smooth(x,100*abs(rpkm-rpkm[length(rpkm)])/(rpkm[length(rpkm)]),type="p",ylab="Precent Relative Error",xlab="Resampling Percentage") + dev.off() #close graphical device + +.. image:: http://rseqc.sourceforge.net/_images/saturation_eg.png + :height: 600 px + :width: 600 px + :scale: 80 % + +----- + +About RSeQC ++++++++++++ + +The RSeQC_ package provides a number of useful modules that can comprehensively evaluate high throughput sequence data especially RNA-seq data. "Basic modules" quickly inspect sequence quality, nucleotide composition bias, PCR bias and GC bias, while "RNA-seq specific modules" investigate sequencing saturation status of both splicing junction detection and expression estimation, mapped reads clipping profile, mapped reads distribution, coverage uniformity over gene body, reproducibility, strand specificity and splice junction annotation. + +The RSeQC package is licensed under the GNU GPL v3 license. + +.. image:: http://rseqc.sourceforge.net/_static/logo.png + +.. _RSeQC: http://rseqc.sourceforge.net/ +]]> + </help> + + <expand macro="citations" /> + +</tool>
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/bam2wig.xml Tue Jul 07 10:39:47 2015 -0400 @@ -0,0 +1,192 @@ +<tool id="rseqc_bam2wig" name="BAM to Wiggle" version="2.4galaxy1"> + <description> + converts all types of RNA-seq data from .bam to .wig + </description> + + <macros> + <import>rseqc_macros.xml</import> + </macros> + + <requirements> + <expand macro="requirement_package_r" /> + <expand macro="requirement_package_numpy" /> + <expand macro="requirement_package_rseqc" /> + </requirements> + + <expand macro="stdio" /> + + <version_command><![CDATA[bam2wig.py --version]]></version_command> + + <command><![CDATA[ + ln -sfn '${input}' 'input.bam' && + ln -sfn '${input.metadata.bam_index}' 'input.bam.bai' && + bam2wig.py -i input.bam -s $chromsize -o outfile + + #if str($strand_type.strand_specific) == "pair" + -d + #if str($strand_type.pair_type) == "sd" + '1++,1--,2+-,2-+' + #else + '1+-,1-+,2++,2--' + #end if + #end if + + #if str($strand_type.strand_specific) == "single" + -d + #if str($strand_type.single_type) == "s" + '++,--' + #else + '+-,-+' + #end if + #end if + + #if $wigsum.wigsum_type + -t $wigsum.totalwig + #end if + #if $multihits.skipmultihits + --skip-multi-hits + --mapq=$multihits.mapq + #end if + 2>&1 + ]]> + </command> + <inputs> + <param name="input" type="data" label="Input .bam File" format="bam" help="(--input-file)"/> + <param name="chromsize" type="data" label="Chromosome size file (tab or space separated)" format="txt,tabular" help="(--chromSize)"/> + + <conditional name="multihits"> + <param name="skipmultihits" type="boolean" label="Skip Multiple Hit Reads/Only Use Uniquely Mapped Reads" value="false" help="(--skip-multi-hits)" /> + <when value="true"> + <param name="mapq" value="30" type="integer" label="Minimum mapping quality for an alignment to be called 'uniquly mapped'" help="(--mapq)" /> + </when> + <when value="false" /> + </conditional> + + <conditional name="wigsum"> + <param name="wigsum_type" type="boolean" label="Specify wigsum?" value="false"> + </param> + <when value="true"> + <param name="totalwig" value="0" type="integer" label="specified wigsum" help="(--wigsum)"/> + </when> + <when value="false"/> + </conditional> + + <conditional name="strand_type"> + <param name="strand_specific" type="select" label="Strand-specific?" value="none"> + <option value="none">none</option> + <option value="pair">Pair-End RNA-seq</option> + <option value="single">Single-End RNA-seq</option> + </param> + <when value="pair"> + <param name="pair_type" type="select" display="radio" label="Pair-End Read Type (format: mapped --> parent)" value="sd" help="(--strand)"> + <option value="sd"> read1 (positive --> positive; negative --> negative), read2 (positive --> negative; negative --> positive)</option> + <option value="ds">read1 (positive --> negative; negative --> positive), read2 (positive --> positive; negative --> negative)</option> + </param> + </when> + <when value="single"> + <param name="single_type" type="select" display="radio" label="Single-End Read Type (format: mapped --> parent)" value="s" help="(--strand)"> + <option value="s">positive --> positive; negative --> negative</option> + <option value="d">positive --> negative; negative --> positive</option> + </param> + </when> + <when value="none"></when> + </conditional> + </inputs> + + <outputs> + <data format="wig" name="output" from_work_dir="outfile.wig"> + <filter>strand_type['strand_specific'] == 'none'</filter> + </data> + <data format="wig" name="outputfwd" from_work_dir="outfile.Forward.wig" label="${tool.name} on ${on_string} (Forward Reads)"> + <filter>strand_type['strand_specific'] != 'none'</filter> + </data> + <data format="wig" name="outputrv" from_work_dir="outfile.Reverse.wig" label="${tool.name} on ${on_string} (Reverse Reads)"> + <filter>strand_type['strand_specific'] != 'none'</filter> + </data> + </outputs> + + <tests> + <test> + <param name="input" value="pairend_strandspecific_51mer_hg19_chr1_1-100000.bam"/> + <param name="chromsize" value="hg19.chrom.sizes"/> + <output name="output" file="testwig.wig"/> + </test> + <test> + <param name="input" value="pairend_strandspecific_51mer_hg19_chr1_1-100000.bam"/> + <param name="chromsize" value="hg19.chrom.sizes"/> + <param name="skipmultihits" value="True"/> + <param name="mapq" value="20"/> + <output name="output" file="testwig.wig"/> + </test> + <test> + <param name="input" value="pairend_strandspecific_51mer_hg19_chr1_1-100000.bam"/> + <param name="chromsize" value="hg19.chrom.sizes"/> + <param name="strand_specific" value="pair"/> + <param name="pair_type" value="sd"/> + <output name="outputfwd" file="testwig.Forward.wig"/> + <output name="outputrv" file="testwig.Reverse.wig"/> + </test> + </tests> + + <help><![CDATA[ +bam2wig.py +++++++++++ + +Visualization is the most straightforward and effective way to QC your RNA-seq +data. For example, change of expression or new splicing can be easily checked +by visually comparing two RNA-seq tracks using genome browser such as UCSC_, +IGB_ and IGV_. `bam2wig.py` converts all types of RNA-seq data from BAM_ +format into wiggle_ format in one-stop. wiggle_ files can then be easily +converted into bigwig_. Bigwig is indexed, binary format of wiggle file, and +it's particular useful to display large, continuous dataset on genome +browser. + +Inputs +++++++++++++++ + +Input BAM file + Alignment file in BAM format (SAM is not supported). BAM file will be sorted and indexed using samTools. + +Chromosome size file + Tab or space separated text file with 2 columns: first column is chromosome name, second column is size of the chromosome. Chromosome names (such as "chr1") should be consistent between this file and BAM file. + +Specified wigsum (default=none) + Specified wigsum. Wigsum of 100000000 equals to coverage achieved by 1 million 100nt reads. Ignore this option to disable normalization. + +Skip multiple Hit reads + skips multiple hit reads or only use uniquely mapped reads + +Strand-specific (default=none) + How read(s) were stranded during sequencing. If you are not sure about the strand rule, run infer_experiment.py + +Outputs +++++++++++++++ + +If RNA-seq is not strand specific, one wig file will be generated, if RNA-seq +is strand specific, two wig files corresponding to Forward and Reverse will be generated. + +----- + +About RSeQC ++++++++++++ + +The RSeQC_ package provides a number of useful modules that can comprehensively evaluate high throughput sequence data especially RNA-seq data. "Basic modules" quickly inspect sequence quality, nucleotide composition bias, PCR bias and GC bias, while "RNA-seq specific modules" investigate sequencing saturation status of both splicing junction detection and expression estimation, mapped reads clipping profile, mapped reads distribution, coverage uniformity over gene body, reproducibility, strand specificity and splice junction annotation. + +The RSeQC package is licensed under the GNU GPL v3 license. + +.. image:: http://rseqc.sourceforge.net/_static/logo.png + +.. _RSeQC: http://rseqc.sourceforge.net/ + +.. _UCSC: http://genome.ucsc.edu/index.html +.. _IGB: http://bioviz.org/igb/ +.. _IGV: http://www.broadinstitute.org/igv/home +.. _BAM: http://genome.ucsc.edu/goldenPath/help/bam.html +.. _wiggle: http://genome.ucsc.edu/goldenPath/help/wiggle.html +.. _bigwig: http://genome.ucsc.edu/FAQ/FAQformat.html#format6.1 +]]> + </help> + + <expand macro="citations" /> + +</tool>
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/bam_stat.xml Tue Jul 07 10:39:47 2015 -0400 @@ -0,0 +1,85 @@ +<tool id="rseqc_bam_stat" name="BAM/SAM Mapping Stats" version="2.4galaxy1"> + <description> + reads mapping statistics for a provided BAM or SAM file. + </description> + + <macros> + <import>rseqc_macros.xml</import> + </macros> + + <requirements> + <expand macro="requirement_package_numpy" /> + <expand macro="requirement_package_rseqc" /> + </requirements> + + <expand macro="stdio" /> + + <version_command><![CDATA[bam_stat.py --version]]></version_command> + + <command><![CDATA[ + bam_stat.py -i $input -q $mapqual 2> $output + ]]> + </command> + + <inputs> + <param name="input" type="data" label="Input .bam/.sam File" format="bam,sam" /> + <param label="Minimum mapping quality (default=30" type="integer" value="30" name="mapqual" /> + </inputs> + + <outputs> + <data format="txt" name="output" /> + </outputs> + + <tests> + <test> + <param name="input" value="pairend_strandspecific_51mer_hg19_chr1_1-100000.bam"/> + <output name="output" file="bamstats.txt"/> + </test> + </tests> + + <help><![CDATA[ +bam_stat.py ++++++++++++ + +This program is used to calculate reads mapping statistics from provided BAM +file. This script determines "uniquely mapped reads" from `mapping quality`_, +which quality the probability that a read is misplaced (Do NOT confused with +sequence quality, sequence quality measures the probability that a base-calling +was wrong) . + +Inputs +++++++++++++++ + +Input BAM/SAM file + Alignment file in BAM/SAM format. + +Minimum mapping quality + Minimum mapping quality for an alignment to be called “uniquely mapped” (default=30) + +Output +++++++++++++++ + +- Total Reads (Total records) = {Multiple mapped reads} + {Uniquely mapped} +- Uniquely mapped Reads = {read-1} + {read-2} (if paired end) +- Uniquely mapped Reads = {Reads map to '+'} + {Reads map to '-'} +- Uniquely mapped Reads = {Splice reads} + {Non-splice reads} + +----- + +About RSeQC ++++++++++++ + + +The RSeQC_ package provides a number of useful modules that can comprehensively evaluate high throughput sequence data especially RNA-seq data. "Basic modules" quickly inspect sequence quality, nucleotide composition bias, PCR bias and GC bias, while "RNA-seq specific modules" investigate sequencing saturation status of both splicing junction detection and expression estimation, mapped reads clipping profile, mapped reads distribution, coverage uniformity over gene body, reproducibility, strand specificity and splice junction annotation. + +The RSeQC package is licensed under the GNU GPL v3 license. + +.. image:: http://rseqc.sourceforge.net/_static/logo.png + +.. _RSeQC: http://rseqc.sourceforge.net/ + +]]> + </help> + + <expand macro="citations" /> +</tool>
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/clipping_profile.xml Tue Jul 07 10:39:47 2015 -0400 @@ -0,0 +1,84 @@ +<tool id="rseqc_clipping_profile" name="Clipping Profile" version="2.4galaxy1"> + <description> + estimates clipping profile of RNA-seq reads from BAM or SAM file + </description> + + <macros> + <import>rseqc_macros.xml</import> + </macros> + + <requirements> + <expand macro="requirement_package_r" /> + <expand macro="requirement_package_numpy" /> + <expand macro="requirement_package_rseqc" /> + </requirements> + + <expand macro="stdio" /> + + <version_command><![CDATA[clipping_profile.py --version]]></version_command> + + <command><![CDATA[ + clipping_profile.py -i $input -o output + ]]> + </command> + + <inputs> + <param name="input" type="data" label="Input .bam/.sam File" format="bam,sam" /> + </inputs> + + <outputs> + <data format="pdf" name="outputpdf" from_work_dir="output.clipping_profile.pdf" /> + <data format="xls" name="outputxls" from_work_dir="output.clipping_profile.xls" /> + <data format="txt" name="outputr" from_work_dir="output.clipping_profile.r" /> + </outputs> + + <tests> + <test> + <param name="input" value="pairend_strandspecific_51mer_hg19_chr1_1-100000.bam"/> + <output name="outputpdf" file="output.clipping_profile.pdf"/> + <output name="outputxls" file="output.clipping_profile.xls"/> + <output name="outputr" file="output.clipping_profile.r"/> + </test> + </tests> + + <help><![CDATA[ +clipping_profile.py ++++++++++++++++++++ + +This program is used to estimate clipping profile of RNA-seq reads from BAM or SAM file. +Note that to use this funciton, CIGAR strings within SAM/BAM file should have 'S' operation +(This means your reads aligner should support clipped mapping). + +Inputs +++++++++++++++ + +Input BAM/SAM file + Alignment file in BAM/SAM format. + + +Sample Output +++++++++++++++ + +.. image:: http://rseqc.sourceforge.net/_images/clipping_good.png + :height: 600 px + :width: 600 px + :scale: 80 % + +----- + +About RSeQC ++++++++++++ + +The RSeQC_ package provides a number of useful modules that can comprehensively evaluate high throughput sequence data especially RNA-seq data. "Basic modules" quickly inspect sequence quality, nucleotide composition bias, PCR bias and GC bias, while "RNA-seq specific modules" investigate sequencing saturation status of both splicing junction detection and expression estimation, mapped reads clipping profile, mapped reads distribution, coverage uniformity over gene body, reproducibility, strand specificity and splice junction annotation. + +The RSeQC package is licensed under the GNU GPL v3 license. + +.. image:: http://rseqc.sourceforge.net/_static/logo.png + +.. _RSeQC: http://rseqc.sourceforge.net/ +]]> + </help> + + <expand macro="citations" /> + +</tool>
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/geneBody_coverage.xml Tue Jul 07 10:39:47 2015 -0400 @@ -0,0 +1,144 @@ +<tool id="rseqc_geneBody_coverage" name="Gene Body Converage (BAM)" version="2.4galaxy1"> + <description> + Read coverage over gene body. + </description> + + <macros> + <import>rseqc_macros.xml</import> + </macros> + + <requirements> + <expand macro="requirement_package_r" /> + <expand macro="requirement_package_numpy" /> + <expand macro="requirement_package_rseqc" /> + </requirements> + + <expand macro="stdio" /> + + <version_command><![CDATA[geneBody_coverage.py --version]]></version_command> + + <command><![CDATA[ + #set $safename = ''.join(c in '_0123456789abcdefghijklmnopqrstuvwxyzABCDEFGHIJKLMNOPQRSTUVWXYZ' and c or '_' for c in $input.display_name) + #set $fname = "d1_" + str($safename) + ".bam" + ln -s '${input}' '${fname}' && + ln -s '${input.metadata.bam_index}' '${fname}.bai' && + echo '${fname}' > input_list.txt && + #for $i, $additional_input in enumerate($additionalinputs): + #set $index = $i+2 + #set $safename = ''.join(c in '_0123456789abcdefghijklmnopqrstuvwxyzABCDEFGHIJKLMNOPQRSTUVWXYZ' and c or '_' for c in $additional_input.file.display_name) + #set $fname = 'd' + str($index) + '_' + str($safename) + ".bam" + ln -s '$additional_input.file' '${fname}' && + ln -s '$additional_input.file.metadata.bam_index' '${fname}.bai' && + echo '${fname}' >> input_list.txt && + #end for + geneBody_coverage.py -i input_list.txt -r $refgene --minimum_length $minimum_length -o output + ]]> + </command> + + <inputs> + <param name="input" type="data" label="Additional input .bam files" format="bam" /> + <repeat name="additionalinputs" title="Input .bam file"> + <param name="file" type="data" label="Input .bam file" format="bam" /> + </repeat> + <param name="refgene" type="data" label="Reference Genome" format="bed" /> + <param name="minimum_length" type="integer" value="100" label="Minimum mRNA length" help="Minimum mRNA length (bp). mRNA that are shorter than this value will be skipped (default is 100)." /> + </inputs> + + <outputs> + <data name="outputcurvespdf" format="pdf" from_work_dir="output.geneBodyCoverage.curves.pdf" label="${tool.name} on ${on_string} (Curves PDF)" /> + <data name="outputheatmappdf" format="pdf" from_work_dir="output.geneBodyCoverage.heatMap.pdf" label="${tool.name} on ${on_string} (HeatMap PDF)"> + <filter>len(additionalinputs) >= 2</filter> + </data> + <data name="outputr" format="txt" from_work_dir="output.geneBodyCoverage.r" label="${tool.name} on ${on_string} (R Script)" /> + <data name="outputtxt" format="txt" from_work_dir="output.geneBodyCoverage.txt" label="${tool.name} on ${on_string} (Text)" /> + </outputs> + + <tests> + <test> + <param name="input" value="pairend_strandspecific_51mer_hg19_chr1_1-100000.bam"/> + <param name="refgene" value="hg19_RefSeq_chr1_1-100000.bed"/> + <output name="outputcurvespdf" file="output.geneBodyCoverage.curves.pdf"/> + <output name="outputr" file="output.geneBodyCoverage.r"/> + <output name="outputtxt" file="output.geneBodyCoverage.txt"/> + </test> + <test> + <param name="input" value="pairend_strandspecific_51mer_hg19_chr1_1-100000.bam"/> + <param name="file_0" value="pairend_strandspecific_51mer_hg19_chr1_1-100000.bam"/> + <param name="file_1" value="pairend_strandspecific_51mer_hg19_chr1_1-100000.bam"/> + <param name="refgene" value="hg19_RefSeq_chr1_1-100000.bed"/> + <output name="outputcurvespdf" file="output2.geneBodyCoverage.curves.pdf"/> + <output name="outputcurvespdf" file="output2.geneBodyCoverage.heatMap.pdf"/> + <output name="outputr" file="output2.geneBodycoverage.r"/> + <output name="outputtxt" file="output2.geneBodyCoverage.txt"/> + </test> + + </tests> + + <help><![CDATA[ +geneBody_coverage.py +++++++++++++++++++++ + +Read coverage over gene body. This module is used to check if read coverage is uniform and if there is any 5\'/3\' bias. This module scales all transcripts to 100 nt and calculates the number of reads covering each nucleotide position. Finally, it generates plots illustrating the coverage profile along the gene body. + +If 3 or more BAM files were provided. This program generates a lineGraph and a heatmap. If fewer than 3 BAM files were provided, only lineGraph is generated. See below for examples. + +When heatmap is generated, samples are ranked by the "skewness" of the coverage: Sample with best (worst) coverage will be displayed at the top (bottom) of the heatmap. +Coverage skewness was measured by `Pearson’s skewness coefficients <http://en.wikipedia.org/wiki/Skewness#Pearson.27s_skewness_coefficients>`_ + + .. image:: http://rseqc.sourceforge.net/_images/geneBody_workflow.png + :width: 800 px + :scale: 80 % + + +Inputs +++++++++++++++ + +Input BAM/SAM file + Alignment file in BAM/SAM format. + +Reference gene model + Gene Model in BED format. + +Minimum mRNA length + Minimum mRNA length (bp). mRNA that are shorter than this value will be skipped (default is 100). + +Outputs +++++++++++++++ +Text + Table that includes the data used to generate the plots + +R Script + R script file that reads the data and generates the plot + +PDF + The final plot, in PDF format + +Example plots: + .. image:: http://rseqc.sourceforge.net/_images/Aug_26.geneBodyCoverage.curves.png + :height: 600 px + :width: 600 px + :scale: 80 % + + .. image:: http://rseqc.sourceforge.net/_images/Aug_26.geneBodyCoverage.heatMap.png + :height: 600 px + :width: 600 px + :scale: 80 % + +----- + +About RSeQC ++++++++++++ + +The RSeQC_ package provides a number of useful modules that can comprehensively evaluate high throughput sequence data especially RNA-seq data. "Basic modules" quickly inspect sequence quality, nucleotide composition bias, PCR bias and GC bias, while "RNA-seq specific modules" investigate sequencing saturation status of both splicing junction detection and expression estimation, mapped reads clipping profile, mapped reads distribution, coverage uniformity over gene body, reproducibility, strand specificity and splice junction annotation. + +The RSeQC package is licensed under the GNU GPL v3 license. + +.. image:: http://rseqc.sourceforge.net/_static/logo.png + +.. _RSeQC: http://rseqc.sourceforge.net/ +]]> + </help> + + <expand macro="citations" /> + +</tool>
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/geneBody_coverage2.xml Tue Jul 07 10:39:47 2015 -0400 @@ -0,0 +1,95 @@ +<tool id="rseqc_geneBody_coverage2" name="Gene Body Converage (Bigwig)" version="2.4galaxy1"> + <description> + Read coverage over gene body + </description> + + <macros> + <import>rseqc_macros.xml</import> + </macros> + + <requirements> + <expand macro="requirement_package_r" /> + <expand macro="requirement_package_numpy" /> + <expand macro="requirement_package_rseqc" /> + </requirements> + + <expand macro="stdio" /> + + <version_command><![CDATA[geneBody_coverage2.py --version]]></version_command> + + <command><![CDATA[ + geneBody_coverage2.py -i $input -r $refgene -o output + ]]> + </command> + + <inputs> + <param name="input" type="data" label="Input bigwig file" format="bigwig" /> + <param name="refgene" type="data" label="Reference Genome" format="bed" /> + </inputs> + + <outputs> + <data name="outputpdf" format="pdf" from_work_dir="output.geneBodyCoverage.pdf" label="${tool.name} on ${on_string} (PDF)" /> + <data name="outputr" format="txt" from_work_dir="output.geneBodyCoverage_plot.r" label="${tool.name} on ${on_string} (R Script)" /> + <data name="outputtxt" format="txt" from_work_dir="output.geneBodyCoverage.txt" label="${tool.name} on ${on_string} (Text)" /> + </outputs> + + <!-- Unable to succefully run this script, it seems deprecated and should probably be dropped + <tests> + <test> + <param name="input" value="pairend_strandspecific_51mer_hg19_chr1_1-100000.bigwig"/> + <param name="refgene" value="hg19_RefSeq_chr1_1-100000.bed"/> + <output name="outputcurvespdf" file="output.geneBodyCoverage.curves.pdf"/> + <output name="outputr" file="output.geneBodyCoverage.r"/> + <output name="outputtxt" file="output.geneBodyCoverage.txt"/> + </test> + </tests> + --> + + <help><![CDATA[ +geneBody_coverage2.py ++++++++++++++++++++++ + +Similar to geneBody_coverage.py. This module takes bigwig instead of BAM as input, and thus +requires much less memory. The BigWig file could be arbitrarily large. + + +Inputs +++++++++++++++ + +Input BAM/SAM file + Alignment file in BAM/SAM format. + +Reference gene model + Gene Model in BED format. + + +Outputs +++++++++++++++ + +Read coverage over gene body. This module is used to check if reads coverage is uniform and if there is any 5’/3’ bias. This module scales all transcripts to 100 nt and calculates the number of reads covering each nucleotide position. Finally, it generates a plot illustrating the coverage profile along the gene body. NOTE: this module requires lots of memory for large BAM files, because it load the entire BAM file into memory. We add another script "geneBody_coverage2.py" into v2.3.1 which takes bigwig (instead of BAM) as input. It only use 200M RAM, but users need to convert BAM into WIG, and then WIG into BigWig. + +Example output: + .. image:: http://dldcc-web.brc.bcm.edu/lilab/liguow/RSeQC/figure/geneBody_coverage.png + :height: 600 px + :width: 600 px + :scale: 80 % + +----- + +About RSeQC ++++++++++++ + +The RSeQC_ package provides a number of useful modules that can comprehensively evaluate high throughput sequence data especially RNA-seq data. "Basic modules" quickly inspect sequence quality, nucleotide composition bias, PCR bias and GC bias, while "RNA-seq specific modules" investigate sequencing saturation status of both splicing junction detection and expression estimation, mapped reads clipping profile, mapped reads distribution, coverage uniformity over gene body, reproducibility, strand specificity and splice junction annotation. + +The RSeQC package is licensed under the GNU GPL v3 license. + +.. image:: http://rseqc.sourceforge.net/_static/logo.png + +.. _RSeQC: http://rseqc.sourceforge.net/ + +]]> + </help> + + <expand macro="citations" /> + +</tool>
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/infer_experiment.xml Tue Jul 07 10:39:47 2015 -0400 @@ -0,0 +1,156 @@ +<tool id="rseqc_infer_experiment" name="Infer Experiment" version="2.4galaxy1"> + <description>speculates how RNA-seq were configured</description> + + <macros> + <import>rseqc_macros.xml</import> + </macros> + + <requirements> + <expand macro="requirement_package_numpy" /> + <expand macro="requirement_package_rseqc" /> + </requirements> + + <expand macro="stdio" /> + + <version_command><![CDATA[infer_experiment.py --version]]></version_command> + + <command><![CDATA[ + infer_experiment.py -i $input -r $refgene + --sample-size $sample_size + --mapq $mapq + > $output + ]]> + </command> + + <inputs> + <param name="input" type="data" format="bam,sam" label="Input BAM/SAM file" help="(--input-file)"/> + <param name="refgene" type="data" format="bed" label="Reference gene model in bed format" help="(--refgene)" /> + <param name="sample_size" type="integer" label="Number of reads sampled from SAM/BAM file (default = 200000)" value="200000" help="(--sample-size)"/> + <param name="mapq" type="integer" label="Minimum mapping quality (default=30)" help="Minimum phred scale mapping quality to consider a read 'uniquely mapped' (--mapq)" value="30" /> + </inputs> + + <outputs> + <data format="txt" name="output" /> + </outputs> + + <tests> + <test> + <param name="input" value="pairend_strandspecific_51mer_hg19_chr1_1-100000.bam"/> + <param name="refgene" value="hg19_RefSeq_chr1_1-100000.bed"/> + <output name="output" file="output.infer_experiment.txt"/> + </test> + </tests> + + <help><![CDATA[ +infer_experiment.py ++++++++++++++++++++ + +This program is used to speculate how RNA-seq sequencing were configured, especially how +reads were stranded for strand-specific RNA-seq data, through comparing reads' mapping +information to the underneath gene model. + + +Inputs +++++++++++++++ + +Input BAM/SAM file + Alignment file in BAM/SAM format. + +Reference gene model + Gene model in BED format. + +Number of usable sampled reads (default=200000) + Number of usable reads sampled from SAM/BAM file. More reads will give more accurate estimation, but make program little slower. + +Outputs ++++++++ + +For pair-end RNA-seq, there are two different +ways to strand reads (such as Illumina ScriptSeq protocol): + +1. 1++,1--,2+-,2-+ + +* read1 mapped to '+' strand indicates parental gene on '+' strand +* read1 mapped to '-' strand indicates parental gene on '-' strand +* read2 mapped to '+' strand indicates parental gene on '-' strand +* read2 mapped to '-' strand indicates parental gene on '+' strand + +2. 1+-,1-+,2++,2-- + +* read1 mapped to '+' strand indicates parental gene on '-' strand +* read1 mapped to '-' strand indicates parental gene on '+' strand +* read2 mapped to '+' strand indicates parental gene on '+' strand +* read2 mapped to '-' strand indicates parental gene on '-' strand + +For single-end RNA-seq, there are also two different ways to strand reads: + +1. ++,-- + +* read mapped to '+' strand indicates parental gene on '+' strand +* read mapped to '-' strand indicates parental gene on '-' strand + +2. +-,-+ + +* read mapped to '+' strand indicates parental gene on '-' strand +* read mapped to '-' strand indicates parental gene on '+' strand + + +Example Output +++++++++++++++ + +**Example1** :: + + ========================================================= + This is PairEnd Data :: + + Fraction of reads explained by "1++,1--,2+-,2-+": 0.4992 + Fraction of reads explained by "1+-,1-+,2++,2--": 0.5008 + Fraction of reads explained by other combinations: 0.0000 + ========================================================= + +*Conclusion*: We can infer that this is NOT a strand specific because 50% of reads can be explained by "1++,1--,2+-,2-+", while the other 50% can be explained by "1+-,1-+,2++,2--". + +**Example2** :: + + ============================================================ + This is PairEnd Data + + Fraction of reads explained by "1++,1--,2+-,2-+": 0.9644 :: + Fraction of reads explained by "1+-,1-+,2++,2--": 0.0356 + Fraction of reads explained by other combinations: 0.0000 + ============================================================ + +*Conclusion*: We can infer that this is a strand-specific RNA-seq data. strandness of read1 is consistent with that of gene model, while strandness of read2 is opposite to the strand of reference gene model. + +**Example3** :: + + ========================================================= + This is SingleEnd Data :: + + Fraction of reads explained by "++,--": 0.9840 :: + Fraction of reads explained by "+-,-+": 0.0160 + Fraction of reads explained by other combinations: 0.0000 + ========================================================= + +*Conclusion*: This is single-end, strand specific RNA-seq data. Strandness of reads are concordant with strandness of reference gene. + + +----- + +About RSeQC ++++++++++++ + +The RSeQC_ package provides a number of useful modules that can comprehensively evaluate high throughput sequence data especially RNA-seq data. "Basic modules" quickly inspect sequence quality, nucleotide composition bias, PCR bias and GC bias, while "RNA-seq specific modules" investigate sequencing saturation status of both splicing junction detection and expression estimation, mapped reads clipping profile, mapped reads distribution, coverage uniformity over gene body, reproducibility, strand specificity and splice junction annotation. + +The RSeQC package is licensed under the GNU GPL v3 license. + +.. image:: http://rseqc.sourceforge.net/_static/logo.png + +.. _RSeQC: http://rseqc.sourceforge.net/ + +]]> + </help> + + <expand macro="citations" /> + +</tool>
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/inner_distance.xml Tue Jul 07 10:39:47 2015 -0400 @@ -0,0 +1,127 @@ +<tool id="rseqc_inner_distance" name="Inner Distance" version="2.4galaxy1"> + <description>calculate the inner distance (or insert size) between two paired RNA reads</description> + + <macros> + <import>rseqc_macros.xml</import> + </macros> + + <requirements> + <expand macro="requirement_package_r" /> + <expand macro="requirement_package_numpy" /> + <expand macro="requirement_package_rseqc" /> + </requirements> + + <expand macro="stdio" /> + + <version_command><![CDATA[inner_distance.py --version]]></version_command> + + <command><![CDATA[ + inner_distance.py -i $input -o output -r $refgene + --sample-size $sample_size + --lower-bound $lowerBound + --upper-bound $upperBound + --step $step + --mapq $mapq + ]]> + </command> + + <inputs> + <param name="input" type="data" format="bam,sam" label="input bam/sam file" help="(--input-file)" /> + <param name="refgene" type="data" format="bed" label="reference gene model" help="(--refgene)" /> + <param name="sample_size" type="integer" label="Number of read-pairs used to estimate inner distance (default = 1000000)" value="1000000" help="(--sample-size)"/> + <param name="lowerBound" type="integer" value="-250" label="Lower bound (bp, default=-250)" help="Used for plotting histogram (--lower-bound)"/> + <param name="upperBound" type="integer" value="250" label="Upper bound (bp, default=250)" help="Used for plotting histogram (--upper-bound)"/> + <param name="step" type="integer" value="5" label="Step size of histogram (bp, default=5)" help="(--step)"/> + <param name="mapq" type="integer" label="Minimum mapping quality (default=30)" help="Minimum phred scale mapping quality to consider a read 'uniquely mapped' (--mapq)" value="30" /> + </inputs> + + <outputs> + <data format="txt" name="outputtxt" from_work_dir="output.inner_distance.txt" label="${tool.name} on ${on_string} (Text)"/> + <data format="txt" name="outputfreqtxt" from_work_dir="output.inner_distance_freq.txt" label="${tool.name} on ${on_string} (Freq Text)" /> + <data format="pdf" name="outputpdf" from_work_dir="output.inner_distance_plot.pdf" label="${tool.name} on ${on_string} (PDF)" /> + <data format="txt" name="outputr" from_work_dir="output.inner_distance_plot.r" label="${tool.name} on ${on_string} (R Script)" /> + </outputs> + + <tests> + <test> + <param name="input" value="pairend_strandspecific_51mer_hg19_chr1_1-100000.bam"/> + <param name="refgene" value="hg19_RefSeq_chr1_1-100000.bed"/> + <output name="outputtxt" file="output.inner_distance.txt"/> + <output name="outputfreqtxt" file="output.inner_distance_freq.txt"/> + <output name="outputpdf" file="output.inner_distance_plot.pdf"/> + <output name="outputr" file="output.inner_distance_plot.r"/> + </test> + </tests> + + <help><![CDATA[ +inner_distance.py ++++++++++++++++++ + +This module is used to calculate the inner distance (or insert size) between two paired RNA +reads. The distance is the mRNA length between two paired fragments. We first determine the +genomic (DNA) size between two paired reads: D_size = read2_start - read1_end, then + +* if two paired reads map to the same exon: inner distance = D_size +* if two paired reads map to different exons:inner distance = D_size - intron_size +* if two paired reads map non-exonic region (such as intron and intergenic region): inner distance = D_size +* The inner_distance might be a negative value if two fragments were overlapped. + +NOTE: Not all read pairs were used to estimate the inner distance distribution. Those low +quality, PCR duplication, multiple mapped reads were skipped. + +Inputs +++++++++++++++ + +Input BAM/SAM file + Alignment file in BAM/SAM format. + +Reference gene model + Gene model in BED format. + +Estimated Upper/Lower Bounds (defaults=250 and -250) + Estimated upper/lower bounds of inner distance (bp). + +Step size (default=5) + Step size of histogram + + +Output +++++++++++++++ + +1. output.inner_distance.txt: + - first column is read ID + -second column is inner distance. Could be negative value if PE reads were overlapped or mapping error (e.g. Read1_start < Read2_start, while Read1_end >> Read2_end due to spliced mapping of read1) + - third column indicates how paired reads were mapped: PE_within_same_exon, PE_within_diff_exon,PE_reads_overlap +2. output..inner_distance_freq.txt: + - inner distance starts + - inner distance ends + - number of read pairs + - note the first 2 columns are left side half open interval +3. output.inner_distance_plot.r: R script to generate histogram +4. output.inner_distance_plot.pdf: histogram plot + +.. image:: http://rseqc.sourceforge.net/_images/inner_distance.png + :height: 600 px + :width: 600 px + :scale: 80 % + + +----- + +About RSeQC ++++++++++++ + +The RSeQC_ package provides a number of useful modules that can comprehensively evaluate high throughput sequence data especially RNA-seq data. "Basic modules" quickly inspect sequence quality, nucleotide composition bias, PCR bias and GC bias, while "RNA-seq specific modules" investigate sequencing saturation status of both splicing junction detection and expression estimation, mapped reads clipping profile, mapped reads distribution, coverage uniformity over gene body, reproducibility, strand specificity and splice junction annotation. + +The RSeQC package is licensed under the GNU GPL v3 license. + +.. image:: http://rseqc.sourceforge.net/_static/logo.png + +.. _RSeQC: http://rseqc.sourceforge.net/ + +]]> + </help> + + <expand macro="citations" /> + +</tool>
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/junction_annotation.xml Tue Jul 07 10:39:47 2015 -0400 @@ -0,0 +1,119 @@ +<tool id="rseqc_junction_annotation" name="Junction Annotation" version="2.4galaxy1"> + <description>compares detected splice junctions to reference gene model</description> + + <macros> + <import>rseqc_macros.xml</import> + </macros> + + <requirements> + <expand macro="requirement_package_r" /> + <expand macro="requirement_package_numpy" /> + <expand macro="requirement_package_rseqc" /> + </requirements> + + <expand macro="stdio" /> + + <version_command><![CDATA[junction_annotation.py --version]]></version_command> + + <command><![CDATA[ + junction_annotation.py + --input-file $input + --refgene $refgene + --out-prefix output + --min-intron $min_intron + --mapq $mapq + ]]> + </command> + + <inputs> + <param name="input" type="data" format="bam,sam" label="input bam/sam file" help="(--input-file)"/> + <param name="refgene" type="data" format="bed" label="reference gene model" help="(--refgene)"/> + <param name="min_intron" type="integer" value="50" label="Minimum intron length (bp, default=50)" help="(--min-intron)" /> + <param name="mapq" type="integer" label="Minimum mapping quality (default=30)" help="Minimum phred scale mapping quality to consider a read 'uniquely mapped' (--mapq)" value="30" /> + </inputs> + + <outputs> + <data format="xls" name="outputxls" from_work_dir="output.junction.xls" label="${tool.name} on ${on_string} (XLS)"/> + <data format="txt" name="outputr" from_work_dir="output.junction_plot.r" label="${tool.name} on ${on_string} (R Script)" /> + <data format="pdf" name="outputpdf" from_work_dir="output.splice_events.pdf" label="${tool.name} on ${on_string} (Splice Events PDF)"/> + <data format="pdf" name="outputjpdf" from_work_dir="output.splice_junction.pdf" label="${tool.name} on ${on_string} (Splice Junction PDF)" /> + </outputs> + + <tests> + <test> + <param name="input" value="pairend_strandspecific_51mer_hg19_chr1_1-100000.bam"/> + <param name="refgene" value="hg19_RefSeq_chr1_1-100000.bed"/> + <output name="outputxls" file="output.junction.xls"/> + <output name="outputr" file="output.junction_plot.r"/> + <output name="outputpdf" file="output.splice_events.pdf"/> + <output name="outputjpdf" file="output.splice_junction.pdf"/> + </test> + </tests> + + <help><![CDATA[ +junction_annotation.py +++++++++++++++++++++++ + +For a given alignment file (-i) in BAM or SAM format and a reference gene model (-r) in BED +format, this program will compare detected splice junctions to reference gene model. splicing +annotation is performed in two levels: splice event level and splice junction level. + +* splice event: An RNA read, especially long read, can be spliced 2 or more times, each time is called a splicing event; In this sense, 100 spliced reads can produce >= 100 splicing events. +* splice junction: multiple splicing events spanning the same intron can be consolidated into one splicing junction. + +All detected junctions can be grouped to 3 exclusive categories: + +1. Annotated: The junction is part of the gene model. Both splice sites, 5' splice site + (5'SS) and 3'splice site (3'SS) can be annotated by reference gene model. +2. complete_novel: Complete new junction. Neither of the two splice sites cannot be annotated by gene model +3. partial_novel: One of the splice site (5'SS or 3'SS) is new, while the other splice site is annotated (known) + +Inputs +++++++++++++++ + +Input BAM/SAM file + Alignment file in BAM/SAM format. + +Reference gene model + Gene model in BED format. + +Minimum intron length (default=50) + Minimum intron length (bp). + + +Output +++++++++++++++ + +1. output.junc.anno.junction.xls: + - chrom ID + - start position of junction (coordinate is 0 based) + - end position of junction (coordinate is 1 based) + - number of splice events supporting this junction + - 'annotated', 'complete_novel' or 'partial_novel'. +2. output.anno.junction_plot.r: R script to generate pie chart +3. output.splice_junction.pdf: plot of splice junctions +4. output.splice_events.pdf: plot of splice events + +.. image:: http://rseqc.sourceforge.net/_images/junction.png + :height: 400 px + :width: 850 px + :scale: 80 % + +----- + +About RSeQC ++++++++++++ + +The RSeQC_ package provides a number of useful modules that can comprehensively evaluate high throughput sequence data especially RNA-seq data. "Basic modules" quickly inspect sequence quality, nucleotide composition bias, PCR bias and GC bias, while "RNA-seq specific modules" investigate sequencing saturation status of both splicing junction detection and expression estimation, mapped reads clipping profile, mapped reads distribution, coverage uniformity over gene body, reproducibility, strand specificity and splice junction annotation. + +The RSeQC package is licensed under the GNU GPL v3 license. + +.. image:: http://rseqc.sourceforge.net/_static/logo.png + +.. _RSeQC: http://rseqc.sourceforge.net/ +]]> + </help> + + <expand macro="citations" /> + +</tool>
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/junction_saturation.xml Tue Jul 07 10:39:47 2015 -0400 @@ -0,0 +1,132 @@ +<tool id="rseqc_junction_saturation" name="Junction Saturation" version="2.4galaxy1"> + <description>detects splice junctions from each subset and compares them to reference gene model</description> + + <macros> + <import>rseqc_macros.xml</import> + </macros> + + <requirements> + <expand macro="requirement_package_r" /> + <expand macro="requirement_package_numpy" /> + <expand macro="requirement_package_rseqc" /> + </requirements> + + <expand macro="stdio" /> + + <version_command><![CDATA[junction_saturation.py --version]]></version_command> + + <command><![CDATA[ + junction_saturation.py + --input-file $input + --refgene $refgene + --out-prefix output + --min-intron $min_intron + --min-coverage $min_coverage + --mapq $mapq + #if $percentiles.specifyPercentiles + --percentile-floor $percentiles.lowBound + --percentile-ceiling $percentiles.upBound + --percentile-step $percentiles.percentileStep + #end if + ]]> + </command> + + <inputs> + <param name="input" type="data" format="bam,sam" label="input bam/sam file" help="(--input-file)"/> + <param name="refgene" type="data" format="bed" label="reference gene model" help="(--refgene)"/> + <param name="min_intron" type="integer" value="50" label="Minimum intron length (bp, default=50)" help="(--min-intron)" /> + <param name="min_coverage" type="integer" label="Minimum number of supporting reads to call a junction (default=1)" value="1" help="(--min-coverage)" /> + <param name="mapq" type="integer" label="Minimum mapping quality (default=30)" help="Minimum phred scale mapping quality to consider a read 'uniquely mapped' (--mapq)" value="30" /> + <conditional name="percentiles"> + <param name="specifyPercentiles" type="boolean" label="Specify sampling bounds and frequency" value="false"/> + <when value="true"> + <param name="lowBound" type="integer" value="5" label="Lower Bound Sampling Frequency (bp, default=5)" help="(--percentile-floor)"> + <validator type="in_range" min="0" max="100" /> + </param> + <param name="upBound" type="integer" value="100" label="Upper Bound Sampling Frequency (bp, default=100)" help="(--percentile-ceiling)"> + <validator type="in_range" min="0" max="100" /> + </param> + <param name="percentileStep" type="integer" value="5" label="Sampling increment (default=5)" help="(--percentile-step)"> + <validator type="in_range" min="0" max="100" /> + </param> + </when> + </conditional> + </inputs> + + <outputs> + <data format="txt" name="outputr" from_work_dir="output.junctionSaturation_plot.r" label="${tool.name} on ${on_string} (R Script)"/> + <data format="pdf" name="outputpdf" from_work_dir="output.junctionSaturation_plot.pdf" label="${tool.name} on ${on_string} (PDF)"/> + </outputs> + + <tests> + <test> + <param name="input" value="pairend_strandspecific_51mer_hg19_chr1_1-100000.bam"/> + <param name="refgene" value="hg19_RefSeq_chr1_1-100000.bed"/> + <output name="outputr" file="output.junctionSaturation_plot.r"/> + </test> + </tests> + + <help><![CDATA[ +junction_saturation.py +++++++++++++++++++++++ + +It's very important to check if current sequencing depth is deep enough to perform +alternative splicing analyses. For a well annotated organism, the number of expressed genes +in particular tissue is almost fixed so the number of splice junctions is also fixed. The fixed +splice junctions can be predetermined from reference gene model. All (annotated) splice +junctions should be rediscovered from a saturated RNA-seq data, otherwise, downstream +alternative splicing analysis is problematic because low abundance splice junctions are +missing. This module checks for saturation by resampling 5%, 10%, 15%, ..., 95% of total +alignments from BAM or SAM file, and then detects splice junctions from each subset and +compares them to reference gene model. + +Inputs +++++++++++++++ + +Input BAM/SAM file + Alignment file in BAM/SAM format. + +Reference gene model + Gene model in BED format. + +Sampling Percentiles - Upper Bound, Lower Bound, Sampling Increment (defaults= 100, 5, and 5) + Sampling starts from the Lower Bound and increments to the Upper Bound at the rate of the Sampling Increment. + +Minimum intron length (default=50) + Minimum intron length (bp). + +Minimum coverage (default=1) + Minimum number of supportting reads to call a junction. + +Output +++++++++++++++ + +1. output.junctionSaturation_plot.r: R script to generate plot +2. output.junctionSaturation_plot.pdf + +.. image:: http://rseqc.sourceforge.net/_images/junction_saturation.png + :height: 600 px + :width: 600 px + :scale: 80 % + +In this example, current sequencing depth is almost saturated for "known junction" (red line) detection because the number of "known junction" reaches a plateau. In other words, nearly all "known junctions" (expressed in this particular tissue) have already been detected, and continue sequencing will not detect additional "known junction" and will only increase junction coverage (i.e. junction covered by more reads). While current sequencing depth is not saturated for novel junctions (green). + + +----- + +About RSeQC ++++++++++++ + +The RSeQC_ package provides a number of useful modules that can comprehensively evaluate high throughput sequence data especially RNA-seq data. "Basic modules" quickly inspect sequence quality, nucleotide composition bias, PCR bias and GC bias, while "RNA-seq specific modules" investigate sequencing saturation status of both splicing junction detection and expression estimation, mapped reads clipping profile, mapped reads distribution, coverage uniformity over gene body, reproducibility, strand specificity and splice junction annotation. + +The RSeQC package is licensed under the GNU GPL v3 license. + +.. image:: http://rseqc.sourceforge.net/_static/logo.png + +.. _RSeQC: http://rseqc.sourceforge.net/ +]]> + </help> + + <expand macro="citations" /> + +</tool>
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/read_GC.xml Tue Jul 07 10:39:47 2015 -0400 @@ -0,0 +1,85 @@ +<tool id="rseqc_read_GC" name="Read GC" version="2.4galaxy1"> + <description>determines GC% and read count</description> + + <macros> + <import>rseqc_macros.xml</import> + </macros> + + <requirements> + <expand macro="requirement_package_r" /> + <expand macro="requirement_package_numpy" /> + <expand macro="requirement_package_rseqc" /> + </requirements> + + <expand macro="stdio" /> + + <version_command><![CDATA[read_GC.py --version]]></version_command> + + <command><![CDATA[ + read_GC.py + --input-file $input + --out-prefix output + --mapq $mapq + ]]> + </command> + + <inputs> + <param name="input" type="data" format="bam,sam" label="input bam/sam file" help="(--input-file)"/> + <param name="mapq" type="integer" label="Minimum mapping quality (default=30)" help="Minimum phred scale mapping quality to consider a read 'uniquely mapped' (--mapq)" value="30" /> + </inputs> + + <outputs> + <data format="xls" name="outputxls" from_work_dir="output.GC.xls" label="${tool.name} on ${on_string} (XLS)"/> + <data format="txt" name="outputr" from_work_dir="output.GC_plot.r" label="${tool.name} on ${on_string} (R Script)" /> + <data format="pdf" name="outputpdf" from_work_dir="output.GC_plot.pdf" label="${tool.name} on ${on_string} (PDF)" /> + </outputs> + + <tests> + <test> + <param name="input" value="pairend_strandspecific_51mer_hg19_chr1_1-100000.bam"/> + <output name="outputxls" file="output.GC.xls"/> + <output name="outputr" file="output.GC_plot.r"/> + </test> + </tests> + + <help><![CDATA[ +read_GC.py +++++++++++ + + +Inputs +++++++++++++++ + +Input BAM/SAM file + Alignment file in BAM/SAM format. + +Output +++++++++++++++ + +1. output.GC.xls: Two column, plain text file, first column is GC%, second column is read count +2. output.GC_plot.r: R script to generate pdf file. +3. output.GC_plot.pdf: graphical output generated from R script. + +.. image:: http://rseqc.sourceforge.net/_images/read_gc.png + :height: 600 px + :width: 600 px + :scale: 80 % + +----- + +About RSeQC ++++++++++++ + +The RSeQC_ package provides a number of useful modules that can comprehensively evaluate high throughput sequence data especially RNA-seq data. "Basic modules" quickly inspect sequence quality, nucleotide composition bias, PCR bias and GC bias, while "RNA-seq specific modules" investigate sequencing saturation status of both splicing junction detection and expression estimation, mapped reads clipping profile, mapped reads distribution, coverage uniformity over gene body, reproducibility, strand specificity and splice junction annotation. + +The RSeQC package is licensed under the GNU GPL v3 license. + +.. image:: http://rseqc.sourceforge.net/_static/logo.png + +.. _RSeQC: http://rseqc.sourceforge.net/ +]]> + </help> + + <expand macro="citations" /> + +</tool>
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/read_NVC.xml Tue Jul 07 10:39:47 2015 -0400 @@ -0,0 +1,101 @@ +<tool id="rseqc_read_NVC" name="Read NVC" version="2.4galaxy1"> + <description>to check the nucleotide composition bias</description> + + <macros> + <import>rseqc_macros.xml</import> + </macros> + + <requirements> + <expand macro="requirement_package_r" /> + <expand macro="requirement_package_numpy" /> + <expand macro="requirement_package_rseqc" /> + </requirements> + + <expand macro="stdio" /> + + <version_command><![CDATA[read_NVC.py --version]]></version_command> + + <command> + read_NVC.py + --input-file $input + --out-prefix output + $nx + --mapq $mapq + </command> + + <inputs> + <param name="input" type="data" format="bam,sam" label="input bam/sam file" help="(--input-file)"/> + <param name="nx" type="boolean" value="false" truevalue="--nx" falsevalue="" label="Include N,X in NVC plot" help="(--nx)"/> + <param name="mapq" type="integer" label="Minimum mapping quality (default=30)" help="Minimum phred scale mapping quality to consider a read 'uniquely mapped' (--mapq)" value="30" /> + </inputs> + + <outputs> + <data format="xls" name="outputxls" from_work_dir="output.NVC.xls" label="${tool.name} on ${on_string} (XLS)" /> + <data format="txt" name="outputr" from_work_dir="output.NVC_plot.r" label="${tool.name} on ${on_string} (R Script)" /> + <data format="pdf" name="outputpdf" from_work_dir="output.NVC_plot.pdf" label="${tool.name} on ${on_string} (PDF)" /> + </outputs> + + <tests> + <test> + <param name="input" value="pairend_strandspecific_51mer_hg19_chr1_1-100000.bam"/> + <output name="outputxls" file="output.NVC.xls"/> + <output name="outputr" file="output.NVC_plot.r"/> + </test> + </tests> + + <help><![CDATA[ +read_NVC.py ++++++++++++ + +This module is used to check the nucleotide composition bias. Due to random priming, certain +patterns are over represented at the beginning (5'end) of reads. This bias could be easily +examined by NVC (Nucleotide versus cycle) plot. NVC plot is generated by overlaying all +reads together, then calculating nucleotide composition for each position of read +(or each sequencing cycle). In ideal condition (genome is random and RNA-seq reads is +randomly sampled from genome), we expect A%=C%=G%=T%=25% at each position of reads. + +NOTE: this program expect a fixed read length + +Inputs +++++++++++++++ + +Input BAM/SAM file + Alignment file in BAM/SAM format. + +Include N,X in NVC plot + Plots N and X alongside A, T, C, and G in plot. + +Output +++++++++++++++ + +This module is used to check the nucleotide composition bias. Due to random priming, certain patterns are over represented at the beginning (5'end) of reads. This bias could be easily examined by NVC (Nucleotide versus cycle) plot. NVC plot is generated by overlaying all reads together, then calculating nucleotide composition for each position of read (or each sequencing cycle). In ideal condition (genome is random and RNA-seq reads is randomly sampled from genome), we expect A%=C%=G%=T%=25% at each position of reads. + + +1. output.NVC.xls: plain text file, each row is position of read (or sequencing cycle), each column is nucleotide (A,C,G,T,N,X) +2. output.NVC_plot.r: R script to generate NVC plot. +3. output.NVC_plot.pdf: NVC plot. + + +.. image:: http://rseqc.sourceforge.net/_images/NVC_plot.png + :height: 600 px + :width: 600 px + :scale: 80 % + +----- + +About RSeQC ++++++++++++ + +The RSeQC_ package provides a number of useful modules that can comprehensively evaluate high throughput sequence data especially RNA-seq data. "Basic modules" quickly inspect sequence quality, nucleotide composition bias, PCR bias and GC bias, while "RNA-seq specific modules" investigate sequencing saturation status of both splicing junction detection and expression estimation, mapped reads clipping profile, mapped reads distribution, coverage uniformity over gene body, reproducibility, strand specificity and splice junction annotation. + +The RSeQC package is licensed under the GNU GPL v3 license. + +.. image:: http://rseqc.sourceforge.net/_static/logo.png + +.. _RSeQC: http://rseqc.sourceforge.net/ +]]> + </help> + + <expand macro="citations" /> + +</tool>
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/read_distribution.xml Tue Jul 07 10:39:47 2015 -0400 @@ -0,0 +1,109 @@ +<tool id="rseqc_read_distribution" name="Read Distribution" version="2.4galaxy1"> + <description>calculates how mapped reads were distributed over genome feature</description> + + <macros> + <import>rseqc_macros.xml</import> + </macros> + + <requirements> + <expand macro="requirement_package_numpy" /> + <expand macro="requirement_package_rseqc" /> + </requirements> + + <expand macro="stdio" /> + + <version_command><![CDATA[read_distribution.py --version]]></version_command> + + <command><![CDATA[ + read_distribution.py -i $input -r $refgene > $output + ]]> + </command> + + <inputs> + <param name="input" type="data" format="bam,sam" label="input bam/sam file" help="(--input-file)"/> + <param name="refgene" type="data" format="bed" label="reference gene model" help="(--refgene)"/> + </inputs> + + <outputs> + <data format="txt" name="output" /> + </outputs> + + <tests> + <test> + <param name="input" value="pairend_strandspecific_51mer_hg19_chr1_1-100000.bam"/> + <param name="refgene" value="hg19_RefSeq_chr1_1-100000.bed"/> + <output name="output" file="output.read_distribution.txt"/> + </test> + </tests> + + <help><![CDATA[ +read_distribution.py +++++++++++++++++++++ + +Provided a BAM/SAM file and reference gene model, this module will calculate how mapped +reads were distributed over genome feature (like CDS exon, 5'UTR exon, 3' UTR exon, Intron, +Intergenic regions). When genome features are overlapped (e.g. a region could be annotated +as both exon and intron by two different transcripts) , they are prioritize as: +CDS exons > UTR exons > Introns > Intergenic regions, for example, if a read was mapped to +both CDS exon and intron, it will be assigned to CDS exons. + +* "Total Reads": This does NOT include those QC fail,duplicate and non-primary hit reads +* "Total Tags": reads spliced once will be counted as 2 tags, reads spliced twice will be counted as 3 tags, etc. And because of this, "Total Tags" >= "Total Reads" +* "Total Assigned Tags": number of tags that can be unambiguously assigned the 10 groups (see below table). +* Tags assigned to "TSS_up_1kb" were also assigned to "TSS_up_5kb" and "TSS_up_10kb", tags assigned to "TSS_up_5kb" were also assigned to "TSS_up_10kb". Therefore, "Total Assigned Tags" = CDS_Exons + 5'UTR_Exons + 3'UTR_Exons + Introns + TSS_up_10kb + TES_down_10kb. +* When assign tags to genome features, each tag is represented by its middle point. + +RSeQC cannot assign those reads that: + +* hit to intergenic regions that beyond region starting from TSS upstream 10Kb to TES downstream 10Kb. +* hit to regions covered by both 5'UTR and 3' UTR. This is possible when two head-to-tail transcripts are overlapped in UTR regions. +* hit to regions covered by both TSS upstream 10Kb and TES downstream 10Kb. + + +Inputs +++++++++++++++ + +Input BAM/SAM file + Alignment file in BAM/SAM format. + +Reference gene model + Gene model in BED format. + +Sample Output +++++++++++++++ + +Output: + +=============== ============ =========== =========== +Group Total_bases Tag_count Tags/Kb +=============== ============ =========== =========== +CDS_Exons 33302033 20002271 600.63 +5'UTR_Exons 21717577 4408991 203.01 +3'UTR_Exons 15347845 3643326 237.38 +Introns 1132597354 6325392 5.58 +TSS_up_1kb 17957047 215331 11.99 +TSS_up_5kb 81621382 392296 4.81 +TSS_up_10kb 149730983 769231 5.14 +TES_down_1kb 18298543 266161 14.55 +TES_down_5kb 78900674 729997 9.25 +TES_down_10kb 140361190 896882 6.39 +=============== ============ =========== =========== + +----- + +About RSeQC ++++++++++++ + +The RSeQC_ package provides a number of useful modules that can comprehensively evaluate high throughput sequence data especially RNA-seq data. "Basic modules" quickly inspect sequence quality, nucleotide composition bias, PCR bias and GC bias, while "RNA-seq specific modules" investigate sequencing saturation status of both splicing junction detection and expression estimation, mapped reads clipping profile, mapped reads distribution, coverage uniformity over gene body, reproducibility, strand specificity and splice junction annotation. + +The RSeQC package is licensed under the GNU GPL v3 license. + +.. image:: http://rseqc.sourceforge.net/_static/logo.png + +.. _RSeQC: http://rseqc.sourceforge.net/ +]]> + </help> + + <expand macro="citations" /> + +</tool>
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/read_duplication.xml Tue Jul 07 10:39:47 2015 -0400 @@ -0,0 +1,92 @@ +<tool id="rseqc_read_duplication" name="Read Duplication" version="2.4galaxy1"> + <description>determines reads duplication rate with sequence-based and mapping-based strategies</description> + + <macros> + <import>rseqc_macros.xml</import> + </macros> + + <requirements> + <expand macro="requirement_package_r" /> + <expand macro="requirement_package_numpy" /> + <expand macro="requirement_package_rseqc" /> + </requirements> + + <expand macro="stdio" /> + + <version_command><![CDATA[read_duplication.py --version]]></version_command> + + <command><![CDATA[ + read_duplication.py -i $input -o output -u $upLimit + ]]> + </command> + + <inputs> + <param name="input" type="data" format="bam,sam" label="input bam/sam file" help="(--input-file)"/> + <param name="upLimit" type="integer" label="Upper Limit of Plotted Duplicated Times (default=500)" value="500" help="(--up-limit)"/> + </inputs> + + <outputs> + <data format="xls" name="outputxls" from_work_dir="output.pos.DupRate.xls" label="${tool.name} on ${on_string} (Position XLS)"/> + <data format="xls" name="outputseqxls" from_work_dir="output.seq.DupRate.xls" label="${tool.name} on ${on_string} (Sequence XLS)"/> + <data format="txt" name="outputr" from_work_dir="output.DupRate_plot.r" label="${tool.name} on ${on_string} (R Script)" /> + <data format="pdf" name="outputpdf" from_work_dir="output.DupRate_plot.pdf" label="${tool.name} on ${on_string} (PDF)" /> + </outputs> + + <tests> + <test> + <param name="input" value="pairend_strandspecific_51mer_hg19_chr1_1-100000.bam"/> + <output name="outputxls" file="output.pos.DupRate.xls"/> + <output name="outputseqxls" file="output.seq.DupRate.xls"/> + <output name="outputr" file="output.DupRate_plot.r"/> + </test> + </tests> + + <help><![CDATA[ +read_duplication.py ++++++++++++++++++++ + +Two strategies were used to determine reads duplication rate: + +* Sequence based: reads with exactly the same sequence content are regarded as duplicated reads. +* Mapping based: reads mapped to the same genomic location are regarded as duplicated reads. For splice reads, reads mapped to the same starting position and splice the same way are regarded as duplicated reads. + +Inputs +++++++++++++++ + +Input BAM/SAM file + Alignment file in BAM/SAM format. + +Upper Limit of Plotted Duplicated Times (default=500) + Only used for plotting. + +Output +++++++++++++++ + +1. output.dup.pos.DupRate.xls: Read duplication rate determined from mapping position of read. First column is "occurrence" or duplication times, second column is number of uniquely mapped reads. +2. output.dup.seq.DupRate.xls: Read duplication rate determined from sequence of read. First column is "occurrence" or duplication times, second column is number of uniquely mapped reads. +3. output.DupRate_plot.r: R script to generate pdf file +4. output.DupRate_plot.pdf: graphical output generated from R script + +.. image:: http://rseqc.sourceforge.net/_images/duplicate.png + :height: 600 px + :width: 600 px + :scale: 80 % + +----- + +About RSeQC ++++++++++++ + +The RSeQC_ package provides a number of useful modules that can comprehensively evaluate high throughput sequence data especially RNA-seq data. "Basic modules" quickly inspect sequence quality, nucleotide composition bias, PCR bias and GC bias, while "RNA-seq specific modules" investigate sequencing saturation status of both splicing junction detection and expression estimation, mapped reads clipping profile, mapped reads distribution, coverage uniformity over gene body, reproducibility, strand specificity and splice junction annotation. + +The RSeQC package is licensed under the GNU GPL v3 license. + +.. image:: http://rseqc.sourceforge.net/_static/logo.png + +.. _RSeQC: http://rseqc.sourceforge.net/ +]]> + </help> + + <expand macro="citations" /> + +</tool>
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/read_quality.xml Tue Jul 07 10:39:47 2015 -0400 @@ -0,0 +1,106 @@ +<tool id="rseqc_read_quality" name="Read Quality" version="2.4galaxy1"> + <description>determines Phred quality score</description> + + <macros> + <import>rseqc_macros.xml</import> + </macros> + + <requirements> + <expand macro="requirement_package_r" /> + <expand macro="requirement_package_numpy" /> + <expand macro="requirement_package_rseqc" /> + </requirements> + + <expand macro="stdio" /> + + <version_command><![CDATA[read_quality.py --version]]></version_command> + + <command><![CDATA[ + read_quality.py + --input-file $input + --out-prefix output + -r $reduce + --mapq $mapq + ]]> + </command> + + <inputs> + <param name="input" type="data" format="bam,sam" label="input bam/sam file" help="(--input-file)"/> + <param name="reduce" type="integer" label="Ignore Phred scores less than this amount (only applies to 'boxplot', default=1000)" value="1000" help="(--reduce)"/> + <param name="mapq" type="integer" label="Minimum mapping quality (default=30)" help="Minimum phred scale mapping quality to consider a read 'uniquely mapped' (--mapq)" value="30" /> + </inputs> + + <outputs> + <data format="txt" name="outputr" from_work_dir="output.qual.r" label="${tool.name} on ${on_string} (R Script)" /> + <data format="pdf" name="outputheatpdf" from_work_dir="output.qual.heatmap.pdf" label="${tool.name} on ${on_string} (Heatmap PDF)" /> + <data format="pdf" name="outputboxpdf" from_work_dir="output.qual.boxplot.pdf" label="${tool.name} on ${on_string} (Boxplot PDF)" /> + </outputs> + + <!-- Unable to succefully run this script with test data + <tests> + <test> + <param name="input" value="pairend_strandspecific_51mer_hg19_chr1_1-100000.bigwig"/> + <output name="outputr" file="output.qual.r"/> + <output name="outputheatpdf" file="output.qual.heatmap.pdf"/> + <output name="outputboxpdf" file="output.qual.boxplot.pdf"/> + </test> + </tests> + --> + + <help><![CDATA[ +read_quality.py ++++++++++++++++ + +According to SAM specification, if Q is the character to represent "base calling quality" +in SAM file, then Phred Quality Score = ord(Q) - 33. Here ord() is python function that +returns an integer representing the Unicode code point of the character when the argument +is a unicode object, for example, ord('a') returns 97. Phred quality score is widely used +to measure "reliability" of base-calling, for example, phred quality score of 20 means +there is 1/100 chance that the base-calling is wrong, phred quality score of 30 means there +is 1/1000 chance that the base-calling is wrong. In general: Phred quality score = -10xlog(10)P, +here P is probability that base-calling is wrong. + +Inputs +++++++++++++++ + +Input BAM/SAM file + Alignment file in BAM/SAM format. + +Ignore phred scores less than this number (default=1000) + To avoid making huge vector in R, nucleotide with certain phred score represented less than this number will be ignored. Increase this number save more memory while reduce precision. This option only applies to the 'boxplot'. + +Output +++++++++++++++ + +1. output.qual.r +2. output.qual.boxplot.pdf + .. image:: http://rseqc.sourceforge.net/_images/36mer.qual.plot.png + :height: 600 px + :width: 600 px + :scale: 80 % +3. output.qual.heatmap.pdf + .. image:: http://rseqc.sourceforge.net/_images/36mer.qual.heatmap.png + :height: 600 px + :width: 600 px + :scale: 80 % + +Heatmap: use different color to represent nucleotide density ("blue"=low density,"orange"=median density,"red"=high density") + +----- + +About RSeQC ++++++++++++ + +The RSeQC_ package provides a number of useful modules that can comprehensively evaluate high throughput sequence data especially RNA-seq data. "Basic modules" quickly inspect sequence quality, nucleotide composition bias, PCR bias and GC bias, while "RNA-seq specific modules" investigate sequencing saturation status of both splicing junction detection and expression estimation, mapped reads clipping profile, mapped reads distribution, coverage uniformity over gene body, reproducibility, strand specificity and splice junction annotation. + +The RSeQC package is licensed under the GNU GPL v3 license. + +.. image:: http://rseqc.sourceforge.net/_static/logo.png + +.. _RSeQC: http://rseqc.sourceforge.net/ +]]> + </help> + + <expand macro="citations" /> + +</tool>
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/rseqc_macros.xml Tue Jul 07 10:39:47 2015 -0400 @@ -0,0 +1,42 @@ +<macros> + + <xml name="requirement_package_r"><requirement type="package" version="3.0.3">R</requirement></xml> + <xml name="requirement_package_numpy"><requirement type="package" version="1.7.1">numpy</requirement></xml> + <xml name="requirement_package_rseqc"><requirement type="package" version="2.4">rseqc</requirement></xml> + + <xml name="stdio"> + <stdio> + <exit_code range="1:" level="fatal" description="An error occured during execution, see stderr and stdout for more information" /> + <regex match="[Ee]rror" source="both" description="An error occured during execution, see stderr and stdout for more information" /> + </stdio> + </xml> + + <xml name="citations"> + <citations> + <citation type="bibtex"> + @article{wang_rseqc:_2012, + title = {{RSeQC}: quality control of {RNA}-seq experiments}, + volume = {28}, + issn = {1367-4803, 1460-2059}, + shorttitle = {{RSeQC}}, + url = {http://bioinformatics.oxfordjournals.org/content/28/16/2184}, + doi = {10.1093/bioinformatics/bts356}, + abstract = {Motivation: RNA-seq has been extensively used for transcriptome study. Quality control (QC) is critical to ensure that RNA-seq data are of high quality and suitable for subsequent analyses. However, QC is a time-consuming and complex task, due to the massive size and versatile nature of RNA-seq data. Therefore, a convenient and comprehensive QC tool to assess RNA-seq quality is sorely needed. + Results: We developed the RSeQC package to comprehensively evaluate different aspects of RNA-seq experiments, such as sequence quality, GC bias, polymerase chain reaction bias, nucleotide composition bias, sequencing depth, strand specificity, coverage uniformity and read distribution over the genome structure. RSeQC takes both SAM and BAM files as input, which can be produced by most RNA-seq mapping tools as well as BED files, which are widely used for gene models. Most modules in RSeQC take advantage of R scripts for visualization, and they are notably efficient in dealing with large BAM/SAM files containing hundreds of millions of alignments. + Availability and implementation: RSeQC is written in Python and C. Source code and a comprehensive user's manual are freely available at: http://code.google.com/p/rseqc/. + Contact: WL1\{at\}bcm.edu + Supplementary Information: Supplementary data are available at Bioinformatics online.}, + language = {en}, + number = {16}, + urldate = {2015-06-30}, + journal = {Bioinformatics}, + author = {Wang, Liguo and Wang, Shengqin and Li, Wei}, + month = aug, + year = {2012}, + pmid = {22743226}, + pages = {2184--2185}, + } + </citation> + </citations> + </xml> +</macros>
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/bamstats.txt Tue Jul 07 10:39:47 2015 -0400 @@ -0,0 +1,23 @@ +Load BAM file ... Done + +#================================================== +#All numbers are READ count +#================================================== + +Total records: 40 + +QC failed: 0 +Optical/PCR duplicate: 0 +Non primary hits 0 +Unmapped reads: 0 +mapq < mapq_cut (non-unique): 0 + +mapq >= mapq_cut (unique): 40 +Read-1: 20 +Read-2: 20 +Reads map to '+': 20 +Reads map to '-': 20 +Non-splice reads: 36 +Splice reads: 4 +Reads mapped in proper pairs: 39 +Proper-paired reads map to different chrom:0
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/hg19.chrom.sizes Tue Jul 07 10:39:47 2015 -0400 @@ -0,0 +1,93 @@ +chr1 249250621 +chr2 243199373 +chr3 198022430 +chr4 191154276 +chr5 180915260 +chr6 171115067 +chr7 159138663 +chrX 155270560 +chr8 146364022 +chr9 141213431 +chr10 135534747 +chr11 135006516 +chr12 133851895 +chr13 115169878 +chr14 107349540 +chr15 102531392 +chr16 90354753 +chr17 81195210 +chr18 78077248 +chr20 63025520 +chrY 59373566 +chr19 59128983 +chr22 51304566 +chr21 48129895 +chr6_ssto_hap7 4928567 +chr6_mcf_hap5 4833398 +chr6_cox_hap2 4795371 +chr6_mann_hap4 4683263 +chr6_apd_hap1 4622290 +chr6_qbl_hap6 4611984 +chr6_dbb_hap3 4610396 +chr17_ctg5_hap1 1680828 +chr4_ctg9_hap1 590426 +chr1_gl000192_random 547496 +chrUn_gl000225 211173 +chr4_gl000194_random 191469 +chr4_gl000193_random 189789 +chr9_gl000200_random 187035 +chrUn_gl000222 186861 +chrUn_gl000212 186858 +chr7_gl000195_random 182896 +chrUn_gl000223 180455 +chrUn_gl000224 179693 +chrUn_gl000219 179198 +chr17_gl000205_random 174588 +chrUn_gl000215 172545 +chrUn_gl000216 172294 +chrUn_gl000217 172149 +chr9_gl000199_random 169874 +chrUn_gl000211 166566 +chrUn_gl000213 164239 +chrUn_gl000220 161802 +chrUn_gl000218 161147 +chr19_gl000209_random 159169 +chrUn_gl000221 155397 +chrUn_gl000214 137718 +chrUn_gl000228 129120 +chrUn_gl000227 128374 +chr1_gl000191_random 106433 +chr19_gl000208_random 92689 +chr9_gl000198_random 90085 +chr17_gl000204_random 81310 +chrUn_gl000233 45941 +chrUn_gl000237 45867 +chrUn_gl000230 43691 +chrUn_gl000242 43523 +chrUn_gl000243 43341 +chrUn_gl000241 42152 +chrUn_gl000236 41934 +chrUn_gl000240 41933 +chr17_gl000206_random 41001 +chrUn_gl000232 40652 +chrUn_gl000234 40531 +chr11_gl000202_random 40103 +chrUn_gl000238 39939 +chrUn_gl000244 39929 +chrUn_gl000248 39786 +chr8_gl000196_random 38914 +chrUn_gl000249 38502 +chrUn_gl000246 38154 +chr17_gl000203_random 37498 +chr8_gl000197_random 37175 +chrUn_gl000245 36651 +chrUn_gl000247 36422 +chr9_gl000201_random 36148 +chrUn_gl000235 34474 +chrUn_gl000239 33824 +chr21_gl000210_random 27682 +chrUn_gl000231 27386 +chrUn_gl000229 19913 +chrM 16571 +chrUn_gl000226 15008 +chr18_gl000207_random 4262
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/hg19_RefSeq_chr1_1-100000.bed Tue Jul 07 10:39:47 2015 -0400 @@ -0,0 +1,7 @@ +chr1 11873 14409 NR_046018 0 + 14409 14409 0 3 354,109,1189, 0,739,1347, +chr1 14361 29370 NR_024540 0 - 29370 29370 0 11 468,69,152,159,198,136,137,147,99,154,50, 0,608,1434,2245,2496,2871,3244,3553,3906,10376,14959, +chr1 17368 17436 NR_106918 0 - 17436 17436 0 1 68, 0, +chr1 17368 17436 NR_107062 0 - 17436 17436 0 1 68, 0, +chr1 34610 36081 NR_026818 0 - 36081 36081 0 3 564,205,361, 0,666,1110, +chr1 34610 36081 NR_026820 0 - 36081 36081 0 3 564,205,361, 0,666,1110, +chr1 69090 70008 NM_001005484 0 + 69090 70008 0 1 918, 0,
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/output.DupRate_plot.r Tue Jul 07 10:39:47 2015 -0400 @@ -0,0 +1,14 @@ +pdf('output.DupRate_plot.pdf') +par(mar=c(5,4,4,5),las=0) +seq_occ=c(1) +seq_uniqRead=c(40) +pos_occ=c(1) +pos_uniqRead=c(40) +plot(pos_occ,log10(pos_uniqRead),ylab='Number of Reads (log10)',xlab='Frequency',pch=4,cex=0.8,col='blue',xlim=c(1,500),yaxt='n') +points(seq_occ,log10(seq_uniqRead),pch=20,cex=0.8,col='red') +ym=floor(max(log10(pos_uniqRead))) +legend(300,ym,legend=c('Sequence-base','Mapping-base'),col=c('blue','red'),pch=c(4,20)) +axis(side=2,at=0:ym,labels=0:ym) +axis(side=4,at=c(log10(pos_uniqRead[1]),log10(pos_uniqRead[2]),log10(pos_uniqRead[3]),log10(pos_uniqRead[4])), labels=c(round(pos_uniqRead[1]*100/sum(pos_uniqRead)),round(pos_uniqRead[2]*100/sum(pos_uniqRead)),round(pos_uniqRead[3]*100/sum(pos_uniqRead)),round(pos_uniqRead[4]*100/sum(pos_uniqRead)))) +mtext(4, text = "Reads %", line = 2) +dev.off()
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/output.GC.xls Tue Jul 07 10:39:47 2015 -0400 @@ -0,0 +1,19 @@ +GC% read_count +60.78 3 +41.18 3 +47.06 5 +56.86 7 +29.41 1 +27.45 2 +37.25 2 +78.43 1 +58.82 1 +50.98 3 +49.02 2 +62.75 1 +68.63 1 +54.90 1 +52.94 3 +35.29 1 +43.14 2 +39.22 1
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/output.GC_plot.r Tue Jul 07 10:39:47 2015 -0400 @@ -0,0 +1,4 @@ +pdf("output.GC_plot.pdf") +gc=rep(c(60.78,41.18,47.06,56.86,29.41,27.45,37.25,78.43,58.82,50.98,49.02,62.75,68.63,54.90,52.94,35.29,43.14,39.22),times=c(3,3,5,7,1,2,2,1,1,3,2,1,1,1,3,1,2,1)) +hist(gc,probability=T,breaks=100,xlab="GC content (%)",ylab="Density of Reads",border="blue",main="") +dev.off()
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/output.NVC.xls Tue Jul 07 10:39:47 2015 -0400 @@ -0,0 +1,52 @@ +Position A C G T N X +0 5 7 18 10 0 0 +1 6 7 15 8 4 0 +2 5 9 18 5 3 0 +3 11 9 14 4 2 0 +4 5 9 12 14 0 0 +5 4 11 19 6 0 0 +6 11 7 12 10 0 0 +7 9 8 12 9 2 0 +8 12 9 11 8 0 0 +9 8 9 8 10 5 0 +10 9 8 9 14 0 0 +11 9 6 11 14 0 0 +12 14 8 12 6 0 0 +13 10 6 9 15 0 0 +14 9 9 7 15 0 0 +15 10 10 9 9 2 0 +16 8 4 6 14 8 0 +17 9 9 10 9 3 0 +18 7 5 11 12 5 0 +19 12 8 4 10 6 0 +20 10 6 9 15 0 0 +21 9 9 15 7 0 0 +22 14 6 11 9 0 0 +23 13 11 11 5 0 0 +24 12 8 7 10 3 0 +25 9 13 4 8 6 0 +26 11 16 7 6 0 0 +27 11 8 13 8 0 0 +28 13 6 9 12 0 0 +29 9 9 12 10 0 0 +30 8 6 15 11 0 0 +31 7 9 11 13 0 0 +32 7 8 14 11 0 0 +33 11 11 10 8 0 0 +34 6 12 13 9 0 0 +35 8 17 11 4 0 0 +36 9 8 7 16 0 0 +37 11 9 12 8 0 0 +38 8 9 10 13 0 0 +39 8 12 11 9 0 0 +40 12 9 10 9 0 0 +41 9 13 11 7 0 0 +42 10 12 9 9 0 0 +43 7 13 11 9 0 0 +44 10 12 6 12 0 0 +45 10 10 9 11 0 0 +46 7 10 10 13 0 0 +47 9 9 12 10 0 0 +48 10 6 14 10 0 0 +49 8 10 13 9 0 0 +50 7 8 9 16 0 0
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/output.NVC_plot.r Tue Jul 07 10:39:47 2015 -0400 @@ -0,0 +1,17 @@ +position=c(0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50) +A_count=c(5,6,5,11,5,4,11,9,12,8,9,9,14,10,9,10,8,9,7,12,10,9,14,13,12,9,11,11,13,9,8,7,7,11,6,8,9,11,8,8,12,9,10,7,10,10,7,9,10,8,7) +C_count=c(7,7,9,9,9,11,7,8,9,9,8,6,8,6,9,10,4,9,5,8,6,9,6,11,8,13,16,8,6,9,6,9,8,11,12,17,8,9,9,12,9,13,12,13,12,10,10,9,6,10,8) +G_count=c(18,15,18,14,12,19,12,12,11,8,9,11,12,9,7,9,6,10,11,4,9,15,11,11,7,4,7,13,9,12,15,11,14,10,13,11,7,12,10,11,10,11,9,11,6,9,10,12,14,13,9) +T_count=c(10,8,5,4,14,6,10,9,8,10,14,14,6,15,15,9,14,9,12,10,15,7,9,5,10,8,6,8,12,10,11,13,11,8,9,4,16,8,13,9,9,7,9,9,12,11,13,10,10,9,16) +N_count=c(0,4,3,2,0,0,0,2,0,5,0,0,0,0,0,2,8,3,5,6,0,0,0,0,3,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0) +X_count=c(0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0) +total= A_count + C_count + G_count + T_count +ym=max(A_count/total,C_count/total,G_count/total,T_count/total) + 0.05 +yn=min(A_count/total,C_count/total,G_count/total,T_count/total) +pdf("output.NVC_plot.pdf") +plot(position,A_count/total,type="o",pch=20,ylim=c(yn,ym),col="dark green",xlab="Position of Read",ylab="Nucleotide Frequency") +lines(position,T_count/total,type="o",pch=20,col="red") +lines(position,G_count/total,type="o",pch=20,col="blue") +lines(position,C_count/total,type="o",pch=20,col="cyan") +legend(41,ym,legend=c("A","T","G","C"),col=c("dark green","red","blue","cyan"),lwd=2,pch=20,text.col=c("dark green","red","blue","cyan")) +dev.off()
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/output.clipping_profile.r Tue Jul 07 10:39:47 2015 -0400 @@ -0,0 +1,5 @@ +pdf("output.clipping_profile.pdf") +read_pos=c(0,1,2,3,4,5,6,7,8,9,44,45,46,47,48,49,50) +count=c(16,12,11,8,6,5,1,1,1,1,1,2,2,2,3,4,4) +plot(read_pos,1-(count/40),col="blue",main="clipping profile",xlab="Position of reads",ylab="Mappability",type="b") +dev.off()
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/output.clipping_profile.xls Tue Jul 07 10:39:47 2015 -0400 @@ -0,0 +1,18 @@ +Position Read_Total Read_clipped +0 40 16 +1 40 12 +2 40 11 +3 40 8 +4 40 6 +5 40 5 +6 40 1 +7 40 1 +8 40 1 +9 40 1 +44 40 1 +45 40 2 +46 40 2 +47 40 2 +48 40 3 +49 40 4 +50 40 4
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/output.eRPKM.xls Tue Jul 07 10:39:47 2015 -0400 @@ -0,0 +1,8 @@ +#chr start end name score strand 5% 10% 15% 20% 25% 30% 35% 40% 45% 50% 55% 60% 65% 70% 75% 80% 85% 90% 95% 100% +chr1 17368 17436 NR_106918 0 - 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 +chr1 17368 17436 NR_107062 0 - 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 +chr1 34610 36081 NR_026818 0 - 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 +chr1 69090 70008 NM_001005484 0 + 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 +chr1 14361 29370 NR_024540 0 - 256950.511332 128475.255666 85650.1704438 128475.255666 102780.204533 85650.1704438 73414.431809 64237.6278329 57100.1136292 51390.1022663 46718.2747875 64237.6278329 59296.2718457 55060.8238568 51390.1022663 48178.2208747 45344.207882 57100.1136292 54094.8444908 51390.1022663 +chr1 34610 36081 NR_026820 0 - 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 +chr1 11873 14409 NR_046018 0 + 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 30572.0644704 27514.8580233 25013.5072939 22929.0483528 42330.5508051 39306.9400333 36686.4773644 34393.5725292 32370.4212039 45858.0967056 43444.5126684 41272.287035
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/output.geneBodyCoverage.r Tue Jul 07 10:39:47 2015 -0400 @@ -0,0 +1,8 @@ +d1_pairend_strandspecific_51mer_hg19_chr1_1_100000_bam <- c(0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,1.0,1.0,0.0,1.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,1.0,1.0,0.0,0.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0) + + +pdf("output.geneBodyCoverage.curves.pdf") +x=1:100 +icolor = colorRampPalette(c("#7fc97f","#beaed4","#fdc086","#ffff99","#386cb0","#f0027f"))(1) +plot(x,d1_pairend_strandspecific_51mer_hg19_chr1_1_100000_bam,type='l',xlab="Gene body percentile (5'->3')", ylab="Coverage",lwd=0.8,col=icolor[1]) +dev.off()
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/output.geneBodyCoverage.txt Tue Jul 07 10:39:47 2015 -0400 @@ -0,0 +1,2 @@ +Percentile 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 +d1_pairend_strandspecific_51mer_hg19_chr1_1_100000_bam 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 0.0 1.0 1.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 0.0 0.0 1.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/output.infer_experiment.txt Tue Jul 07 10:39:47 2015 -0400 @@ -0,0 +1,6 @@ + + +This is PairEnd Data +Fraction of reads explained by "1++,1--,2+-,2-+": 1.0000 +Fraction of reads explained by "1+-,1-+,2++,2--": 0.0000 +Fraction of reads explained by other combinations: 0.0000
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/output.inner_distance.txt Tue Jul 07 10:39:47 2015 -0400 @@ -0,0 +1,20 @@ +seq.11990047 235 sameTranscript=No,dist=genomic +seq.14614493 31 sameTranscript=Yes,sameExon=Yes,dist=mRNA +seq.24018133 2 sameTranscript=Yes,nonExonic=Yes,dist=genomic +seq.10608403 158 sameTranscript=Yes,sameExon=No,dist=mRNA +seq.10820209 146 sameTranscript=Yes,nonExonic=Yes,dist=genomic +seq.1537155 33 sameTranscript=Yes,nonExonic=Yes,dist=genomic +seq.25274725 17 sameTranscript=Yes,nonExonic=Yes,dist=genomic +seq.26326595 211 sameTranscript=Yes,nonExonic=Yes,dist=genomic +seq.28833653 55 sameTranscript=Yes,nonExonic=Yes,dist=genomic +seq.25049090 61 sameTranscript=Yes,nonExonic=Yes,dist=genomic +seq.23476912 69 sameTranscript=Yes,nonExonic=Yes,dist=genomic +seq.28059536 225 sameTranscript=Yes,nonExonic=Yes,dist=genomic +seq.13270875 200 sameTranscript=Yes,nonExonic=Yes,dist=genomic +seq.2214586 132 sameTranscript=Yes,nonExonic=Yes,dist=genomic +seq.31061198 -31 readPairOverlap +seq.13539256 208 sameTranscript=No,dist=genomic +seq.13835843 -7 sameTranscript=No,dist=genomic +seq.5556605 88 sameTranscript=No,dist=genomic +seq.20367385 17 sameTranscript=No,dist=genomic +seq.17373919 146394 sameTranscript=No,dist=genomic
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/output.inner_distance_freq.txt Tue Jul 07 10:39:47 2015 -0400 @@ -0,0 +1,100 @@ +-250 -245 0 +-245 -240 0 +-240 -235 0 +-235 -230 0 +-230 -225 0 +-225 -220 0 +-220 -215 0 +-215 -210 0 +-210 -205 0 +-205 -200 0 +-200 -195 0 +-195 -190 0 +-190 -185 0 +-185 -180 0 +-180 -175 0 +-175 -170 0 +-170 -165 0 +-165 -160 0 +-160 -155 0 +-155 -150 0 +-150 -145 0 +-145 -140 0 +-140 -135 0 +-135 -130 0 +-130 -125 0 +-125 -120 0 +-120 -115 0 +-115 -110 0 +-110 -105 0 +-105 -100 0 +-100 -95 0 +-95 -90 0 +-90 -85 0 +-85 -80 0 +-80 -75 0 +-75 -70 0 +-70 -65 0 +-65 -60 0 +-60 -55 0 +-55 -50 0 +-50 -45 0 +-45 -40 0 +-40 -35 0 +-35 -30 1 +-30 -25 0 +-25 -20 0 +-20 -15 0 +-15 -10 0 +-10 -5 1 +-5 0 0 +0 5 1 +5 10 0 +10 15 0 +15 20 2 +20 25 0 +25 30 0 +30 35 2 +35 40 0 +40 45 0 +45 50 0 +50 55 1 +55 60 0 +60 65 1 +65 70 1 +70 75 0 +75 80 0 +80 85 0 +85 90 1 +90 95 0 +95 100 0 +100 105 0 +105 110 0 +110 115 0 +115 120 0 +120 125 0 +125 130 0 +130 135 1 +135 140 0 +140 145 0 +145 150 1 +150 155 0 +155 160 1 +160 165 0 +165 170 0 +170 175 0 +175 180 0 +180 185 0 +185 190 0 +190 195 0 +195 200 1 +200 205 0 +205 210 1 +210 215 1 +215 220 0 +220 225 1 +225 230 0 +230 235 1 +235 240 0 +240 245 0 +245 250 0
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/output.inner_distance_plot.r Tue Jul 07 10:39:47 2015 -0400 @@ -0,0 +1,11 @@ +pdf('output.inner_distance_plot.pdf') +fragsize=rep(c(-248,-243,-238,-233,-228,-223,-218,-213,-208,-203,-198,-193,-188,-183,-178,-173,-168,-163,-158,-153,-148,-143,-138,-133,-128,-123,-118,-113,-108,-103,-98,-93,-88,-83,-78,-73,-68,-63,-58,-53,-48,-43,-38,-33,-28,-23,-18,-13,-8,-3,2,7,12,17,22,27,32,37,42,47,52,57,62,67,72,77,82,87,92,97,102,107,112,117,122,127,132,137,142,147,152,157,162,167,172,177,182,187,192,197,202,207,212,217,222,227,232,237,242,247),times=c(0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,1,0,1,0,0,2,0,0,2,0,0,0,1,0,1,1,0,0,0,1,0,0,0,0,0,0,0,0,1,0,0,1,0,1,0,0,0,0,0,0,0,1,0,1,1,0,1,0,1,0,0,0)) +frag_sd = sd(fragsize) +frag_mean = mean(fragsize) +frag_median = median(fragsize) +write(c("Mean insert size",frag_mean), stdout()) +write(c("Median insert size",frag_median), stdout()) +write(c("Standard deviation",frag_sd), stdout()) +hist(fragsize,probability=T,breaks=100,xlab="mRNA insert size (bp)",main=paste(c("Mean=",frag_mean,";","SD=",frag_sd),collapse=""),border="blue") +lines(density(fragsize,bw=10),col='red') +dev.off()
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/output.junction.xls Tue Jul 07 10:39:47 2015 -0400 @@ -0,0 +1,4 @@ +chrom intron_st(0-based) intron_end(1-based) read_count annotation +chr1 17055 17232 1 annotated +chr1 21768 22000 1 complete_novel +chr1 12697 13220 1 partial_novel
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/output.junctionSaturation_plot.r Tue Jul 07 10:39:47 2015 -0400 @@ -0,0 +1,12 @@ +pdf('output.junctionSaturation_plot.pdf') +x=c(5,10,15,20,25,30,35,40,45,50,55,60,65,70,75,80,85,90,95,100) +y=c(0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,1,1,1,1,1) +z=c(0,0,0,0,0,0,1,1,1,1,1,1,1,2,2,2,2,2,2,3) +w=c(0,0,0,0,0,0,1,1,1,1,1,1,1,1,1,1,1,1,1,2) +m=max(0,0,0) +n=min(0,0,0) +plot(x,z/1000,xlab='percent of total reads',ylab='Number of splicing junctions (x1000)',type='o',col='blue',ylim=c(n,m)) +points(x,y/1000,type='o',col='red') +points(x,w/1000,type='o',col='green') +legend(5,0, legend=c("All junctions","known junctions", "novel junctions"),col=c("blue","red","green"),lwd=1,pch=1) +dev.off()
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/output.junction_plot.r Tue Jul 07 10:39:47 2015 -0400 @@ -0,0 +1,8 @@ +pdf("output.splice_events.pdf") +events=c(25.0,25.0,25.0) +pie(events,col=c(2,3,4),init.angle=30,angle=c(60,120,150),density=c(70,70,70),main="splicing events",labels=c("partial_novel 25%","complete_novel 25%","known 25%")) +dev.off() +pdf("output.splice_junction.pdf") +junction=c(33.3333333333,33.3333333333,33.3333333333) +pie(junction,col=c(2,3,4),init.angle=30,angle=c(60,120,150),density=c(70,70,70),main="splicing junctions",labels=c("partial_novel 33%","complete_novel 33%","known 33%")) +dev.off()
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/output.pos.DupRate.xls Tue Jul 07 10:39:47 2015 -0400 @@ -0,0 +1,2 @@ +Occurrence UniqReadNumber +1 40
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/output.qual.r Tue Jul 07 10:39:47 2015 -0400 @@ -0,0 +1,62 @@ +pdf('output.qual.boxplot.pdf') +p0<-rep(c(33,43,45,51,54,58,59,60,61,62,63,64,66,67,69,70,71),times=c(1,2,1,3,1,1,1,1,1,3,1,1,1,2,5,5,10)/1000) +p1<-rep(c(43,45,51,56,57,58,60,61,62,63,64,65,66,67,68,69,70,71),times=c(1,1,1,1,1,1,1,2,1,1,2,1,3,3,1,8,5,6)/1000) +p2<-rep(c(43,49,51,53,54,56,58,59,60,61,64,65,66,67,69,70,71),times=c(1,1,1,1,2,1,1,1,2,1,1,2,2,2,7,6,8)/1000) +p3<-rep(c(33,39,53,54,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71),times=c(1,1,1,1,1,1,1,1,1,1,2,1,1,1,4,4,1,5,5,6)/1000) +p4<-rep(c(33,55,58,59,60,61,62,64,65,66,67,68,69,70,71),times=c(1,1,1,1,5,1,1,4,1,2,5,2,3,3,9)/1000) +p5<-rep(c(33,53,58,60,61,62,64,65,67,68,69,70,71),times=c(1,1,1,2,1,4,3,2,2,3,5,4,11)/1000) +p6<-rep(c(33,40,54,56,58,60,64,66,67,68,69,70,71),times=c(3,2,1,1,1,1,2,2,4,4,4,2,13)/1000) +p7<-rep(c(41,42,43,49,50,51,57,58,62,64,65,66,67,68,69,70,71),times=c(1,1,1,1,2,1,1,1,2,1,1,2,7,1,2,3,12)/1000) +p8<-rep(c(33,39,53,56,58,59,60,62,63,66,67,68,69,70,71),times=c(1,1,1,1,1,1,2,1,2,4,3,2,3,5,12)/1000) +p9<-rep(c(33,40,50,52,53,57,58,60,64,65,66,67,68,69,70,71),times=c(1,1,1,1,1,1,3,1,4,1,1,3,2,5,4,10)/1000) +p10<-rep(c(33,40,53,54,55,58,60,63,64,66,67,68,69,70,71),times=c(2,1,1,2,1,1,2,1,4,1,1,1,7,6,9)/1000) +p11<-rep(c(45,50,51,52,53,56,57,58,60,62,63,64,65,66,67,68,69,70,71),times=c(1,2,1,1,1,1,1,1,3,2,1,1,1,3,1,1,3,5,10)/1000) +p12<-rep(c(33,41,52,53,54,58,59,60,64,65,66,67,69,70,71),times=c(3,1,1,2,1,1,1,3,1,2,3,2,5,6,8)/1000) +p13<-rep(c(33,40,51,53,55,56,58,59,60,63,64,65,66,67,68,69,70,71),times=c(4,1,1,1,1,1,1,1,1,1,3,1,2,2,2,3,5,9)/1000) +p14<-rep(c(33,39,54,56,57,59,60,61,62,63,65,66,67,68,69,70,71),times=c(4,1,3,1,1,1,1,2,1,1,1,2,2,1,7,2,9)/1000) +p15<-rep(c(33,39,40,42,45,50,52,53,57,58,59,60,61,64,66,68,69,70,71),times=c(2,2,1,1,1,1,1,1,1,3,1,1,1,3,1,1,4,5,9)/1000) +p16<-rep(c(33,47,51,52,53,58,59,60,61,64,67,69,70,71),times=c(4,1,1,1,2,3,1,1,2,3,3,7,2,9)/1000) +p17<-rep(c(33,48,50,51,53,54,55,56,58,60,61,63,64,65,66,67,69,70,71),times=c(1,1,1,1,2,1,1,1,2,2,3,2,2,1,2,1,7,2,7)/1000) +p18<-rep(c(33,43,48,51,53,58,59,60,61,63,64,66,67,68,69,70,71),times=c(2,1,1,1,2,2,1,2,2,3,1,1,3,1,7,2,8)/1000) +p19<-rep(c(33,44,47,50,51,52,54,58,59,61,62,64,65,67,69,70,71),times=c(2,1,1,2,1,1,2,1,1,1,1,3,1,1,8,4,9)/1000) +p20<-rep(c(33,46,47,51,54,56,58,59,61,62,63,64,66,67,69,70,71),times=c(1,1,1,1,2,1,1,2,1,2,1,5,5,3,5,2,6)/1000) +p21<-rep(c(33,43,54,55,57,58,62,64,65,66,67,68,69,70,71),times=c(1,1,1,1,1,5,2,3,1,3,2,4,5,4,6)/1000) +p22<-rep(c(33,47,51,53,54,57,58,60,62,63,64,65,66,68,69,70,71),times=c(1,1,1,1,1,1,1,1,2,1,5,1,4,3,5,5,6)/1000) +p23<-rep(c(33,42,53,54,55,57,58,62,63,64,65,66,67,68,69,70,71),times=c(1,1,1,1,1,1,2,1,1,5,2,2,3,2,9,3,4)/1000) +p24<-rep(c(33,42,52,54,57,60,61,63,64,65,66,67,69,70,71),times=c(1,1,1,1,1,2,1,1,5,1,6,4,5,4,6)/1000) +p25<-rep(c(33,53,54,57,61,62,63,64,66,67,68,69,70,71),times=c(1,1,1,2,1,1,1,2,4,5,2,9,5,5)/1000) +p26<-rep(c(46,53,54,57,58,60,61,62,64,66,67,68,69,70,71),times=c(1,1,1,1,1,1,1,2,5,8,4,1,5,3,5)/1000) +p27<-rep(c(42,43,48,54,56,57,60,61,62,66,67,68,69,70,71),times=c(1,1,1,2,1,1,4,1,2,1,4,1,5,6,9)/1000) +p28<-rep(c(51,55,56,57,60,62,64,65,66,67,68,69,70,71),times=c(1,1,1,1,1,2,4,2,2,2,4,10,1,8)/1000) +p29<-rep(c(49,52,56,57,58,60,63,64,65,66,67,69,70,71),times=c(2,1,1,1,2,1,1,3,1,3,6,8,2,8)/1000) +p30<-rep(c(45,47,50,57,61,62,64,66,67,68,69,70,71),times=c(1,1,1,1,1,1,2,6,3,2,8,5,8)/1000) +p31<-rep(c(48,52,53,54,57,58,59,60,61,62,64,65,66,67,68,69,70,71),times=c(1,1,1,1,1,1,1,1,2,1,4,1,2,3,3,7,3,6)/1000) +p32<-rep(c(43,47,48,54,56,62,64,66,67,68,69,70,71),times=c(1,1,1,1,2,1,2,1,5,3,10,5,7)/1000) +p33<-rep(c(52,55,58,60,61,63,64,68,69,70,71),times=c(1,1,2,1,1,1,5,4,11,5,8)/1000) +p34<-rep(c(42,43,50,56,59,60,63,64,67,68,69,70,71),times=c(1,1,1,1,1,1,1,3,1,4,9,5,11)/1000) +p35<-rep(c(42,53,57,58,60,64,66,68,69,70,71),times=c(1,1,1,2,1,3,2,2,12,7,8)/1000) +p36<-rep(c(48,53,56,61,63,64,66,67,69,70,71),times=c(2,1,1,2,1,1,2,6,7,3,14)/1000) +p37<-rep(c(53,56,60,63,64,66,68,69,70,71),times=c(2,2,1,1,3,2,6,7,8,8)/1000) +p38<-rep(c(41,48,53,57,59,61,62,63,64,66,67,68,69,70,71),times=c(1,1,1,1,1,1,1,1,3,3,4,1,4,6,11)/1000) +p39<-rep(c(38,42,51,53,56,58,61,63,64,65,66,67,68,69,70,71),times=c(1,1,1,1,1,1,1,1,2,1,4,2,2,7,3,11)/1000) +p40<-rep(c(53,58,61,62,63,64,66,67,68,69,70,71),times=c(1,1,3,1,2,2,1,2,2,9,4,12)/1000) +p41<-rep(c(48,53,54,57,58,59,60,61,63,64,66,67,68,69,70,71),times=c(1,1,1,1,1,1,2,1,1,1,3,3,1,5,7,9)/1000) +p42<-rep(c(38,49,54,58,59,64,65,66,67,68,69,70,71),times=c(1,1,3,1,1,3,1,2,1,1,7,7,9)/1000) +p43<-rep(c(50,51,62,63,64,65,66,67,69,70,71),times=c(2,1,1,1,3,1,2,4,3,8,12)/1000) +p44<-rep(c(48,54,63,64,65,66,67,68,69,70,71),times=c(1,2,4,1,1,2,2,1,7,8,8)/1000) +p45<-rep(c(50,57,58,59,60,62,64,67,69,70,71),times=c(1,1,1,1,1,1,1,1,10,8,7)/1000) +p46<-rep(c(43,48,54,59,60,64,65,66,67,68,69,70,71),times=c(2,1,1,2,1,2,1,1,4,2,1,6,8)/1000) +p47<-rep(c(49,53,56,61,64,66,67,69,70,71),times=c(1,1,1,1,2,2,2,3,10,7)/1000) +p48<-rep(c(61,64,66,67,68,69,70,71),times=c(2,1,2,2,2,6,5,7)/1000) +p49<-rep(c(33,56,60,64,66,68,69,70,71),times=c(1,1,2,1,1,2,2,5,10)/1000) +p50<-rep(c(33,66,67,68,69,70,71),times=c(1,1,1,2,4,5,7)/1000) +boxplot(p0,p1,p2,p3,p4,p5,p6,p7,p8,p9,p10,p11,p12,p13,p14,p15,p16,p17,p18,p19,p20,p21,p22,p23,p24,p25,p26,p27,p28,p29,p30,p31,p32,p33,p34,p35,p36,p37,p38,p39,p40,p41,p42,p43,p44,p45,p46,p47,p48,p49,p50,xlab="Position of Read(5'->3')",ylab="Phred Quality Score",outline=F) +dev.off() + + +pdf('output.qual.heatmap.pdf') +qual=c(1,0,0,0,0,0,0,0,0,0,2,0,1,0,0,0,0,0,3,0,0,1,0,0,0,1,1,1,1,3,1,1,0,1,2,0,5,5,10,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,1,0,0,0,0,1,1,1,0,1,2,1,1,2,1,3,3,1,8,5,6,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,1,0,1,2,0,1,0,1,1,2,1,0,0,1,2,2,2,0,7,6,8,1,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,0,1,1,1,1,1,1,2,1,1,1,4,4,1,5,5,6,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,1,1,5,1,1,0,4,1,2,5,2,3,3,9,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,1,0,2,1,4,0,3,2,0,2,3,5,4,11,3,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,1,0,1,0,0,0,2,0,2,4,4,4,2,13,0,0,0,0,0,0,0,0,1,1,1,0,0,0,0,0,1,2,1,0,0,0,0,0,1,1,0,0,0,2,0,1,1,2,7,1,2,3,12,1,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,1,1,2,0,1,2,0,0,4,3,2,3,5,12,1,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,1,0,1,1,0,0,0,1,3,0,1,0,0,0,4,1,1,3,2,5,4,10,2,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,2,1,0,0,1,0,2,0,0,1,4,0,1,1,1,7,6,9,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,2,1,1,1,0,0,1,1,1,0,3,0,2,1,1,1,3,1,1,3,5,10,3,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,1,2,1,0,0,0,1,1,3,0,0,0,1,2,3,2,0,5,6,8,4,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,1,0,1,0,1,1,0,1,1,1,0,0,1,3,1,2,2,2,3,5,9,4,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,1,1,0,1,1,2,1,1,0,1,2,2,1,7,2,9,2,0,0,0,0,0,2,1,0,1,0,0,1,0,0,0,0,1,0,1,1,0,0,0,1,3,1,1,1,0,0,3,0,1,0,1,4,5,9,4,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,1,1,2,0,0,0,0,3,1,1,2,0,0,3,0,0,3,0,7,2,9,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,1,0,2,1,1,1,0,2,0,2,3,0,2,2,1,2,1,0,7,2,7,2,0,0,0,0,0,0,0,0,0,1,0,0,0,0,1,0,0,1,0,2,0,0,0,0,2,1,2,2,0,3,1,0,1,3,1,7,2,8,2,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,0,2,1,1,0,2,0,0,0,1,1,0,1,1,0,3,1,0,1,0,8,4,9,1,0,0,0,0,0,0,0,0,0,0,0,0,1,1,0,0,0,1,0,0,2,0,1,0,1,2,0,1,2,1,5,0,5,3,0,5,2,6,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,1,1,0,1,5,0,0,0,2,0,3,1,3,2,4,5,4,6,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,1,0,1,1,0,0,1,1,0,1,0,2,1,5,1,4,0,3,5,5,6,1,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,1,1,1,0,1,2,0,0,0,1,1,5,2,2,3,2,9,3,4,1,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,1,0,1,0,0,1,0,0,2,1,0,1,5,1,6,4,0,5,4,6,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,0,0,2,0,0,0,1,1,1,2,0,4,5,2,9,5,5,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,1,1,0,0,1,1,0,1,1,2,0,5,0,8,4,1,5,3,5,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,1,0,0,0,0,0,2,0,1,1,0,0,4,1,2,0,0,0,1,4,1,5,6,9,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,1,1,1,0,0,1,0,2,0,4,2,2,2,4,10,1,8,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,1,0,0,0,1,1,2,0,1,0,0,1,3,1,3,6,0,8,2,8,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,1,0,0,0,0,0,0,1,0,0,0,1,1,0,2,0,6,3,2,8,5,8,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,1,1,1,0,0,1,1,1,1,2,1,0,4,1,2,3,3,7,3,6,0,0,0,0,0,0,0,0,0,0,1,0,0,0,1,1,0,0,0,0,0,1,0,2,0,0,0,0,0,1,0,2,0,1,5,3,10,5,7,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,0,2,0,1,1,0,1,5,0,0,0,4,11,5,8,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,1,0,0,0,0,0,1,0,0,1,1,0,0,1,3,0,0,1,4,9,5,11,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,1,0,0,0,1,2,0,1,0,0,0,3,0,2,0,2,12,7,8,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,1,0,0,1,0,0,0,0,2,0,1,1,0,2,6,0,7,3,14,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,2,0,0,0,1,0,0,1,3,0,2,0,6,7,8,8,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,1,0,0,0,0,1,0,0,0,1,0,1,0,1,1,1,3,0,3,4,1,4,6,11,0,0,0,0,0,1,0,0,0,1,0,0,0,0,0,0,0,0,1,0,1,0,0,1,0,1,0,0,1,0,1,2,1,4,2,2,7,3,11,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,1,0,0,3,1,2,2,0,1,2,2,9,4,12,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,1,1,0,0,1,1,1,2,1,0,1,1,0,3,3,1,5,7,9,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,3,0,0,0,1,1,0,0,0,0,3,1,2,1,1,7,7,9,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,1,0,0,0,0,0,0,0,0,0,0,1,1,3,1,2,4,0,3,8,12,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,2,0,0,0,0,0,0,0,0,4,1,1,2,2,1,7,8,8,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,1,1,1,1,0,1,0,1,0,0,1,0,10,8,7,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,2,1,0,0,0,2,1,1,4,2,1,6,8,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,1,0,0,1,0,0,0,0,1,0,0,2,0,2,2,0,3,10,7,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,1,0,2,2,2,6,5,7,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,2,0,0,0,1,0,1,0,2,2,5,10,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,2,4,5,7) +mat=matrix(qual,ncol=51,byrow=F) +Lab.palette <- colorRampPalette(c("blue", "orange", "red3","red2","red1","red"), space = "rgb",interpolate=c('spline')) +heatmap(mat,Rowv=NA,Colv=NA,xlab="Position of Read",ylab="Phred Quality Score",labRow=seq(from=33,to=71),col = Lab.palette(256),scale="none" ) +dev.off()
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/output.rawCount.xls Tue Jul 07 10:39:47 2015 -0400 @@ -0,0 +1,8 @@ +#chr start end name score strand 5% 10% 15% 20% 25% 30% 35% 40% 45% 50% 55% 60% 65% 70% 75% 80% 85% 90% 95% 100% +chr1 17368 17436 NR_106918 0 - 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 +chr1 17368 17436 NR_107062 0 - 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 +chr1 34610 36081 NR_026818 0 - 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 +chr1 69090 70008 NM_001005484 0 + 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 +chr1 14361 29370 NR_024540 0 - 1 1 1 2 2 2 2 2 2 2 2 3 3 3 3 3 3 4 4 4 +chr1 34610 36081 NR_026820 0 - 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 +chr1 11873 14409 NR_046018 0 + 0 0 0 0 0 0 0 0 1 1 1 1 2 2 2 2 2 3 3 3
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/output.read_distribution.txt Tue Jul 07 10:39:47 2015 -0400 @@ -0,0 +1,16 @@ +Total Reads 40 +Total Tags 44 +Total Assigned Tags 38 +===================================================================== +Group Total_bases Tag_count Tags/Kb +CDS_Exons 918 0 0.00 +5'UTR_Exons 1652 3 1.81 +3'UTR_Exons 2967 4 1.35 +Introns 14397 27 1.88 +TSS_up_1kb 4000 0 0.00 +TSS_up_5kb 20000 4 0.20 +TSS_up_10kb 35240 4 0.11 +TES_down_1kb 2000 0 0.00 +TES_down_5kb 12512 0 0.00 +TES_down_10kb 22752 0 0.00 +=====================================================================
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/output.saturation.r Tue Jul 07 10:39:47 2015 -0400 @@ -0,0 +1,87 @@ +pdf('output.saturation.pdf') +par(mfrow=c(2,2)) +name=c(5,10,15,20,25,30,35,40,45,50,55,60,65,70,75,80,85,90,95) +S5=c() +S10=c() +S15=c() +S20=c() +S25=c() +S30=c() +S35=c() +S40=c() +S45=c() +S50=c() +S55=c() +S60=c() +S65=c() +S70=c() +S75=c() +S80=c() +S85=c() +S90=c() +S95=c() +boxplot(100*S5,100*S10,100*S15,100*S20,100*S25,100*S30,100*S35,100*S40,100*S45,100*S50,100*S55,100*S60,100*S65,100*S70,100*S75,100*S80,100*S85,100*S90,100*S95,names=name,outline=F,ylab='Percent Relative Error',main='Q1',xlab='Resampling percentage') +name=c(5,10,15,20,25,30,35,40,45,50,55,60,65,70,75,80,85,90,95) +S5=c(1.0) +S10=c(1.0) +S15=c(1.0) +S20=c(1.0) +S25=c(1.0) +S30=c(1.0) +S35=c(1.0) +S40=c(1.0) +S45=c(0.259259259259) +S50=c(0.333333333334) +S55=c(0.39393939394) +S60=c(0.444444444444) +S65=c(0.0256410256403) +S70=c(0.0476190476199) +S75=c(0.111111111112) +S80=c(0.166666666666) +S85=c(0.21568627451) +S90=c(0.111111111112) +S95=c(0.0526315789469) +boxplot(100*S5,100*S10,100*S15,100*S20,100*S25,100*S30,100*S35,100*S40,100*S45,100*S50,100*S55,100*S60,100*S65,100*S70,100*S75,100*S80,100*S85,100*S90,100*S95,names=name,outline=F,ylab='Percent Relative Error',main='Q2',xlab='Resampling percentage') +name=c(5,10,15,20,25,30,35,40,45,50,55,60,65,70,75,80,85,90,95) +S5=c() +S10=c() +S15=c() +S20=c() +S25=c() +S30=c() +S35=c() +S40=c() +S45=c() +S50=c() +S55=c() +S60=c() +S65=c() +S70=c() +S75=c() +S80=c() +S85=c() +S90=c() +S95=c() +boxplot(100*S5,100*S10,100*S15,100*S20,100*S25,100*S30,100*S35,100*S40,100*S45,100*S50,100*S55,100*S60,100*S65,100*S70,100*S75,100*S80,100*S85,100*S90,100*S95,names=name,outline=F,ylab='Percent Relative Error',main='Q3',xlab='Resampling percentage') +name=c(5,10,15,20,25,30,35,40,45,50,55,60,65,70,75,80,85,90,95) +S5=c(4.00000000001) +S10=c(1.5) +S15=c(0.666666666666) +S20=c(1.5) +S25=c(1.00000000001) +S30=c(0.666666666666) +S35=c(0.428571428571) +S40=c(0.25) +S45=c(0.111111111111) +S50=c(0.0) +S55=c(0.09090909091) +S60=c(0.25) +S65=c(0.153846153846) +S70=c(0.0714285714295) +S75=c(0.0) +S80=c(0.0624999999991) +S85=c(0.117647058824) +S90=c(0.111111111111) +S95=c(0.0526315789465) +boxplot(100*S5,100*S10,100*S15,100*S20,100*S25,100*S30,100*S35,100*S40,100*S45,100*S50,100*S55,100*S60,100*S65,100*S70,100*S75,100*S80,100*S85,100*S90,100*S95,names=name,outline=F,ylab='Percent Relative Error',main='Q4',xlab='Resampling percentage') +dev.off()
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/output.seq.DupRate.xls Tue Jul 07 10:39:47 2015 -0400 @@ -0,0 +1,2 @@ +Occurrence UniqReadNumber +1 40
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/output2.geneBodyCoverage.r Tue Jul 07 10:39:47 2015 -0400 @@ -0,0 +1,8 @@ +d1_pairend_strandspecific_51mer_hg19_chr1_1_100000_bam <- c(0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,1.0,1.0,0.0,1.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,1.0,1.0,0.0,0.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0) + + +pdf("output.geneBodyCoverage.curves.pdf") +x=1:100 +icolor = colorRampPalette(c("#7fc97f","#beaed4","#fdc086","#ffff99","#386cb0","#f0027f"))(1) +plot(x,d1_pairend_strandspecific_51mer_hg19_chr1_1_100000_bam,type='l',xlab="Gene body percentile (5'->3')", ylab="Coverage",lwd=0.8,col=icolor[1]) +dev.off()
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/output2.geneBodyCoverage.txt Tue Jul 07 10:39:47 2015 -0400 @@ -0,0 +1,2 @@ +Percentile 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 +d1_pairend_strandspecific_51mer_hg19_chr1_1_100000_bam 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 0.0 1.0 1.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 0.0 0.0 1.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/output_read_count.xls Tue Jul 07 10:39:47 2015 -0400 @@ -0,0 +1,47 @@ +#chrom st end accession score gene_strand tag_count RPKM +chr1 12227 12612 NR_046018_intron_1 0 + 0 0.000 +chr1 12721 13220 NR_046018_intron_2 0 + 0 0.000 +chr1 11873 12227 NR_046018_exon_1 0 + 0 0.000 +chr1 12612 12721 NR_046018_exon_2 0 + 1 208507.089 +chr1 13220 14409 NR_046018_exon_3 0 + 2 38229.222 +chr1 11873 14409 NR_046018_mRNA 0 + 3 41272.287 +chr1 14829 14969 NR_024540_intron_10 0 - 0 0.000 +chr1 15038 15795 NR_024540_intron_9 0 - 0 0.000 +chr1 15947 16606 NR_024540_intron_8 0 - 2 68975.031 +chr1 16765 16857 NR_024540_intron_7 0 - 0 0.000 +chr1 17055 17232 NR_024540_intron_6 0 - 0 0.000 +chr1 17368 17605 NR_024540_intron_5 0 - 1 95895.666 +chr1 17742 17914 NR_024540_intron_4 0 - 0 0.000 +chr1 18061 18267 NR_024540_intron_3 0 - 0 0.000 +chr1 18366 24737 NR_024540_intron_2 0 - 22 78480.615 +chr1 24891 29320 NR_024540_intron_1 0 - 2 10262.936 +chr1 14361 14829 NR_024540_exon_11 0 - 2 97125.097 +chr1 14969 15038 NR_024540_exon_10 0 - 0 0.000 +chr1 15795 15947 NR_024540_exon_9 0 - 0 0.000 +chr1 16606 16765 NR_024540_exon_8 0 - 0 0.000 +chr1 16857 17055 NR_024540_exon_7 0 - 1 114784.206 +chr1 17232 17368 NR_024540_exon_6 0 - 1 167112.299 +chr1 17605 17742 NR_024540_exon_5 0 - 0 0.000 +chr1 17914 18061 NR_024540_exon_4 0 - 0 0.000 +chr1 18267 18366 NR_024540_exon_3 0 - 0 0.000 +chr1 24737 24891 NR_024540_exon_2 0 - 0 0.000 +chr1 29320 29370 NR_024540_exon_1 0 - 0 0.000 +chr1 14361 29370 NR_024540_mRNA 0 - 4 51390.102 +chr1 17368 17436 NR_106918_exon_1 0 - 0 0.000 +chr1 17368 17436 NR_106918_mRNA 0 - 0 0.000 +chr1 17368 17436 NR_107062_exon_1 0 - 0 0.000 +chr1 17368 17436 NR_107062_mRNA 0 - 0 0.000 +chr1 35174 35276 NR_026818_intron_2 0 - 0 0.000 +chr1 35481 35720 NR_026818_intron_1 0 - 0 0.000 +chr1 34610 35174 NR_026818_exon_3 0 - 0 0.000 +chr1 35276 35481 NR_026818_exon_2 0 - 0 0.000 +chr1 35720 36081 NR_026818_exon_1 0 - 0 0.000 +chr1 34610 36081 NR_026818_mRNA 0 - 0 0.000 +chr1 35174 35276 NR_026820_intron_2 0 - 0 0.000 +chr1 35481 35720 NR_026820_intron_1 0 - 0 0.000 +chr1 34610 35174 NR_026820_exon_3 0 - 0 0.000 +chr1 35276 35481 NR_026820_exon_2 0 - 0 0.000 +chr1 35720 36081 NR_026820_exon_1 0 - 0 0.000 +chr1 34610 36081 NR_026820_mRNA 0 - 0 0.000 +chr1 69090 70008 NM_001005484_exon_1 0 + 0 0.000 +chr1 69090 70008 NM_001005484_mRNA 0 + 0 0.000
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/testwig.Forward.wig Tue Jul 07 10:39:47 2015 -0400 @@ -0,0 +1,127 @@ +variableStep chrom=chr13 +variableStep chrom=chr12 +variableStep chrom=chr11 +variableStep chrom=chr10 +variableStep chrom=chr17 +variableStep chrom=chr16 +variableStep chrom=chr15 +variableStep chrom=chr14 +variableStep chrom=chr19 +variableStep chrom=chr18 +variableStep chrom=chr8 +variableStep chrom=chr3 +variableStep chrom=chr1 +12674 1.00 +12675 1.00 +12676 1.00 +12677 1.00 +12678 1.00 +12679 1.00 +12680 1.00 +12681 1.00 +12682 1.00 +12683 1.00 +12684 1.00 +12685 1.00 +12686 1.00 +12687 1.00 +12688 1.00 +12689 1.00 +12690 1.00 +12691 1.00 +12692 1.00 +12693 1.00 +12694 1.00 +12695 1.00 +12696 1.00 +12697 1.00 +13221 1.00 +13222 1.00 +13223 1.00 +13224 1.00 +13225 1.00 +13226 1.00 +13227 1.00 +13228 1.00 +13229 1.00 +13230 1.00 +13231 1.00 +13232 1.00 +13233 1.00 +13234 1.00 +13235 1.00 +13236 1.00 +13237 1.00 +13238 1.00 +13239 1.00 +13240 1.00 +13241 1.00 +13242 1.00 +13243 1.00 +13244 1.00 +13245 1.00 +13246 1.00 +13247 1.00 +13483 1.00 +13484 1.00 +13485 1.00 +13486 1.00 +13487 1.00 +13488 1.00 +13489 1.00 +13490 1.00 +13491 1.00 +13492 1.00 +13493 1.00 +13494 1.00 +13495 1.00 +13496 1.00 +13497 1.00 +13498 1.00 +13499 1.00 +13500 1.00 +13501 1.00 +13502 1.00 +13503 1.00 +13504 1.00 +13505 1.00 +13506 1.00 +13507 1.00 +13508 1.00 +13509 1.00 +13510 1.00 +13511 1.00 +13512 1.00 +13513 1.00 +13514 1.00 +13515 1.00 +13516 1.00 +13517 1.00 +13518 1.00 +13519 1.00 +13520 1.00 +13521 1.00 +13522 1.00 +13523 1.00 +13524 1.00 +13525 1.00 +13526 1.00 +13527 1.00 +13528 1.00 +13529 1.00 +13530 1.00 +13531 1.00 +13532 1.00 +13533 1.00 +variableStep chrom=chrY +variableStep chrom=chrX +variableStep chrom=chr9 +variableStep chrom=chrM +variableStep chrom=chr22 +variableStep chrom=chr20 +variableStep chrom=chr21 +variableStep chrom=chr7 +variableStep chrom=chr6 +variableStep chrom=chr5 +variableStep chrom=chr4 +variableStep chrom=chr2
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/testwig.Reverse.wig Tue Jul 07 10:39:47 2015 -0400 @@ -0,0 +1,1686 @@ +variableStep chrom=chr13 +variableStep chrom=chr12 +variableStep chrom=chr11 +variableStep chrom=chr10 +variableStep chrom=chr17 +variableStep chrom=chr16 +variableStep chrom=chr15 +variableStep chrom=chr14 +variableStep chrom=chr19 +variableStep chrom=chr18 +variableStep chrom=chr8 +variableStep chrom=chr3 +variableStep chrom=chr1 +14596 -1.00 +14597 -1.00 +14598 -1.00 +14599 -1.00 +14600 -1.00 +14601 -1.00 +14602 -1.00 +14603 -1.00 +14604 -1.00 +14605 -1.00 +14606 -1.00 +14607 -1.00 +14608 -1.00 +14609 -1.00 +14610 -1.00 +14611 -1.00 +14612 -1.00 +14613 -1.00 +14614 -1.00 +14615 -1.00 +14616 -1.00 +14617 -1.00 +14618 -1.00 +14619 -1.00 +14620 -1.00 +14621 -1.00 +14622 -1.00 +14623 -1.00 +14624 -1.00 +14625 -1.00 +14626 -1.00 +14627 -1.00 +14628 -1.00 +14629 -1.00 +14630 -1.00 +14631 -1.00 +14632 -1.00 +14633 -1.00 +14634 -1.00 +14635 -1.00 +14636 -1.00 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--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/tool_dependencies.xml Tue Jul 07 10:39:47 2015 -0400 @@ -0,0 +1,12 @@ +<?xml version="1.0"?> +<tool_dependency> + <package name="R" version="3.0.3"> + <repository changeset_revision="6ce19fda3a9a" name="package_r_3_0_3" owner="iuc" toolshed="https://testtoolshed.g2.bx.psu.edu" /> + </package> + <package name="numpy" version="1.7.1"> + <repository changeset_revision="bbfe29f56566" name="package_numpy_1_7" owner="iuc" toolshed="https://testtoolshed.g2.bx.psu.edu" /> + </package> + <package name="rseqc" version="2.4"> + <repository changeset_revision="04560daaa40f" name="package_rseqc_2_4" owner="lparsons" toolshed="https://testtoolshed.g2.bx.psu.edu" /> + </package> +</tool_dependency>