Mercurial > repos > nilesh > rseqc
changeset 50:f242ee103277 draft
planemo upload for repository https://github.com/lparsons/galaxy_tools/tree/master/tools/rseqc commit 91ad241aa3f34b70649d13a5f18611da7577a5ee
| author | lparsons |
|---|---|
| date | Tue, 03 May 2016 16:36:57 -0400 |
| parents | 6b33e31bda10 |
| children | 09846d5169fa |
| files | README.txt RPKM_saturation.xml geneBody_coverage.xml junction_saturation.xml read_quality.xml test-data/hg19.HouseKeepingGenes_30.bed test-data/output.eRPKM.xls test-data/output.geneBodyCoverage.curves.pdf test-data/output.qual.r test-data/output.rawCount.xls test-data/output.saturation.r test-data/output2.geneBodyCoverage.curves.pdf test-data/output2.geneBodyCoverage.heatMap.pdf test-data/output2.geneBodyCoverage.r test-data/output2.geneBodyCoverage.txt test-data/pairend_strandspecific_51mer_hg19_chr1_1-100000.bigwig test-data/pairend_strandspecific_51mer_hg19_random.bam tool_dependencies.xml |
| diffstat | 18 files changed, 216 insertions(+), 279 deletions(-) [+] |
line wrap: on
line diff
--- a/README.txt Thu Jul 16 17:43:43 2015 -0400 +++ b/README.txt Tue May 03 16:36:57 2016 -0400 @@ -16,9 +16,8 @@ The RSeQC package and associated documentation can be found at: http://rseqc.sourceforge.net/ -The galaxy wrapper code was written by +The galaxy wrapper code was written by Nilesh Kavthekar, School of Engineering and Applied Sciences, University of Pennsylvania, Class of 2016 -Modified by +Modified by Lance Parsons, Lewis-Sigler Institute for Integrative Genomics, Princeton University, Bjorn Gruning, University of Freiburg, bjoern.gruening@gmail.com -The development of the wrapper code is housed on BitBucket at: https://bitbucket.org/lance_parsons/rseqc_galaxy_wrapper
--- a/RPKM_saturation.xml Thu Jul 16 17:43:43 2015 -0400 +++ b/RPKM_saturation.xml Tue May 03 16:36:57 2016 -0400 @@ -77,17 +77,30 @@ <data format="pdf" name="outputpdf" from_work_dir="output.saturation.pdf" label="${tool.name} on ${on_string} (PDF)"/> </outputs> - <!-- Unable to succefully run this script with test data <tests> <test> - <param name="input" value="pairend_strandspecific_51mer_hg19_chr1_1-100000.bam"/> - <param name="refgene" value="hg19_RefSeq_chr1_1-100000.bed"/> - <output name="outputxls" file="output.eRPKM.xls"/> - <output name="outputrawxls" file="output.rawCount.xls"/> - <output name="outputr" file="output.saturation.r"/> + <param name="input" value="pairend_strandspecific_51mer_hg19_random.bam"/> + <param name="refgene" value="hg19.HouseKeepingGenes_30.bed"/> + <output name="outputxls"> + <assert_contents> + <has_n_columns n="26" /> + <has_line_matching expression="chr1\t16174358\t16266950\tNM_015001.*" /> + </assert_contents> + </output> + <output name="outputrawxls"> + <assert_contents> + <has_n_columns n="26" /> + <has_line_matching expression="chr1\t16174358\t16266950\tNM_015001.*" /> + </assert_contents> + </output> + <output name="outputr"> + <assert_contents> + <has_text text="pdf('output.saturation.pdf')" /> + <has_line_matching expression="S5=c\(\d+\.\d+\)" /> + </assert_contents> + </output> </test> </tests> - --> <help><![CDATA[ RPKM_saturation.py
--- a/geneBody_coverage.xml Thu Jul 16 17:43:43 2015 -0400 +++ b/geneBody_coverage.xml Tue May 03 16:36:57 2016 -0400 @@ -1,144 +1,130 @@ -<tool id="rseqc_geneBody_coverage" name="Gene Body Converage (BAM)" version="2.4galaxy1"> - <description> - Read coverage over gene body. - </description> +<tool id="rseqc_geneBody_coverage" name="Gene Body Converage (BAM)" version="2.4galaxy2"> + <description> + Read coverage over gene body. + </description> + + <macros> + <import>rseqc_macros.xml</import> + </macros> - <macros> - <import>rseqc_macros.xml</import> - </macros> + <requirements> + <expand macro="requirement_package_r" /> + <expand macro="requirement_package_numpy" /> + <expand macro="requirement_package_rseqc" /> + </requirements> - <requirements> - <expand macro="requirement_package_r" /> - <expand macro="requirement_package_numpy" /> - <expand macro="requirement_package_rseqc" /> - </requirements> + <expand macro="stdio" /> - <expand macro="stdio" /> + <version_command><![CDATA[geneBody_coverage.py --version]]></version_command> - <version_command><![CDATA[geneBody_coverage.py --version]]></version_command> + <command><![CDATA[ + #for $i, $input in enumerate($inputs): + #set $index = $i+1 + #set $safename = ''.join(c in '_0123456789abcdefghijklmnopqrstuvwxyzABCDEFGHIJKLMNOPQRSTUVWXYZ' and c or '_' for c in $input.display_name) + #set $fname = 'd' + str($index) + '_' + str($safename) + ".bam" + ln -s '$input' '${fname}' && + ln -s '$input.metadata.bam_index' '${fname}.bai' && + echo '${fname}' >> input_list.txt && + #end for + geneBody_coverage.py -i input_list.txt -r $refgene --minimum_length $minimum_length -o output + ]]> + </command> - <command><![CDATA[ - #set $safename = ''.join(c in '_0123456789abcdefghijklmnopqrstuvwxyzABCDEFGHIJKLMNOPQRSTUVWXYZ' and c or '_' for c in $input.display_name) - #set $fname = "d1_" + str($safename) + ".bam" - ln -s '${input}' '${fname}' && - ln -s '${input.metadata.bam_index}' '${fname}.bai' && - echo '${fname}' > input_list.txt && - #for $i, $additional_input in enumerate($additionalinputs): - #set $index = $i+2 - #set $safename = ''.join(c in '_0123456789abcdefghijklmnopqrstuvwxyzABCDEFGHIJKLMNOPQRSTUVWXYZ' and c or '_' for c in $additional_input.file.display_name) - #set $fname = 'd' + str($index) + '_' + str($safename) + ".bam" - ln -s '$additional_input.file' '${fname}' && - ln -s '$additional_input.file.metadata.bam_index' '${fname}.bai' && - echo '${fname}' >> input_list.txt && - #end for - geneBody_coverage.py -i input_list.txt -r $refgene --minimum_length $minimum_length -o output - ]]> - </command> + <inputs> + <param name="inputs" type="data" label="Input .bam File(s)" format="bam" help="(--input-file)" multiple="true"/> + <param name="refgene" type="data" format="bed" label="reference gene model" help="(--refgene)"/> + <param name="minimum_length" type="integer" value="100" label="Minimum mRNA length in bp (default: 100)" help="mRNA that are shorter than this value will be skipped (--minimum_length)." /> + </inputs> - <inputs> - <param name="input" type="data" label="Input .bam File" format="bam" help="(--input-file)"/> - <repeat name="additionalinputs" title="Additional input .bam files"> - <param name="file" type="data" label="Additional input .bam file" format="bam" /> - </repeat> - <param name="refgene" type="data" format="bed" label="reference gene model" help="(--refgene)"/> - <param name="minimum_length" type="integer" value="100" label="Minimum mRNA length in bp (default: 100)" help="mRNA that are shorter than this value will be skipped (--minimum_length)." /> - </inputs> - - <outputs> - <data name="outputcurvespdf" format="pdf" from_work_dir="output.geneBodyCoverage.curves.pdf" label="${tool.name} on ${on_string} (Curves PDF)" /> - <data name="outputheatmappdf" format="pdf" from_work_dir="output.geneBodyCoverage.heatMap.pdf" label="${tool.name} on ${on_string} (HeatMap PDF)"> - <filter>len(additionalinputs) >= 2</filter> - </data> - <data name="outputr" format="txt" from_work_dir="output.geneBodyCoverage.r" label="${tool.name} on ${on_string} (R Script)" /> - <data name="outputtxt" format="txt" from_work_dir="output.geneBodyCoverage.txt" label="${tool.name} on ${on_string} (Text)" /> - </outputs> + <outputs> + <data name="outputcurvespdf" format="pdf" from_work_dir="output.geneBodyCoverage.curves.pdf" label="${tool.name} on ${on_string} (Curves PDF)" /> + <data name="outputheatmappdf" format="pdf" from_work_dir="output.geneBodyCoverage.heatMap.pdf" label="${tool.name} on ${on_string} (HeatMap PDF)"> + <filter>len(inputs) >= 3</filter> + </data> + <data name="outputr" format="txt" from_work_dir="output.geneBodyCoverage.r" label="${tool.name} on ${on_string} (R Script)" /> + <data name="outputtxt" format="txt" from_work_dir="output.geneBodyCoverage.txt" label="${tool.name} on ${on_string} (Text)" /> + </outputs> - <tests> - <test> - <param name="input" value="pairend_strandspecific_51mer_hg19_chr1_1-100000.bam"/> - <param name="refgene" value="hg19_RefSeq_chr1_1-100000.bed"/> - <output name="outputcurvespdf" file="output.geneBodyCoverage.curves.pdf"/> - <output name="outputr" file="output.geneBodyCoverage.r"/> - <output name="outputtxt" file="output.geneBodyCoverage.txt"/> - </test> - <test> - <param name="input" value="pairend_strandspecific_51mer_hg19_chr1_1-100000.bam"/> - <param name="file_0" value="pairend_strandspecific_51mer_hg19_chr1_1-100000.bam"/> - <param name="file_1" value="pairend_strandspecific_51mer_hg19_chr1_1-100000.bam"/> - <param name="refgene" value="hg19_RefSeq_chr1_1-100000.bed"/> - <output name="outputcurvespdf" file="output2.geneBodyCoverage.curves.pdf"/> - <output name="outputcurvespdf" file="output2.geneBodyCoverage.heatMap.pdf"/> - <output name="outputr" file="output2.geneBodycoverage.r"/> - <output name="outputtxt" file="output2.geneBodyCoverage.txt"/> - </test> + <!-- PDF Files contain R version, must avoid checking for diff --> + <tests> + <test> + <param name="inputs" value="pairend_strandspecific_51mer_hg19_chr1_1-100000.bam"/> + <param name="refgene" value="hg19_RefSeq_chr1_1-100000.bed"/> + <!-- <output name="outputcurvespdf" file="output.geneBodyCoverage.curves.pdf"/> --> + <output name="outputr" file="output.geneBodyCoverage.r"/> + <output name="outputtxt" file="output.geneBodyCoverage.txt"/> + </test> + <test> + <param name="inputs" value="pairend_strandspecific_51mer_hg19_chr1_1-100000.bam,pairend_strandspecific_51mer_hg19_chr1_1-100000.bam,pairend_strandspecific_51mer_hg19_chr1_1-100000.bam"/> + <param name="refgene" value="hg19_RefSeq_chr1_1-100000.bed"/> + <!-- <output name="outputcurvespdf" file="output2.geneBodyCoverage.curves.pdf"/> --> + <!-- <output name="outputheatmappdf" file="output2.geneBodyCoverage.heatMap.pdf"/> --> + <output name="outputr" file="output2.geneBodycoverage.r"/> + <output name="outputtxt" file="output2.geneBodyCoverage.txt"/> + </test> - </tests> + </tests> - <help><![CDATA[ -geneBody_coverage.py -++++++++++++++++++++ + <help><![CDATA[ + ## geneBody_coverage.py -Read coverage over gene body. This module is used to check if read coverage is uniform and if there is any 5\'/3\' bias. This module scales all transcripts to 100 nt and calculates the number of reads covering each nucleotide position. Finally, it generates plots illustrating the coverage profile along the gene body. + Read coverage over gene body. This module is used to check if read coverage is uniform and if there is any 5\'/3\' bias. This module scales all transcripts to 100 nt and calculates the number of reads covering each nucleotide position. Finally, it generates plots illustrating the coverage profile along the gene body. -If 3 or more BAM files were provided. This program generates a lineGraph and a heatmap. If fewer than 3 BAM files were provided, only lineGraph is generated. See below for examples. + If 3 or more BAM files were provided. This program generates a lineGraph and a heatmap. If fewer than 3 BAM files were provided, only lineGraph is generated. See below for examples. -When heatmap is generated, samples are ranked by the "skewness" of the coverage: Sample with best (worst) coverage will be displayed at the top (bottom) of the heatmap. -Coverage skewness was measured by `Pearson’s skewness coefficients <http://en.wikipedia.org/wiki/Skewness#Pearson.27s_skewness_coefficients>`_ + When heatmap is generated, samples are ranked by the "skewness" of the coverage: Sample with best (worst) coverage will be displayed at the top (bottom) of the heatmap. + Coverage skewness was measured by `Pearson’s skewness coefficients <http://en.wikipedia.org/wiki/Skewness#Pearson.27s_skewness_coefficients>`_ .. image:: http://rseqc.sourceforge.net/_images/geneBody_workflow.png - :width: 800 px - :scale: 80 % + :width: 800 px + :scale: 80 % -Inputs -++++++++++++++ + ## Inputs + + Input BAM/SAM file + Alignment file in BAM/SAM format. -Input BAM/SAM file - Alignment file in BAM/SAM format. + Reference gene model + Gene Model in BED format. -Reference gene model - Gene Model in BED format. - -Minimum mRNA length + Minimum mRNA length Minimum mRNA length (bp). mRNA that are shorter than this value will be skipped (default is 100). -Outputs -++++++++++++++ -Text + ## Outputs + + Text Table that includes the data used to generate the plots -R Script + R Script R script file that reads the data and generates the plot -PDF + PDF The final plot, in PDF format -Example plots: + Example plots: .. image:: http://rseqc.sourceforge.net/_images/Aug_26.geneBodyCoverage.curves.png - :height: 600 px - :width: 600 px - :scale: 80 % + :height: 600 px + :width: 600 px + :scale: 80 % .. image:: http://rseqc.sourceforge.net/_images/Aug_26.geneBodyCoverage.heatMap.png - :height: 600 px - :width: 600 px - :scale: 80 % + :height: 600 px + :width: 600 px + :scale: 80 % ------ + ## About RSeQC -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 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. -The RSeQC package is licensed under the GNU GPL v3 license. - -.. image:: http://rseqc.sourceforge.net/_static/logo.png + .. image:: http://rseqc.sourceforge.net/_static/logo.png -.. _RSeQC: http://rseqc.sourceforge.net/ -]]> - </help> + .. _RSeQC: http://rseqc.sourceforge.net/ + ]]> + </help> - <expand macro="citations" /> + <expand macro="citations" /> </tool>
--- a/junction_saturation.xml Thu Jul 16 17:43:43 2015 -0400 +++ b/junction_saturation.xml Tue May 03 16:36:57 2016 -0400 @@ -50,6 +50,7 @@ <validator type="in_range" min="0" max="100" /> </param> </when> + <when value="false"/> </conditional> </inputs>
--- a/read_quality.xml Thu Jul 16 17:43:43 2015 -0400 +++ b/read_quality.xml Tue May 03 16:36:57 2016 -0400 @@ -36,16 +36,12 @@ <data format="pdf" name="outputboxpdf" from_work_dir="output.qual.boxplot.pdf" label="${tool.name} on ${on_string} (Boxplot PDF)" /> </outputs> - <!-- Unable to succefully run this script with test data <tests> <test> - <param name="input" value="pairend_strandspecific_51mer_hg19_chr1_1-100000.bigwig"/> + <param name="input" value="pairend_strandspecific_51mer_hg19_random.bam"/> <output name="outputr" file="output.qual.r"/> - <output name="outputheatpdf" file="output.qual.heatmap.pdf"/> - <output name="outputboxpdf" file="output.qual.boxplot.pdf"/> </test> </tests> - --> <help><![CDATA[ read_quality.py
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/hg19.HouseKeepingGenes_30.bed Tue May 03 16:36:57 2016 -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,
--- a/test-data/output.eRPKM.xls Thu Jul 16 17:43:43 2015 -0400 +++ /dev/null Thu Jan 01 00:00:00 1970 +0000 @@ -1,8 +0,0 @@ -#chr start end name score strand 5% 10% 15% 20% 25% 30% 35% 40% 45% 50% 55% 60% 65% 70% 75% 80% 85% 90% 95% 100% -chr1 17368 17436 NR_106918 0 - 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 -chr1 17368 17436 NR_107062 0 - 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 -chr1 34610 36081 NR_026818 0 - 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 -chr1 69090 70008 NM_001005484 0 + 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 -chr1 14361 29370 NR_024540 0 - 256950.511332 128475.255666 85650.1704438 128475.255666 102780.204533 85650.1704438 73414.431809 64237.6278329 57100.1136292 51390.1022663 46718.2747875 64237.6278329 59296.2718457 55060.8238568 51390.1022663 48178.2208747 45344.207882 57100.1136292 54094.8444908 51390.1022663 -chr1 34610 36081 NR_026820 0 - 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 -chr1 11873 14409 NR_046018 0 + 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 30572.0644704 27514.8580233 25013.5072939 22929.0483528 42330.5508051 39306.9400333 36686.4773644 34393.5725292 32370.4212039 45858.0967056 43444.5126684 41272.287035
--- a/test-data/output.qual.r Thu Jul 16 17:43:43 2015 -0400 +++ b/test-data/output.qual.r Tue May 03 16:36:57 2016 -0400 @@ -1,61 +1,61 @@ pdf('output.qual.boxplot.pdf') -p0<-rep(c(33,43,45,51,54,58,59,60,61,62,63,64,66,67,69,70,71),times=c(1,2,1,3,1,1,1,1,1,3,1,1,1,2,5,5,10)/1000) -p1<-rep(c(43,45,51,56,57,58,60,61,62,63,64,65,66,67,68,69,70,71),times=c(1,1,1,1,1,1,1,2,1,1,2,1,3,3,1,8,5,6)/1000) -p2<-rep(c(43,49,51,53,54,56,58,59,60,61,64,65,66,67,69,70,71),times=c(1,1,1,1,2,1,1,1,2,1,1,2,2,2,7,6,8)/1000) -p3<-rep(c(33,39,53,54,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71),times=c(1,1,1,1,1,1,1,1,1,1,2,1,1,1,4,4,1,5,5,6)/1000) -p4<-rep(c(33,55,58,59,60,61,62,64,65,66,67,68,69,70,71),times=c(1,1,1,1,5,1,1,4,1,2,5,2,3,3,9)/1000) -p5<-rep(c(33,53,58,60,61,62,64,65,67,68,69,70,71),times=c(1,1,1,2,1,4,3,2,2,3,5,4,11)/1000) -p6<-rep(c(33,40,54,56,58,60,64,66,67,68,69,70,71),times=c(3,2,1,1,1,1,2,2,4,4,4,2,13)/1000) -p7<-rep(c(41,42,43,49,50,51,57,58,62,64,65,66,67,68,69,70,71),times=c(1,1,1,1,2,1,1,1,2,1,1,2,7,1,2,3,12)/1000) -p8<-rep(c(33,39,53,56,58,59,60,62,63,66,67,68,69,70,71),times=c(1,1,1,1,1,1,2,1,2,4,3,2,3,5,12)/1000) -p9<-rep(c(33,40,50,52,53,57,58,60,64,65,66,67,68,69,70,71),times=c(1,1,1,1,1,1,3,1,4,1,1,3,2,5,4,10)/1000) -p10<-rep(c(33,40,53,54,55,58,60,63,64,66,67,68,69,70,71),times=c(2,1,1,2,1,1,2,1,4,1,1,1,7,6,9)/1000) -p11<-rep(c(45,50,51,52,53,56,57,58,60,62,63,64,65,66,67,68,69,70,71),times=c(1,2,1,1,1,1,1,1,3,2,1,1,1,3,1,1,3,5,10)/1000) -p12<-rep(c(33,41,52,53,54,58,59,60,64,65,66,67,69,70,71),times=c(3,1,1,2,1,1,1,3,1,2,3,2,5,6,8)/1000) -p13<-rep(c(33,40,51,53,55,56,58,59,60,63,64,65,66,67,68,69,70,71),times=c(4,1,1,1,1,1,1,1,1,1,3,1,2,2,2,3,5,9)/1000) -p14<-rep(c(33,39,54,56,57,59,60,61,62,63,65,66,67,68,69,70,71),times=c(4,1,3,1,1,1,1,2,1,1,1,2,2,1,7,2,9)/1000) -p15<-rep(c(33,39,40,42,45,50,52,53,57,58,59,60,61,64,66,68,69,70,71),times=c(2,2,1,1,1,1,1,1,1,3,1,1,1,3,1,1,4,5,9)/1000) -p16<-rep(c(33,47,51,52,53,58,59,60,61,64,67,69,70,71),times=c(4,1,1,1,2,3,1,1,2,3,3,7,2,9)/1000) -p17<-rep(c(33,48,50,51,53,54,55,56,58,60,61,63,64,65,66,67,69,70,71),times=c(1,1,1,1,2,1,1,1,2,2,3,2,2,1,2,1,7,2,7)/1000) -p18<-rep(c(33,43,48,51,53,58,59,60,61,63,64,66,67,68,69,70,71),times=c(2,1,1,1,2,2,1,2,2,3,1,1,3,1,7,2,8)/1000) -p19<-rep(c(33,44,47,50,51,52,54,58,59,61,62,64,65,67,69,70,71),times=c(2,1,1,2,1,1,2,1,1,1,1,3,1,1,8,4,9)/1000) -p20<-rep(c(33,46,47,51,54,56,58,59,61,62,63,64,66,67,69,70,71),times=c(1,1,1,1,2,1,1,2,1,2,1,5,5,3,5,2,6)/1000) -p21<-rep(c(33,43,54,55,57,58,62,64,65,66,67,68,69,70,71),times=c(1,1,1,1,1,5,2,3,1,3,2,4,5,4,6)/1000) -p22<-rep(c(33,47,51,53,54,57,58,60,62,63,64,65,66,68,69,70,71),times=c(1,1,1,1,1,1,1,1,2,1,5,1,4,3,5,5,6)/1000) -p23<-rep(c(33,42,53,54,55,57,58,62,63,64,65,66,67,68,69,70,71),times=c(1,1,1,1,1,1,2,1,1,5,2,2,3,2,9,3,4)/1000) -p24<-rep(c(33,42,52,54,57,60,61,63,64,65,66,67,69,70,71),times=c(1,1,1,1,1,2,1,1,5,1,6,4,5,4,6)/1000) -p25<-rep(c(33,53,54,57,61,62,63,64,66,67,68,69,70,71),times=c(1,1,1,2,1,1,1,2,4,5,2,9,5,5)/1000) -p26<-rep(c(46,53,54,57,58,60,61,62,64,66,67,68,69,70,71),times=c(1,1,1,1,1,1,1,2,5,8,4,1,5,3,5)/1000) -p27<-rep(c(42,43,48,54,56,57,60,61,62,66,67,68,69,70,71),times=c(1,1,1,2,1,1,4,1,2,1,4,1,5,6,9)/1000) -p28<-rep(c(51,55,56,57,60,62,64,65,66,67,68,69,70,71),times=c(1,1,1,1,1,2,4,2,2,2,4,10,1,8)/1000) -p29<-rep(c(49,52,56,57,58,60,63,64,65,66,67,69,70,71),times=c(2,1,1,1,2,1,1,3,1,3,6,8,2,8)/1000) -p30<-rep(c(45,47,50,57,61,62,64,66,67,68,69,70,71),times=c(1,1,1,1,1,1,2,6,3,2,8,5,8)/1000) -p31<-rep(c(48,52,53,54,57,58,59,60,61,62,64,65,66,67,68,69,70,71),times=c(1,1,1,1,1,1,1,1,2,1,4,1,2,3,3,7,3,6)/1000) -p32<-rep(c(43,47,48,54,56,62,64,66,67,68,69,70,71),times=c(1,1,1,1,2,1,2,1,5,3,10,5,7)/1000) -p33<-rep(c(52,55,58,60,61,63,64,68,69,70,71),times=c(1,1,2,1,1,1,5,4,11,5,8)/1000) -p34<-rep(c(42,43,50,56,59,60,63,64,67,68,69,70,71),times=c(1,1,1,1,1,1,1,3,1,4,9,5,11)/1000) -p35<-rep(c(42,53,57,58,60,64,66,68,69,70,71),times=c(1,1,1,2,1,3,2,2,12,7,8)/1000) -p36<-rep(c(48,53,56,61,63,64,66,67,69,70,71),times=c(2,1,1,2,1,1,2,6,7,3,14)/1000) -p37<-rep(c(53,56,60,63,64,66,68,69,70,71),times=c(2,2,1,1,3,2,6,7,8,8)/1000) -p38<-rep(c(41,48,53,57,59,61,62,63,64,66,67,68,69,70,71),times=c(1,1,1,1,1,1,1,1,3,3,4,1,4,6,11)/1000) -p39<-rep(c(38,42,51,53,56,58,61,63,64,65,66,67,68,69,70,71),times=c(1,1,1,1,1,1,1,1,2,1,4,2,2,7,3,11)/1000) -p40<-rep(c(53,58,61,62,63,64,66,67,68,69,70,71),times=c(1,1,3,1,2,2,1,2,2,9,4,12)/1000) -p41<-rep(c(48,53,54,57,58,59,60,61,63,64,66,67,68,69,70,71),times=c(1,1,1,1,1,1,2,1,1,1,3,3,1,5,7,9)/1000) -p42<-rep(c(38,49,54,58,59,64,65,66,67,68,69,70,71),times=c(1,1,3,1,1,3,1,2,1,1,7,7,9)/1000) -p43<-rep(c(50,51,62,63,64,65,66,67,69,70,71),times=c(2,1,1,1,3,1,2,4,3,8,12)/1000) -p44<-rep(c(48,54,63,64,65,66,67,68,69,70,71),times=c(1,2,4,1,1,2,2,1,7,8,8)/1000) -p45<-rep(c(50,57,58,59,60,62,64,67,69,70,71),times=c(1,1,1,1,1,1,1,1,10,8,7)/1000) -p46<-rep(c(43,48,54,59,60,64,65,66,67,68,69,70,71),times=c(2,1,1,2,1,2,1,1,4,2,1,6,8)/1000) -p47<-rep(c(49,53,56,61,64,66,67,69,70,71),times=c(1,1,1,1,2,2,2,3,10,7)/1000) -p48<-rep(c(61,64,66,67,68,69,70,71),times=c(2,1,2,2,2,6,5,7)/1000) -p49<-rep(c(33,56,60,64,66,68,69,70,71),times=c(1,1,2,1,1,2,2,5,10)/1000) -p50<-rep(c(33,66,67,68,69,70,71),times=c(1,1,1,2,4,5,7)/1000) +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(1,0,0,0,0,0,0,0,0,0,2,0,1,0,0,0,0,0,3,0,0,1,0,0,0,1,1,1,1,3,1,1,0,1,2,0,5,5,10,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,1,0,0,0,0,1,1,1,0,1,2,1,1,2,1,3,3,1,8,5,6,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,1,0,1,2,0,1,0,1,1,2,1,0,0,1,2,2,2,0,7,6,8,1,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,0,1,1,1,1,1,1,2,1,1,1,4,4,1,5,5,6,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,1,1,5,1,1,0,4,1,2,5,2,3,3,9,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,1,0,2,1,4,0,3,2,0,2,3,5,4,11,3,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,1,0,1,0,0,0,2,0,2,4,4,4,2,13,0,0,0,0,0,0,0,0,1,1,1,0,0,0,0,0,1,2,1,0,0,0,0,0,1,1,0,0,0,2,0,1,1,2,7,1,2,3,12,1,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,1,1,2,0,1,2,0,0,4,3,2,3,5,12,1,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,1,0,1,1,0,0,0,1,3,0,1,0,0,0,4,1,1,3,2,5,4,10,2,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,2,1,0,0,1,0,2,0,0,1,4,0,1,1,1,7,6,9,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,2,1,1,1,0,0,1,1,1,0,3,0,2,1,1,1,3,1,1,3,5,10,3,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,1,2,1,0,0,0,1,1,3,0,0,0,1,2,3,2,0,5,6,8,4,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,1,0,1,0,1,1,0,1,1,1,0,0,1,3,1,2,2,2,3,5,9,4,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,1,1,0,1,1,2,1,1,0,1,2,2,1,7,2,9,2,0,0,0,0,0,2,1,0,1,0,0,1,0,0,0,0,1,0,1,1,0,0,0,1,3,1,1,1,0,0,3,0,1,0,1,4,5,9,4,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,1,1,2,0,0,0,0,3,1,1,2,0,0,3,0,0,3,0,7,2,9,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,1,0,2,1,1,1,0,2,0,2,3,0,2,2,1,2,1,0,7,2,7,2,0,0,0,0,0,0,0,0,0,1,0,0,0,0,1,0,0,1,0,2,0,0,0,0,2,1,2,2,0,3,1,0,1,3,1,7,2,8,2,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,0,2,1,1,0,2,0,0,0,1,1,0,1,1,0,3,1,0,1,0,8,4,9,1,0,0,0,0,0,0,0,0,0,0,0,0,1,1,0,0,0,1,0,0,2,0,1,0,1,2,0,1,2,1,5,0,5,3,0,5,2,6,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,1,1,0,1,5,0,0,0,2,0,3,1,3,2,4,5,4,6,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,1,0,1,1,0,0,1,1,0,1,0,2,1,5,1,4,0,3,5,5,6,1,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,1,1,1,0,1,2,0,0,0,1,1,5,2,2,3,2,9,3,4,1,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,1,0,1,0,0,1,0,0,2,1,0,1,5,1,6,4,0,5,4,6,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,0,0,2,0,0,0,1,1,1,2,0,4,5,2,9,5,5,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,1,1,0,0,1,1,0,1,1,2,0,5,0,8,4,1,5,3,5,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,1,0,0,0,0,0,2,0,1,1,0,0,4,1,2,0,0,0,1,4,1,5,6,9,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,1,1,1,0,0,1,0,2,0,4,2,2,2,4,10,1,8,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,1,0,0,0,1,1,2,0,1,0,0,1,3,1,3,6,0,8,2,8,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,1,0,0,0,0,0,0,1,0,0,0,1,1,0,2,0,6,3,2,8,5,8,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,1,1,1,0,0,1,1,1,1,2,1,0,4,1,2,3,3,7,3,6,0,0,0,0,0,0,0,0,0,0,1,0,0,0,1,1,0,0,0,0,0,1,0,2,0,0,0,0,0,1,0,2,0,1,5,3,10,5,7,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,0,2,0,1,1,0,1,5,0,0,0,4,11,5,8,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,1,0,0,0,0,0,1,0,0,1,1,0,0,1,3,0,0,1,4,9,5,11,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,1,0,0,0,1,2,0,1,0,0,0,3,0,2,0,2,12,7,8,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,1,0,0,1,0,0,0,0,2,0,1,1,0,2,6,0,7,3,14,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,2,0,0,0,1,0,0,1,3,0,2,0,6,7,8,8,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,1,0,0,0,0,1,0,0,0,1,0,1,0,1,1,1,3,0,3,4,1,4,6,11,0,0,0,0,0,1,0,0,0,1,0,0,0,0,0,0,0,0,1,0,1,0,0,1,0,1,0,0,1,0,1,2,1,4,2,2,7,3,11,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,1,0,0,3,1,2,2,0,1,2,2,9,4,12,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,1,1,0,0,1,1,1,2,1,0,1,1,0,3,3,1,5,7,9,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,3,0,0,0,1,1,0,0,0,0,3,1,2,1,1,7,7,9,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,1,0,0,0,0,0,0,0,0,0,0,1,1,3,1,2,4,0,3,8,12,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,2,0,0,0,0,0,0,0,0,4,1,1,2,2,1,7,8,8,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,1,1,1,1,0,1,0,1,0,0,1,0,10,8,7,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,2,1,0,0,0,2,1,1,4,2,1,6,8,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,1,0,0,1,0,0,0,0,1,0,0,2,0,2,2,0,3,10,7,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,1,0,2,2,2,6,5,7,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,2,0,0,0,1,0,1,0,2,2,5,10,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,2,4,5,7) +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" )
--- a/test-data/output.rawCount.xls Thu Jul 16 17:43:43 2015 -0400 +++ /dev/null Thu Jan 01 00:00:00 1970 +0000 @@ -1,8 +0,0 @@ -#chr start end name score strand 5% 10% 15% 20% 25% 30% 35% 40% 45% 50% 55% 60% 65% 70% 75% 80% 85% 90% 95% 100% -chr1 17368 17436 NR_106918 0 - 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 -chr1 17368 17436 NR_107062 0 - 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 -chr1 34610 36081 NR_026818 0 - 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 -chr1 69090 70008 NM_001005484 0 + 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 -chr1 14361 29370 NR_024540 0 - 1 1 1 2 2 2 2 2 2 2 2 3 3 3 3 3 3 4 4 4 -chr1 34610 36081 NR_026820 0 - 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 -chr1 11873 14409 NR_046018 0 + 0 0 0 0 0 0 0 0 1 1 1 1 2 2 2 2 2 3 3 3
--- a/test-data/output.saturation.r Thu Jul 16 17:43:43 2015 -0400 +++ /dev/null Thu Jan 01 00:00:00 1970 +0000 @@ -1,87 +0,0 @@ -pdf('output.saturation.pdf') -par(mfrow=c(2,2)) -name=c(5,10,15,20,25,30,35,40,45,50,55,60,65,70,75,80,85,90,95) -S5=c() -S10=c() -S15=c() -S20=c() -S25=c() -S30=c() -S35=c() -S40=c() -S45=c() -S50=c() -S55=c() -S60=c() -S65=c() -S70=c() -S75=c() -S80=c() -S85=c() -S90=c() -S95=c() -boxplot(100*S5,100*S10,100*S15,100*S20,100*S25,100*S30,100*S35,100*S40,100*S45,100*S50,100*S55,100*S60,100*S65,100*S70,100*S75,100*S80,100*S85,100*S90,100*S95,names=name,outline=F,ylab='Percent Relative Error',main='Q1',xlab='Resampling percentage') -name=c(5,10,15,20,25,30,35,40,45,50,55,60,65,70,75,80,85,90,95) -S5=c(1.0) -S10=c(1.0) -S15=c(1.0) -S20=c(1.0) -S25=c(1.0) -S30=c(1.0) -S35=c(1.0) -S40=c(1.0) -S45=c(0.259259259259) -S50=c(0.333333333334) -S55=c(0.39393939394) -S60=c(0.444444444444) -S65=c(0.0256410256403) -S70=c(0.0476190476199) -S75=c(0.111111111112) -S80=c(0.166666666666) -S85=c(0.21568627451) -S90=c(0.111111111112) -S95=c(0.0526315789469) -boxplot(100*S5,100*S10,100*S15,100*S20,100*S25,100*S30,100*S35,100*S40,100*S45,100*S50,100*S55,100*S60,100*S65,100*S70,100*S75,100*S80,100*S85,100*S90,100*S95,names=name,outline=F,ylab='Percent Relative Error',main='Q2',xlab='Resampling percentage') -name=c(5,10,15,20,25,30,35,40,45,50,55,60,65,70,75,80,85,90,95) -S5=c() -S10=c() -S15=c() -S20=c() -S25=c() -S30=c() -S35=c() -S40=c() -S45=c() -S50=c() -S55=c() -S60=c() -S65=c() -S70=c() -S75=c() -S80=c() -S85=c() -S90=c() -S95=c() -boxplot(100*S5,100*S10,100*S15,100*S20,100*S25,100*S30,100*S35,100*S40,100*S45,100*S50,100*S55,100*S60,100*S65,100*S70,100*S75,100*S80,100*S85,100*S90,100*S95,names=name,outline=F,ylab='Percent Relative Error',main='Q3',xlab='Resampling percentage') -name=c(5,10,15,20,25,30,35,40,45,50,55,60,65,70,75,80,85,90,95) -S5=c(4.00000000001) -S10=c(1.5) -S15=c(0.666666666666) -S20=c(1.5) -S25=c(1.00000000001) -S30=c(0.666666666666) -S35=c(0.428571428571) -S40=c(0.25) -S45=c(0.111111111111) -S50=c(0.0) -S55=c(0.09090909091) -S60=c(0.25) -S65=c(0.153846153846) -S70=c(0.0714285714295) -S75=c(0.0) -S80=c(0.0624999999991) -S85=c(0.117647058824) -S90=c(0.111111111111) -S95=c(0.0526315789465) -boxplot(100*S5,100*S10,100*S15,100*S20,100*S25,100*S30,100*S35,100*S40,100*S45,100*S50,100*S55,100*S60,100*S65,100*S70,100*S75,100*S80,100*S85,100*S90,100*S95,names=name,outline=F,ylab='Percent Relative Error',main='Q4',xlab='Resampling percentage') -dev.off()
--- a/test-data/output2.geneBodyCoverage.r Thu Jul 16 17:43:43 2015 -0400 +++ b/test-data/output2.geneBodyCoverage.r Tue May 03 16:36:57 2016 -0400 @@ -1,8 +1,21 @@ d1_pairend_strandspecific_51mer_hg19_chr1_1_100000_bam <- c(0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,1.0,1.0,0.0,1.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,1.0,1.0,0.0,0.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0) +d2_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) +d3_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) +data_matrix <- matrix(c(d1_pairend_strandspecific_51mer_hg19_chr1_1_100000_bam,d2_pairend_strandspecific_51mer_hg19_chr1_1_100000_bam,d3_pairend_strandspecific_51mer_hg19_chr1_1_100000_bam), byrow=T, ncol=100) +rowLabel <- c("d1_pairend_strandspecific_51mer_hg19_chr1_1_100000_bam","d2_pairend_strandspecific_51mer_hg19_chr1_1_100000_bam","d3_pairend_strandspecific_51mer_hg19_chr1_1_100000_bam") + + +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"))(1) +icolor = colorRampPalette(c("#7fc97f","#beaed4","#fdc086","#ffff99","#386cb0","#f0027f"))(3) plot(x,d1_pairend_strandspecific_51mer_hg19_chr1_1_100000_bam,type='l',xlab="Gene body percentile (5'->3')", ylab="Coverage",lwd=0.8,col=icolor[1]) +lines(x,d2_pairend_strandspecific_51mer_hg19_chr1_1_100000_bam,type='l',col=icolor[2]) +lines(x,d3_pairend_strandspecific_51mer_hg19_chr1_1_100000_bam,type='l',col=icolor[3]) +legend(0,1,fill=icolor[1:3], legend=c('d1_pairend_strandspecific_51mer_hg19_chr1_1_100000_bam','d2_pairend_strandspecific_51mer_hg19_chr1_1_100000_bam','d3_pairend_strandspecific_51mer_hg19_chr1_1_100000_bam')) dev.off()
--- a/test-data/output2.geneBodyCoverage.txt Thu Jul 16 17:43:43 2015 -0400 +++ b/test-data/output2.geneBodyCoverage.txt Tue May 03 16:36:57 2016 -0400 @@ -1,2 +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 d1_pairend_strandspecific_51mer_hg19_chr1_1_100000_bam 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 0.0 1.0 1.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 0.0 0.0 1.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 +d2_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 +d3_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
--- a/tool_dependencies.xml Thu Jul 16 17:43:43 2015 -0400 +++ b/tool_dependencies.xml Tue May 03 16:36:57 2016 -0400 @@ -1,7 +1,7 @@ <?xml version="1.0"?> <tool_dependency> <package name="R" version="3.0.3"> - <repository changeset_revision="f386d7431fe0" name="package_r_3_0_3" owner="iuc" toolshed="https://toolshed.g2.bx.psu.edu" /> + <repository changeset_revision="afc48696ee5c" name="package_r_3_0_3" owner="iuc" toolshed="https://toolshed.g2.bx.psu.edu" /> </package> <package name="numpy" version="1.7.1"> <repository changeset_revision="300877695495" name="package_numpy_1_7" owner="iuc" toolshed="https://toolshed.g2.bx.psu.edu" />