Mercurial > repos > iuc > vsnp_add_zero_coverage
changeset 0:0ad85e7db2fc draft
"planemo upload for repository https://github.com/galaxyproject/tools-iuc/tree/master/tools/vsnp commit 6a0c9a857c1f4638ef18e106b1f8c0681303acc5"
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--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/macros.xml Sun Sep 27 10:07:44 2020 +0000 @@ -0,0 +1,30 @@ +<?xml version='1.0' encoding='UTF-8'?> +<macros> + <token name="@WRAPPER_VERSION@">1.0</token> + <token name="@PROFILE@">19.09</token> + <xml name="param_input_type"> + <param name="input_type" type="select" label="Choose the category of the files to be analyzed"> + <option value="single" selected="true">Single files</option> + <option value="collection">Collections of files</option> + </param> + </xml> + <xml name="param_reference_source"> + <param name="reference_source" type="select" label="Choose the source for the reference genome"> + <option value="cached" selected="true">locally cached</option> + <option value="history">from history</option> + </param> + </xml> + <xml name="citations"> + <citations> + <citation type="bibtex"> + @misc{None, + journal = {None}, + author = {1. Stuber T}, + title = {Manuscript in preparation}, + year = {None}, + url = {https://github.com/USDA-VS/vSNP},} + </citation> + </citations> + </xml> +</macros> +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/Mbovis-01D6_avg_mq.json Sun Sep 27 10:07:44 2020 +0000 @@ -0,0 +1,1 @@ 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--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/Mbovis-01D6_snps.newick Sun Sep 27 10:07:44 2020 +0000 @@ -0,0 +1,1 @@ +(root,((((SRR1792271_zc,SRR1792272_zc),SRR1791772_zc),SRR8073662_zc),SRR1791698_zc_vcf),SRR1792265_zc);
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--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/Mbovis-01D_snps.newick Sun Sep 27 10:07:44 2020 +0000 @@ -0,0 +1,1 @@ +(root,((((SRR1792271_zc,SRR1792272_zc),SRR1791772_zc),SRR8073662_zc),SRR1791698_zc_vcf),SRR1792265_zc);
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--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/Mbovis-01_snps.json Sun Sep 27 10:07:44 2020 +0000 @@ -0,0 +1,1 @@ +{"columns":["NC_002945.4:1005705","NC_002945.4:1348342","NC_002945.4:1382465","NC_002945.4:1463503","NC_002945.4:1704859","NC_002945.4:1723583","NC_002945.4:1911237","NC_002945.4:1961826","NC_002945.4:228109","NC_002945.4:2412437","NC_002945.4:2413021","NC_002945.4:3069493","NC_002945.4:3319244","NC_002945.4:3373966","NC_002945.4:3413486","NC_002945.4:3941254","NC_002945.4:3942270","NC_002945.4:4236320","NC_002945.4:4278315","NC_002945.4:960995","NC_002945.4:997676"],"index":["SRR1792265_zc","SRR1792272_zc","SRR1792271_zc","SRR8073662_zc","SRR1791772_zc","SRR1791698_zc_vcf","root"],"data":[["C","G","G","A","C","G","C","G","C","R","C","A","C","G","A","G","A","G","T","T","C"],["G","A","G","A","C","A","C","C","T","A","T","C","A","A","G","A","A","A","C","G","T"],["G","A","G","A","C","A","C","C","T","A","T","C","A","A","G","A","A","A","C","G","T"],["G","A","G","A","C","G","C","C","T","A","T","C","A","G","G","G","A","G","C","G","T"],["G","A","C","G","T","G","C","C","T","A","T","C","A","G","G","G","A","G","C","G","T"],["G","A","G","A","C","G","T","C","T","A","T","C","A","G","G","G","C","G","C","G","T"],["C","G","G","A","C","G","C","G","C","G","T","C","A","G","G","G","A","G","C","T","C"]]} \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/Mbovis-01_snps.newick Sun Sep 27 10:07:44 2020 +0000 @@ -0,0 +1,1 @@ +(root,((((SRR1792271_zc,SRR1792272_zc),SRR1791772_zc),SRR8073662_zc),SRR1791698_zc_vcf),SRR1792265_zc);
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/NC_002945v4.fasta Sun Sep 27 10:07:44 2020 +0000 @@ -0,0 +1,101 @@ +>NC_002945.4 Mycobacterium bovis AF2122/97 genome assembly, chromosome: Mycobacterium_bovis_AF2122/97 +TTGACCGATGACCCCGGTTCAGGCTTCACCACAGTGTGGAACGCGGTCGTCTCCGAACTTAACGGCGACC +CTAAGGTTGACGACGGACCCAGCAGTGATGCTAATCTCAGCGCTCCGCTGACCCCTCAGCAAAGGGCTTG +GCTCAATCTCGTCCAGCCATTGACCATCGTCGAGGGGTTTGCTCTGTTATCCGTGCCGAGCAGCTTTGTC +CAAAACGAAATCGAGCGCCATCTGCGGGCCCCGATTACCGACGCTCTCAGCCGCCGACTCGGACATCAGA +TCCAACTCGGGGTCCGCATCGCTCCGCCGGCGACCGACGAAGCCGACGACACTACCGTGCCGCCTTCCGA +AAATCCTGCTACCACATCGCCAGACACCACAACCGACAACGACGAGATTGATGACAGCGCTGCGGCACGG +GGCGATAACCAGCACAGTTGGCCAAGTTACTTCACCGAGCGCCCGCGCAATACCGATTCCGCTACCGCTG +GCGTAACCAGCCTTAACCGTCGCTACACCTTTGATACGTTCGTTATCGGCGCCTCCAACCGGTTCGCGCA +CGCCGCCGCCTTGGCGATCGCAGAAGCACCCGCCCGCGCTTACAACCCCCTGTTCATCTGGGGCGAGTCC +GGTCTCGGCAAGACACACCTGCTACACGCGGCAGGCAACTATGCCCAACGGTTGTTCCCGGGAATGCGGG +TCAAATATGTCTCCACCGAGGAATTCACCAACGACTTCATTAACTCGCTCCGCGATGACCGCAAGGTCGC +ATTCAAACGCAGCTACCGCGACGTAGACGTGCTGTTGGTCGACGACATCCAATTCATTGAAGGCAAAGAG +GGTATTCAAGAGGAGTTCTTCCACACCTTCAACACCTTGCACAATGCCAACAAGCAAATCGTCATCTCAT +CTGACCGCCCACCCAAGCAGCTCGCCACCCTCGAGGACCGGCTGAGAACCCGCTTTGAGTGGGGGCTGAT +CACTGACGTACAACCACCCGAGCTGGAGACCCGCATCGCCATCTTGCGCAAGAAAGCACAGATGGAACGG +CTCGCGATCCCCGACGATGTCCTCGAACTCATCGCCAGCAGTATCGAACGCAATATCCGTGAACTCGAGG +GCGCGCTGATCCGGGTCACCGCGTTCGCCTCATTGAACAAAACACCAATCGACAAAGCGCTGGCCGAGAT +TGTGCTTCGCGATCTGATCGCCGACGCCAACACCATGCAAATCAGCGCGGCGACGATCATGGCTGCCACC +GCCGAATACTTCGACACTACCGTCGAAGAGCTTCGCGGGCCCGGCAAGACCCGAGCACTGGCCCAGTCAC +GACAGATTGCGATGTACCTGTGTCGTGAGCTCACCGATCTTTCGTTGCCCAAAATCGGCCAAGCGTTCGG +CCGTGATCACACAACCGTCATGTACGCCCAACGCAAGATCCTGTCCGAGATGGCCGAGCGCCGTGAGGTC +TTTGATCACGTCAAAGAACTCACCACTCGCATCCGTCAGCGCTCCAAGCGCTAGCACGGCGTGTTCTTCC +GACAACGTTCTTAAAAAAACTTCTCTCTCCCAGGTCACACCAGTCACAGAGATTGGCTGTGAGTGTCGCT +GTGCACAAACCGCGCACAGACTCATACAGTCCCGGCGGTTCCGTTCACAACCCACGCCTCATCCCCACCG +ACCCAACACACACCCCACAGTCATCGCCACCGTCATCCACAACTCCGACCGACGTCGACCTGCACCAAGA +CCAGACTGTCCCCAAACTGCACACCCTCTAATACTGTTACCGAGATTTCTTCGTCGTTTGTTCTTGGAAA +GACAGCGCTGGGGATCGTTCGCTGGATACCACCCGCATAACTGGCTCGTCGCGGTGGGTCAGAGGTCAAT +GATGAACTTTCAAGTTGACGTGAGAAGCTCTACGGTTGTTGTTCGACTGCTGTTGCGGCCGTCGTGGCGG +GTCACGCGTCATGGGCGTTCGTCGTTGGCAGTCCCCACGCTAGCGGGGCGCTAGCCACGGGATCGAACTC +ATCGTGAGGTGAAAGGGCGCAATGGACGCGGCTACGACAAGAGTTGGCCTCACCGACTTGACGTTTCGTT +TGCTACGAGAGTCTTTCGCCGATGCGGTGTCGTGGGTGGCTAAAAATCTGCCAGCCAGGCCCGCGGTGCC +GGTGCTCTCCGGCGTGTTGTTGACCGGCTCGGACAACGGTCTGACGATTTCCGGATTCGACTACGAGGTT +TCCGCCGAGGCCCAGGTTGGCGCTGAAATTGTTTCTCCTGGAAGCGTTTTAGTTTCTGGCCGATTGTTGT +CCGATATTACCCGGGCGTTGCCTAACAAGCCCGTAGGCGTTCATGTCGAAGGTAACCGGGTCGCATTGAC +CTGCGGTAACGCCAGGTTTTCGCTACCGACGATGCCAGTCGAGGATTATCCGACGCTGCCGACGCTGCCG +GAAGAGACCGGATTGTTGCCTGCGGAATTATTCGCCGAGGCAATCAGTCAGGTCGCTATCGCCGCCGGCC +GGGACGACACGCTGCCTATGTTGACCGGCATCCGGGTCGAAATCCTCGGTGAGACGGTGGTTTTGGCCGC +TACCGACAGGTTTCGCCTGGCTGTTCGAGAACTGAAGTGGTCGGCGTCGTCGCCAGATATCGAAGCGGCT +GTGCTGGTCCCGGCCAAGACGCTGGCCGAGGCCGCCAAAGCGGGCATCGGCGGCTCTGACGTTCGTTTGT +CGTTGGGTACTGGGCCGGGGGTGGGCAAGGATGGCCTGCTCGGTATCAGTGGGAACGGCAAGCGCAGCAC +CACGCGACTTCTTGATGCCGAGTTCCCGAAGTTTCGGCAGTTGCTACCAACCGAACACACCGCGGTGGCC +ACCATGGACGTGGCCGAGTTGATCGAAGCGATCAAGCTGGTTGCGTTGGTAGCTGATCGGGGCGCGCAGG +TGCGCATGGAGTTCGCTGATGGCAGCGTGCGGCTTTCTGCGGGTGCCGATGATGTTGGACGAGCCGAGGA +AGATCTTGTTGTTGACTATGCCGGTGAACCATTGACGATTGCGTTTAACCCAACCTATCTAACGGACGGT +TTGAGTTCGTTGCGCTCGGAGCGAGTGTCTTTCGGGTTTACGACTGCGGGTAAGCCTGCCTTGCTACGTC +CGGTGTCCGGGGACGATCGCCCTGTGGCGGGTCTGAATGGCAACGGTCCGTTCCCGGCGGTGTCGACGGA +CTATGTCTATCTGTTGATGCCGGTTCGGTTGCCGGGCTGAGCACTTGGCGCCCGGGTAGGTGTACGTCCG +TCATTTGGGGCTGCGTGACTTCCGGTCCTGGGCATGTGTAGATCTGGAATTGCATCCAGGGCGGACGGTT +TTTGTTGGGCCTAACGGTTATGGTAAGACGAATCTTATTGAGGCACTGTGGTATTCGACGACGTTAGGTT +CGCACCGCGTTAGCGCCGATTTGCCGTTGATCCGGGTAGGTACCGATCGTGCGGTGATCTCCACGATCGT +GGTGAACGACGGTAGAGAATGTGCCGTCGACCTCGAGATCGCCACGGGGCGAGTCAACAAAGCGCGATTG +AATCGATCATCGGTCCGAAGTACACGTGATGTGGTCGGAGTGCTTCGAGCTGTGTTGTTTGCCCCTGAGG +ATCTGGGGTTGGTTCGTGGGGATCCCGCTGACCGGCGGCGCTATCTGGATGATCTGGCGATCGTGCGTAG +GCCTGCGATCGCTGCGGTACGAGCCGAATATGAGAGGGTGGTGCGCCAGCGGACGGCGTTATTGAAGTCC +GTACCTGGAGCACGGTATCGGGGTGACCGGGGTGTGTTTGACACTCTTGAGGTATGGGACAGTCGTTTGG +CGGAGCACGGGGCTGAACTGGTGGCCGCCCGCATCGATTTGGTCAACCAGTTGGCACCGGAAGTGAAGAA +GGCATACCAGCTGTTGGCGCCGGAATCGCGATCGGCGTCTATCGGTTATCGGGCCAGCATGGATGTAACC +GGTCCCAGCGAGCAGTCAGATACCGATCGGCAATTGTTAGCAGCTCGGCTGTTGGCGGCGCTGGCGGCCC +GTCGGGATGCCGAACTCGAGCGTGGGGTTTGTCTAGTTGGTCCGCACCGTGACGACCTAATACTGCGACT +AGGCGATCAACCCGCGAAAGGATTTGCTAGCCATGGGGAGGCGTGGTCGTTGGCGGTGGCACTGCGGTTG +GCGGCCTATCAACTGTTACGCGTTGATGGTGGTGAGCCGGTGTTGTTGCTCGACGACGTGTTCGCCGAAC +TGGATGTCATGCGCCGTCGAGCGTTGGCGACGGCGGCCGAGTCCGCCGAACAGGTGTTGGTGACTGCCGC +GGTGCTCGAGGATATTCCCGCCGGCTGGGACGCCAGGCGGGTGCACATCGATGTGCGTGCCGATGACACC +GGATCGATGTCGGTGGTTCTGCCATGACGGGTTCTGTTGACCGGCCCGACCAGAATCGCGGTGAGCGATT +AATGAAGTCACCAGGGTTGGATTTGGTCAGGCGCACCCTGGACGAAGCTCGTGCTGCTGCCCGCGCGCGC +GGACAAGACGCCGGTCGAGGGCGGGTCGCTTCCGTTGCGTCGGGTCGGGTGGCCGGACGGCGACGAAGCT +GGTCGGGTCCGGGGCCCGACATTCGTGATCCACAACCGCTGGGTAAGGCCGCTCGTGAGCTGGCAAAGAA +ACGCGGCTGGTCGGTGCGGGTCGCCGAGGGTATGGTGCTCGGCCAGTGGTCTGCGGTGGTCGGCCACCAG +ATCGCCGAACATGCACGCCCGACTGCGCTAAACGACGGGGTGTTGAGCGTGATTGCGGAGTCGACGGCGT +GGGCGACGCAGTTGAGGATCATGCAGGCCCAGCTTCTGGCCAAGATCGCCGCAGCGGTTGGCAACGATGT +GGTGCGATCGCTAAAGATCACCGGGCCGGCGGCACCATCGTGGCGCAAGGGGCCTCGCCATATTGCCGGT +AGGGGTCCGCGCGACACCTACGGATAACACGTCGATCGGCCCAGAACAAGGCGCTCCGGTCCCGGCCTGA +GAGCCTCGAGGACGAAGCGGATCCGTATGCCGGACGTCGGGACGCACCAGGAAGAAAGATGTCCGACGCA +CGGCGCGGTTAGATGGGTAAAAACGAGGCCAGAAGATCGGCCCTGGCGCCCGATCACGGTACAGTGGTGT +GCGACCCCCTGCGGCGACTCAACCGCATGCACGCAACCCCTGAGGAGAGTATTCGGATCGTGGCTGCCCA +GAAAAAGAAGGCCCAAGACGAATACGGCGCTGCGTCTATCACCATTCTCGAAGGGCTGGAGGCCGTCCGC +AAACGTCCCGGCATGTACATTGGCTCGACCGGTGAGCGCGGTTTACACCATCTCATTTGGGAGGTGGTCG +ACAACGCGGTCGACGAGGCGATGGCCGGTTATGCAACCACAGTGAACGTAGTGCTGCTTGAGGATGGCGG +TGTCGAGGTCGCCGACGACGGCCGCGGCATTCCGGTCGCCACCCACGCCTCCGGCATACCGACCGTCGAC +GTGGTGATGACACAACTACATGCCGGCGGCAAGTTCGACTCGGACGCGTATGCGATATCTGGTGGTCTGC +ACGGCGTCGGCGTGTCGGTGGTTAACGCGCTATCCACCCGGCTCGAAGTCGAGATCAAGCGCGACGGGTA +CGAGTGGTCTCAGGTTTATGAGAAGTCGGAACCCCTGGGCCTCAAGCAAGGGGCGCCGACCAAGAAGACG +GGGTCAACGGTACGGTTCTGGGCCGACCCCGCTGTTTTCGAAACCACGGAATACGACTTCGAAACCGTCG +CCCGCCGGCTGCAAGAGATGGCGTTCCTCAACAAGGGGCTGACCATCAACCTGACCGACGAGAGGGTGAC +CCAAGACGAGGTCGTCGACGAAGTGGTCAGCGACGTCGCCGAGGCGCCGAAGTCGGCAAGTGAACGCGCA +GCCGAATCCACTGCACCGCACAAAGTTAAGAGCCGCACCTTTCACTATCCGGGTGGCCTGGTGGACTTCG +TGAAACACATCAACCGCACCAAGAACGCGATTCATAGCAGCATCGTGGACTTTTCCGGCAAGGGCACCGG +GCACGAGGTGGAGATCGCGATGCAATGGAACGCCGGGTATTCGGAGTCGGTGCACACCTTCGCCAACACC +ATCAACACCCACGAGGGCGGCACCCACGAAGAGGGCTTCCGCAGCGCGCTGACGTCGGTGGTGAACAAGT +ACGCCAAGGACCGCAAGCTACTGAAGGACAAGGACCCCAACCTCACCGGTGACGATATCCGGGAAGGCCT +GGCCGCTGTGATCTCGGTGAAGGTCAGCGAACCGCAGTTCGAGGGCCAGACCAAGACCAAGTTGGGCAAC +ACCGAGGTCAAATCGTTTGTGCAGAAGGTCTGTAATGAACAGCTGACCCACTGGTTTGAAGCCAACCCCA +CCGACTCGAAAGTCGTTGTGAACAAGGCTGTGTCCTCGGCGCAAGCCCGTATCGCGGCACGTAAGGCACG +AGAGTTGGTGCGGCGTAAGAGCGCCACCGACATCGGTGGATTGCCCGGCAAGCTGGCCGATTGCCGTTCC +ACGGATCCGCGCAAGTCCGAACTGTATGTCGTAGAAGGTGACTCGGCCGGCGGTTCTGCAAAAAGCGGTC +GCGATTCGATGTTCCAGGCGATACTTCCGCTGCGCGGCAAGATCATCAATGTGGAGAAAGCGCGCATCGA +CCGGGTGCTAAAGAACACCGAAGTTCAGGCGATCATCACGGCGCTGGGCACCGGGATCCACGACGAGTTC +GATATCGGCAAGCTGCGCTACCACAAGATCGTGCTGATGGCCGACGCCGATGTTGACGGCCAACATATTT +CCACGCTGTTGTTGACGTTGTTGTTCCGGTTCATGCGGCCGCTCATCGAGAACGGGCATGTGTTTTTGGC +ACAACCGCCGCTGTACAAACTCAAGTGGCAGCGCAGTGACCCGGAATTCGCATACTCCGACCGCGAGCGC
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/fasta_indexes.loc Sun Sep 27 10:07:44 2020 +0000 @@ -0,0 +1,1 @@ +89 89 Mycobacterium_AF2122 ${__HERE__}/NC_002945v4.fasta
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/input_avg_mq_json.json Sun Sep 27 10:07:44 2020 +0000 @@ -0,0 +1,1 @@ 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--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/input_newick.newick Sun Sep 27 10:07:44 2020 +0000 @@ -0,0 +1,1 @@ +(root,((((SRR1792271_zc,SRR1792272_zc),SRR1791772_zc),SRR8073662_zc),SRR1791698_zc_vcf),SRR1792265_zc);
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/input_snps_json.json Sun Sep 27 10:07:44 2020 +0000 @@ -0,0 +1,1 @@ +{"columns":["NC_002945.4:1005705","NC_002945.4:1348342","NC_002945.4:1382465","NC_002945.4:1463503","NC_002945.4:1704859","NC_002945.4:1723583","NC_002945.4:1911237","NC_002945.4:1961826","NC_002945.4:228109","NC_002945.4:2412437","NC_002945.4:2413021","NC_002945.4:3069493","NC_002945.4:3319244","NC_002945.4:3373966","NC_002945.4:3413486","NC_002945.4:3941254","NC_002945.4:3942270","NC_002945.4:4236320","NC_002945.4:4278315","NC_002945.4:960995","NC_002945.4:997676"],"index":["SRR1792265_zc","SRR1792272_zc","SRR1792271_zc","SRR8073662_zc","SRR1791772_zc","SRR1791698_zc_vcf","root"],"data":[["C","G","G","A","C","G","C","G","C","R","C","A","C","G","A","G","A","G","T","T","C"],["G","A","G","A","C","A","C","C","T","A","T","C","A","A","G","A","A","A","C","G","T"],["G","A","G","A","C","A","C","C","T","A","T","C","A","A","G","A","A","A","C","G","T"],["G","A","G","A","C","G","C","C","T","A","T","C","A","G","G","G","A","G","C","G","T"],["G","A","C","G","T","G","C","C","T","A","T","C","A","G","G","G","A","G","C","G","T"],["G","A","G","A","C","G","T","C","T","A","T","C","A","G","G","G","C","G","C","G","T"],["C","G","G","A","C","G","C","G","C","G","T","C","A","G","G","G","A","G","C","T","C"]]} \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/output_metrics.tabular Sun Sep 27 10:07:44 2020 +0000 @@ -0,0 +1,2 @@ +# File Number of Good SNPs Average Coverage Genome Coverage + 0
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/output_vcf.vcf Sun Sep 27 10:07:44 2020 +0000 @@ -0,0 +1,100 @@ +##fileformat=VCFv4.2 +##fileDate=20200302 +##source=freeBayes v1.3.1-dirty +##reference=/home/galaxy/galaxy/tool-data/AF2122/seq/AF2122.fa +##contig=<ID=NC_002945.4,length=4349904> +##phasing=none +##commandline="freebayes --region NC_002945.4:0..4349904 --bam b_0.bam --fasta-reference /home/galaxy/galaxy/tool-data/AF2122/seq/AF2122.fa --vcf ./vcf_output/part_NC_002945.4:0..4349904.vcf -u -n 0 --haplotype-length -1 --min-repeat-size 5 --min-repeat-entropy 1 -m 1 -q 0 -R 0 -Y 0 -e 1 -F 0.05 -C 2 -G 1 --min-alternate-qsum 0" +##filter="QUAL > 0" +##INFO=<ID=NS,Number=1,Type=Integer,Description="Number of samples with data"> +##INFO=<ID=DP,Number=1,Type=Integer,Description="Total read depth at the locus"> +##INFO=<ID=DPB,Number=1,Type=Float,Description="Total read depth per bp at the locus; bases in reads overlapping / bases in haplotype"> +##INFO=<ID=AC,Number=A,Type=Integer,Description="Total number of alternate alleles in called genotypes"> +##INFO=<ID=AN,Number=1,Type=Integer,Description="Total number of alleles in called genotypes"> +##INFO=<ID=AF,Number=A,Type=Float,Description="Estimated allele frequency in the range (0,1]"> +##INFO=<ID=RO,Number=1,Type=Integer,Description="Count of full observations of the reference haplotype."> +##INFO=<ID=AO,Number=A,Type=Integer,Description="Count of full observations of this alternate haplotype."> +##INFO=<ID=PRO,Number=1,Type=Float,Description="Reference allele observation count, with partial observations recorded fractionally"> +##INFO=<ID=PAO,Number=A,Type=Float,Description="Alternate allele observations, with partial observations recorded fractionally"> +##INFO=<ID=QR,Number=1,Type=Integer,Description="Reference allele quality sum in phred"> +##INFO=<ID=QA,Number=A,Type=Integer,Description="Alternate allele quality sum in phred"> +##INFO=<ID=PQR,Number=1,Type=Float,Description="Reference allele quality sum in phred for partial observations"> +##INFO=<ID=PQA,Number=A,Type=Float,Description="Alternate allele quality sum in phred for partial observations"> +##INFO=<ID=SRF,Number=1,Type=Integer,Description="Number of reference observations on the forward strand"> +##INFO=<ID=SRR,Number=1,Type=Integer,Description="Number of reference observations on the reverse strand"> +##INFO=<ID=SAF,Number=A,Type=Integer,Description="Number of alternate observations on the forward strand"> +##INFO=<ID=SAR,Number=A,Type=Integer,Description="Number of alternate observations on the reverse strand"> +##INFO=<ID=SRP,Number=1,Type=Float,Description="Strand balance probability for the reference allele: Phred-scaled upper-bounds estimate of the probability of observing the deviation between SRF and SRR given E(SRF/SRR) ~ 0.5, derived using Hoeffding's inequality"> +##INFO=<ID=SAP,Number=A,Type=Float,Description="Strand balance probability for the alternate allele: Phred-scaled upper-bounds estimate of the probability of observing the deviation between SAF and SAR given E(SAF/SAR) ~ 0.5, derived using Hoeffding's inequality"> +##INFO=<ID=AB,Number=A,Type=Float,Description="Allele balance at heterozygous sites: a number between 0 and 1 representing the ratio of reads showing the reference allele to all reads, considering only reads from individuals called as heterozygous"> +##INFO=<ID=ABP,Number=A,Type=Float,Description="Allele balance probability at heterozygous sites: Phred-scaled upper-bounds estimate of the probability of observing the deviation between ABR and ABA given E(ABR/ABA) ~ 0.5, derived using Hoeffding's inequality"> +##INFO=<ID=RUN,Number=A,Type=Integer,Description="Run length: the number of consecutive repeats of the alternate allele in the reference genome"> +##INFO=<ID=RPP,Number=A,Type=Float,Description="Read Placement Probability: Phred-scaled upper-bounds estimate of the probability of observing the deviation between RPL and RPR given E(RPL/RPR) ~ 0.5, derived using Hoeffding's inequality"> +##INFO=<ID=RPPR,Number=1,Type=Float,Description="Read Placement Probability for reference observations: Phred-scaled upper-bounds estimate of the probability of observing the deviation between RPL and RPR given E(RPL/RPR) ~ 0.5, derived using Hoeffding's inequality"> +##INFO=<ID=RPL,Number=A,Type=Float,Description="Reads Placed Left: number of reads supporting the alternate balanced to the left (5') of the alternate allele"> +##INFO=<ID=RPR,Number=A,Type=Float,Description="Reads Placed Right: number of reads supporting the alternate balanced to the right (3') of the alternate allele"> +##INFO=<ID=EPP,Number=A,Type=Float,Description="End Placement Probability: Phred-scaled upper-bounds estimate of the probability of observing the deviation between EL and ER given E(EL/ER) ~ 0.5, derived using Hoeffding's inequality"> +##INFO=<ID=EPPR,Number=1,Type=Float,Description="End Placement Probability for reference observations: Phred-scaled upper-bounds estimate of the probability of observing the deviation between EL and ER given E(EL/ER) ~ 0.5, derived using Hoeffding's inequality"> +##INFO=<ID=DPRA,Number=A,Type=Float,Description="Alternate allele depth ratio. Ratio between depth in samples with each called alternate allele and those without."> +##INFO=<ID=ODDS,Number=1,Type=Float,Description="The log odds ratio of the best genotype combination to the second-best."> +##INFO=<ID=GTI,Number=1,Type=Integer,Description="Number of genotyping iterations required to reach convergence or bailout."> +##INFO=<ID=TYPE,Number=A,Type=String,Description="The type of allele, either snp, mnp, ins, del, or complex."> +##INFO=<ID=CIGAR,Number=A,Type=String,Description="The extended CIGAR representation of each alternate allele, with the exception that '=' is replaced by 'M' to ease VCF parsing. Note that INDEL alleles do not have the first matched base (which is provided by default, per the spec) referred to by the CIGAR."> +##INFO=<ID=NUMALT,Number=1,Type=Integer,Description="Number of unique non-reference alleles in called genotypes at this position."> +##INFO=<ID=MEANALT,Number=A,Type=Float,Description="Mean number of unique non-reference allele observations per sample with the corresponding alternate alleles."> +##INFO=<ID=LEN,Number=A,Type=Integer,Description="allele length"> +##INFO=<ID=MQM,Number=A,Type=Float,Description="Mean mapping quality of observed alternate alleles"> +##INFO=<ID=MQMR,Number=1,Type=Float,Description="Mean mapping quality of observed reference alleles"> +##INFO=<ID=PAIRED,Number=A,Type=Float,Description="Proportion of observed alternate alleles which are supported by properly paired read fragments"> +##INFO=<ID=PAIREDR,Number=1,Type=Float,Description="Proportion of observed reference alleles which are supported by properly paired read fragments"> +##INFO=<ID=MIN_DP,Number=1,Type=Integer,Description="Minimum depth in gVCF output block."> +##INFO=<ID=END,Number=1,Type=Integer,Description="Last position (inclusive) in gVCF output record."> +##INFO=<ID=technology.ILLUMINA,Number=A,Type=Float,Description="Fraction of observations supporting the alternate observed in reads from ILLUMINA"> +##FORMAT=<ID=GT,Number=1,Type=String,Description="Genotype"> +##FORMAT=<ID=GQ,Number=1,Type=Float,Description="Genotype Quality, the Phred-scaled marginal (or unconditional) probability of the called genotype"> +##FORMAT=<ID=GL,Number=G,Type=Float,Description="Genotype Likelihood, log10-scaled likelihoods of the data given the called genotype for each possible genotype generated from the reference and alternate alleles given the sample ploidy"> +##FORMAT=<ID=DP,Number=1,Type=Integer,Description="Read Depth"> +##FORMAT=<ID=AD,Number=R,Type=Integer,Description="Number of observation for each allele"> +##FORMAT=<ID=RO,Number=1,Type=Integer,Description="Reference allele observation count"> +##FORMAT=<ID=QR,Number=1,Type=Integer,Description="Sum of quality of the reference observations"> +##FORMAT=<ID=AO,Number=A,Type=Integer,Description="Alternate allele observation count"> +##FORMAT=<ID=QA,Number=A,Type=Integer,Description="Sum of quality of the alternate observations"> +##FORMAT=<ID=MIN_DP,Number=1,Type=Integer,Description="Minimum depth in gVCF output block."> +#CHROM POS ID REF ALT QUAL FILTER INFO FORMAT 13-1941-6 +NC_002945.4 1 . N . . . . GT ./. +NC_002945.4 2 . N . . . . GT ./. +NC_002945.4 3 . N . . . . GT ./. +NC_002945.4 4 . N . . . . GT ./. +NC_002945.4 5 . N . . . . GT ./. +NC_002945.4 6 . N . . . . GT ./. +NC_002945.4 7 . N . . . . GT ./. +NC_002945.4 8 . N . . . . GT ./. +NC_002945.4 9 . N . . . . GT ./. +NC_002945.4 10 . N . . . . GT ./. +NC_002945.4 11 . N . . . . GT ./. +NC_002945.4 12 . N . . . . GT ./. +NC_002945.4 13 . N . . . . GT ./. +NC_002945.4 14 . N . . . . GT ./. +NC_002945.4 15 . N . . . . GT ./. +NC_002945.4 16 . N . . . . GT ./. +NC_002945.4 17 . N . . . . GT ./. +NC_002945.4 18 . N . . . . GT ./. +NC_002945.4 19 . N . . . . GT ./. +NC_002945.4 20 . N . . . . GT ./. +NC_002945.4 21 . N . . . . GT ./. +NC_002945.4 22 . N . . . . GT ./. +NC_002945.4 23 . N . . . . GT ./. +NC_002945.4 24 . N . . . . GT ./. +NC_002945.4 25 . N . . . . GT ./. +NC_002945.4 26 . N . . . . GT ./. +NC_002945.4 27 . N . . . . GT ./. +NC_002945.4 28 . N . . . . GT ./. +NC_002945.4 29 . N . . . . GT ./. +NC_002945.4 30 . N . . . . GT ./. +NC_002945.4 31 . N . . . . GT ./. +NC_002945.4 32 . N . . . . GT ./. +NC_002945.4 33 . N . . . . GT ./. +NC_002945.4 34 . N . . . . GT ./. +NC_002945.4 35 . N . . . . GT ./. +NC_002945.4 36 . N . . . . GT ./. +NC_002945.4 37 . N . . . . GT ./.
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/vcf_input.vcf Sun Sep 27 10:07:44 2020 +0000 @@ -0,0 +1,64 @@ +##fileformat=VCFv4.2 +##fileDate=20200302 +##source=freeBayes v1.3.1-dirty +##reference=/home/galaxy/galaxy/tool-data/AF2122/seq/AF2122.fa +##contig=<ID=NC_002945.4,length=4349904> +##phasing=none +##commandline="freebayes --region NC_002945.4:0..4349904 --bam b_0.bam --fasta-reference /home/galaxy/galaxy/tool-data/AF2122/seq/AF2122.fa --vcf ./vcf_output/part_NC_002945.4:0..4349904.vcf -u -n 0 --haplotype-length -1 --min-repeat-size 5 --min-repeat-entropy 1 -m 1 -q 0 -R 0 -Y 0 -e 1 -F 0.05 -C 2 -G 1 --min-alternate-qsum 0" +##filter="QUAL > 0" +##INFO=<ID=NS,Number=1,Type=Integer,Description="Number of samples with data"> +##INFO=<ID=DP,Number=1,Type=Integer,Description="Total read depth at the locus"> +##INFO=<ID=DPB,Number=1,Type=Float,Description="Total read depth per bp at the locus; bases in reads overlapping / bases in haplotype"> +##INFO=<ID=AC,Number=A,Type=Integer,Description="Total number of alternate alleles in called genotypes"> +##INFO=<ID=AN,Number=1,Type=Integer,Description="Total number of alleles in called genotypes"> +##INFO=<ID=AF,Number=A,Type=Float,Description="Estimated allele frequency in the range (0,1]"> +##INFO=<ID=RO,Number=1,Type=Integer,Description="Count of full observations of the reference haplotype."> +##INFO=<ID=AO,Number=A,Type=Integer,Description="Count of full observations of this alternate haplotype."> +##INFO=<ID=PRO,Number=1,Type=Float,Description="Reference allele observation count, with partial observations recorded fractionally"> +##INFO=<ID=PAO,Number=A,Type=Float,Description="Alternate allele observations, with partial observations recorded fractionally"> +##INFO=<ID=QR,Number=1,Type=Integer,Description="Reference allele quality sum in phred"> +##INFO=<ID=QA,Number=A,Type=Integer,Description="Alternate allele quality sum in phred"> +##INFO=<ID=PQR,Number=1,Type=Float,Description="Reference allele quality sum in phred for partial observations"> +##INFO=<ID=PQA,Number=A,Type=Float,Description="Alternate allele quality sum in phred for partial observations"> +##INFO=<ID=SRF,Number=1,Type=Integer,Description="Number of reference observations on the forward strand"> +##INFO=<ID=SRR,Number=1,Type=Integer,Description="Number of reference observations on the reverse strand"> +##INFO=<ID=SAF,Number=A,Type=Integer,Description="Number of alternate observations on the forward strand"> +##INFO=<ID=SAR,Number=A,Type=Integer,Description="Number of alternate observations on the reverse strand"> +##INFO=<ID=SRP,Number=1,Type=Float,Description="Strand balance probability for the reference allele: Phred-scaled upper-bounds estimate of the probability of observing the deviation between SRF and SRR given E(SRF/SRR) ~ 0.5, derived using Hoeffding's inequality"> +##INFO=<ID=SAP,Number=A,Type=Float,Description="Strand balance probability for the alternate allele: Phred-scaled upper-bounds estimate of the probability of observing the deviation between SAF and SAR given E(SAF/SAR) ~ 0.5, derived using Hoeffding's inequality"> +##INFO=<ID=AB,Number=A,Type=Float,Description="Allele balance at heterozygous sites: a number between 0 and 1 representing the ratio of reads showing the reference allele to all reads, considering only reads from individuals called as heterozygous"> +##INFO=<ID=ABP,Number=A,Type=Float,Description="Allele balance probability at heterozygous sites: Phred-scaled upper-bounds estimate of the probability of observing the deviation between ABR and ABA given E(ABR/ABA) ~ 0.5, derived using Hoeffding's inequality"> +##INFO=<ID=RUN,Number=A,Type=Integer,Description="Run length: the number of consecutive repeats of the alternate allele in the reference genome"> +##INFO=<ID=RPP,Number=A,Type=Float,Description="Read Placement Probability: Phred-scaled upper-bounds estimate of the probability of observing the deviation between RPL and RPR given E(RPL/RPR) ~ 0.5, derived using Hoeffding's inequality"> +##INFO=<ID=RPPR,Number=1,Type=Float,Description="Read Placement Probability for reference observations: Phred-scaled upper-bounds estimate of the probability of observing the deviation between RPL and RPR given E(RPL/RPR) ~ 0.5, derived using Hoeffding's inequality"> +##INFO=<ID=RPL,Number=A,Type=Float,Description="Reads Placed Left: number of reads supporting the alternate balanced to the left (5') of the alternate allele"> +##INFO=<ID=RPR,Number=A,Type=Float,Description="Reads Placed Right: number of reads supporting the alternate balanced to the right (3') of the alternate allele"> +##INFO=<ID=EPP,Number=A,Type=Float,Description="End Placement Probability: Phred-scaled upper-bounds estimate of the probability of observing the deviation between EL and ER given E(EL/ER) ~ 0.5, derived using Hoeffding's inequality"> +##INFO=<ID=EPPR,Number=1,Type=Float,Description="End Placement Probability for reference observations: Phred-scaled upper-bounds estimate of the probability of observing the deviation between EL and ER given E(EL/ER) ~ 0.5, derived using Hoeffding's inequality"> +##INFO=<ID=DPRA,Number=A,Type=Float,Description="Alternate allele depth ratio. Ratio between depth in samples with each called alternate allele and those without."> +##INFO=<ID=ODDS,Number=1,Type=Float,Description="The log odds ratio of the best genotype combination to the second-best."> +##INFO=<ID=GTI,Number=1,Type=Integer,Description="Number of genotyping iterations required to reach convergence or bailout."> +##INFO=<ID=TYPE,Number=A,Type=String,Description="The type of allele, either snp, mnp, ins, del, or complex."> +##INFO=<ID=CIGAR,Number=A,Type=String,Description="The extended CIGAR representation of each alternate allele, with the exception that '=' is replaced by 'M' to ease VCF parsing. Note that INDEL alleles do not have the first matched base (which is provided by default, per the spec) referred to by the CIGAR."> +##INFO=<ID=NUMALT,Number=1,Type=Integer,Description="Number of unique non-reference alleles in called genotypes at this position."> +##INFO=<ID=MEANALT,Number=A,Type=Float,Description="Mean number of unique non-reference allele observations per sample with the corresponding alternate alleles."> +##INFO=<ID=LEN,Number=A,Type=Integer,Description="allele length"> +##INFO=<ID=MQM,Number=A,Type=Float,Description="Mean mapping quality of observed alternate alleles"> +##INFO=<ID=MQMR,Number=1,Type=Float,Description="Mean mapping quality of observed reference alleles"> +##INFO=<ID=PAIRED,Number=A,Type=Float,Description="Proportion of observed alternate alleles which are supported by properly paired read fragments"> +##INFO=<ID=PAIREDR,Number=1,Type=Float,Description="Proportion of observed reference alleles which are supported by properly paired read fragments"> +##INFO=<ID=MIN_DP,Number=1,Type=Integer,Description="Minimum depth in gVCF output block."> +##INFO=<ID=END,Number=1,Type=Integer,Description="Last position (inclusive) in gVCF output record."> +##INFO=<ID=technology.ILLUMINA,Number=A,Type=Float,Description="Fraction of observations supporting the alternate observed in reads from ILLUMINA"> +##FORMAT=<ID=GT,Number=1,Type=String,Description="Genotype"> +##FORMAT=<ID=GQ,Number=1,Type=Float,Description="Genotype Quality, the Phred-scaled marginal (or unconditional) probability of the called genotype"> +##FORMAT=<ID=GL,Number=G,Type=Float,Description="Genotype Likelihood, log10-scaled likelihoods of the data given the called genotype for each possible genotype generated from the reference and alternate alleles given the sample ploidy"> +##FORMAT=<ID=DP,Number=1,Type=Integer,Description="Read Depth"> +##FORMAT=<ID=AD,Number=R,Type=Integer,Description="Number of observation for each allele"> +##FORMAT=<ID=RO,Number=1,Type=Integer,Description="Reference allele observation count"> +##FORMAT=<ID=QR,Number=1,Type=Integer,Description="Sum of quality of the reference observations"> +##FORMAT=<ID=AO,Number=A,Type=Integer,Description="Alternate allele observation count"> +##FORMAT=<ID=QA,Number=A,Type=Integer,Description="Sum of quality of the alternate observations"> +##FORMAT=<ID=MIN_DP,Number=1,Type=Integer,Description="Minimum depth in gVCF output block."> +#CHROM POS ID REF ALT QUAL FILTER INFO FORMAT 13-1941-6 +NC_002945.4 2898437 . T G 0.263449 . AB=0;ABP=0;AC=0;AF=0;AN=2;AO=2;CIGAR=1X;DP=2;DPB=2;DPRA=0;EPP=3.0103;EPPR=0;GTI=0;LEN=1;MEANALT=1;MQM=60;MQMR=0;NS=1;NUMALT=1;ODDS=2.77259;PAIRED=1;PAIREDR=0;PAO=0;PQA=0;PQR=0;PRO=0;QA=0;QR=0;RO=0;RPL=2;RPP=7.35324;RPPR=0;RPR=0;RUN=1;SAF=1;SAP=3.0103;SAR=1;SRF=0;SRP=0;SRR=0;TYPE=snp;technology.ILLUMINA=1 GT:DP:AD:RO:QR:AO:QA:GL 0/0:2:0,2:0:0:2:0:0,-0.60206,-8.68589e-09
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/tool-data/fasta_indexes.loc.sample Sun Sep 27 10:07:44 2020 +0000 @@ -0,0 +1,29 @@ +#This is a sample file distributed with Galaxy that enables tools +#to use a directory of Samtools indexed sequences data files. You will need +#to create these data files and then create a fasta_indexes.loc file +#similar to this one (store it in this directory) that points to +#the directories in which those files are stored. The fasta_indexes.loc +#file has this format (white space characters are TAB characters): +# +# <unique_build_id> <dbkey> <display_name> <file_base_path> +# +#So, for example, if you had hg19 Canonical indexed stored in +# +# /depot/data2/galaxy/hg19/sam/, +# +#then the fasta_indexes.loc entry would look like this: +# +#hg19canon hg19 Human (Homo sapiens): hg19 Canonical /depot/data2/galaxy/hg19/sam/hg19canon.fa +# +#and your /depot/data2/galaxy/hg19/sam/ directory +#would contain hg19canon.fa and hg19canon.fa.fai files. +# +#Your fasta_indexes.loc file should include an entry per line for +#each index set you have stored. The file in the path does actually +#exist, but it should never be directly used. Instead, the name serves +#as a prefix for the index file. For example: +# +#hg18canon hg18 Human (Homo sapiens): hg18 Canonical /depot/data2/galaxy/hg18/sam/hg18canon.fa +#hg18full hg18 Human (Homo sapiens): hg18 Full /depot/data2/galaxy/hg18/sam/hg18full.fa +#hg19canon hg19 Human (Homo sapiens): hg19 Canonical /depot/data2/galaxy/hg19/sam/hg19canon.fa +#hg19full hg19 Human (Homo sapiens): hg19 Full /depot/data2/galaxy/hg19/sam/hg19full.fa
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/tool-data/vsnp_genbank.loc.sample Sun Sep 27 10:07:44 2020 +0000 @@ -0,0 +1,4 @@ +## vSNP Genbank files +#Value Name Path Description +#AF2122 Mycobacterium_AF2122/NC_002945v4.gbk vsnp/AF2122/Mycobacterium_AF2122/NC_002945v4.gbk Genbank file for Mycobacterium bovis AF2122/97 +#NC_006932 Brucella_abortus1/NC_006932-NC_006933.gbk vsnp/NC_006932/Brucella_abortus1/NC_006932-NC_006933.gbk Genbank file for Brucella abortus bv. 1 str. 9-941
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/tool_data_table_conf.xml.sample Sun Sep 27 10:07:44 2020 +0000 @@ -0,0 +1,13 @@ +<tables> + <!-- Location of SAMTools indexes for FASTA files --> + <table name="fasta_indexes" comment_char="#"> + <columns>value, dbkey, name, path</columns> + <file path="tool-data/fasta_indexes.loc" /> + </table> + <!-- Location of genbank files for vsnp_build_tables tool --> + <table name="vsnp_genbank" comment_char="#"> + <columns>value, name, path, description</columns> + <file path="tool-data/vsnp_genbank.loc" /> + </table> +</tables> +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/tool_data_table_conf.xml.test Sun Sep 27 10:07:44 2020 +0000 @@ -0,0 +1,6 @@ +<tables> + <table name="fasta_indexes" comment_char="#"> + <columns>value, dbkey, name, path</columns> + <file path="${__HERE__}/test-data/fasta_indexes.loc" /> + </table> +</tables>
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/vsnp_add_zero_coverage.py Sun Sep 27 10:07:44 2020 +0000 @@ -0,0 +1,188 @@ +#!/usr/bin/env python + +import argparse +import multiprocessing +import os +import pandas +import pysam +import queue +import re +import shutil +from Bio import SeqIO + +INPUT_BAM_DIR = 'input_bam_dir' +INPUT_VCF_DIR = 'input_vcf_dir' +OUTPUT_VCF_DIR = 'output_vcf_dir' +OUTPUT_METRICS_DIR = 'output_metrics_dir' + + +def get_base_file_name(file_path): + base_file_name = os.path.basename(file_path) + if base_file_name.find(".") > 0: + # Eliminate the extension. + return os.path.splitext(base_file_name)[0] + elif base_file_name.endswith("_vcf"): + # The "." character has likely + # changed to an "_" character. + return base_file_name.rstrip("_vcf") + return base_file_name + + +def get_coverage_and_snp_count(task_queue, reference, output_metrics, output_vcf, timeout): + while True: + try: + tup = task_queue.get(block=True, timeout=timeout) + except queue.Empty: + break + bam_file, vcf_file = tup + # Create a coverage dictionary. + coverage_dict = {} + coverage_list = pysam.depth(bam_file, split_lines=True) + for line in coverage_list: + chrom, position, depth = line.split('\t') + coverage_dict["%s-%s" % (chrom, position)] = depth + # Convert it to a data frame. + coverage_df = pandas.DataFrame.from_dict(coverage_dict, orient='index', columns=["depth"]) + # Create a zero coverage dictionary. + zero_dict = {} + for record in SeqIO.parse(reference, "fasta"): + chrom = record.id + total_len = len(record.seq) + for pos in list(range(1, total_len + 1)): + zero_dict["%s-%s" % (str(chrom), str(pos))] = 0 + # Convert it to a data frame with depth_x + # and depth_y columns - index is NaN. + zero_df = pandas.DataFrame.from_dict(zero_dict, orient='index', columns=["depth"]) + coverage_df = zero_df.merge(coverage_df, left_index=True, right_index=True, how='outer') + # depth_x "0" column no longer needed. + coverage_df = coverage_df.drop(columns=['depth_x']) + coverage_df = coverage_df.rename(columns={'depth_y': 'depth'}) + # Covert the NaN to 0 coverage and get some metrics. + coverage_df = coverage_df.fillna(0) + coverage_df['depth'] = coverage_df['depth'].apply(int) + total_length = len(coverage_df) + average_coverage = coverage_df['depth'].mean() + zero_df = coverage_df[coverage_df['depth'] == 0] + total_zero_coverage = len(zero_df) + total_coverage = total_length - total_zero_coverage + genome_coverage = "{:.2%}".format(total_coverage / total_length) + # Process the associated VCF input. + column_names = ["CHROM", "POS", "ID", "REF", "ALT", "QUAL", "FILTER", "INFO", "FORMAT", "Sample"] + vcf_df = pandas.read_csv(vcf_file, sep='\t', header=None, names=column_names, comment='#') + good_snp_count = len(vcf_df[(vcf_df['ALT'].str.len() == 1) & (vcf_df['REF'].str.len() == 1) & (vcf_df['QUAL'] > 150)]) + base_file_name = get_base_file_name(vcf_file) + if total_zero_coverage > 0: + header_file = "%s_header.csv" % base_file_name + with open(header_file, 'w') as outfile: + with open(vcf_file) as infile: + for line in infile: + if re.search('^#', line): + outfile.write("%s" % line) + vcf_df_snp = vcf_df[vcf_df['REF'].str.len() == 1] + vcf_df_snp = vcf_df_snp[vcf_df_snp['ALT'].str.len() == 1] + vcf_df_snp['ABS_VALUE'] = vcf_df_snp['CHROM'].map(str) + "-" + vcf_df_snp['POS'].map(str) + vcf_df_snp = vcf_df_snp.set_index('ABS_VALUE') + cat_df = pandas.concat([vcf_df_snp, zero_df], axis=1, sort=False) + cat_df = cat_df.drop(columns=['CHROM', 'POS', 'depth']) + cat_df[['ID', 'ALT', 'QUAL', 'FILTER', 'INFO']] = cat_df[['ID', 'ALT', 'QUAL', 'FILTER', 'INFO']].fillna('.') + cat_df['REF'] = cat_df['REF'].fillna('N') + cat_df['FORMAT'] = cat_df['FORMAT'].fillna('GT') + cat_df['Sample'] = cat_df['Sample'].fillna('./.') + cat_df['temp'] = cat_df.index.str.rsplit('-', n=1) + cat_df[['CHROM', 'POS']] = pandas.DataFrame(cat_df.temp.values.tolist(), index=cat_df.index) + cat_df = cat_df[['CHROM', 'POS', 'ID', 'REF', 'ALT', 'QUAL', 'FILTER', 'INFO', 'FORMAT', 'Sample']] + cat_df['POS'] = cat_df['POS'].astype(int) + cat_df = cat_df.sort_values(['CHROM', 'POS']) + body_file = "%s_body.csv" % base_file_name + cat_df.to_csv(body_file, sep='\t', header=False, index=False) + if output_vcf is None: + output_vcf_file = os.path.join(OUTPUT_VCF_DIR, "%s.vcf" % base_file_name) + else: + output_vcf_file = output_vcf + with open(output_vcf_file, "w") as outfile: + for cf in [header_file, body_file]: + with open(cf, "r") as infile: + for line in infile: + outfile.write("%s" % line) + else: + if output_vcf is None: + output_vcf_file = os.path.join(OUTPUT_VCF_DIR, "%s.vcf" % base_file_name) + else: + output_vcf_file = output_vcf + shutil.copyfile(vcf_file, output_vcf_file) + bam_metrics = [base_file_name, "", "%4f" % average_coverage, genome_coverage] + vcf_metrics = [base_file_name, str(good_snp_count), "", ""] + if output_metrics is None: + output_metrics_file = os.path.join(OUTPUT_METRICS_DIR, "%s.tabular" % base_file_name) + else: + output_metrics_file = output_metrics + metrics_columns = ["File", "Number of Good SNPs", "Average Coverage", "Genome Coverage"] + with open(output_metrics_file, "w") as fh: + fh.write("# %s\n" % "\t".join(metrics_columns)) + fh.write("%s\n" % "\t".join(bam_metrics)) + fh.write("%s\n" % "\t".join(vcf_metrics)) + task_queue.task_done() + + +def set_num_cpus(num_files, processes): + num_cpus = int(multiprocessing.cpu_count()) + if num_files < num_cpus and num_files < processes: + return num_files + if num_cpus < processes: + half_cpus = int(num_cpus / 2) + if num_files < half_cpus: + return num_files + return half_cpus + return processes + + +if __name__ == '__main__': + parser = argparse.ArgumentParser() + + parser.add_argument('--output_metrics', action='store', dest='output_metrics', required=False, default=None, help='Output metrics text file') + parser.add_argument('--output_vcf', action='store', dest='output_vcf', required=False, default=None, help='Output VCF file') + parser.add_argument('--reference', action='store', dest='reference', help='Reference dataset') + parser.add_argument('--processes', action='store', dest='processes', type=int, help='User-selected number of processes to use for job splitting') + + args = parser.parse_args() + + # The assumption here is that the list of files + # in both INPUT_BAM_DIR and INPUT_VCF_DIR are + # equal in number and named such that they are + # properly matched if the directories contain + # more than 1 file (i.e., hopefully the bam file + # names and vcf file names will be something like + # Mbovis-01D6_* so they can be # sorted and properly + # associated with each other). + bam_files = [] + for file_name in sorted(os.listdir(INPUT_BAM_DIR)): + file_path = os.path.abspath(os.path.join(INPUT_BAM_DIR, file_name)) + bam_files.append(file_path) + vcf_files = [] + for file_name in sorted(os.listdir(INPUT_VCF_DIR)): + file_path = os.path.abspath(os.path.join(INPUT_VCF_DIR, file_name)) + vcf_files.append(file_path) + + multiprocessing.set_start_method('spawn') + queue1 = multiprocessing.JoinableQueue() + num_files = len(bam_files) + cpus = set_num_cpus(num_files, args.processes) + # Set a timeout for get()s in the queue. + timeout = 0.05 + + # Add each associated bam and vcf file pair to the queue. + for i, bam_file in enumerate(bam_files): + vcf_file = vcf_files[i] + queue1.put((bam_file, vcf_file)) + + # Complete the get_coverage_and_snp_count task. + processes = [multiprocessing.Process(target=get_coverage_and_snp_count, args=(queue1, args.reference, args.output_metrics, args.output_vcf, timeout, )) for _ in range(cpus)] + for p in processes: + p.start() + for p in processes: + p.join() + queue1.join() + + if queue1.empty(): + queue1.close() + queue1.join_thread()
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/vsnp_add_zero_coverage.xml Sun Sep 27 10:07:44 2020 +0000 @@ -0,0 +1,167 @@ +<tool id="vsnp_add_zero_coverage" name="vSNP: add zero coverage" version="@WRAPPER_VERSION@.0" profile="@PROFILE@"> + <description></description> + <macros> + <import>macros.xml</import> + </macros> + <requirements> + <requirement type="package" version="1.76">biopython</requirement> + <requirement type="package" version="0.25.3">pandas</requirement> + <requirement type="package" version="0.15.4">pysam</requirement> + </requirements> + <command detect_errors="exit_code"><![CDATA[ +#import os +#import re +#set input_type = $input_type_cond.input_type +#set input_bam_dir = 'input_bam_dir' +#set input_vcf_dir = 'input_vcf_dir' +#set output_vcf_dir = 'output_vcf_dir' +#set output_metrics_dir = 'output_metrics_dir' +mkdir -p $input_bam_dir && +mkdir -p $input_vcf_dir && +mkdir -p $output_vcf_dir && +mkdir -p $output_metrics_dir && +#if str($input_type) == "single": + #set bam_input = $input_type_cond.bam_input + #set file_name = $bam_input.file_name + #set file_name_base = $os.path.basename($file_name) + ln -s '$file_name' '$input_bam_dir/$file_name_base' && + #set vcf_input = $input_type_cond.vcf_input + #set file_name = $vcf_input.file_name + #set file_name_base = $os.path.basename($file_name) + ln -s '$file_name' '$input_vcf_dir/$file_name_base' && +#else: + #for $i in $input_type_cond.bam_input_collection: + #set filename = $i.file_name + #set identifier = re.sub('[^\s\w\-]', '_', str($i.element_identifier)) + ln -s '$filename' '$input_bam_dir/$identifier' && + #end for + #for $i in $input_type_cond.vcf_input_collection: + #set filename = $i.file_name + #set identifier = re.sub('[^\s\w\-]', '_', str($i.element_identifier)) + ln -s '$filename' '$input_vcf_dir/$identifier' && + #end for +#end if +python '$__tool_directory__/vsnp_add_zero_coverage.py' +--processes \${GALAXY_SLOTS:-4} +#if str($reference_cond.reference_source) == "cached" + --reference '$reference_cond.reference.fields.path' +#else: + --reference '$reference_cond.reference' +#end if +#if str($input_type) == "single": + --output_metrics '$output_metrics' + --output_vcf '$output_vcf' +#end if +]]></command> + <inputs> + <conditional name="input_type_cond"> + <expand macro="param_input_type"/> + <when value="single"> + <param name="bam_input" type="data" format="bam" label="BAM file"> + <validator type="unspecified_build"/> + </param> + <param name="vcf_input" type="data" format="vcf" label="VCF file"> + <validator type="unspecified_build"/> + </param> + </when> + <when value="collection"> + <param name="bam_input_collection" type="data_collection" format="bam" collection_type="list" label="Collection of BAM files"> + <validator type="unspecified_build"/> + </param> + <param name="vcf_input_collection" type="data_collection" format="vcf" collection_type="list" label="Collection of VCF files"> + <validator type="unspecified_build"/> + </param> + </when> + </conditional> + <conditional name="reference_cond"> + <expand macro="param_reference_source"/> + <when value="cached"> + <param name="reference" type="select" label="Using reference genome"> + <options from_data_table="fasta_indexes"/> + <!-- No <filter> tag here! --> + <validator type="no_options" message="A built-in reference genome is not available for the build associated with the selected BAM file"/> + </param> + </when> + <when value="history"> + <param name="reference" type="data" format="fasta" label="Using reference genome"> + <validator type="no_options" message="The current history does not include a fasta dataset"/> + </param> + </when> + </conditional> + </inputs> + <outputs> + <data name="output_vcf" format="vcf" label="${tool.name} (filtered VCF) on ${on_string}"> + <filter>input_type_cond['input_type'] == 'single'</filter> + </data> + <collection name="output_vcf_collection" type="list" label="${tool.name} (filtered VCFs) on ${on_string}"> + <discover_datasets pattern="__name__" directory="output_vcf_dir" format="vcf" /> + <filter>input_type_cond['input_type'] == 'collection'</filter> + </collection> + <data name="output_metrics" format="tabular" label="${tool.name} (metrics) on ${on_string}"> + <filter>input_type_cond['input_type'] == 'single'</filter> + </data> + <collection name="output_metrics_collection" type="list" label="${tool.name} (metrics) on ${on_string}"> + <discover_datasets pattern="__name__" directory="output_metrics_dir" format="tabular" /> + <filter>input_type_cond['input_type'] == 'collection'</filter> + </collection> + </outputs> + <tests> + <test> + <param name="input_type" value="collection"/> + <param name="bam_input_collection"> + <collection type="list"> + <element name="bam_input.bam" value="bam_input.bam" dbkey="89"/> + <element name="bam_input2.bam" value="bam_input.bam" dbkey="89"/> + </collection> + </param> + <param name="vcf_input_collection"> + <collection type="list"> + <element name="vcf_input.vcf" value="vcf_input.vcf" dbkey="89"/> + <element name="vcf_input2.vcf" value="vcf_input.vcf" dbkey="89"/> + </collection> + </param> + <param name="reference_source" value="history"/> + <param name="reference" value="NC_002945v4.fasta" ftype="fasta"/> + <output_collection name="output_vcf_collection" type="list"> + <element name="vcf_input.vcf" file="output_vcf.vcf" ftype="vcf" compare="contains"/> + <element name="vcf_input2.vcf" file="output_vcf.vcf" ftype="vcf" compare="contains"/> + </output_collection> + <output_collection name="output_metrics_collection" type="list"> + <element name="vcf_input.tabular" file="output_metrics.tabular" ftype="tabular" compare="contains"/> + <element name="vcf_input2.tabular" file="output_metrics.tabular" ftype="tabular" compare="contains"/> + </output_collection> + </test> + <test> + <param name="bam_input" value="bam_input.bam" ftype="bam" dbkey="89"/> + <param name="vcf_input" value="vcf_input.vcf" ftype="vcf" dbkey="89"/> + <param name="reference_source" value="history"/> + <param name="reference" value="NC_002945v4.fasta" ftype="fasta"/> + <output name="output_vcf" value="output_vcf.vcf" ftype="vcf" compare="contains"/> + <output name="output_metrics" file="output_metrics.tabular" ftype="tabular" compare="contains"/> + </test> + <test> + <param name="bam_input" value="bam_input.bam" ftype="bam" dbkey="89"/> + <param name="vcf_input" value="vcf_input.vcf" ftype="vcf" dbkey="89"/> + <param name="reference_source" value="cached"/> + <output name="output_vcf" value="output_vcf.vcf" ftype="vcf" compare="contains"/> + <output name="output_metrics" file="output_metrics.tabular" ftype="tabular" compare="contains"/> + </test> + </tests> + <help> +**What it does** + +Accepts a combination of single BAM and associated VCF files (or associated collections of each) to produce a VCF file for each +combination whose positions with no coverage are represented as "N". These outputs are restricted to SNPs and those regions +along the reference with no coverage. + +A metrics file is produced for each combination which provides the number of good SNPs, the average coverage and the genome +coverage percentage. + +**Required Options** + + * **Choose the category of the files to be analyzed** - select "Single files" or "Collections of files", then select the appropriate history items (single BAM and VCF files or collections of BAM and VCF files) based on the selected option. + * **Choose the source for the reference genome** - select "locally cached" if the reference associated with the BAM and VCF files is available within the Galaxy environment or "from history" to select the reference from the current history. + </help> + <expand macro="citations" /> +</tool> +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/vsnp_build_tables.py Sun Sep 27 10:07:44 2020 +0000 @@ -0,0 +1,381 @@ +#!/usr/bin/env python + +import argparse +import multiprocessing +import os +import pandas +import queue +import pandas.io.formats.excel +import re +from Bio import SeqIO + +INPUT_JSON_AVG_MQ_DIR = 'input_json_avg_mq_dir' +INPUT_JSON_DIR = 'input_json_dir' +INPUT_NEWICK_DIR = 'input_newick_dir' +# Maximum columns allowed in a LibreOffice +# spreadsheet is 1024. Excel allows for +# 16,384 columns, but we'll set the lower +# number as the maximum. Some browsers +# (e.g., Firefox on Linux) are configured +# to use LibreOffice for Excel spreadsheets. +MAXCOLS = 1024 +OUTPUT_EXCEL_DIR = 'output_excel_dir' + + +def annotate_table(table_df, group, annotation_dict): + for gbk_chrome, pro in list(annotation_dict.items()): + ref_pos = list(table_df) + ref_series = pandas.Series(ref_pos) + ref_df = pandas.DataFrame(ref_series.str.split(':', expand=True).values, columns=['reference', 'position']) + all_ref = ref_df[ref_df['reference'] == gbk_chrome] + positions = all_ref.position.to_frame() + # Create an annotation file. + annotation_file = "%s_annotations.csv" % group + with open(annotation_file, "a") as fh: + for index, row in positions.iterrows(): + pos = row.position + try: + aaa = pro.iloc[pro.index.get_loc(int(pos))][['chrom', 'locus', 'product', 'gene']] + try: + chrom, name, locus, tag = aaa.values[0] + print("{}:{}\t{}, {}, {}".format(chrom, pos, locus, tag, name), file=fh) + except ValueError: + # If only one annotation for the entire + # chromosome (e.g., flu) then having [0] fails + chrom, name, locus, tag = aaa.values + print("{}:{}\t{}, {}, {}".format(chrom, pos, locus, tag, name), file=fh) + except KeyError: + print("{}:{}\tNo annotated product".format(gbk_chrome, pos), file=fh) + # Read the annotation file into a data frame. + annotations_df = pandas.read_csv(annotation_file, sep='\t', header=None, names=['index', 'annotations'], index_col='index') + # Remove the annotation_file from disk since both + # cascade and sort tables are built using the file, + # and it is opened for writing in append mode. + os.remove(annotation_file) + # Process the data. + table_df_transposed = table_df.T + table_df_transposed.index = table_df_transposed.index.rename('index') + table_df_transposed = table_df_transposed.merge(annotations_df, left_index=True, right_index=True) + table_df = table_df_transposed.T + return table_df + + +def excel_formatter(json_file_name, excel_file_name, group, annotation_dict): + pandas.io.formats.excel.header_style = None + table_df = pandas.read_json(json_file_name, orient='split') + if annotation_dict is not None: + table_df = annotate_table(table_df, group, annotation_dict) + else: + table_df = table_df.append(pandas.Series(name='no annotations')) + writer = pandas.ExcelWriter(excel_file_name, engine='xlsxwriter') + table_df.to_excel(writer, sheet_name='Sheet1') + writer_book = writer.book + ws = writer.sheets['Sheet1'] + format_a = writer_book.add_format({'bg_color': '#58FA82'}) + format_g = writer_book.add_format({'bg_color': '#F7FE2E'}) + format_c = writer_book.add_format({'bg_color': '#0000FF'}) + format_t = writer_book.add_format({'bg_color': '#FF0000'}) + format_normal = writer_book.add_format({'bg_color': '#FDFEFE'}) + formatlowqual = writer_book.add_format({'font_color': '#C70039', 'bg_color': '#E2CFDD'}) + format_ambigous = writer_book.add_format({'font_color': '#C70039', 'bg_color': '#E2CFDD'}) + format_n = writer_book.add_format({'bg_color': '#E2CFDD'}) + rows, cols = table_df.shape + ws.set_column(0, 0, 30) + ws.set_column(1, cols, 2.1) + ws.freeze_panes(2, 1) + format_annotation = writer_book.add_format({'font_color': '#0A028C', 'rotation': '-90', 'align': 'top'}) + # Set last row. + ws.set_row(rows + 1, cols + 1, format_annotation) + # Make sure that row/column locations don't overlap. + ws.conditional_format(rows - 2, 1, rows - 1, cols, {'type': 'cell', 'criteria': '<', 'value': 55, 'format': formatlowqual}) + ws.conditional_format(2, 1, rows - 2, cols, {'type': 'cell', 'criteria': '==', 'value': 'B$2', 'format': format_normal}) + ws.conditional_format(2, 1, rows - 2, cols, {'type': 'text', 'criteria': 'containing', 'value': 'A', 'format': format_a}) + ws.conditional_format(2, 1, rows - 2, cols, {'type': 'text', 'criteria': 'containing', 'value': 'G', 'format': format_g}) + ws.conditional_format(2, 1, rows - 2, cols, {'type': 'text', 'criteria': 'containing', 'value': 'C', 'format': format_c}) + ws.conditional_format(2, 1, rows - 2, cols, {'type': 'text', 'criteria': 'containing', 'value': 'T', 'format': format_t}) + ws.conditional_format(2, 1, rows - 2, cols, {'type': 'text', 'criteria': 'containing', 'value': 'S', 'format': format_ambigous}) + ws.conditional_format(2, 1, rows - 2, cols, {'type': 'text', 'criteria': 'containing', 'value': 'Y', 'format': format_ambigous}) + ws.conditional_format(2, 1, rows - 2, cols, {'type': 'text', 'criteria': 'containing', 'value': 'R', 'format': format_ambigous}) + ws.conditional_format(2, 1, rows - 2, cols, {'type': 'text', 'criteria': 'containing', 'value': 'W', 'format': format_ambigous}) + ws.conditional_format(2, 1, rows - 2, cols, {'type': 'text', 'criteria': 'containing', 'value': 'K', 'format': format_ambigous}) + ws.conditional_format(2, 1, rows - 2, cols, {'type': 'text', 'criteria': 'containing', 'value': 'M', 'format': format_ambigous}) + ws.conditional_format(2, 1, rows - 2, cols, {'type': 'text', 'criteria': 'containing', 'value': 'N', 'format': format_n}) + ws.conditional_format(2, 1, rows - 2, cols, {'type': 'text', 'criteria': 'containing', 'value': '-', 'format': format_n}) + format_rotation = writer_book.add_format({}) + format_rotation.set_rotation(90) + for column_num, column_name in enumerate(list(table_df.columns)): + ws.write(0, column_num + 1, column_name, format_rotation) + format_annotation = writer_book.add_format({'font_color': '#0A028C', 'rotation': '-90', 'align': 'top'}) + # Set last row. + ws.set_row(rows, 400, format_annotation) + writer.save() + + +def get_annotation_dict(gbk_file): + gbk_dict = SeqIO.to_dict(SeqIO.parse(gbk_file, "genbank")) + annotation_dict = {} + tmp_file = "features.csv" + # Create a file of chromosomes and features. + for chromosome in list(gbk_dict.keys()): + with open(tmp_file, 'w+') as fh: + for feature in gbk_dict[chromosome].features: + if "CDS" in feature.type or "rRNA" in feature.type: + try: + product = feature.qualifiers['product'][0] + except KeyError: + product = None + try: + locus = feature.qualifiers['locus_tag'][0] + except KeyError: + locus = None + try: + gene = feature.qualifiers['gene'][0] + except KeyError: + gene = None + fh.write("%s\t%d\t%d\t%s\t%s\t%s\n" % (chromosome, int(feature.location.start), int(feature.location.end), locus, product, gene)) + # Read the chromosomes and features file into a data frame. + df = pandas.read_csv(tmp_file, sep='\t', names=["chrom", "start", "stop", "locus", "product", "gene"]) + # Process the data. + df = df.sort_values(['start', 'gene'], ascending=[True, False]) + df = df.drop_duplicates('start') + pro = df.reset_index(drop=True) + pro.index = pandas.IntervalIndex.from_arrays(pro['start'], pro['stop'], closed='both') + annotation_dict[chromosome] = pro + return annotation_dict + + +def get_base_file_name(file_path): + base_file_name = os.path.basename(file_path) + if base_file_name.find(".") > 0: + # Eliminate the extension. + return os.path.splitext(base_file_name)[0] + elif base_file_name.find("_") > 0: + # The dot extension was likely changed to + # the " character. + items = base_file_name.split("_") + return "_".join(items[0:-1]) + else: + return base_file_name + + +def output_cascade_table(cascade_order, mqdf, group, annotation_dict): + cascade_order_mq = pandas.concat([cascade_order, mqdf], join='inner') + output_table(cascade_order_mq, "cascade", group, annotation_dict) + + +def output_excel(df, type_str, group, annotation_dict, count=None): + # Output the temporary json file that + # is used by the excel_formatter. + if count is None: + if group is None: + json_file_name = "%s_order_mq.json" % type_str + excel_file_name = os.path.join(OUTPUT_EXCEL_DIR, "%s_table.xlsx" % type_str) + else: + json_file_name = "%s_%s_order_mq.json" % (group, type_str) + excel_file_name = os.path.join(OUTPUT_EXCEL_DIR, "%s_%s_table.xlsx" % (group, type_str)) + else: + if group is None: + json_file_name = "%s_order_mq_%d.json" % (type_str, count) + excel_file_name = os.path.join(OUTPUT_EXCEL_DIR, "%s_table_%d.xlsx" % (type_str, count)) + else: + json_file_name = "%s_%s_order_mq_%d.json" % (group, type_str, count) + excel_file_name = os.path.join(OUTPUT_EXCEL_DIR, "%s_%s_table_%d.xlsx" % (group, type_str, count)) + df.to_json(json_file_name, orient='split') + # Output the Excel file. + excel_formatter(json_file_name, excel_file_name, group, annotation_dict) + + +def output_sort_table(cascade_order, mqdf, group, annotation_dict): + sort_df = cascade_order.T + sort_df['abs_value'] = sort_df.index + sort_df[['chrom', 'pos']] = sort_df['abs_value'].str.split(':', expand=True) + sort_df = sort_df.drop(['abs_value', 'chrom'], axis=1) + sort_df.pos = sort_df.pos.astype(int) + sort_df = sort_df.sort_values(by=['pos']) + sort_df = sort_df.drop(['pos'], axis=1) + sort_df = sort_df.T + sort_order_mq = pandas.concat([sort_df, mqdf], join='inner') + output_table(sort_order_mq, "sort", group, annotation_dict) + + +def output_table(df, type_str, group, annotation_dict): + if isinstance(group, str) and group.startswith("dataset"): + # Inputs are single files, not collections, + # so input file names are not useful for naming + # output files. + group_str = None + else: + group_str = group + count = 0 + chunk_start = 0 + chunk_end = 0 + column_count = df.shape[1] + if column_count >= MAXCOLS: + # Here the number of columns is greater than + # the maximum allowed by Excel, so multiple + # outputs will be produced. + while column_count >= MAXCOLS: + count += 1 + chunk_end += MAXCOLS + df_of_type = df.iloc[:, chunk_start:chunk_end] + output_excel(df_of_type, type_str, group_str, annotation_dict, count=count) + chunk_start += MAXCOLS + column_count -= MAXCOLS + count += 1 + df_of_type = df.iloc[:, chunk_start:] + output_excel(df_of_type, type_str, group_str, annotation_dict, count=count) + else: + output_excel(df, type_str, group_str, annotation_dict) + + +def preprocess_tables(task_queue, annotation_dict, timeout): + while True: + try: + tup = task_queue.get(block=True, timeout=timeout) + except queue.Empty: + break + newick_file, json_file, json_avg_mq_file = tup + avg_mq_series = pandas.read_json(json_avg_mq_file, typ='series', orient='split') + # Map quality to dataframe. + mqdf = avg_mq_series.to_frame(name='MQ') + mqdf = mqdf.T + # Get the group. + group = get_base_file_name(newick_file) + snps_df = pandas.read_json(json_file, orient='split') + with open(newick_file, 'r') as fh: + for line in fh: + line = re.sub('[:,]', '\n', line) + line = re.sub('[)(]', '', line) + line = re.sub(r'[0-9].*\.[0-9].*\n', '', line) + line = re.sub('root\n', '', line) + sample_order = line.split('\n') + sample_order = list([_f for _f in sample_order if _f]) + sample_order.insert(0, 'root') + tree_order = snps_df.loc[sample_order] + # Count number of SNPs in each column. + snp_per_column = [] + for column_header in tree_order: + count = 0 + column = tree_order[column_header] + for element in column: + if element != column[0]: + count = count + 1 + snp_per_column.append(count) + row1 = pandas.Series(snp_per_column, tree_order.columns, name="snp_per_column") + # Count number of SNPS from the + # top of each column in the table. + snp_from_top = [] + for column_header in tree_order: + count = 0 + column = tree_order[column_header] + # for each element in the column + # skip the first element + for element in column[1:]: + if element == column[0]: + count = count + 1 + else: + break + snp_from_top.append(count) + row2 = pandas.Series(snp_from_top, tree_order.columns, name="snp_from_top") + tree_order = tree_order.append([row1]) + tree_order = tree_order.append([row2]) + # In pandas=0.18.1 even this does not work: + # abc = row1.to_frame() + # abc = abc.T --> tree_order.shape (5, 18), abc.shape (1, 18) + # tree_order.append(abc) + # Continue to get error: "*** ValueError: all the input arrays must have same number of dimensions" + tree_order = tree_order.T + tree_order = tree_order.sort_values(['snp_from_top', 'snp_per_column'], ascending=[True, False]) + tree_order = tree_order.T + # Remove snp_per_column and snp_from_top rows. + cascade_order = tree_order[:-2] + # Output the cascade table. + output_cascade_table(cascade_order, mqdf, group, annotation_dict) + # Output the sorted table. + output_sort_table(cascade_order, mqdf, group, annotation_dict) + task_queue.task_done() + + +def set_num_cpus(num_files, processes): + num_cpus = int(multiprocessing.cpu_count()) + if num_files < num_cpus and num_files < processes: + return num_files + if num_cpus < processes: + half_cpus = int(num_cpus / 2) + if num_files < half_cpus: + return num_files + return half_cpus + return processes + + +if __name__ == '__main__': + parser = argparse.ArgumentParser() + + parser.add_argument('--input_avg_mq_json', action='store', dest='input_avg_mq_json', required=False, default=None, help='Average MQ json file') + parser.add_argument('--input_newick', action='store', dest='input_newick', required=False, default=None, help='Newick file') + parser.add_argument('--input_snps_json', action='store', dest='input_snps_json', required=False, default=None, help='SNPs json file') + parser.add_argument('--gbk_file', action='store', dest='gbk_file', required=False, default=None, help='Optional gbk file'), + parser.add_argument('--processes', action='store', dest='processes', type=int, help='User-selected number of processes to use for job splitting') + + args = parser.parse_args() + + if args.gbk_file is not None: + # Create the annotation_dict for annotating + # the Excel tables. + annotation_dict = get_annotation_dict(args.gbk_file) + else: + annotation_dict = None + + # The assumption here is that the list of files + # in both INPUT_NEWICK_DIR and INPUT_JSON_DIR are + # named such that they are properly matched if + # the directories contain more than 1 file (i.e., + # hopefully the newick file names and json file names + # will be something like Mbovis-01D6_* so they can be + # sorted and properly associated with each other). + if args.input_newick is not None: + newick_files = [args.input_newick] + else: + newick_files = [] + for file_name in sorted(os.listdir(INPUT_NEWICK_DIR)): + file_path = os.path.abspath(os.path.join(INPUT_NEWICK_DIR, file_name)) + newick_files.append(file_path) + if args.input_snps_json is not None: + json_files = [args.input_snps_json] + else: + json_files = [] + for file_name in sorted(os.listdir(INPUT_JSON_DIR)): + file_path = os.path.abspath(os.path.join(INPUT_JSON_DIR, file_name)) + json_files.append(file_path) + if args.input_avg_mq_json is not None: + json_avg_mq_files = [args.input_avg_mq_json] + else: + json_avg_mq_files = [] + for file_name in sorted(os.listdir(INPUT_JSON_AVG_MQ_DIR)): + file_path = os.path.abspath(os.path.join(INPUT_JSON_AVG_MQ_DIR, file_name)) + json_avg_mq_files.append(file_path) + + multiprocessing.set_start_method('spawn') + queue1 = multiprocessing.JoinableQueue() + queue2 = multiprocessing.JoinableQueue() + num_files = len(newick_files) + cpus = set_num_cpus(num_files, args.processes) + # Set a timeout for get()s in the queue. + timeout = 0.05 + + for i, newick_file in enumerate(newick_files): + json_file = json_files[i] + json_avg_mq_file = json_avg_mq_files[i] + queue1.put((newick_file, json_file, json_avg_mq_file)) + + # Complete the preprocess_tables task. + processes = [multiprocessing.Process(target=preprocess_tables, args=(queue1, annotation_dict, timeout, )) for _ in range(cpus)] + for p in processes: + p.start() + for p in processes: + p.join() + queue1.join() + + if queue1.empty(): + queue1.close() + queue1.join_thread()