# HG changeset patch # User iuc # Date 1489501377 14400 # Node ID 71ed55a3515a983f83a0fe555cd2bf1d1b78e014 # Parent ebadf9ee2d08ccbf2429bd98562dc804b8f69944 planemo upload for repository https://github.com/galaxyproject/tools-iuc/tree/master/tools/rseqc commit 37fb1988971807c6a072e1afd98eeea02329ee83 diff -r ebadf9ee2d08 -r 71ed55a3515a .hgignore --- a/.hgignore Thu Jul 18 11:01:08 2013 -0500 +++ /dev/null Thu Jan 01 00:00:00 1970 +0000 @@ -1,4 +0,0 @@ -# use glob syntax -syntax: regexp - -^test-data/.* \ No newline at end of file diff -r ebadf9ee2d08 -r 71ed55a3515a FPKM_count.xml --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/FPKM_count.xml Tue Mar 14 10:22:57 2017 -0400 @@ -0,0 +1,132 @@ + + calculates raw read count, FPM, and FPKM for each gene + + + rseqc_macros.xml + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + diff -r ebadf9ee2d08 -r 71ed55a3515a README.txt --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/README.txt Tue Mar 14 10:22:57 2017 -0400 @@ -0,0 +1,23 @@ +== 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 diff -r ebadf9ee2d08 -r 71ed55a3515a RNA_fragment_size.xml --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/RNA_fragment_size.xml Tue Mar 14 10:22:57 2017 -0400 @@ -0,0 +1,76 @@ + + + calculates the fragment size for each gene/transcript + + + + rseqc_macros.xml + + + + + + + + + '${output}' + ]]> + + + + + + + + + + + + + + + + + + + + + + + + + + + + diff -r ebadf9ee2d08 -r 71ed55a3515a RPKM_count.xml --- a/RPKM_count.xml Thu Jul 18 11:01:08 2013 -0500 +++ /dev/null Thu Jan 01 00:00:00 1970 +0000 @@ -1,110 +0,0 @@ - - calculates raw count and RPKM values for transcript at exon, intron, and mRNA level - - samtools - rseqc - - samtoolshelper.py RPKM_count.py -i $input -o output -r $refgene - - #if $nx - -x - #end if - - #if str($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 $skiphits - -u - #end if - - #if $onlyexonic - -e - #end if - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -.. image:: https://code.google.com/p/rseqc/logo?cct=1336721062 - ------ - -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. - -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. - - - diff -r ebadf9ee2d08 -r 71ed55a3515a RPKM_saturation.xml --- a/RPKM_saturation.xml Thu Jul 18 11:01:08 2013 -0500 +++ b/RPKM_saturation.xml Tue Mar 14 10:22:57 2017 -0400 @@ -1,108 +1,130 @@ - - calculates raw count and RPKM values for transcript at exon, intron, and mRNA level - - R - rseqc - - RPKM_saturation.py -i $input -o output -r $refgene + + calculates raw count and RPKM values for transcript at exon, intron, and mRNA level + + + rseqc_macros.xml + + + + + + + + + + - -l $percentileFloor -u $percentileCeiling -s $percentileStep -c $rpkmCutoff + + + + + + + + + + + - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -.. image:: https://code.google.com/p/rseqc/logo?cct=1336721062 + + + + + + ------ + + + + + + + + + + + + + + + + + + + + + + + + + + -About RSeQC -+++++++++++ + +.. image:: $PATH_TO_IMAGES/saturation_eg.png + :height: 600 px + :width: 600 px + :scale: 80 % + +@ABOUT@ + +]]> + + + + diff -r ebadf9ee2d08 -r 71ed55a3515a bam2wig.xml --- a/bam2wig.xml Thu Jul 18 11:01:08 2013 -0500 +++ b/bam2wig.xml Tue Mar 14 10:22:57 2017 -0400 @@ -1,124 +1,130 @@ - - - converts all types of RNA-seq data from .bam to .wig - - - R - samtools - rseqc - - - samtoolshelper.py /home/nilesh/RSeQC-2.3.3/scripts/bam2wig.py -i $input -s $chromsize -o outfile + + + converts all types of RNA-seq data from .bam to .wig + + + + rseqc_macros.xml + + + + + + + + + - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - strand_type['strand_specific'] == 'none' - - - strand_type['strand_specific'] != 'none' - - - strand_type['strand_specific'] != 'none' - - - - - - - - - - - - - -.. image:: https://code.google.com/p/rseqc/logo?cct=1336721062 + @MULTIHITS@ + ]]> + + + + + + + + + + + + + + + + + + + + + strand_type['strand_specific'] == 'none' + + + strand_type['strand_specific'] != 'none' + + + strand_type['strand_specific'] != 'none' + + ------ + + + + + + + + + + + + + + + + + + + + + + -About RSeQC -+++++++++++ + - \ No newline at end of file +.. _UCSC: http://genome.ucsc.edu/index.html +.. _IGB: http://bioviz.org/igb/ +.. _IGV: http://software.broadinstitute.org/software/igv/ +.. _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 +]]> + + + + + diff -r ebadf9ee2d08 -r 71ed55a3515a bam_stat.xml --- a/bam_stat.xml Thu Jul 18 11:01:08 2013 -0500 +++ b/bam_stat.xml Tue Mar 14 10:22:57 2017 -0400 @@ -1,46 +1,58 @@ - - - reads mapping statistics for a provided BAM or SAM file. - - - rseqc - s - - bam_stat.py -i $input -q $mapqual &> $output - - - - - - - - - - - - - - -.. image:: https://code.google.com/p/rseqc/logo?cct=1336721062 + + + reads mapping statistics for a provided BAM or SAM file. + + + + rseqc_macros.xml + + + + + + + ------ + '${output}' + ]]> + + + + + + -About RSeQC + + + + + + + + + + + + `_, +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. + Alignment file in BAM/SAM format. Minimum mapping quality - Minimum mapping quality for an alignment to be called “uniquely mapped” (default=30) + Minimum mapping quality for an alignment to be called "uniquely mapped" (default=30) Output ++++++++++++++ @@ -50,6 +62,10 @@ - Uniquely mapped Reads = {Reads map to '+'} + {Reads map to '-'} - Uniquely mapped Reads = {Splice reads} + {Non-splice reads} +@ABOUT@ - - \ No newline at end of file +]]> + + + + diff -r ebadf9ee2d08 -r 71ed55a3515a clipping_profile.xml --- a/clipping_profile.xml Thu Jul 18 11:01:08 2013 -0500 +++ b/clipping_profile.xml Tue Mar 14 10:22:57 2017 -0400 @@ -1,54 +1,85 @@ - - - estimates clipping profile of RNA-seq reads from BAM or SAM file - - - R - rseqc - - - clipping_profile.py -i $input -o output - - - - - - - - - - - - - - - - - - -.. image:: https://code.google.com/p/rseqc/logo?cct=1336721062 + + + estimates clipping profile of RNA-seq reads from BAM or SAM file + + + + rseqc_macros.xml + + + + + + + + + + + + + + + + + ------ + + + + + -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. + - \ No newline at end of file +]]> + + + + + diff -r ebadf9ee2d08 -r 71ed55a3515a deletion_profile.xml --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/deletion_profile.xml Tue Mar 14 10:22:57 2017 -0400 @@ -0,0 +1,87 @@ + + + calculates the distributions of deleted nucleotides across reads + + + + rseqc_macros.xml + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + diff -r ebadf9ee2d08 -r 71ed55a3515a geneBody_coverage.xml --- a/geneBody_coverage.xml Thu Jul 18 11:01:08 2013 -0500 +++ b/geneBody_coverage.xml Tue Mar 14 10:22:57 2017 -0400 @@ -1,63 +1,126 @@ - - - Read coverage over gene body. - - - R - rseqc - - - geneBody_coverage.py -i $input -r $refgene -o output - - - - - - - - - - - - - - - - - - - - -.. image:: https://code.google.com/p/rseqc/logo?cct=1336721062 + + + Read coverage over gene body. + + + + rseqc_macros.xml + + + + + + + + + > 'input_list.txt' && + #end for + geneBody_coverage.py -i 'input_list.txt' -r '${refgene}' --minimum_length ${minimum_length} -o output + ]]> + + + + + + + + ------ - -About RSeQC -+++++++++++ + + + + len(inputs) >= 3 + + + + -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:: $PATH_TO_IMAGES/geneBody_workflow.png +:width: 800 px +:scale: 80 % + + +## Inputs Input BAM/SAM file - Alignment file in BAM/SAM format. + Alignment file in BAM/SAM format. Reference gene model - Gene Model in BED format. + 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 -Outputs -++++++++++++++ +Text + Table that includes the data used to generate the plots -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. +R Script + R script file that reads the data and generates the plot + +PDF + The final plot, in PDF format -Example output: - .. image:: http://dldcc-web.brc.bcm.edu/lilab/liguow/RSeQC/figure/geneBody_coverage.png - +Example plots: +.. image:: $PATH_TO_IMAGES/Aug_26.geneBodyCoverage.curves.png +:height: 600 px +:width: 600 px +:scale: 80 % +.. image:: $PATH_TO_IMAGES/Aug_26.geneBodyCoverage.heatMap.png +:height: 600 px +:width: 600 px +:scale: 80 % - - \ No newline at end of file +@ABOUT@ + + ]]> + + + + + diff -r ebadf9ee2d08 -r 71ed55a3515a geneBody_coverage2.xml --- a/geneBody_coverage2.xml Thu Jul 18 11:01:08 2013 -0500 +++ b/geneBody_coverage2.xml Tue Mar 14 10:22:57 2017 -0400 @@ -1,43 +1,61 @@ - - - Read coverage over gene body. - - - R - rseqc - - - geneBody_coverage2.py -i $input -r $refgene -o output - - - - - - - - - - - -.. image:: https://code.google.com/p/rseqc/logo?cct=1336721062 + + + Read coverage over gene body + + + + rseqc_macros.xml + + + + + + + + + + + + + + + ------ + + + + + -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. + + - - \ No newline at end of file + + + diff -r ebadf9ee2d08 -r 71ed55a3515a infer_experiment.xml --- a/infer_experiment.xml Thu Jul 18 11:01:08 2013 -0500 +++ b/infer_experiment.xml Tue Mar 14 10:22:57 2017 -0400 @@ -1,124 +1,141 @@ - - speculates how RNA-seq were configured - - rseqc - - infer_experiment.py -i $input -r $refgene - - #if $sample_size.boolean - -s $sample_size.size - #end if - - > $output - - - - - - - - - - - - - - - - - - - - - - -.. image:: https://code.google.com/p/rseqc/logo?cct=1336721062 + + speculates how RNA-seq were configured + + + rseqc_macros.xml + + + + + + + + + '${output}' + ]]> + ------ + + + + + + + + + + -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. + - \ No newline at end of file + +@ABOUT@ + +]]> + + + + + diff -r ebadf9ee2d08 -r 71ed55a3515a inner_distance.xml --- a/inner_distance.xml Thu Jul 18 11:01:08 2013 -0500 +++ b/inner_distance.xml Tue Mar 14 10:22:57 2017 -0400 @@ -1,105 +1,113 @@ - - calculate the inner distance (or insert size) between two paired RNA reads - - R - rseqc - - inner_distance.py -i $input -o output -r $refgene + + calculate the inner distance (or insert size) between two paired RNA reads + + + rseqc_macros.xml + + + + + + + - #if $bounds.hasLowerBound - -l $bounds.lowerBound - #end if + + - #if $bounds2.hasUpperBound - -u $bounds2.upperBound - #end if + + + + + + + + + + - #if $steps.step - -s $steps.stepSize - #end if - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -.. image:: https://code.google.com/p/rseqc/logo?cct=1336721062 + + + + + + ------ + + + + + + + + + + + -About RSeQC -+++++++++++ + > 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 + - 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 + - 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://dldcc-web.brc.bcm.edu/lilab/liguow/RSeQC/figure/inner_distance.png +.. image:: $PATH_TO_IMAGES/inner_distance.png + :height: 600 px + :width: 600 px + :scale: 80 % + +@ABOUT@ - +]]> + + + + diff -r ebadf9ee2d08 -r 71ed55a3515a insertion_profile.xml --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/insertion_profile.xml Tue Mar 14 10:22:57 2017 -0400 @@ -0,0 +1,88 @@ + + + calculates the distribution of inserted nucleotides across reads + + + + rseqc_macros.xml + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + diff -r ebadf9ee2d08 -r 71ed55a3515a junction_annotation.xml --- a/junction_annotation.xml Thu Jul 18 11:01:08 2013 -0500 +++ b/junction_annotation.xml Tue Mar 14 10:22:57 2017 -0400 @@ -1,83 +1,107 @@ - - compares detected splice junctions to reference gene model - - R - rseqc - - junction_annotation.py -i $input -o output -r $refgene + + compares detected splice junctions to reference gene model + + + rseqc_macros.xml + + + + + + + - #if $intron.hasIntron - -m $intron.min_Intron - #end if + + + + + + + + + + - - - - - - - - - - - - - - - - - - - - - - - - - - - - -.. image:: https://code.google.com/p/rseqc/logo?cct=1336721062 + + + + + + ------ + + + + + + + + + + + -About RSeQC -+++++++++++ + = 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: -The RSeQC package is licensed under the GNU GPL v3 license. +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. + Alignment file in BAM/SAM format. Reference gene model - Gene model in BED format. + Gene model in BED format. Minimum intron length (default=50) - Minimum intron length (bp). + 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'. + - 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://dldcc-web.brc.bcm.edu/lilab/liguow/RSeQC/figure/junction.png - +.. image:: $PATH_TO_IMAGES/junction.png + :height: 400 px + :width: 850 px + :scale: 80 % +@ABOUT@ - +]]> + + + + diff -r ebadf9ee2d08 -r 71ed55a3515a junction_saturation.xml --- a/junction_saturation.xml Thu Jul 18 11:01:08 2013 -0500 +++ b/junction_saturation.xml Tue Mar 14 10:22:57 2017 -0400 @@ -1,71 +1,110 @@ - - detects splice junctions from each subset and compares them to reference gene model - - R - rseqc - - junction_saturation.py -i $input -o output -r $refgene -m $intronSize -v $minSplice + + detects splice junctions from each subset and compares them to reference gene model + + + rseqc_macros.xml + + + + + + + - #if $percentiles.specifyPercentiles - -l $percentiles.lowBound -u $percentiles.upBound -s $percentiles.percentileStep - #end if + + - - - - - - - - - - - - - - - - - - - - - - - - - - - - -.. image:: https://code.google.com/p/rseqc/logo?cct=1336721062 + + + + + + + + + + + + + + + + + + + + + + + + + + ------ + + + + -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. + - \ No newline at end of file +]]> + + + + + diff -r ebadf9ee2d08 -r 71ed55a3515a mismatch_profile.xml --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/mismatch_profile.xml Tue Mar 14 10:22:57 2017 -0400 @@ -0,0 +1,89 @@ + + + calculates the distribution of mismatches across reads + + + + rseqc_macros.xml + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + diff -r ebadf9ee2d08 -r 71ed55a3515a read_GC.xml --- a/read_GC.xml Thu Jul 18 11:01:08 2013 -0500 +++ b/read_GC.xml Tue Mar 14 10:22:57 2017 -0400 @@ -1,53 +1,74 @@ - - determines GC% and read count - - R - rseqc - - read_GC.py -i $input -o output - - - - - - - - - - - - - - - - - - - .. image:: https://code.google.com/p/rseqc/logo?cct=1336721062 + + determines GC% and read count + + + rseqc_macros.xml + + + + + + + + + + ------ + + + + + + + + + + + -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. + +]]> + + + + diff -r ebadf9ee2d08 -r 71ed55a3515a read_NVC.xml --- a/read_NVC.xml Thu Jul 18 11:01:08 2013 -0500 +++ b/read_NVC.xml Tue Mar 14 10:22:57 2017 -0400 @@ -1,52 +1,69 @@ - - to check the nucleotide composition bias - - R - rseqc - - read_NVC.py -i $input -o output + + to check the nucleotide composition bias + + + rseqc_macros.xml + + + + + + + + + + - #if $nx - -x - #end if - - - - - - - - - - - - - - - - - - - -.. image:: https://code.google.com/p/rseqc/logo?cct=1336721062 + + + + + + + + + + + + ------ + + + + + + + + + -About RSeQC + +]]> + + + + diff -r ebadf9ee2d08 -r 71ed55a3515a read_distribution.xml --- a/read_distribution.xml Thu Jul 18 11:01:08 2013 -0500 +++ b/read_distribution.xml Tue Mar 14 10:22:57 2017 -0400 @@ -1,69 +1,96 @@ - - calculates how mapped reads were distributed over genome feature - - rseqc - - read_distribution.py -i $input -r $refgene > $output - - - - - - - - - -.. image:: https://code.google.com/p/rseqc/logo?cct=1336721062 + + calculates how mapped reads were distributed over genome feature + + + rseqc_macros.xml + + + + + + + + + '${output}' + ]]> + + + + + + + + + + ------ + + + + + + + + + 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. -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. +* "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. -The RSeQC package is licensed under the GNU GPL v3 license. +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. + Alignment file in BAM/SAM format. Reference gene model - Gene model in BED format. + Gene model in BED format. Sample Output ++++++++++++++ -:: - - Total Read: 44,826,454 :: - - Total Tags: 50,023,249 :: - - Total Assigned Tags: 36,057,402 :: +Output: - Group Total_bases Tag_count Tags/Kb - CDS_Exons 33302033 20022538 601.24 - 5'UTR_Exons 21717577 4414913 203.29 - 3'UTR_Exons 15347845 3641689 237.28 - Introns 1132597354 6312099 5.57 - TSS_up_1kb 17957047 215220 11.99 - TSS_up_5kb 81621382 392192 4.81 - TSS_up_10kb 149730983 769210 5.14 - TES_down_1kb 18298543 266157 14.55 - TES_down_5kb 78900674 730072 9.25 - TES_down_10kb 140361190 896953 6.39 +=============== ============ =========== =========== +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 +=============== ============ =========== =========== -Note: -- "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 Fragments" >= "Total Reads" -- "Total Assigned Tags": number of tags that can be unambiguously assigned the 10 groups (above 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 assigning tags to genome features, each tag is represented by its middle point. -- RSeQC cannot assign those reads that: 1) hit to intergenic regions that beyond region starting from TSS upstream 10Kb to TES downstream 10Kb. 2) 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. 3) hit to regions covered by both TSS upstream 10Kb and TES downstream 10Kb. +@ABOUT@ +]]> + - + + diff -r ebadf9ee2d08 -r 71ed55a3515a read_duplication.xml --- a/read_duplication.xml Thu Jul 18 11:01:08 2013 -0500 +++ b/read_duplication.xml Tue Mar 14 10:22:57 2017 -0400 @@ -1,50 +1,63 @@ - - determines reads duplication rate with sequence-based and mapping-based strategies - - R - rseqc - - read_duplication.py -i $input -o output -u $upLimit - - - - - - - - - - - - - - - - - - - - - -.. image:: https://code.google.com/p/rseqc/logo?cct=1336721062 + + determines reads duplication rate with sequence-based and mapping-based strategies + + + rseqc_macros.xml + + + + + + + + + + + + + + + + + ------ + + + + + + -About RSeQC -+++++++++++ + + + + + + + + + + -The RSeQC package provides a number of useful modules that can comprehensively evaluate high throughput sequence data especially RNA-seq data. “Basic modules” quickly inspect sequence quality, nucleotide composition bias, PCR bias and GC bias, while “RNA-seq specific modules” investigate sequencing saturation status of both splicing junction detection and expression estimation, mapped reads clipping profile, mapped reads distribution, coverage uniformity over gene body, reproducibility, strand specificity and splice junction annotation. + +]]> + + + + diff -r ebadf9ee2d08 -r 71ed55a3515a read_hexamer.xml --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/read_hexamer.xml Tue Mar 14 10:22:57 2017 -0400 @@ -0,0 +1,137 @@ + + + calculates hexamer (6mer) frequency for reads, genomes, and mRNA sequences + + + + rseqc_macros.xml + + + + + + + + + "${safename}" && + #else: + ln -sf '${input}' "${safename}" && + #end if + #end for + read_hexamer.py -i '${ ','.join( [ $name for $name in $input_list ] ) }' + #if $refgenome: + -r '${refgenome}' + #end if + #if $refgene: + -g '${refgene}' + #end if + > '${output}' + ]]> + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + diff -r ebadf9ee2d08 -r 71ed55a3515a read_quality.xml --- a/read_quality.xml Thu Jul 18 11:01:08 2013 -0500 +++ b/read_quality.xml Tue Mar 14 10:22:57 2017 -0400 @@ -1,58 +1,92 @@ - - determines Phred quality score - - R - rseqc - - read_quality.py -i $input -o output -r $reduce - - - - - - - - - - - - - - - - - - - -.. image:: https://code.google.com/p/rseqc/logo?cct=1336721062 + + determines Phred quality score + + + rseqc_macros.xml + + + + + + + + + + + + + + + + + ------ + + + + + -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. + +@ABOUT@ + +]]> + + + + diff -r ebadf9ee2d08 -r 71ed55a3515a rseqc_macros.xml --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/rseqc_macros.xml Tue Mar 14 10:22:57 2017 -0400 @@ -0,0 +1,155 @@ + + + 2.6.4 + + + + rseqc + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + rscript_output + + + + + + + + + + +----- + +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:: $PATH_TO_IMAGES/logo.png + +.. _RSeQC: http://rseqc.sourceforge.net/ + + + + + + + 10.1093/bioinformatics/bts356 + + + diff -r ebadf9ee2d08 -r 71ed55a3515a samtoolshelper.py --- a/samtoolshelper.py Thu Jul 18 11:01:08 2013 -0500 +++ /dev/null Thu Jan 01 00:00:00 1970 +0000 @@ -1,20 +0,0 @@ -import sys -import subprocess as sp -import os - -# Creates the sorted and indexed bam/bai files that are requried for both bam2wig and RSEQC_count -def samtools_sorted(bam): - sortedbam = bam + ".sorted" - indexedbam = ".".join([sortedbam,"bam.bai"]) - sp.call(['samtools', 'sort', '-m 1000000000', bam, sortedbam]) - sortedbam = sortedbam + '.bam' - sp.call(['samtools', 'index', sortedbam, indexedbam]) - return sortedbam - -def main(args): - args[2] = samtools_sorted(args[2]) - sp.call(args) - - -if __name__ == "__main__": - main(sys.argv[1:]) \ No newline at end of file diff -r ebadf9ee2d08 -r 71ed55a3515a static/images/36mer.qual.heatmap.png Binary file static/images/36mer.qual.heatmap.png has changed diff -r ebadf9ee2d08 -r 71ed55a3515a static/images/36mer.qual.plot.png Binary file static/images/36mer.qual.plot.png has changed diff -r ebadf9ee2d08 -r 71ed55a3515a static/images/Aug_26.geneBodyCoverage.curves.png Binary file static/images/Aug_26.geneBodyCoverage.curves.png has changed diff -r ebadf9ee2d08 -r 71ed55a3515a static/images/Aug_26.geneBodyCoverage.heatMap.png Binary file static/images/Aug_26.geneBodyCoverage.heatMap.png has changed diff -r ebadf9ee2d08 -r 71ed55a3515a 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changed diff -r ebadf9ee2d08 -r 71ed55a3515a static/images/saturation_eg.png Binary file static/images/saturation_eg.png has changed diff -r ebadf9ee2d08 -r 71ed55a3515a test-data/hg19.HouseKeepingGenes_30.bed --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/hg19.HouseKeepingGenes_30.bed Tue Mar 14 10:22:57 2017 -0400 @@ -0,0 +1,30 @@ +chr1 100652477 100715409 NM_001918 0 - 100661810 100715376 0 11 9501,72,192,78,167,217,122,182,76,124,84, 0,19308,19523,23772,27895,29061,31704,43811,48514,53839,62848, +chr1 175913961 176176380 NM_022457 0 - 175914288 176176114 0 20 345,45,161,125,118,117,82,109,144,136,115,58,77,60,69,120,77,98,60,673, 0,2369,42117,43462,44536,82746,98360,98884,101355,136326,140950,171798,190184,191662,204180,218043,218989,231084,239807,261746, +chr1 150980972 151008189 NM_021222 0 + 150981108 151006710 0 8 175,93,203,185,159,95,159,1908, 0,9315,9970,16114,17018,18736,20289,25309, +chr1 6281252 6296044 NM_012405 0 - 6285139 6295971 0 5 4070,218,170,89,268, 0,10709,12281,13693,14524, +chr1 20959947 20978004 NM_032409 0 + 20960041 20977184 0 8 481,288,101,183,164,128,237,1078, 0,4387,6437,11035,12105,15050,15540,16979, +chr1 32479294 32509482 NM_006559 0 + 32479596 32508225 0 9 684,125,117,147,134,202,68,59,1355, 0,16604,17830,19494,23216,24141,24858,25821,28833, +chr1 27248212 27273362 NM_006600 0 + 27248339 27272672 0 9 208,78,204,66,117,195,84,119,742, 0,2367,19735,20031,20938,21149,23668,23846,24408, +chr1 31404352 31538564 NM_014676 0 - 31406057 31532413 0 22 1837,193,122,126,138,129,130,268,237,297,144,139,152,102,94,271,167,179,109,69,374,102, 0,5137,9645,10492,13840,18627,20728,22208,33168,34476,35661,36848,43145,48556,49806,60884,63548,74347,75488,97290,127698,134110, +chr1 36690016 36770957 NM_005119 0 + 36748164 36769618 0 12 90,103,168,903,705,173,112,85,188,199,144,1561, 0,34966,58117,61952,64644,66958,68182,69435,72167,76470,77137,79380, +chr1 46092975 46152302 NM_021639 0 - 46093927 46124759 0 13 1105,103,125,159,141,194,73,287,130,115,1042,45,219, 0,2272,3178,6185,6792,12906,15123,27239,27886,31724,32880,58272,59108, +chr1 44870959 45117396 NM_018150 0 + 44877769 45116447 0 15 243,742,133,46,102,43,44,133,97,87,56,79,109,75,1021, 0,6693,208877,217454,221009,227055,230257,230742,239410,239707,239933,240122,244373,244595,245416, +chr1 54519273 54565416 NM_153035 0 + 54520095 54562146 0 5 158,144,142,194,3459, 0,780,15155,34996,42684, +chr1 50906934 51425936 NM_007051 0 - 50907111 51425483 0 19 261,216,78,81,89,137,155,82,64,127,96,87,106,92,92,206,47,69,498, 0,34201,49325,50458,94106,98329,125814,141355,142389,143422,154858,214179,264523,297600,303421,346737,360368,416666,518504, +chr1 77554666 77685132 NM_005482 0 - 77558058 77685087 0 11 3509,85,173,111,118,97,112,136,92,54,138, 0,33293,65467,72313,72612,74864,77737,80278,117658,121454,130328, +chr1 94352589 94375012 NM_002061 0 - 94354545 94374719 0 7 2126,115,203,60,85,66,419, 0,7580,9584,10805,14551,17489,22004, +chr1 112162404 112259317 NM_002884 0 + 112233982 112251857 0 8 152,84,69,57,141,144,116,4265, 0,71551,75558,77658,83553,84560,89366,92648, +chr1 118472371 118503049 NM_006784 0 + 118475942 118502070 0 27 34,203,210,119,79,96,114,102,98,108,231,94,102,86,136,57,101,112,135,51,66,93,48,44,129,94,1135, 0,3539,4724,7020,8731,9728,11078,11375,12001,12688,13647,16337,18656,20002,20246,21085,22227,22548,22779,23197,23727,24258,24831,25566,27319,29161,29543, +chr1 156219014 156252620 NM_015327 0 - 156220377 156252471 0 22 1447,139,75,91,160,60,159,176,76,176,600,138,209,69,126,79,90,90,157,124,99,223, 0,1634,2179,3190,3695,4225,9781,11227,12109,14171,16557,17317,18246,18891,19066,23096,24137,25373,27861,28701,29712,33383, +chr1 180257351 180472022 NM_032360 0 - 180257497 180471401 0 8 301,31,90,106,83,97,65,843, 0,26475,109299,125149,141963,204052,207244,213828, +chr1 183441505 183523328 NM_173156 0 + 183441755 183521066 0 22 279,32,118,133,172,72,151,136,163,157,71,61,120,427,145,383,372,81,160,171,146,2376, 0,40466,43503,45317,54225,55585,56521,57027,60793,61305,64774,66009,68613,69705,71982,72559,73595,76837,77393,78380,78674,79447, +chr1 220141941 220220000 NM_004446 0 - 220142147 220219730 0 32 357,65,79,161,174,198,156,102,80,73,210,52,263,234,360,118,113,208,137,111,60,85,234,172,193,127,95,140,157,100,85,316, 0,458,3469,4638,9946,10818,11485,12156,12778,14172,14589,15606,18542,19990,28383,32538,36648,37506,38602,42346,49849,50395,51388,53747,55664,56532,61786,63787,64902,66314,71585,77743, +chr1 229577043 229644088 NM_018230 0 - 229577650 229643996 0 26 744,89,65,81,119,136,159,134,252,100,123,225,95,164,92,158,148,148,71,156,171,135,108,104,119,274, 0,3617,7829,9228,11228,16864,19314,22246,23327,24123,25337,29283,34341,36300,42757,45074,46169,48658,54198,54595,56839,58387,59459,60702,64743,66771, +chr1 1309109 1310818 NM_017900 0 - 1309180 1310136 0 4 173,446,86,285, 0,270,975,1424, +chr1 9908333 9970316 NM_020248 0 - 9910775 9932122 0 6 2501,91,120,85,34,164, 0,22911,23693,29629,35429,61819, +chr1 10093040 10241296 NM_006048 0 + 10093728 10240014 0 27 712,187,136,88,145,229,142,101,115,84,57,117,99,114,199,139,100,128,99,236,127,145,135,192,175,147,1344, 0,39045,62478,68125,69965,72533,84476,86530,88979,93811,96409,97517,97732,99386,102005,104084,111957,113980,116201,118343,125373,128159,135153,138155,145661,146433,146912, +chr1 11126675 11159938 NM_002685 0 - 11126774 11159888 0 24 130,77,62,172,74,85,96,107,79,51,112,51,149,157,191,144,111,76,115,166,105,124,137,161, 0,1389,2026,2940,4281,5468,7612,10223,10746,10982,13092,13880,14145,14463,16069,20829,21181,21504,23935,24395,24874,29139,31401,33102, +chr1 10535002 10690815 NM_004565 0 + 10535023 10690044 0 9 57,48,85,129,86,103,98,92,1228, 0,20328,61267,124292,143386,148073,149394,152326,154585, +chr1 16576558 16678948 NM_018994 0 - 16577164 16641913 0 10 1722,117,57,97,111,154,135,117,267,199, 0,2224,3032,3571,5647,6542,44719,55739,65105,102191, +chr1 16174358 16266950 NM_015001 0 + 16174562 16265922 0 15 287,321,477,161,201,152,126,114,114,101,8176,483,195,159,1160, 0,24952,28338,61457,63237,68264,71062,71540,73006,74385,80227,89299,89948,90854,91432, +chr1 17866329 18024370 NM_018125 0 + 17907090 18023875 0 29 116,80,186,34,92,84,176,117,109,107,78,180,117,93,174,146,15,182,116,128,101,122,87,224,155,149,175,123,1028, 0,40718,47625,48611,62292,63673,67967,73223,76259,79504,82029,83161,84552,86121,87495,92486,94713,95000,98053,98727,100367,108719,114801,116044,116719,124612,147738,155323,157013, diff -r ebadf9ee2d08 -r 71ed55a3515a test-data/hg19.chrom.sizes --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/hg19.chrom.sizes Tue Mar 14 10:22:57 2017 -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 diff -r ebadf9ee2d08 -r 71ed55a3515a test-data/hg19_RefSeq_chr1_1-100000.bed --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/hg19_RefSeq_chr1_1-100000.bed Tue Mar 14 10:22:57 2017 -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, diff -r ebadf9ee2d08 -r 71ed55a3515a test-data/output.DupRate_plot.pdf Binary file test-data/output.DupRate_plot.pdf has changed diff -r ebadf9ee2d08 -r 71ed55a3515a test-data/output.DupRate_plot.r --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/output.DupRate_plot.r Tue Mar 14 10:22:57 2017 -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='Occurrence of read',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-based','Mapping-based'),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*pos_occ)),round(pos_uniqRead[2]*100/sum(pos_uniqRead*pos_occ)),round(pos_uniqRead[3]*100/sum(pos_uniqRead*pos_occ)),round(pos_uniqRead[4]*100/sum(pos_uniqRead*pos_occ)))) +mtext(4, text = "Reads %", line = 2) +dev.off() diff -r ebadf9ee2d08 -r 71ed55a3515a test-data/output.FPKM.xls --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/output.FPKM.xls Tue Mar 14 10:22:57 2017 -0400 @@ -0,0 +1,8 @@ +#chrom st end accession mRNA_size gene_strand Frag_count FPM FPKM +chr1 11873 14409 NR_046018 1652.0 + 1.0 50000.0 30266.3438257 +chr1 14361 29370 NR_024540 1769.0 - 2.0 100000.0 56529.1124929 +chr1 17368 17436 NR_106918 68.0 - 0.0 0.0 0.0 +chr1 17368 17436 NR_107062 68.0 - 0.0 0.0 0.0 +chr1 34610 36081 NR_026818 1130.0 - 0.0 0.0 0.0 +chr1 34610 36081 NR_026820 1130.0 - 0.0 0.0 0.0 +chr1 69090 70008 NM_001005484 918.0 + 0.0 0.0 0.0 diff -r ebadf9ee2d08 -r 71ed55a3515a test-data/output.GC.xls --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/output.GC.xls Tue Mar 14 10:22:57 2017 -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 diff -r ebadf9ee2d08 -r 71ed55a3515a test-data/output.GC_plot.pdf Binary file test-data/output.GC_plot.pdf has changed diff -r ebadf9ee2d08 -r 71ed55a3515a test-data/output.GC_plot.r --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/output.GC_plot.r Tue Mar 14 10:22:57 2017 -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() diff -r ebadf9ee2d08 -r 71ed55a3515a test-data/output.NVC.xls --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/output.NVC.xls Tue Mar 14 10:22:57 2017 -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 diff -r ebadf9ee2d08 -r 71ed55a3515a test-data/output.NVC_plot.pdf Binary file test-data/output.NVC_plot.pdf has changed diff -r ebadf9ee2d08 -r 71ed55a3515a test-data/output.NVC_plot.r --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/output.NVC_plot.r Tue Mar 14 10:22:57 2017 -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() diff -r ebadf9ee2d08 -r 71ed55a3515a test-data/output.RNA_fragment_size.txt --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/output.RNA_fragment_size.txt Tue Mar 14 10:22:57 2017 -0400 @@ -0,0 +1,8 @@ +chrom tx_start tx_end symbol frag_count frag_mean frag_median frag_std +chr1 11873 14409 NR_046018 1 0 0 0 +chr1 14361 29370 NR_024540 14 66.5 51.0 41.1195990809 +chr1 17368 17436 NR_106918 0 0 0 0 +chr1 17368 17436 NR_107062 0 0 0 0 +chr1 34610 36081 NR_026818 0 0 0 0 +chr1 34610 36081 NR_026820 0 0 0 0 +chr1 69090 70008 NM_001005484 0 0 0 0 diff -r ebadf9ee2d08 -r 71ed55a3515a test-data/output.bamstats.txt --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/output.bamstats.txt Tue Mar 14 10:22:57 2017 -0400 @@ -0,0 +1,22 @@ + +#================================================== +#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 diff -r ebadf9ee2d08 -r 71ed55a3515a test-data/output.clipping_profile.pdf Binary file test-data/output.clipping_profile.pdf has changed diff -r ebadf9ee2d08 -r 71ed55a3515a test-data/output.clipping_profile.r --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/output.clipping_profile.r Tue Mar 14 10:22:57 2017 -0400 @@ -0,0 +1,6 @@ +pdf("output.clipping_profile.pdf") +read_pos=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) +clip_count=c(16.0,12.0,11.0,8.0,7.0,6.0,1.0,1.0,1.0,1.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.0,1.0,1.0,2.0,3.0,4.0,4.0) +nonclip_count= 40 - clip_count +plot(read_pos, nonclip_count*100/(clip_count+nonclip_count),col="blue",main="clipping profile",xlab="Position of read",ylab="Non-clipped %",type="b") +dev.off() diff -r ebadf9ee2d08 -r 71ed55a3515a test-data/output.clipping_profile.xls --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/output.clipping_profile.xls Tue Mar 14 10:22:57 2017 -0400 @@ -0,0 +1,52 @@ +Position Clipped_nt Non_clipped_nt +0 16.0 24.0 +1 12.0 28.0 +2 11.0 29.0 +3 8.0 32.0 +4 7.0 33.0 +5 6.0 34.0 +6 1.0 39.0 +7 1.0 39.0 +8 1.0 39.0 +9 1.0 39.0 +10 0 40.0 +11 0 40.0 +12 0 40.0 +13 0 40.0 +14 0 40.0 +15 0 40.0 +16 0 40.0 +17 0 40.0 +18 0 40.0 +19 0 40.0 +20 0 40.0 +21 0 40.0 +22 0 40.0 +23 0 40.0 +24 0 40.0 +25 0 40.0 +26 0 40.0 +27 0 40.0 +28 0 40.0 +29 0 40.0 +30 0 40.0 +31 0 40.0 +32 0 40.0 +33 0 40.0 +34 0 40.0 +35 0 40.0 +36 0 40.0 +37 0 40.0 +38 0 40.0 +39 0 40.0 +40 0 40.0 +41 0 40.0 +42 0 40.0 +43 0 40.0 +44 1.0 39.0 +45 1.0 39.0 +46 1.0 39.0 +47 2.0 38.0 +48 3.0 37.0 +49 4.0 36.0 +50 4.0 36.0 diff -r ebadf9ee2d08 -r 71ed55a3515a test-data/output.deletion_profile.pdf Binary file test-data/output.deletion_profile.pdf has changed diff -r ebadf9ee2d08 -r 71ed55a3515a test-data/output.deletion_profile.r --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/output.deletion_profile.r Tue Mar 14 10:22:57 2017 -0400 @@ -0,0 +1,5 @@ +pdf("output.deletion_profile.pdf") +pos=c(0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100) +value=c(0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0) +plot(pos,value,type='b', col='blue',xlab="Read position (5'->3')", ylab='Deletion count') +dev.off() diff -r ebadf9ee2d08 -r 71ed55a3515a test-data/output.deletion_profile.txt --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/output.deletion_profile.txt Tue Mar 14 10:22:57 2017 -0400 @@ -0,0 +1,102 @@ +read_position deletion_count +0 0 +1 0 +2 0 +3 0 +4 0 +5 0 +6 0 +7 0 +8 0 +9 0 +10 0 +11 0 +12 0 +13 0 +14 0 +15 0 +16 0 +17 0 +18 0 +19 0 +20 0 +21 0 +22 0 +23 0 +24 0 +25 0 +26 0 +27 0 +28 0 +29 0 +30 0 +31 0 +32 0 +33 0 +34 0 +35 0 +36 0 +37 0 +38 0 +39 0 +40 0 +41 0 +42 0 +43 0 +44 0 +45 0 +46 0 +47 0 +48 0 +49 0 +50 0 +51 0 +52 0 +53 0 +54 0 +55 0 +56 0 +57 0 +58 0 +59 0 +60 0 +61 0 +62 0 +63 0 +64 0 +65 0 +66 0 +67 0 +68 0 +69 0 +70 0 +71 0 +72 0 +73 0 +74 0 +75 0 +76 0 +77 0 +78 0 +79 0 +80 0 +81 0 +82 0 +83 0 +84 0 +85 0 +86 0 +87 0 +88 0 +89 0 +90 0 +91 0 +92 0 +93 0 +94 0 +95 0 +96 0 +97 0 +98 0 +99 0 +100 0 diff -r ebadf9ee2d08 -r 71ed55a3515a test-data/output.geneBodyCoverage.curves.pdf Binary file test-data/output.geneBodyCoverage.curves.pdf has changed diff -r ebadf9ee2d08 -r 71ed55a3515a test-data/output.geneBodyCoverage.r --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/output.geneBodyCoverage.r Tue Mar 14 10:22:57 2017 -0400 @@ -0,0 +1,8 @@ +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,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() diff -r ebadf9ee2d08 -r 71ed55a3515a test-data/output.geneBodyCoverage.txt --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/output.geneBodyCoverage.txt Tue Mar 14 10:22:57 2017 -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 +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 diff -r ebadf9ee2d08 -r 71ed55a3515a test-data/output.geneBodyCoverage2.curves.pdf Binary file test-data/output.geneBodyCoverage2.curves.pdf has changed diff -r ebadf9ee2d08 -r 71ed55a3515a test-data/output.geneBodyCoverage2.r --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/output.geneBodyCoverage2.r Tue Mar 14 10:22:57 2017 -0400 @@ -0,0 +1,5 @@ +pdf('output.geneBodyCoverage.pdf') +x=1:100 +y=c(0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,1.0,1.0,0.0,0.0,1.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,1.0,1.0,0.0,1.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0) +plot(x,y/7,xlab="percentile of gene body (5'->3')",ylab='average wigsum',type='s') +dev.off() diff -r ebadf9ee2d08 -r 71ed55a3515a test-data/output.geneBodyCoverage2.txt --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/output.geneBodyCoverage2.txt Tue Mar 14 10:22:57 2017 -0400 @@ -0,0 +1,101 @@ +percentile count +0 0.0 +1 0.0 +2 0.0 +3 0.0 +4 0.0 +5 0.0 +6 0.0 +7 0.0 +8 0.0 +9 0.0 +10 0.0 +11 0.0 +12 0.0 +13 0.0 +14 0.0 +15 0.0 +16 0.0 +17 0.0 +18 0.0 +19 0.0 +20 0.0 +21 0.0 +22 0.0 +23 0.0 +24 0.0 +25 1.0 +26 0.0 +27 0.0 +28 1.0 +29 0.0 +30 0.0 +31 0.0 +32 0.0 +33 0.0 +34 0.0 +35 0.0 +36 0.0 +37 0.0 +38 1.0 +39 1.0 +40 1.0 +41 0.0 +42 0.0 +43 1.0 +44 1.0 +45 1.0 +46 0.0 +47 0.0 +48 0.0 +49 0.0 +50 0.0 +51 0.0 +52 0.0 +53 0.0 +54 0.0 +55 0.0 +56 0.0 +57 0.0 +58 0.0 +59 0.0 +60 0.0 +61 0.0 +62 0.0 +63 0.0 +64 0.0 +65 0.0 +66 0.0 +67 0.0 +68 0.0 +69 0.0 +70 0.0 +71 0.0 +72 0.0 +73 0.0 +74 0.0 +75 0.0 +76 0.0 +77 0.0 +78 0.0 +79 1.0 +80 1.0 +81 1.0 +82 0.0 +83 1.0 +84 1.0 +85 1.0 +86 0.0 +87 0.0 +88 0.0 +89 0.0 +90 0.0 +91 0.0 +92 0.0 +93 0.0 +94 0.0 +95 0.0 +96 0.0 +97 0.0 +98 0.0 +99 0.0 diff -r ebadf9ee2d08 -r 71ed55a3515a test-data/output.infer_experiment.txt --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/output.infer_experiment.txt Tue Mar 14 10:22:57 2017 -0400 @@ -0,0 +1,6 @@ + + +This is PairEnd Data +Fraction of reads failed to determine: 0.0000 +Fraction of reads explained by "1++,1--,2+-,2-+": 1.0000 +Fraction of reads explained by "1+-,1-+,2++,2--": 0.0000 diff -r ebadf9ee2d08 -r 71ed55a3515a test-data/output.inner_distance.txt --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/output.inner_distance.txt Tue Mar 14 10:22:57 2017 -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 diff -r ebadf9ee2d08 -r 71ed55a3515a test-data/output.inner_distance_freq.txt --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/output.inner_distance_freq.txt Tue Mar 14 10:22:57 2017 -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 diff -r ebadf9ee2d08 -r 71ed55a3515a test-data/output.inner_distance_plot.pdf Binary file test-data/output.inner_distance_plot.pdf has changed diff -r ebadf9ee2d08 -r 71ed55a3515a test-data/output.inner_distance_plot.r --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/output.inner_distance_plot.r Tue Mar 14 10:22:57 2017 -0400 @@ -0,0 +1,11 @@ +out_file = 'output' +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(x=c("Name","Mean","Median","sd"), sep=" ", file=stdout(),ncolumns=4) +write(c(out_file,frag_mean,frag_median,frag_sd),sep=" ", file=stdout(),ncolumns=4) +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() diff -r ebadf9ee2d08 -r 71ed55a3515a test-data/output.insertion_profile.pdf Binary file test-data/output.insertion_profile.pdf has changed diff -r ebadf9ee2d08 -r 71ed55a3515a test-data/output.insertion_profile.r --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/output.insertion_profile.r Tue Mar 14 10:22:57 2017 -0400 @@ -0,0 +1,6 @@ +pdf("output.insertion_profile.pdf") +read_pos=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) +insert_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) +noninsert_count= 40 - insert_count +plot(read_pos, insert_count*100/(insert_count+noninsert_count),col="blue",main="Insertion profile",xlab="Position of read",ylab="Insertion %",type="b") +dev.off() diff -r ebadf9ee2d08 -r 71ed55a3515a test-data/output.insertion_profile.xls --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/output.insertion_profile.xls Tue Mar 14 10:22:57 2017 -0400 @@ -0,0 +1,52 @@ +Position Insert_nt Non_insert_nt +0 0 40.0 +1 0 40.0 +2 0 40.0 +3 0 40.0 +4 0 40.0 +5 0 40.0 +6 0 40.0 +7 0 40.0 +8 0 40.0 +9 0 40.0 +10 0 40.0 +11 0 40.0 +12 0 40.0 +13 0 40.0 +14 0 40.0 +15 0 40.0 +16 0 40.0 +17 0 40.0 +18 0 40.0 +19 0 40.0 +20 0 40.0 +21 0 40.0 +22 0 40.0 +23 0 40.0 +24 0 40.0 +25 0 40.0 +26 0 40.0 +27 0 40.0 +28 0 40.0 +29 0 40.0 +30 0 40.0 +31 0 40.0 +32 0 40.0 +33 0 40.0 +34 0 40.0 +35 0 40.0 +36 0 40.0 +37 0 40.0 +38 0 40.0 +39 0 40.0 +40 0 40.0 +41 0 40.0 +42 0 40.0 +43 0 40.0 +44 0 40.0 +45 0 40.0 +46 0 40.0 +47 0 40.0 +48 0 40.0 +49 0 40.0 +50 0 40.0 diff -r ebadf9ee2d08 -r 71ed55a3515a test-data/output.junction.xls --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/output.junction.xls Tue Mar 14 10:22:57 2017 -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 diff -r ebadf9ee2d08 -r 71ed55a3515a test-data/output.junctionSaturation_plot.pdf Binary file test-data/output.junctionSaturation_plot.pdf has changed diff -r ebadf9ee2d08 -r 71ed55a3515a test-data/output.junctionSaturation_plot.r --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/output.junctionSaturation_plot.r Tue Mar 14 10:22:57 2017 -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(0,0,0,0,0,0,1,1,1,1,1,1,1,1,1,1,1,1,1,1) +z=c(0,0,0,0,0,0,1,1,1,1,1,1,1,2,2,2,2,2,2,3) +w=c(0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,1,1,1,1,2) +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() diff -r ebadf9ee2d08 -r 71ed55a3515a test-data/output.junction_plot.r --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/output.junction_plot.r Tue Mar 14 10:22:57 2017 -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() diff -r ebadf9ee2d08 -r 71ed55a3515a test-data/output.mismatch_profile.pdf diff -r ebadf9ee2d08 -r 71ed55a3515a test-data/output.mismatch_profile.r diff -r ebadf9ee2d08 -r 71ed55a3515a test-data/output.mismatch_profile.xls --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/output.mismatch_profile.xls Tue Mar 14 10:22:57 2017 -0400 @@ -0,0 +1,1 @@ +Total reads used: 0 diff -r ebadf9ee2d08 -r 71ed55a3515a test-data/output.pos.DupRate.xls --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/output.pos.DupRate.xls Tue Mar 14 10:22:57 2017 -0400 @@ -0,0 +1,2 @@ +Occurrence UniqReadNumber +1 40 diff -r ebadf9ee2d08 -r 71ed55a3515a test-data/output.qual.boxplot.pdf Binary file test-data/output.qual.boxplot.pdf has changed diff -r ebadf9ee2d08 -r 71ed55a3515a test-data/output.qual.heatmap.pdf --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/output.qual.heatmap.pdf Tue Mar 14 10:22:57 2017 -0400 @@ -0,0 +1,62 @@ +pdf('output.qual.boxplot.pdf') +p0<-rep(c(33,37,38,39,40,41,42,43,44,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71),times=c(119,2,3,2,5,6,8,6,2,3,11,16,6,26,11,13,25,39,7,40,33,33,58,51,116,87,55,256,54,323,263,140,812,654,1119)/1000) +p1<-rep(c(33,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71),times=c(105,2,2,2,4,8,6,21,3,1,1,8,13,13,16,16,14,29,32,18,50,30,57,66,73,97,105,60,253,57,330,270,142,801,630,1069)/1000) +p2<-rep(c(33,35,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71),times=c(109,1,1,3,2,7,11,14,13,2,4,3,8,14,21,27,17,14,26,39,11,37,28,74,64,55,86,106,62,234,56,326,269,147,787,645,1081)/1000) +p3<-rep(c(33,37,38,39,40,41,42,43,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71),times=c(108,1,6,4,2,9,12,7,3,3,9,14,13,24,20,8,24,46,14,43,28,59,67,75,88,107,51,285,56,293,239,139,802,660,1084)/1000) +p4<-rep(c(33,35,37,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71),times=c(97,1,1,3,9,8,11,5,4,2,4,10,16,19,24,7,8,35,43,19,49,29,51,67,51,93,107,43,306,65,345,223,123,789,661,1075)/1000) +p5<-rep(c(33,37,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71),times=c(96,3,2,5,11,6,8,2,7,2,12,17,15,16,12,11,25,31,12,32,36,59,70,69,74,99,56,277,59,343,249,111,845,650,1081)/1000) +p6<-rep(c(33,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71),times=c(86,2,4,2,6,12,8,10,1,7,7,9,11,14,26,14,9,14,53,17,34,41,55,71,76,76,117,62,238,62,339,229,155,798,607,1131)/1000) +p7<-rep(c(33,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71),times=c(76,1,4,4,6,11,9,13,5,5,6,6,17,19,20,17,8,19,45,15,30,33,60,68,58,76,99,59,291,54,349,251,129,818,602,1120)/1000) +p8<-rep(c(33,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71),times=c(74,1,2,3,1,5,6,6,7,7,2,4,11,11,16,24,13,9,24,48,19,33,39,63,67,68,78,104,66,284,62,329,240,147,749,649,1132)/1000) +p9<-rep(c(33,37,38,39,40,41,42,43,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71),times=c(98,1,2,2,6,11,19,10,5,3,8,18,19,24,14,5,18,53,21,41,39,56,79,64,70,93,57,291,42,334,259,143,795,616,1087)/1000) +p10<-rep(c(33,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71),times=c(71,3,3,4,3,5,6,7,2,3,5,13,14,6,17,21,12,27,40,16,34,39,46,64,78,103,103,63,279,37,314,239,118,805,674,1129)/1000) +p11<-rep(c(33,34,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71),times=c(76,1,4,3,3,7,10,11,8,5,6,3,12,13,18,21,16,18,21,46,21,32,41,77,56,77,103,105,54,269,40,320,247,144,796,621,1098)/1000) +p12<-rep(c(33,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71),times=c(87,3,2,1,7,12,8,6,5,13,8,11,9,16,23,13,14,22,40,21,53,48,51,59,77,84,126,75,282,48,306,254,151,808,586,1074)/1000) +p13<-rep(c(33,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71),times=c(76,5,1,3,6,3,7,8,4,3,10,12,14,13,23,12,19,25,43,17,52,42,63,57,92,91,114,61,281,45,342,256,132,812,586,1073)/1000) +p14<-rep(c(33,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71),times=c(81,1,5,4,4,10,11,9,3,5,5,6,18,21,29,26,14,27,51,17,54,47,51,65,84,84,118,66,291,46,316,244,149,782,579,1080)/1000) +p15<-rep(c(33,36,37,38,39,40,41,42,43,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71),times=c(87,1,2,5,2,10,17,12,9,7,2,10,10,12,20,18,21,27,50,17,50,54,42,82,57,84,103,54,285,41,342,265,115,822,582,1085)/1000) +p16<-rep(c(33,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71),times=c(118,4,3,7,7,11,10,5,6,8,11,18,19,30,34,13,34,47,14,62,49,55,83,82,96,101,51,283,45,346,249,152,843,521,985)/1000) +p17<-rep(c(33,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71),times=c(73,2,5,6,7,9,11,7,5,4,11,19,13,15,33,18,17,42,57,25,46,65,67,94,68,93,117,67,279,53,306,295,132,844,504,993)/1000) +p18<-rep(c(33,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71),times=c(72,1,2,3,5,4,16,13,14,2,8,2,18,19,27,37,27,18,29,57,21,47,57,62,87,81,89,111,57,293,49,319,270,142,858,495,990)/1000) +p19<-rep(c(33,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71),times=c(78,1,1,5,3,3,13,10,13,5,7,5,14,15,24,30,23,23,24,57,19,72,49,70,70,72,91,124,60,298,52,347,270,147,841,486,980)/1000) +p20<-rep(c(33,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71),times=c(71,4,5,3,10,9,10,12,5,9,6,23,14,19,33,27,21,34,60,16,47,57,55,82,84,109,117,44,305,45,335,265,146,856,510,954)/1000) +p21<-rep(c(33,37,38,39,40,41,42,43,44,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71),times=c(80,5,4,5,4,6,10,7,4,11,14,19,18,32,25,29,32,75,19,58,56,66,81,79,102,133,52,332,44,306,260,152,879,486,917)/1000) +p22<-rep(c(33,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71),times=c(80,2,5,2,11,11,13,4,4,5,8,12,15,21,34,27,18,44,58,26,72,62,72,90,84,97,137,51,324,54,332,254,143,857,492,881)/1000) +p23<-rep(c(33,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71),times=c(78,3,4,1,9,9,3,8,9,9,5,12,20,19,37,30,23,38,69,29,64,51,71,95,92,99,133,52,320,51,340,275,152,868,467,856)/1000) +p24<-rep(c(33,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71),times=c(73,1,2,3,6,14,19,7,3,3,10,24,15,26,38,27,15,34,71,17,62,72,75,86,84,108,128,65,304,41,356,239,139,864,494,876)/1000) +p25<-rep(c(33,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71),times=c(100,1,1,6,3,6,14,19,11,9,7,11,5,8,25,21,35,16,18,39,61,19,65,42,62,91,83,80,105,38,318,50,372,289,135,847,504,884)/1000) +p26<-rep(c(33,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71),times=c(72,1,1,4,2,12,10,17,11,5,3,3,21,25,29,34,34,19,38,55,20,55,59,82,96,99,106,133,45,299,71,339,265,157,822,474,882)/1000) +p27<-rep(c(33,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71),times=c(73,1,2,5,3,17,16,6,5,7,11,7,16,14,30,31,16,45,71,29,50,62,72,78,77,107,132,62,273,47,366,277,161,892,462,877)/1000) +p28<-rep(c(33,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71),times=c(91,3,2,2,7,10,6,9,9,6,11,15,17,19,33,20,10,30,54,20,68,48,73,84,72,114,131,60,321,60,356,270,159,874,496,840)/1000) +p29<-rep(c(33,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71),times=c(92,1,5,5,4,6,8,7,10,4,7,7,16,26,24,33,20,22,36,49,15,53,65,71,79,80,112,127,63,320,49,359,292,141,837,455,900)/1000) +p30<-rep(c(33,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71),times=c(105,1,2,5,4,2,15,8,13,8,6,7,19,24,21,30,22,17,35,53,13,52,61,45,93,74,87,120,60,302,41,331,272,131,877,513,931)/1000) +p31<-rep(c(33,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71),times=c(117,3,5,3,11,23,24,10,5,7,6,8,13,12,40,18,18,40,41,12,45,57,72,86,71,75,125,68,299,55,302,264,154,874,464,973)/1000) +p32<-rep(c(33,35,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71),times=c(120,1,7,3,4,9,13,19,8,5,3,10,17,25,13,19,18,23,33,49,25,41,51,72,74,56,95,112,60,291,58,281,267,145,916,463,993)/1000) +p33<-rep(c(33,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71),times=c(89,1,4,9,13,7,8,7,8,6,8,8,18,20,12,34,26,14,31,50,17,45,65,58,68,77,84,110,66,289,54,284,282,164,871,489,1003)/1000) +p34<-rep(c(33,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71),times=c(162,1,3,4,4,14,15,24,17,2,6,9,16,15,20,37,20,12,34,49,12,42,50,54,66,62,81,121,56,265,50,292,258,127,878,506,1015)/1000) +p35<-rep(c(33,37,38,39,40,41,42,43,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71),times=c(79,5,1,3,3,13,8,6,7,11,15,19,11,17,25,16,34,49,16,37,45,69,72,76,79,101,48,303,38,326,254,140,811,619,1043)/1000) +p36<-rep(c(33,37,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71),times=c(78,3,4,3,8,5,6,8,6,5,13,18,18,32,19,22,39,43,17,39,45,68,77,74,69,118,47,272,47,332,262,139,833,562,1068)/1000) +p37<-rep(c(33,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71),times=c(96,1,3,1,5,5,6,6,8,4,7,3,12,9,18,35,15,24,27,57,20,40,53,70,81,89,91,117,46,262,44,298,251,130,817,588,1059)/1000) +p38<-rep(c(33,37,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71),times=c(102,5,5,4,10,11,12,3,3,7,8,18,15,22,20,17,20,50,21,46,43,71,70,80,91,110,51,239,34,339,258,119,820,614,1058)/1000) +p39<-rep(c(33,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71),times=c(100,3,5,3,6,10,12,12,5,5,10,6,21,18,28,14,16,33,38,18,45,56,58,71,65,79,109,57,253,47,310,263,129,854,616,1017)/1000) +p40<-rep(c(33,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71),times=c(97,1,4,3,1,5,14,7,11,2,5,2,9,6,19,21,18,21,30,37,22,37,64,40,69,53,89,104,66,281,42,355,233,137,771,615,1095)/1000) +p41<-rep(c(33,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71),times=c(130,5,2,6,10,10,19,20,5,5,4,12,16,17,28,14,12,25,42,16,42,39,57,61,73,84,110,49,261,48,315,254,125,761,643,1028)/1000) +p42<-rep(c(33,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71),times=c(108,2,1,2,1,5,4,12,13,7,6,7,3,6,14,19,20,22,15,22,52,14,50,45,57,67,72,78,119,51,272,45,284,226,127,831,604,1054)/1000) +p43<-rep(c(33,35,36,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71),times=c(124,2,2,1,2,5,13,17,10,1,6,4,14,11,19,31,14,14,24,30,12,42,41,54,64,74,82,112,68,250,49,308,261,142,775,557,1060)/1000) +p44<-rep(c(33,35,37,38,39,40,41,42,43,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71),times=c(106,1,1,4,4,10,9,9,7,9,6,8,8,16,21,12,18,24,50,14,43,43,56,55,100,87,109,51,261,51,308,217,139,759,562,1041)/1000) +p45<-rep(c(33,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71),times=c(120,3,3,2,9,7,12,9,6,4,1,10,9,15,26,11,16,22,35,16,26,45,50,60,56,67,74,62,247,50,282,243,123,747,618,1061)/1000) +p46<-rep(c(33,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71),times=c(116,1,2,3,2,10,15,10,1,2,6,6,10,9,13,8,14,29,26,12,31,42,59,41,57,88,92,58,257,43,304,236,133,707,612,1016)/1000) +p47<-rep(c(33,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71),times=c(130,1,2,4,2,6,8,18,21,3,5,5,7,12,15,17,7,7,23,43,9,28,32,44,42,56,68,83,54,225,38,289,181,133,713,594,991)/1000) +p48<-rep(c(33,35,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71),times=c(105,1,5,2,6,9,10,23,1,5,3,3,9,9,30,13,7,18,27,12,28,24,49,42,63,75,81,45,226,43,274,217,147,676,571,925)/1000) +p49<-rep(c(33,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71),times=c(99,3,1,3,5,5,16,3,3,6,7,4,13,15,11,3,10,34,16,20,37,46,41,52,66,85,35,201,45,253,201,119,685,497,913)/1000) +p50<-rep(c(33,43,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71),times=c(82,2,3,5,3,4,5,13,9,5,11,14,3,35,17,21,41,34,67,67,31,184,49,241,167,93,639,515,797)/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(119,0,0,0,2,3,2,5,6,8,6,2,0,3,11,16,6,26,11,13,25,39,7,40,33,33,58,51,116,87,55,256,54,323,263,140,812,654,1119,105,0,0,0,2,2,2,4,8,6,21,3,1,1,8,13,13,16,16,14,29,32,18,50,30,57,66,73,97,105,60,253,57,330,270,142,801,630,1069,109,0,1,0,1,3,2,7,11,14,13,2,4,3,8,14,21,27,17,14,26,39,11,37,28,74,64,55,86,106,62,234,56,326,269,147,787,645,1081,108,0,0,0,1,6,4,2,9,12,7,0,3,3,9,14,13,24,20,8,24,46,14,43,28,59,67,75,88,107,51,285,56,293,239,139,802,660,1084,97,0,1,0,1,0,3,9,8,11,5,4,2,4,10,16,19,24,7,8,35,43,19,49,29,51,67,51,93,107,43,306,65,345,223,123,789,661,1075,96,0,0,0,3,0,2,5,11,6,8,2,7,2,12,17,15,16,12,11,25,31,12,32,36,59,70,69,74,99,56,277,59,343,249,111,845,650,1081,86,0,0,0,2,4,2,6,12,8,10,1,7,7,9,11,14,26,14,9,14,53,17,34,41,55,71,76,76,117,62,238,62,339,229,155,798,607,1131,76,0,0,0,1,4,4,6,11,9,13,5,5,6,6,17,19,20,17,8,19,45,15,30,33,60,68,58,76,99,59,291,54,349,251,129,818,602,1120,74,0,0,1,2,3,1,5,6,6,7,7,2,4,11,11,16,24,13,9,24,48,19,33,39,63,67,68,78,104,66,284,62,329,240,147,749,649,1132,98,0,0,0,1,2,2,6,11,19,10,0,5,3,8,18,19,24,14,5,18,53,21,41,39,56,79,64,70,93,57,291,42,334,259,143,795,616,1087,71,0,0,0,3,3,4,3,5,6,7,2,3,5,13,14,6,17,21,12,27,40,16,34,39,46,64,78,103,103,63,279,37,314,239,118,805,674,1129,76,1,0,0,4,3,3,7,10,11,8,5,6,3,12,13,18,21,16,18,21,46,21,32,41,77,56,77,103,105,54,269,40,320,247,144,796,621,1098,87,0,0,0,3,2,1,7,12,8,6,5,13,8,11,9,16,23,13,14,22,40,21,53,48,51,59,77,84,126,75,282,48,306,254,151,808,586,1074,76,0,0,0,5,1,3,6,3,7,8,4,3,10,12,14,13,23,12,19,25,43,17,52,42,63,57,92,91,114,61,281,45,342,256,132,812,586,1073,81,0,0,0,1,5,4,4,10,11,9,3,5,5,6,18,21,29,26,14,27,51,17,54,47,51,65,84,84,118,66,291,46,316,244,149,782,579,1080,87,0,0,1,2,5,2,10,17,12,9,0,7,2,10,10,12,20,18,21,27,50,17,50,54,42,82,57,84,103,54,285,41,342,265,115,822,582,1085,118,0,0,0,0,4,3,7,7,11,10,5,6,8,11,18,19,30,34,13,34,47,14,62,49,55,83,82,96,101,51,283,45,346,249,152,843,521,985,73,0,0,0,2,5,6,7,9,11,7,5,4,11,19,13,15,33,18,17,42,57,25,46,65,67,94,68,93,117,67,279,53,306,295,132,844,504,993,72,0,0,1,2,3,5,4,16,13,14,2,8,2,18,19,27,37,27,18,29,57,21,47,57,62,87,81,89,111,57,293,49,319,270,142,858,495,990,78,0,0,1,1,5,3,3,13,10,13,5,7,5,14,15,24,30,23,23,24,57,19,72,49,70,70,72,91,124,60,298,52,347,270,147,841,486,980,71,0,0,0,4,5,3,10,9,10,12,5,9,6,23,14,19,33,27,21,34,60,16,47,57,55,82,84,109,117,44,305,45,335,265,146,856,510,954,80,0,0,0,5,4,5,4,6,10,7,4,0,11,14,19,18,32,25,29,32,75,19,58,56,66,81,79,102,133,52,332,44,306,260,152,879,486,917,80,0,0,0,2,5,2,11,11,13,4,4,5,8,12,15,21,34,27,18,44,58,26,72,62,72,90,84,97,137,51,324,54,332,254,143,857,492,881,78,0,0,0,3,4,1,9,9,3,8,9,9,5,12,20,19,37,30,23,38,69,29,64,51,71,95,92,99,133,52,320,51,340,275,152,868,467,856,73,0,0,0,1,2,3,6,14,19,7,3,3,10,24,15,26,38,27,15,34,71,17,62,72,75,86,84,108,128,65,304,41,356,239,139,864,494,876,100,0,1,1,6,3,6,14,19,11,9,7,11,5,8,25,21,35,16,18,39,61,19,65,42,62,91,83,80,105,38,318,50,372,289,135,847,504,884,72,0,0,1,1,4,2,12,10,17,11,5,3,3,21,25,29,34,34,19,38,55,20,55,59,82,96,99,106,133,45,299,71,339,265,157,822,474,882,73,0,0,0,1,2,5,3,17,16,6,5,7,11,7,16,14,30,31,16,45,71,29,50,62,72,78,77,107,132,62,273,47,366,277,161,892,462,877,91,0,0,0,3,2,2,7,10,6,9,9,6,11,15,17,19,33,20,10,30,54,20,68,48,73,84,72,114,131,60,321,60,356,270,159,874,496,840,92,0,0,1,5,5,4,6,8,7,10,4,7,7,16,26,24,33,20,22,36,49,15,53,65,71,79,80,112,127,63,320,49,359,292,141,837,455,900,105,0,0,1,2,5,4,2,15,8,13,8,6,7,19,24,21,30,22,17,35,53,13,52,61,45,93,74,87,120,60,302,41,331,272,131,877,513,931,117,0,0,0,3,5,3,11,23,24,10,5,7,6,8,13,12,40,18,18,40,41,12,45,57,72,86,71,75,125,68,299,55,302,264,154,874,464,973,120,0,1,0,7,3,4,9,13,19,8,5,3,10,17,25,13,19,18,23,33,49,25,41,51,72,74,56,95,112,60,291,58,281,267,145,916,463,993,89,0,0,1,4,9,13,7,8,7,8,6,8,8,18,20,12,34,26,14,31,50,17,45,65,58,68,77,84,110,66,289,54,284,282,164,871,489,1003,162,0,0,1,3,4,4,14,15,24,17,2,6,9,16,15,20,37,20,12,34,49,12,42,50,54,66,62,81,121,56,265,50,292,258,127,878,506,1015,79,0,0,0,5,1,3,3,13,8,6,0,7,11,15,19,11,17,25,16,34,49,16,37,45,69,72,76,79,101,48,303,38,326,254,140,811,619,1043,78,0,0,0,3,0,4,3,8,5,6,8,6,5,13,18,18,32,19,22,39,43,17,39,45,68,77,74,69,118,47,272,47,332,262,139,833,562,1068,96,0,0,1,3,1,5,5,6,6,8,4,7,3,12,9,18,35,15,24,27,57,20,40,53,70,81,89,91,117,46,262,44,298,251,130,817,588,1059,102,0,0,0,5,0,5,4,10,11,12,3,3,7,8,18,15,22,20,17,20,50,21,46,43,71,70,80,91,110,51,239,34,339,258,119,820,614,1058,100,0,0,0,3,5,3,6,10,12,12,5,5,10,6,21,18,28,14,16,33,38,18,45,56,58,71,65,79,109,57,253,47,310,263,129,854,616,1017,97,0,0,1,4,3,1,5,14,7,11,2,5,2,9,6,19,21,18,21,30,37,22,37,64,40,69,53,89,104,66,281,42,355,233,137,771,615,1095,130,0,0,0,5,2,6,10,10,19,20,5,5,4,12,16,17,28,14,12,25,42,16,42,39,57,61,73,84,110,49,261,48,315,254,125,761,643,1028,108,0,2,1,2,1,5,4,12,13,7,6,7,3,6,14,19,20,22,15,22,52,14,50,45,57,67,72,78,119,51,272,45,284,226,127,831,604,1054,124,0,2,2,0,1,2,5,13,17,10,1,6,4,14,11,19,31,14,14,24,30,12,42,41,54,64,74,82,112,68,250,49,308,261,142,775,557,1060,106,0,1,0,1,4,4,10,9,9,7,0,9,6,8,8,16,21,12,18,24,50,14,43,43,56,55,100,87,109,51,261,51,308,217,139,759,562,1041,120,0,0,0,3,3,2,9,7,12,9,6,4,1,10,9,15,26,11,16,22,35,16,26,45,50,60,56,67,74,62,247,50,282,243,123,747,618,1061,116,0,0,0,1,2,3,2,10,15,10,1,2,6,6,10,9,13,8,14,29,26,12,31,42,59,41,57,88,92,58,257,43,304,236,133,707,612,1016,130,0,0,1,2,4,2,6,8,18,21,3,5,5,7,12,15,17,7,7,23,43,9,28,32,44,42,56,68,83,54,225,38,289,181,133,713,594,991,105,0,1,0,0,5,2,6,9,10,23,1,5,3,3,9,9,30,13,7,18,27,12,28,24,49,42,63,75,81,45,226,43,274,217,147,676,571,925,99,0,0,0,0,3,1,3,5,5,16,3,3,6,7,4,13,15,11,3,10,34,16,20,37,46,41,52,66,85,35,201,45,253,201,119,685,497,913,82,0,0,0,0,0,0,0,0,0,2,0,3,5,3,4,5,13,9,5,11,14,3,35,17,21,41,34,67,67,31,184,49,241,167,93,639,515,797) +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() diff -r ebadf9ee2d08 -r 71ed55a3515a test-data/output.qual.r --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/output.qual.r Tue Mar 14 10:22:57 2017 -0400 @@ -0,0 +1,62 @@ +pdf('output.qual.boxplot.pdf') +p0<-rep(c(33,37,38,39,40,41,42,43,44,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71),times=c(119,2,3,2,5,6,8,6,2,3,11,16,6,26,11,13,25,39,7,40,33,33,58,51,116,87,55,256,54,323,263,140,812,654,1119)/1000) +p1<-rep(c(33,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71),times=c(105,2,2,2,4,8,6,21,3,1,1,8,13,13,16,16,14,29,32,18,50,30,57,66,73,97,105,60,253,57,330,270,142,801,630,1069)/1000) +p2<-rep(c(33,35,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71),times=c(109,1,1,3,2,7,11,14,13,2,4,3,8,14,21,27,17,14,26,39,11,37,28,74,64,55,86,106,62,234,56,326,269,147,787,645,1081)/1000) +p3<-rep(c(33,37,38,39,40,41,42,43,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71),times=c(108,1,6,4,2,9,12,7,3,3,9,14,13,24,20,8,24,46,14,43,28,59,67,75,88,107,51,285,56,293,239,139,802,660,1084)/1000) +p4<-rep(c(33,35,37,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71),times=c(97,1,1,3,9,8,11,5,4,2,4,10,16,19,24,7,8,35,43,19,49,29,51,67,51,93,107,43,306,65,345,223,123,789,661,1075)/1000) +p5<-rep(c(33,37,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71),times=c(96,3,2,5,11,6,8,2,7,2,12,17,15,16,12,11,25,31,12,32,36,59,70,69,74,99,56,277,59,343,249,111,845,650,1081)/1000) +p6<-rep(c(33,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71),times=c(86,2,4,2,6,12,8,10,1,7,7,9,11,14,26,14,9,14,53,17,34,41,55,71,76,76,117,62,238,62,339,229,155,798,607,1131)/1000) +p7<-rep(c(33,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71),times=c(76,1,4,4,6,11,9,13,5,5,6,6,17,19,20,17,8,19,45,15,30,33,60,68,58,76,99,59,291,54,349,251,129,818,602,1120)/1000) +p8<-rep(c(33,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71),times=c(74,1,2,3,1,5,6,6,7,7,2,4,11,11,16,24,13,9,24,48,19,33,39,63,67,68,78,104,66,284,62,329,240,147,749,649,1132)/1000) +p9<-rep(c(33,37,38,39,40,41,42,43,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71),times=c(98,1,2,2,6,11,19,10,5,3,8,18,19,24,14,5,18,53,21,41,39,56,79,64,70,93,57,291,42,334,259,143,795,616,1087)/1000) +p10<-rep(c(33,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71),times=c(71,3,3,4,3,5,6,7,2,3,5,13,14,6,17,21,12,27,40,16,34,39,46,64,78,103,103,63,279,37,314,239,118,805,674,1129)/1000) +p11<-rep(c(33,34,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71),times=c(76,1,4,3,3,7,10,11,8,5,6,3,12,13,18,21,16,18,21,46,21,32,41,77,56,77,103,105,54,269,40,320,247,144,796,621,1098)/1000) +p12<-rep(c(33,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71),times=c(87,3,2,1,7,12,8,6,5,13,8,11,9,16,23,13,14,22,40,21,53,48,51,59,77,84,126,75,282,48,306,254,151,808,586,1074)/1000) +p13<-rep(c(33,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71),times=c(76,5,1,3,6,3,7,8,4,3,10,12,14,13,23,12,19,25,43,17,52,42,63,57,92,91,114,61,281,45,342,256,132,812,586,1073)/1000) +p14<-rep(c(33,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71),times=c(81,1,5,4,4,10,11,9,3,5,5,6,18,21,29,26,14,27,51,17,54,47,51,65,84,84,118,66,291,46,316,244,149,782,579,1080)/1000) +p15<-rep(c(33,36,37,38,39,40,41,42,43,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71),times=c(87,1,2,5,2,10,17,12,9,7,2,10,10,12,20,18,21,27,50,17,50,54,42,82,57,84,103,54,285,41,342,265,115,822,582,1085)/1000) +p16<-rep(c(33,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71),times=c(118,4,3,7,7,11,10,5,6,8,11,18,19,30,34,13,34,47,14,62,49,55,83,82,96,101,51,283,45,346,249,152,843,521,985)/1000) +p17<-rep(c(33,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71),times=c(73,2,5,6,7,9,11,7,5,4,11,19,13,15,33,18,17,42,57,25,46,65,67,94,68,93,117,67,279,53,306,295,132,844,504,993)/1000) +p18<-rep(c(33,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71),times=c(72,1,2,3,5,4,16,13,14,2,8,2,18,19,27,37,27,18,29,57,21,47,57,62,87,81,89,111,57,293,49,319,270,142,858,495,990)/1000) +p19<-rep(c(33,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71),times=c(78,1,1,5,3,3,13,10,13,5,7,5,14,15,24,30,23,23,24,57,19,72,49,70,70,72,91,124,60,298,52,347,270,147,841,486,980)/1000) +p20<-rep(c(33,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71),times=c(71,4,5,3,10,9,10,12,5,9,6,23,14,19,33,27,21,34,60,16,47,57,55,82,84,109,117,44,305,45,335,265,146,856,510,954)/1000) +p21<-rep(c(33,37,38,39,40,41,42,43,44,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71),times=c(80,5,4,5,4,6,10,7,4,11,14,19,18,32,25,29,32,75,19,58,56,66,81,79,102,133,52,332,44,306,260,152,879,486,917)/1000) +p22<-rep(c(33,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71),times=c(80,2,5,2,11,11,13,4,4,5,8,12,15,21,34,27,18,44,58,26,72,62,72,90,84,97,137,51,324,54,332,254,143,857,492,881)/1000) +p23<-rep(c(33,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71),times=c(78,3,4,1,9,9,3,8,9,9,5,12,20,19,37,30,23,38,69,29,64,51,71,95,92,99,133,52,320,51,340,275,152,868,467,856)/1000) +p24<-rep(c(33,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71),times=c(73,1,2,3,6,14,19,7,3,3,10,24,15,26,38,27,15,34,71,17,62,72,75,86,84,108,128,65,304,41,356,239,139,864,494,876)/1000) +p25<-rep(c(33,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71),times=c(100,1,1,6,3,6,14,19,11,9,7,11,5,8,25,21,35,16,18,39,61,19,65,42,62,91,83,80,105,38,318,50,372,289,135,847,504,884)/1000) +p26<-rep(c(33,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71),times=c(72,1,1,4,2,12,10,17,11,5,3,3,21,25,29,34,34,19,38,55,20,55,59,82,96,99,106,133,45,299,71,339,265,157,822,474,882)/1000) +p27<-rep(c(33,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71),times=c(73,1,2,5,3,17,16,6,5,7,11,7,16,14,30,31,16,45,71,29,50,62,72,78,77,107,132,62,273,47,366,277,161,892,462,877)/1000) +p28<-rep(c(33,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71),times=c(91,3,2,2,7,10,6,9,9,6,11,15,17,19,33,20,10,30,54,20,68,48,73,84,72,114,131,60,321,60,356,270,159,874,496,840)/1000) +p29<-rep(c(33,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71),times=c(92,1,5,5,4,6,8,7,10,4,7,7,16,26,24,33,20,22,36,49,15,53,65,71,79,80,112,127,63,320,49,359,292,141,837,455,900)/1000) +p30<-rep(c(33,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71),times=c(105,1,2,5,4,2,15,8,13,8,6,7,19,24,21,30,22,17,35,53,13,52,61,45,93,74,87,120,60,302,41,331,272,131,877,513,931)/1000) +p31<-rep(c(33,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71),times=c(117,3,5,3,11,23,24,10,5,7,6,8,13,12,40,18,18,40,41,12,45,57,72,86,71,75,125,68,299,55,302,264,154,874,464,973)/1000) +p32<-rep(c(33,35,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71),times=c(120,1,7,3,4,9,13,19,8,5,3,10,17,25,13,19,18,23,33,49,25,41,51,72,74,56,95,112,60,291,58,281,267,145,916,463,993)/1000) +p33<-rep(c(33,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71),times=c(89,1,4,9,13,7,8,7,8,6,8,8,18,20,12,34,26,14,31,50,17,45,65,58,68,77,84,110,66,289,54,284,282,164,871,489,1003)/1000) +p34<-rep(c(33,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71),times=c(162,1,3,4,4,14,15,24,17,2,6,9,16,15,20,37,20,12,34,49,12,42,50,54,66,62,81,121,56,265,50,292,258,127,878,506,1015)/1000) +p35<-rep(c(33,37,38,39,40,41,42,43,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71),times=c(79,5,1,3,3,13,8,6,7,11,15,19,11,17,25,16,34,49,16,37,45,69,72,76,79,101,48,303,38,326,254,140,811,619,1043)/1000) +p36<-rep(c(33,37,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71),times=c(78,3,4,3,8,5,6,8,6,5,13,18,18,32,19,22,39,43,17,39,45,68,77,74,69,118,47,272,47,332,262,139,833,562,1068)/1000) +p37<-rep(c(33,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71),times=c(96,1,3,1,5,5,6,6,8,4,7,3,12,9,18,35,15,24,27,57,20,40,53,70,81,89,91,117,46,262,44,298,251,130,817,588,1059)/1000) +p38<-rep(c(33,37,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71),times=c(102,5,5,4,10,11,12,3,3,7,8,18,15,22,20,17,20,50,21,46,43,71,70,80,91,110,51,239,34,339,258,119,820,614,1058)/1000) +p39<-rep(c(33,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71),times=c(100,3,5,3,6,10,12,12,5,5,10,6,21,18,28,14,16,33,38,18,45,56,58,71,65,79,109,57,253,47,310,263,129,854,616,1017)/1000) +p40<-rep(c(33,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71),times=c(97,1,4,3,1,5,14,7,11,2,5,2,9,6,19,21,18,21,30,37,22,37,64,40,69,53,89,104,66,281,42,355,233,137,771,615,1095)/1000) +p41<-rep(c(33,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71),times=c(130,5,2,6,10,10,19,20,5,5,4,12,16,17,28,14,12,25,42,16,42,39,57,61,73,84,110,49,261,48,315,254,125,761,643,1028)/1000) +p42<-rep(c(33,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71),times=c(108,2,1,2,1,5,4,12,13,7,6,7,3,6,14,19,20,22,15,22,52,14,50,45,57,67,72,78,119,51,272,45,284,226,127,831,604,1054)/1000) +p43<-rep(c(33,35,36,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71),times=c(124,2,2,1,2,5,13,17,10,1,6,4,14,11,19,31,14,14,24,30,12,42,41,54,64,74,82,112,68,250,49,308,261,142,775,557,1060)/1000) +p44<-rep(c(33,35,37,38,39,40,41,42,43,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71),times=c(106,1,1,4,4,10,9,9,7,9,6,8,8,16,21,12,18,24,50,14,43,43,56,55,100,87,109,51,261,51,308,217,139,759,562,1041)/1000) +p45<-rep(c(33,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71),times=c(120,3,3,2,9,7,12,9,6,4,1,10,9,15,26,11,16,22,35,16,26,45,50,60,56,67,74,62,247,50,282,243,123,747,618,1061)/1000) +p46<-rep(c(33,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71),times=c(116,1,2,3,2,10,15,10,1,2,6,6,10,9,13,8,14,29,26,12,31,42,59,41,57,88,92,58,257,43,304,236,133,707,612,1016)/1000) +p47<-rep(c(33,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71),times=c(130,1,2,4,2,6,8,18,21,3,5,5,7,12,15,17,7,7,23,43,9,28,32,44,42,56,68,83,54,225,38,289,181,133,713,594,991)/1000) +p48<-rep(c(33,35,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71),times=c(105,1,5,2,6,9,10,23,1,5,3,3,9,9,30,13,7,18,27,12,28,24,49,42,63,75,81,45,226,43,274,217,147,676,571,925)/1000) +p49<-rep(c(33,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71),times=c(99,3,1,3,5,5,16,3,3,6,7,4,13,15,11,3,10,34,16,20,37,46,41,52,66,85,35,201,45,253,201,119,685,497,913)/1000) +p50<-rep(c(33,43,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71),times=c(82,2,3,5,3,4,5,13,9,5,11,14,3,35,17,21,41,34,67,67,31,184,49,241,167,93,639,515,797)/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(119,0,0,0,2,3,2,5,6,8,6,2,0,3,11,16,6,26,11,13,25,39,7,40,33,33,58,51,116,87,55,256,54,323,263,140,812,654,1119,105,0,0,0,2,2,2,4,8,6,21,3,1,1,8,13,13,16,16,14,29,32,18,50,30,57,66,73,97,105,60,253,57,330,270,142,801,630,1069,109,0,1,0,1,3,2,7,11,14,13,2,4,3,8,14,21,27,17,14,26,39,11,37,28,74,64,55,86,106,62,234,56,326,269,147,787,645,1081,108,0,0,0,1,6,4,2,9,12,7,0,3,3,9,14,13,24,20,8,24,46,14,43,28,59,67,75,88,107,51,285,56,293,239,139,802,660,1084,97,0,1,0,1,0,3,9,8,11,5,4,2,4,10,16,19,24,7,8,35,43,19,49,29,51,67,51,93,107,43,306,65,345,223,123,789,661,1075,96,0,0,0,3,0,2,5,11,6,8,2,7,2,12,17,15,16,12,11,25,31,12,32,36,59,70,69,74,99,56,277,59,343,249,111,845,650,1081,86,0,0,0,2,4,2,6,12,8,10,1,7,7,9,11,14,26,14,9,14,53,17,34,41,55,71,76,76,117,62,238,62,339,229,155,798,607,1131,76,0,0,0,1,4,4,6,11,9,13,5,5,6,6,17,19,20,17,8,19,45,15,30,33,60,68,58,76,99,59,291,54,349,251,129,818,602,1120,74,0,0,1,2,3,1,5,6,6,7,7,2,4,11,11,16,24,13,9,24,48,19,33,39,63,67,68,78,104,66,284,62,329,240,147,749,649,1132,98,0,0,0,1,2,2,6,11,19,10,0,5,3,8,18,19,24,14,5,18,53,21,41,39,56,79,64,70,93,57,291,42,334,259,143,795,616,1087,71,0,0,0,3,3,4,3,5,6,7,2,3,5,13,14,6,17,21,12,27,40,16,34,39,46,64,78,103,103,63,279,37,314,239,118,805,674,1129,76,1,0,0,4,3,3,7,10,11,8,5,6,3,12,13,18,21,16,18,21,46,21,32,41,77,56,77,103,105,54,269,40,320,247,144,796,621,1098,87,0,0,0,3,2,1,7,12,8,6,5,13,8,11,9,16,23,13,14,22,40,21,53,48,51,59,77,84,126,75,282,48,306,254,151,808,586,1074,76,0,0,0,5,1,3,6,3,7,8,4,3,10,12,14,13,23,12,19,25,43,17,52,42,63,57,92,91,114,61,281,45,342,256,132,812,586,1073,81,0,0,0,1,5,4,4,10,11,9,3,5,5,6,18,21,29,26,14,27,51,17,54,47,51,65,84,84,118,66,291,46,316,244,149,782,579,1080,87,0,0,1,2,5,2,10,17,12,9,0,7,2,10,10,12,20,18,21,27,50,17,50,54,42,82,57,84,103,54,285,41,342,265,115,822,582,1085,118,0,0,0,0,4,3,7,7,11,10,5,6,8,11,18,19,30,34,13,34,47,14,62,49,55,83,82,96,101,51,283,45,346,249,152,843,521,985,73,0,0,0,2,5,6,7,9,11,7,5,4,11,19,13,15,33,18,17,42,57,25,46,65,67,94,68,93,117,67,279,53,306,295,132,844,504,993,72,0,0,1,2,3,5,4,16,13,14,2,8,2,18,19,27,37,27,18,29,57,21,47,57,62,87,81,89,111,57,293,49,319,270,142,858,495,990,78,0,0,1,1,5,3,3,13,10,13,5,7,5,14,15,24,30,23,23,24,57,19,72,49,70,70,72,91,124,60,298,52,347,270,147,841,486,980,71,0,0,0,4,5,3,10,9,10,12,5,9,6,23,14,19,33,27,21,34,60,16,47,57,55,82,84,109,117,44,305,45,335,265,146,856,510,954,80,0,0,0,5,4,5,4,6,10,7,4,0,11,14,19,18,32,25,29,32,75,19,58,56,66,81,79,102,133,52,332,44,306,260,152,879,486,917,80,0,0,0,2,5,2,11,11,13,4,4,5,8,12,15,21,34,27,18,44,58,26,72,62,72,90,84,97,137,51,324,54,332,254,143,857,492,881,78,0,0,0,3,4,1,9,9,3,8,9,9,5,12,20,19,37,30,23,38,69,29,64,51,71,95,92,99,133,52,320,51,340,275,152,868,467,856,73,0,0,0,1,2,3,6,14,19,7,3,3,10,24,15,26,38,27,15,34,71,17,62,72,75,86,84,108,128,65,304,41,356,239,139,864,494,876,100,0,1,1,6,3,6,14,19,11,9,7,11,5,8,25,21,35,16,18,39,61,19,65,42,62,91,83,80,105,38,318,50,372,289,135,847,504,884,72,0,0,1,1,4,2,12,10,17,11,5,3,3,21,25,29,34,34,19,38,55,20,55,59,82,96,99,106,133,45,299,71,339,265,157,822,474,882,73,0,0,0,1,2,5,3,17,16,6,5,7,11,7,16,14,30,31,16,45,71,29,50,62,72,78,77,107,132,62,273,47,366,277,161,892,462,877,91,0,0,0,3,2,2,7,10,6,9,9,6,11,15,17,19,33,20,10,30,54,20,68,48,73,84,72,114,131,60,321,60,356,270,159,874,496,840,92,0,0,1,5,5,4,6,8,7,10,4,7,7,16,26,24,33,20,22,36,49,15,53,65,71,79,80,112,127,63,320,49,359,292,141,837,455,900,105,0,0,1,2,5,4,2,15,8,13,8,6,7,19,24,21,30,22,17,35,53,13,52,61,45,93,74,87,120,60,302,41,331,272,131,877,513,931,117,0,0,0,3,5,3,11,23,24,10,5,7,6,8,13,12,40,18,18,40,41,12,45,57,72,86,71,75,125,68,299,55,302,264,154,874,464,973,120,0,1,0,7,3,4,9,13,19,8,5,3,10,17,25,13,19,18,23,33,49,25,41,51,72,74,56,95,112,60,291,58,281,267,145,916,463,993,89,0,0,1,4,9,13,7,8,7,8,6,8,8,18,20,12,34,26,14,31,50,17,45,65,58,68,77,84,110,66,289,54,284,282,164,871,489,1003,162,0,0,1,3,4,4,14,15,24,17,2,6,9,16,15,20,37,20,12,34,49,12,42,50,54,66,62,81,121,56,265,50,292,258,127,878,506,1015,79,0,0,0,5,1,3,3,13,8,6,0,7,11,15,19,11,17,25,16,34,49,16,37,45,69,72,76,79,101,48,303,38,326,254,140,811,619,1043,78,0,0,0,3,0,4,3,8,5,6,8,6,5,13,18,18,32,19,22,39,43,17,39,45,68,77,74,69,118,47,272,47,332,262,139,833,562,1068,96,0,0,1,3,1,5,5,6,6,8,4,7,3,12,9,18,35,15,24,27,57,20,40,53,70,81,89,91,117,46,262,44,298,251,130,817,588,1059,102,0,0,0,5,0,5,4,10,11,12,3,3,7,8,18,15,22,20,17,20,50,21,46,43,71,70,80,91,110,51,239,34,339,258,119,820,614,1058,100,0,0,0,3,5,3,6,10,12,12,5,5,10,6,21,18,28,14,16,33,38,18,45,56,58,71,65,79,109,57,253,47,310,263,129,854,616,1017,97,0,0,1,4,3,1,5,14,7,11,2,5,2,9,6,19,21,18,21,30,37,22,37,64,40,69,53,89,104,66,281,42,355,233,137,771,615,1095,130,0,0,0,5,2,6,10,10,19,20,5,5,4,12,16,17,28,14,12,25,42,16,42,39,57,61,73,84,110,49,261,48,315,254,125,761,643,1028,108,0,2,1,2,1,5,4,12,13,7,6,7,3,6,14,19,20,22,15,22,52,14,50,45,57,67,72,78,119,51,272,45,284,226,127,831,604,1054,124,0,2,2,0,1,2,5,13,17,10,1,6,4,14,11,19,31,14,14,24,30,12,42,41,54,64,74,82,112,68,250,49,308,261,142,775,557,1060,106,0,1,0,1,4,4,10,9,9,7,0,9,6,8,8,16,21,12,18,24,50,14,43,43,56,55,100,87,109,51,261,51,308,217,139,759,562,1041,120,0,0,0,3,3,2,9,7,12,9,6,4,1,10,9,15,26,11,16,22,35,16,26,45,50,60,56,67,74,62,247,50,282,243,123,747,618,1061,116,0,0,0,1,2,3,2,10,15,10,1,2,6,6,10,9,13,8,14,29,26,12,31,42,59,41,57,88,92,58,257,43,304,236,133,707,612,1016,130,0,0,1,2,4,2,6,8,18,21,3,5,5,7,12,15,17,7,7,23,43,9,28,32,44,42,56,68,83,54,225,38,289,181,133,713,594,991,105,0,1,0,0,5,2,6,9,10,23,1,5,3,3,9,9,30,13,7,18,27,12,28,24,49,42,63,75,81,45,226,43,274,217,147,676,571,925,99,0,0,0,0,3,1,3,5,5,16,3,3,6,7,4,13,15,11,3,10,34,16,20,37,46,41,52,66,85,35,201,45,253,201,119,685,497,913,82,0,0,0,0,0,0,0,0,0,2,0,3,5,3,4,5,13,9,5,11,14,3,35,17,21,41,34,67,67,31,184,49,241,167,93,639,515,797) +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() diff -r ebadf9ee2d08 -r 71ed55a3515a test-data/output.read_distribution.txt --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/output.read_distribution.txt Tue Mar 14 10:22:57 2017 -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 +===================================================================== diff -r ebadf9ee2d08 -r 71ed55a3515a test-data/output.saturation.pdf Binary file test-data/output.saturation.pdf has changed diff -r ebadf9ee2d08 -r 71ed55a3515a test-data/output.seq.DupRate.xls --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/output.seq.DupRate.xls Tue Mar 14 10:22:57 2017 -0400 @@ -0,0 +1,2 @@ +Occurrence UniqReadNumber +1 40 diff -r ebadf9ee2d08 -r 71ed55a3515a test-data/output.splice_events.pdf Binary file test-data/output.splice_events.pdf has changed diff -r ebadf9ee2d08 -r 71ed55a3515a test-data/output.splice_junction.pdf Binary file test-data/output.splice_junction.pdf has changed diff -r ebadf9ee2d08 -r 71ed55a3515a test-data/output.tin.summary.txt --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/output.tin.summary.txt Tue Mar 14 10:22:57 2017 -0400 @@ -0,0 +1,2 @@ +Bam_file TIN(mean) TIN(median) TIN(stdev) +input.bam 8.87096774194 8.87096774194 0.0 diff -r ebadf9ee2d08 -r 71ed55a3515a test-data/output.tin.xls --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/output.tin.xls Tue Mar 14 10:22:57 2017 -0400 @@ -0,0 +1,8 @@ +geneID chrom tx_start tx_end TIN +NR_046018 chr1 11873 14409 0.0 +NR_024540 chr1 14361 29370 8.87096774194 +NR_106918 chr1 17368 17436 0.0 +NR_107062 chr1 17368 17436 0.0 +NR_026818 chr1 34610 36081 0.0 +NR_026820 chr1 34610 36081 0.0 +NM_001005484 chr1 69090 70008 0.0 diff -r ebadf9ee2d08 -r 71ed55a3515a test-data/output2.geneBodyCoverage.curves.pdf Binary file test-data/output2.geneBodyCoverage.curves.pdf has changed diff -r ebadf9ee2d08 -r 71ed55a3515a test-data/output2.geneBodyCoverage.heatMap.pdf Binary file test-data/output2.geneBodyCoverage.heatMap.pdf has changed diff -r ebadf9ee2d08 -r 71ed55a3515a test-data/output2.geneBodyCoverage.r --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/output2.geneBodyCoverage.r Tue Mar 14 10:22:57 2017 -0400 @@ -0,0 +1,21 @@ +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) +pairend_strandspecific_51mer_hg19_chr1_1_100000_bam.1 <- 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) +pairend_strandspecific_51mer_hg19_chr1_1_100000_bam.2 <- 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) +data_matrix <- matrix(c(pairend_strandspecific_51mer_hg19_chr1_1_100000_bam,pairend_strandspecific_51mer_hg19_chr1_1_100000_bam.1,pairend_strandspecific_51mer_hg19_chr1_1_100000_bam.2), byrow=T, ncol=100) +rowLabel <- c("pairend_strandspecific_51mer_hg19_chr1_1_100000_bam","pairend_strandspecific_51mer_hg19_chr1_1_100000_bam.1","pairend_strandspecific_51mer_hg19_chr1_1_100000_bam.2") + + +pdf("output.geneBodyCoverage.heatMap.pdf") +rc <- cm.colors(ncol(data_matrix)) +heatmap(data_matrix, scale=c("none"),keep.dendro=F, labRow = rowLabel ,Colv = NA,Rowv = NA,labCol=NA,col=cm.colors(256),margins = c(6, 8),ColSideColors = rc,cexRow=1,cexCol=1,xlab="Gene body percentile (5'->3')", add.expr=x_axis_expr <- axis(side=1,at=c(1,10,20,30,40,50,60,70,80,90,100),labels=c("1","10","20","30","40","50","60","70","80","90","100"))) +dev.off() + + +pdf("output.geneBodyCoverage.curves.pdf") +x=1:100 +icolor = colorRampPalette(c("#7fc97f","#beaed4","#fdc086","#ffff99","#386cb0","#f0027f"))(3) +plot(x,pairend_strandspecific_51mer_hg19_chr1_1_100000_bam,type='l',xlab="Gene body percentile (5'->3')", ylab="Coverage",lwd=0.8,col=icolor[1]) +lines(x,pairend_strandspecific_51mer_hg19_chr1_1_100000_bam.1,type='l',col=icolor[2]) +lines(x,pairend_strandspecific_51mer_hg19_chr1_1_100000_bam.2,type='l',col=icolor[3]) +legend(0,1,fill=icolor[1:3], legend=c('pairend_strandspecific_51mer_hg19_chr1_1_100000_bam','pairend_strandspecific_51mer_hg19_chr1_1_100000_bam.1','pairend_strandspecific_51mer_hg19_chr1_1_100000_bam.2')) +dev.off() diff -r ebadf9ee2d08 -r 71ed55a3515a test-data/output2.geneBodyCoverage.txt --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/output2.geneBodyCoverage.txt Tue Mar 14 10:22:57 2017 -0400 @@ -0,0 +1,4 @@ +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 +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 +pairend_strandspecific_51mer_hg19_chr1_1_100000_bam.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 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 +pairend_strandspecific_51mer_hg19_chr1_1_100000_bam.2 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 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 diff -r ebadf9ee2d08 -r 71ed55a3515a test-data/pairend_strandspecific_51mer_hg19_chr1_1-100000.R1.fastq --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/pairend_strandspecific_51mer_hg19_chr1_1-100000.R1.fastq Tue Mar 14 10:22:57 2017 -0400 @@ -0,0 +1,80 @@ +@seq.11990047/1 +ACGGCCGACTTGGATCACACTCTTGCAGGGCCATCAGGCACCAAAGGGATT ++ +hgffghhhhfhhchfhghhhhhhhggahh[chffhfhghhhhhhafehfeh +@seq.14614493/1 +AGAGGAGGACGAGGACGACTGGGAATCGTAGGGGGCTCCATGACACCTTCC ++ +hhgghehehghgghhaffffcggchghhhhgfhfhahghhhghhghhhhhe +@seq.24018133/1 +CGGGTGGATTTTCTGTGGGTTTGTTAAGTGGTCAGAAATTCTCAATTTTTT ++ +`aggggecfffa\\^Ua``\af`fffcffffafaffcffffec``fWfffe +@seq.10608403/1 +GTATGGCCAGAGGGCAGGGCCGAGGGGTGTGGGCGGGAGGCCCGGCCTGGC ++ +_dd_]bfggfgcg[egdbdbdXc`cfggaagdgggf^ggfdfggggggggg +@seq.10820209/1 +GTGCTGGCCCCAGTTTTCTAACCAGGTGTTGAATGAACTGGATGGACTCTG ++ +ghhccgfgghhhdhfhhghhhfffdf_hfhhhffhhhgdchhhhhgfhahh +@seq.1537155/1 +GGGAGTGTGCAGAGACTGGAGGGGATGACAGTCACCCTCTGTTTTCTGTGG ++ +aag`hhhhhgghhhhhhfhhchgfacchhhhah]hhdcafhhhhffachhg +@seq.25274725/1 +AGGGTGTGGGGCAAGGCAGTGAGTGAAGAGTTGGGATGAGTGAGTTAGGGC ++ +hhhhdhhhhhhffffcfdffff_fdffffffhchahhhhchgfhhfhfhhh +@seq.26326595/1 +GGGAAGGGGGTGCTTCTGCATGGGAAGCACAGACAGCGCTGCCTCTCCCTT ++ +Wbfdf]ddggggdgggdfdfWggdggf]fffadfffffVfffgfgfdgggg +@seq.28833653/1 +TGGGGCCAGGGGACTATGACACACCACTTGGCTTAGACTGAGGAGCTCTGT ++ +_cffafhdghhhhhhhhhhhghhaffhhhhdhfhhehhhhhhgghfhghhh +@seq.25049090/1 +AGGGCGAGATTGATTGTTAATTGCTAGCATGAACCGCGTGGGCTTCTCAGG ++ +fdffbfdddb[_afffacdggafdbc[fcfcgggfgffccfgagggggfgc +@seq.23476912/1 +GGCCTCTCCACCATGTGCTCCACCTCGTGCTGGACCTTAAGAGATACCAAT ++ +fgggggeggecfefffd^^aY]fdfcaggggfdefdggggggggggggggg +@seq.28059536/1 +GGGATGAGGAGAGGGCAGGAAGGCATTTCCTGGGTAGTGGAGTGCTGTGTT ++ +B_bbea[_V[WZVY`\Pacaaebecd]]fddbaed[decbe]fd`fggggg +@seq.13270875/1 +TGCCCCGAGTTTGTCAAGAATGTCCCAGTAACCAGGGGACACACAGTGAAG ++ +ffffafgagcggggfgfcfffccfcffg]ggggfgcgggggggggggggcf +@seq.2214586/1 +GTGGGAGGGGCTGAAGTGAGAGCCCAACTTGGAAGCTTTTACTCCTGGGAG ++ +gghghghhhhhhhhhhffefafhfhhhhhgghhhghhehhhhehhhhhhhh +@seq.31061198/1 +GAGGAGCTAGGGTTTCTCATAAAACTCCCTGATAGAAGACGACTTTTGATA ++ +cd\WaaaRcaacJdd[dff_f_ffcfddfff_dafffcd[cd\aW\eedcc +@seq.13835843/1 +GGGGAGGCAGAGGTTGCAGTGAGCCGAGATCATGTCACTGCACTCCAGGCT ++ +ggcgafggfgggggggfgggagggagggefgdffffdadeggggggggegg +@seq.13539256/1 +GTGGGGAAACCTAGAATTGCTGTAGAGAAAATGCCCTAGAGCAGCTCTAGA ++ +hfhhchfhhhfhghhhhhhhhghghhhgfhhhhhhhhhghhhhfhhhhfhh +@seq.5556605/1 +GGGATGAGGCCAAATCTTTCTGAATCTGAGATAGCCTCTCAGCCTATGCAT ++ +hfhchhchhhehhhhhhhhdhh_hghchgfhdhhhhhhhhhchhghhhhhh +@seq.32597077/1 +GAGAGACGGGGTTTCACCATGTTGGCCAGGATGGTCTTGATCTCTTGACCT ++ +dcba]fffcdfdWccf\``S_da_cdc_fdafggggfffcfcfcfddafff +@seq.20367385/1 +TTAAGTGCACTCAAATAATGTGATTTTATGAGGCTATAGGAGAAAAAAATT ++ +fdddZ`dc[cdadJ_ddScad[[^\^ddadad__daa^a^\]QY\T^ZZZY diff -r ebadf9ee2d08 -r 71ed55a3515a test-data/pairend_strandspecific_51mer_hg19_chr1_1-100000.R1.fastq.gz Binary file test-data/pairend_strandspecific_51mer_hg19_chr1_1-100000.R1.fastq.gz has changed diff -r ebadf9ee2d08 -r 71ed55a3515a test-data/pairend_strandspecific_51mer_hg19_chr1_1-100000.R2.fastq --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/pairend_strandspecific_51mer_hg19_chr1_1-100000.R2.fastq Tue Mar 14 10:22:57 2017 -0400 @@ -0,0 +1,80 @@ +@seq.11990047/2 +CCCGTGGGCAGAGCAAAGGAAGGGCACAGCGCCAGGCAGTGGTGCAGCTGC ++ +hfadfddd`]f[fa_fh]hhcagffhWhhhh]eeehhhha^hfhhhhghhf +@seq.14614493/2 +TCCCCTCCCAAGGAAGTAGGTCTGAGCAGCTTGTCCTGGCTGTGTCCATGT ++ +hhhhhhhhhehhhhehehhhghhehfhahhdhfdhhfhhhdf]f_ffbdfa +@seq.24018133/2 +TACCACTATTTTATGAACCAAGTATAACAAGATACTTGAAATGGAAACTAT ++ +fLffddhhhhag_gefafffhaefhfffffchffhhggahhhhRhhgccgh +@seq.10608403/2 +GGGACCTGCTGTTCCATCGGCTCTCTCTTGCTGATGGACAAGGGGGCATCA ++ +hefchhhfda`]]b]a^aLa^[Za^WdWb[faff]fd]defQacffdRd]f +@seq.10820209/2 +GCCCGGGGAAAACATGCATCACAGTTCATCTCGAGTCAGCAGGATTTTGAC ++ +ffcddeed]eaTfccfffffceee]ffdcdcee[efdaffffdSfhhdc]d +@seq.1537155/2 +TNNATCAATCAGCAGGNNNCGTGCACTCTCTTTGAGCCACCACAGAAAACA ++ +VBBVT^WZ^^I[]V]YBBBIVS[W[eeKceccaccUfaffff_afghg`gd +@seq.25274725/2 +GGCNCCTCCNTGCCCTNCTNAAAANNCAATCACAGCTCCCTAACAGTCCTG ++ +^UZB]]]Y]B][IS[[B]]BW][XBB\WaadddddhhghgffffaGVV[Se +@seq.26326595/2 +CATGCGTGCCCTGCTCGANATCCAATCACAGCTCCCTAACACTCCTGAATC ++ +^K^K_YT[YVe_eLe[INBTYZUV^S`babhacfhhccghhahghhdaghW +@seq.28833653/2 +CAGCAGCTATTTCCTGNTNACTCAANCAATGGCCCCATTTCCCTGGTGGAA ++ +fLdYfeYdddXbbabSBWBTY[[[]BdeedfffffghhhghdfgLfbfddg +@seq.25049090/2 +CTNCCCTTANTCCGAANGCNGCTCNNCTGATTGGTTAATTTTTGCGTAGCT ++ +VVBNZT[]]BSHZWS[BZHBTQPOBBZUZO]bZ^^hfehffff[fcfd_]g +@seq.23476912/2 +TNACTGATTNCTCTCCACTNTAGANNCTGAGAAGCCCACGCTGTTCATGCT ++ +`Ba_a^]^\Bbab\aa`b`BV]^VBBZV[Z`a^abffYfaa^e^dedbdd] +@seq.28059536/2 +CGTNTGACTCTAGACCNTNNGAAGCCCACGCGGTTCATTCTAGCAAGTAAC ++ +ZXZBY`\`]][dcKcUBVBBWNVV]ghchfdcc]ecccLa`edecf_cfdf +@seq.13270875/2 +TAGATTATCAACAGGGGAGAGATAGCATTTCCTGAAGGCTTCCTAGGTGCC ++ +eghggd_hhhfahhg\K^[[ffafchehg_ffWffhgceghhhhhffLfcY +@seq.2214586/2 +GNNTGCANANATAGANNTTNCCACACTGCCTTGCACAGGAGCACTGCGGGG ++ +VBBSITZBVBTTRHXBBZYBVUUVHH[QV[chhghacKaa_eeeeghgghg +@seq.31061198/2 +GNCGGAAAAAAAAATTNNNNAAAATNCGTCTGCTATCAGGGAGTTTTATGA ++ +VB[NV^\_]_`hfccXBBBBTUT[TBa_aLTRNQQaYcaeaKa^adcS\Vd +@seq.13539256/2 +CCTATTTTTTTTTTTTTTTTTGACACAGGTTCTCTGTCACCCAGCCTGGGG ++ +hhhhhhhhhhhghhhghhghfccWKVVYZRd[_[aZQYZ^``WT`[L^^Q\ +@seq.13835843/2 +TCCATCTTTTTTTTTTTTTTTTTGACACAGGTTCTCTGTCACCCAGCCTGG ++ +ghhhggffhhhhhhhhhhhghhh]fMPLPVW^^WXUZ\WXL[VVJY`\Wbb +@seq.5556605/2 +GCAGCTANGNCCATCNNNTTTGAAANCCAGATTTCGTTTTAAACCAGAGGA ++ +fLfLf]VBTBI]]]]BBB[^WW[]XBdede`eedeffhhehffhhhhahee +@seq.20367385/2 +ATTTGGCAGAGAAGCAAACACCAGTCGGAGAGCTGGGGCCCTCCCAGCCCT ++ +W_\_^W___WdcfceVIW[T^aa\[aaacaQYYSZY`KK````^[GaQZ\Y +@seq.17373919/2 +TTTTTGTTTTTTTTTTTTTTTGAGTCAGAATCTCGCTCTGTTGCCCAGGCT ++ +hgghhhhhhSfffffhgh`h__Wb`ZZZ_]PVPUSVYVQVaWWacaQa^BB diff -r ebadf9ee2d08 -r 71ed55a3515a test-data/pairend_strandspecific_51mer_hg19_chr1_1-100000.bam Binary file test-data/pairend_strandspecific_51mer_hg19_chr1_1-100000.bam has changed diff -r ebadf9ee2d08 -r 71ed55a3515a test-data/pairend_strandspecific_51mer_hg19_chr1_1-100000.bigwig Binary file test-data/pairend_strandspecific_51mer_hg19_chr1_1-100000.bigwig has changed diff -r ebadf9ee2d08 -r 71ed55a3515a test-data/pairend_strandspecific_51mer_hg19_random.bam Binary file test-data/pairend_strandspecific_51mer_hg19_random.bam has changed diff -r ebadf9ee2d08 -r 71ed55a3515a test-data/testwig.Forward.wig --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/testwig.Forward.wig Tue Mar 14 10:22:57 2017 -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 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10:22:57 2017 -0400 @@ -0,0 +1,144 @@ + + + evaluates RNA integrity at a transcript level + + + + rseqc_macros.xml + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + diff -r ebadf9ee2d08 -r 71ed55a3515a tool_dependencies.xml --- a/tool_dependencies.xml Thu Jul 18 11:01:08 2013 -0500 +++ /dev/null Thu Jan 01 00:00:00 1970 +0000 @@ -1,22 +0,0 @@ - - - - - - - https://sourceforge.net/projects/rseqc/files/RSeQC-2.3.7.tar.gz - python setup.py install --root $INSTALL_DIR/lib/rseqc - - $INSTALL_DIR/lib/rseqc/usr/local/lib/python2.7/site-packages - - - $INSTALL_DIR/lib/rseqc/usr/local/bin - - - - - RSeQC version 2.3.7, documentation available at http://dldcc-web.brc.bcm.edu/lilab/liguow/CGI/rseqc/_build/html/index.html#. - - - -