# HG changeset patch
# User iuc
# Date 1630064812 0
# Node ID 50f539302bf4a912fca17bad9cb059eb63ec8ccd

"planemo upload for repository https://github.com/galaxyproject/tools-iuc/tree/master/tools/vsnp commit 92f46d4bb55b582f05ac3c4b094307f114cbf98f"

diff -r 000000000000 -r 50f539302bf4 macros.xml
--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/macros.xml	Fri Aug 27 11:46:52 2021 +0000
@@ -0,0 +1,24 @@
+<?xml version='1.0' encoding='UTF-8'?>
+<macros>
+    <token name="@WRAPPER_VERSION@">1.0</token>
+    <token name="@PROFILE@">19.09</token>
+    <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>
+
diff -r 000000000000 -r 50f539302bf4 static/images/table_description.png
Binary file static/images/table_description.png has changed
diff -r 000000000000 -r 50f539302bf4 test-data/13-1941-6_S4_L001_R1_600000.fastq.gz
Binary file test-data/13-1941-6_S4_L001_R1_600000.fastq.gz has changed
diff -r 000000000000 -r 50f539302bf4 test-data/13-1941-6_S4_L001_R2_600000.fastq.gz
Binary file test-data/13-1941-6_S4_L001_R2_600000.fastq.gz has changed
diff -r 000000000000 -r 50f539302bf4 test-data/CMC_20E1_R1.fastq.gz
Binary file test-data/CMC_20E1_R1.fastq.gz has changed
diff -r 000000000000 -r 50f539302bf4 test-data/CMC_20E1_R2.fastq.gz
Binary file test-data/CMC_20E1_R2.fastq.gz has changed
diff -r 000000000000 -r 50f539302bf4 test-data/Mbovis-01D6_avg_mq.json
--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/Mbovis-01D6_avg_mq.json	Fri Aug 27 11:46:52 2021 +0000
@@ -0,0 +1,1 @@
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\ No newline at end of file
diff -r 000000000000 -r 50f539302bf4 test-data/Mbovis-01D6_cascade_table.xlsx
Binary file test-data/Mbovis-01D6_cascade_table.xlsx has changed
diff -r 000000000000 -r 50f539302bf4 test-data/Mbovis-01D6_snps.json
--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/Mbovis-01D6_snps.json	Fri Aug 27 11:46:52 2021 +0000
@@ -0,0 +1,1 @@
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diff -r 000000000000 -r 50f539302bf4 test-data/Mbovis-01D6_snps.newick
--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/Mbovis-01D6_snps.newick	Fri Aug 27 11:46:52 2021 +0000
@@ -0,0 +1,1 @@
+(root,((((SRR1792271_zc,SRR1792272_zc),SRR1791772_zc),SRR8073662_zc),SRR1791698_zc_vcf),SRR1792265_zc);
diff -r 000000000000 -r 50f539302bf4 test-data/Mbovis-01D6_sort_table.xlsx
Binary file test-data/Mbovis-01D6_sort_table.xlsx has changed
diff -r 000000000000 -r 50f539302bf4 test-data/Mbovis-01D_avg_mq.json
--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/Mbovis-01D_avg_mq.json	Fri Aug 27 11:46:52 2021 +0000
@@ -0,0 +1,1 @@
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\ No newline at end of file
diff -r 000000000000 -r 50f539302bf4 test-data/Mbovis-01D_cascade_table.xlsx
Binary file test-data/Mbovis-01D_cascade_table.xlsx has changed
diff -r 000000000000 -r 50f539302bf4 test-data/Mbovis-01D_snps.json
--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/Mbovis-01D_snps.json	Fri Aug 27 11:46:52 2021 +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"]]}
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diff -r 000000000000 -r 50f539302bf4 test-data/Mbovis-01D_snps.newick
--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/Mbovis-01D_snps.newick	Fri Aug 27 11:46:52 2021 +0000
@@ -0,0 +1,1 @@
+(root,((((SRR1792271_zc,SRR1792272_zc),SRR1791772_zc),SRR8073662_zc),SRR1791698_zc_vcf),SRR1792265_zc);
diff -r 000000000000 -r 50f539302bf4 test-data/Mbovis-01D_sort_table.xlsx
Binary file test-data/Mbovis-01D_sort_table.xlsx has changed
diff -r 000000000000 -r 50f539302bf4 test-data/Mbovis-01_avg_mq.json
--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/Mbovis-01_avg_mq.json	Fri Aug 27 11:46:52 2021 +0000
@@ -0,0 +1,1 @@
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diff -r 000000000000 -r 50f539302bf4 test-data/Mbovis-01_cascade_table.xlsx
Binary file test-data/Mbovis-01_cascade_table.xlsx has changed
diff -r 000000000000 -r 50f539302bf4 test-data/Mbovis-01_snps.json
--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/Mbovis-01_snps.json	Fri Aug 27 11:46:52 2021 +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
diff -r 000000000000 -r 50f539302bf4 test-data/Mbovis-01_snps.newick
--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/Mbovis-01_snps.newick	Fri Aug 27 11:46:52 2021 +0000
@@ -0,0 +1,1 @@
+(root,((((SRR1792271_zc,SRR1792272_zc),SRR1791772_zc),SRR8073662_zc),SRR1791698_zc_vcf),SRR1792265_zc);
diff -r 000000000000 -r 50f539302bf4 test-data/Mbovis-01_sort_table.xlsx
Binary file test-data/Mbovis-01_sort_table.xlsx has changed
diff -r 000000000000 -r 50f539302bf4 test-data/Mcap_Deer_DE_SRR650221.fastq.gz
Binary file test-data/Mcap_Deer_DE_SRR650221.fastq.gz has changed
diff -r 000000000000 -r 50f539302bf4 test-data/NC_002945v4.fasta
--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/NC_002945v4.fasta	Fri Aug 27 11:46:52 2021 +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
diff -r 000000000000 -r 50f539302bf4 test-data/NC_002945v4.yml
--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/NC_002945v4.yml	Fri Aug 27 11:46:52 2021 +0000
@@ -0,0 +1,5 @@
+bovis:
+ - '11001110'
+ - '11011110'
+ - '11001100'
+
diff -r 000000000000 -r 50f539302bf4 test-data/add_zc_metrics.tabular
--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/add_zc_metrics.tabular	Fri Aug 27 11:46:52 2021 +0000
@@ -0,0 +1,3 @@
+# File	Number of Good SNPs	Average Coverage	Genome Coverage
+MarkDuplicates on data 4_ MarkDuplicates BAM output		10.338671	98.74%
+VCFfilter_ on data 7	611		
diff -r 000000000000 -r 50f539302bf4 test-data/add_zc_metrics1.tabular
--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/add_zc_metrics1.tabular	Fri Aug 27 11:46:52 2021 +0000
@@ -0,0 +1,3 @@
+# File	Number of Good SNPs	Average Coverage	Genome Coverage
+Mcap_Deer_DE_SRR650221_fastq_gz		0.439436	8.27%
+Mcap_Deer_DE_SRR650221_fastq_gz	36		
diff -r 000000000000 -r 50f539302bf4 test-data/add_zc_metrics2.tabular
--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/add_zc_metrics2.tabular	Fri Aug 27 11:46:52 2021 +0000
@@ -0,0 +1,3 @@
+# File	Number of Good SNPs	Average Coverage	Genome Coverage
+MarkDuplicates on data 4_ MarkDuplicates BAM output		10.338671	98.74%
+VCFfilter_ on data 7	611		
diff -r 000000000000 -r 50f539302bf4 test-data/add_zc_metrics3.tabular
--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/add_zc_metrics3.tabular	Fri Aug 27 11:46:52 2021 +0000
@@ -0,0 +1,3 @@
+# File	Number of Good SNPs	Average Coverage	Genome Coverage
+13-1941-6_S4_L001_R1_600000_fastq_gz		0.001252	0.13%
+13-1941-6_S4_L001_R1_600000_fastq_gz	0		
diff -r 000000000000 -r 50f539302bf4 test-data/add_zc_metrics4.tabular
--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/add_zc_metrics4.tabular	Fri Aug 27 11:46:52 2021 +0000
@@ -0,0 +1,3 @@
+# File	Number of Good SNPs	Average Coverage	Genome Coverage
+Mcap_Deer_DE_SRR650221_fastq_gz		0.439436	8.27%
+Mcap_Deer_DE_SRR650221_fastq_gz	36		
diff -r 000000000000 -r 50f539302bf4 test-data/add_zc_metrics5.tabular
--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/add_zc_metrics5.tabular	Fri Aug 27 11:46:52 2021 +0000
@@ -0,0 +1,3 @@
+# File	Number of Good SNPs	Average Coverage	Genome Coverage
+13-1941-6_S4_L001_600000_fastq		0.002146	0.16%
+13-1941-6_S4_L001_600000_fastq	0		
diff -r 000000000000 -r 50f539302bf4 test-data/bam_input.bam
Binary file test-data/bam_input.bam has changed
diff -r 000000000000 -r 50f539302bf4 test-data/cascade_table.xlsx
Binary file test-data/cascade_table.xlsx has changed
diff -r 000000000000 -r 50f539302bf4 test-data/fasta_indexes.loc
--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/fasta_indexes.loc	Fri Aug 27 11:46:52 2021 +0000
@@ -0,0 +1,1 @@
+89	89	Mycobacterium_AF2122	${__HERE__}/NC_002945v4.fasta
diff -r 000000000000 -r 50f539302bf4 test-data/input_avg_mq_json.json
--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/input_avg_mq_json.json	Fri Aug 27 11:46:52 2021 +0000
@@ -0,0 +1,1 @@
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\ No newline at end of file
diff -r 000000000000 -r 50f539302bf4 test-data/input_newick.newick
--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/input_newick.newick	Fri Aug 27 11:46:52 2021 +0000
@@ -0,0 +1,1 @@
+(root,((((SRR1792271_zc,SRR1792272_zc),SRR1791772_zc),SRR8073662_zc),SRR1791698_zc_vcf),SRR1792265_zc);
diff -r 000000000000 -r 50f539302bf4 test-data/input_snps_json.json
--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/input_snps_json.json	Fri Aug 27 11:46:52 2021 +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
diff -r 000000000000 -r 50f539302bf4 test-data/output_dbkey.txt
--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/output_dbkey.txt	Fri Aug 27 11:46:52 2021 +0000
@@ -0,0 +1,1 @@
+AF2122
\ No newline at end of file
diff -r 000000000000 -r 50f539302bf4 test-data/output_metrics.tabular
--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/output_metrics.tabular	Fri Aug 27 11:46:52 2021 +0000
@@ -0,0 +1,3 @@
+# File	Number of Good SNPs	Average Coverage	Genome Coverage
+vcf_input_vcf		0.659602	50.49%
+vcf_input_vcf	0		
diff -r 000000000000 -r 50f539302bf4 test-data/output_metrics.txt
--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/output_metrics.txt	Fri Aug 27 11:46:52 2021 +0000
@@ -0,0 +1,6 @@
+Sample: Mcap_Deer_DE_SRR650221_fastq_gz
+Brucella counts: 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,
+TB counts: 2,2,0,0,4,5,0,0,
+Para counts: 0,0,0,
+Group: TB
+dbkey: AF2122
diff -r 000000000000 -r 50f539302bf4 test-data/output_vcf.vcf
--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/output_vcf.vcf	Fri Aug 27 11:46:52 2021 +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	./.
diff -r 000000000000 -r 50f539302bf4 test-data/paired_collection_metrics.txt
--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/paired_collection_metrics.txt	Fri Aug 27 11:46:52 2021 +0000
@@ -0,0 +1,6 @@
+Sample: CMC_20E1_R1_fastq_gz
+Brucella counts: 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,
+TB counts: 4,4,0,0,8,10,0,0,
+Para counts: 0,0,0,
+Group: TB
+dbkey: AF2122
diff -r 000000000000 -r 50f539302bf4 test-data/paired_dbkey.txt
--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/paired_dbkey.txt	Fri Aug 27 11:46:52 2021 +0000
@@ -0,0 +1,1 @@
+AF2122
\ No newline at end of file
diff -r 000000000000 -r 50f539302bf4 test-data/paired_metrics.txt
--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/paired_metrics.txt	Fri Aug 27 11:46:52 2021 +0000
@@ -0,0 +1,6 @@
+Sample: CMC_20E1_R1_fastq_gz
+Brucella counts: 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,
+TB counts: 4,4,0,0,8,10,0,0,
+Para counts: 0,0,0,
+Group: TB
+dbkey: AF2122
diff -r 000000000000 -r 50f539302bf4 test-data/samtools_idxstats.tabular
--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/samtools_idxstats.tabular	Fri Aug 27 11:46:52 2021 +0000
@@ -0,0 +1,2 @@
+NC_002945.4	4349904	45	4047
+*	0	0	5
diff -r 000000000000 -r 50f539302bf4 test-data/samtools_idxstats1.tabular
--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/samtools_idxstats1.tabular	Fri Aug 27 11:46:52 2021 +0000
@@ -0,0 +1,2 @@
+NC_002945.4	4349904	17063	0
+*	0	0	223
diff -r 000000000000 -r 50f539302bf4 test-data/samtools_idxstats2.tabular
--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/samtools_idxstats2.tabular	Fri Aug 27 11:46:52 2021 +0000
@@ -0,0 +1,2 @@
+NC_002945.4	4349904	45	4047
+*	0	0	5
diff -r 000000000000 -r 50f539302bf4 test-data/samtools_idxstats3.tabular
--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/samtools_idxstats3.tabular	Fri Aug 27 11:46:52 2021 +0000
@@ -0,0 +1,2 @@
+NC_002945.4	4349904	24	0
+*	0	0	2
diff -r 000000000000 -r 50f539302bf4 test-data/samtools_idxstats4.tabular
--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/samtools_idxstats4.tabular	Fri Aug 27 11:46:52 2021 +0000
@@ -0,0 +1,2 @@
+NC_002945.4	4349904	17063	0
+*	0	0	223
diff -r 000000000000 -r 50f539302bf4 test-data/samtools_idxstats5.tabular
--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/samtools_idxstats5.tabular	Fri Aug 27 11:46:52 2021 +0000
@@ -0,0 +1,2 @@
+NC_002945.4	4349904	46	2
+*	0	0	4
diff -r 000000000000 -r 50f539302bf4 test-data/sort_table.xlsx
Binary file test-data/sort_table.xlsx has changed
diff -r 000000000000 -r 50f539302bf4 test-data/vcf_input.vcf
--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/vcf_input.vcf	Fri Aug 27 11:46:52 2021 +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
diff -r 000000000000 -r 50f539302bf4 test-data/vsnp_dnaprints.loc
--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/vsnp_dnaprints.loc	Fri Aug 27 11:46:52 2021 +0000
@@ -0,0 +1,4 @@
+## vSNP DNAprints files
+#Value	Name	Path	Description
+AF2122	Mycobacterium_AF2122/NC_002945v4.yml	${__HERE__}/NC_002945v4.yml	DNAprints file for Mycobacterium bovis AF2122/97
+#NC_006932	Brucella_abortus1/NC_006932-NC_006933.yml	/vsnp/NC_006932/Brucella_abortus1/NC_006932-NC_006933.yml	DNAprints file for Brucella abortus bv. 1 str. 9-941
diff -r 000000000000 -r 50f539302bf4 test-data/vsnp_statistics1.tabular
--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/vsnp_statistics1.tabular	Fri Aug 27 11:46:52 2021 +0000
@@ -0,0 +1,2 @@
+	Reference	File Size	Mean Read Length	Mean Read Quality	Reads Passing Q30	Total Reads	All Mapped Reads	Unmapped Reads	Unmapped Reads Percentage of Total	Reference with Coverage	Average Depth of Coverage	Good SNP Count
+Mcap_Deer_DE_SRR650221_fastq_gz	89	1.6 MB	121.0	29.7	      0.53	4317	17063	223	      0.05	8.27%	0.439436	36
diff -r 000000000000 -r 50f539302bf4 test-data/vsnp_statistics2.tabular
--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/vsnp_statistics2.tabular	Fri Aug 27 11:46:52 2021 +0000
@@ -0,0 +1,3 @@
+	Reference	File Size	Mean Read Length	Mean Read Quality	Reads Passing Q30	Total Reads	All Mapped Reads	Unmapped Reads	Unmapped Reads Percentage of Total	Reference with Coverage	Average Depth of Coverage	Good SNP Count
+13-1941-6_S4_L001_R1_600000_fastq_gz	89	8.7 KB	100.0	65.7	      1.00	25	45	5	      0.20	98.74%	10.338671	611
+13-1941-6_S4_L001_R2_600000_fastq_gz	89	8.5 KB	100.0	66.3	      1.00	25	45	5	      0.20	98.74%	10.338671	611
diff -r 000000000000 -r 50f539302bf4 test-data/vsnp_statistics3.tabular
--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/vsnp_statistics3.tabular	Fri Aug 27 11:46:52 2021 +0000
@@ -0,0 +1,3 @@
+	Reference	File Size	Mean Read Length	Mean Read Quality	Reads Passing Q30	Total Reads	All Mapped Reads	Unmapped Reads	Unmapped Reads Percentage of Total	Reference with Coverage	Average Depth of Coverage	Good SNP Count
+13-1941-6_S4_L001_R1_600000_fastq_gz	89	8.7 KB	100.0	65.7	      1.00	25	24	2	      0.08	0.13%	0.001252	0
+Mcap_Deer_DE_SRR650221_fastq_gz	89	1.6 MB	121.0	29.7	      0.53	4317	17063	223	      0.05	8.27%	0.439436	36
diff -r 000000000000 -r 50f539302bf4 test-data/vsnp_statistics4.tabular
--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/vsnp_statistics4.tabular	Fri Aug 27 11:46:52 2021 +0000
@@ -0,0 +1,3 @@
+	Reference	File Size	Mean Read Length	Mean Read Quality	Reads Passing Q30	Total Reads	All Mapped Reads	Unmapped Reads	Unmapped Reads Percentage of Total	Reference with Coverage	Average Depth of Coverage	Good SNP Count
+13-1941-6_S4_L001_R1_600000_fastq_gz	89	8.7 KB	100.0	65.7	      1.00	25	46	4	      0.16	0.16%	0.002146	0
+13-1941-6_S4_L001_R2_600000_fastq_gz	89	8.5 KB	100.0	66.3	      1.00	25	46	4	      0.16	0.16%	0.002146	0
diff -r 000000000000 -r 50f539302bf4 tool-data/fasta_indexes.loc.sample
--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/tool-data/fasta_indexes.loc.sample	Fri Aug 27 11:46:52 2021 +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
diff -r 000000000000 -r 50f539302bf4 tool-data/vsnp_dnaprints.loc.sample
--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/tool-data/vsnp_dnaprints.loc.sample	Fri Aug 27 11:46:52 2021 +0000
@@ -0,0 +1,4 @@
+## vSNP DNAprints files
+#Value	Name	Path	Description
+#AF2122	Mycobacterium_AF2122/NC_002945v4.yml	/vsnp/AF2122/Mycobacterium_AF2122/NC_002945v4.yml	DNAprints file for Mycobacterium bovis AF2122/97
+#NC_006932	Brucella_abortus1/NC_006932-NC_006933.yml	/vsnp/NC_006932/Brucella_abortus1/NC_006932-NC_006933.yml	DNAprints file for Brucella abortus bv. 1 str. 9-941
diff -r 000000000000 -r 50f539302bf4 tool-data/vsnp_genbank.loc.sample
--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/tool-data/vsnp_genbank.loc.sample	Fri Aug 27 11:46:52 2021 +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
diff -r 000000000000 -r 50f539302bf4 tool_data_table_conf.xml.sample
--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/tool_data_table_conf.xml.sample	Fri Aug 27 11:46:52 2021 +0000
@@ -0,0 +1,18 @@
+<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>
+    <!-- Location of dnaprints files for vsnp_dtermine_ref_from_data tool -->
+    <table name="vsnp_dnaprints" comment_char="#">
+        <columns>value, name, path, description</columns>
+        <file path="tool-data/vsnp_dnaprints.loc" />
+    </table>
+</tables>
+
diff -r 000000000000 -r 50f539302bf4 tool_data_table_conf.xml.test
--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/tool_data_table_conf.xml.test	Fri Aug 27 11:46:52 2021 +0000
@@ -0,0 +1,15 @@
+<tables>
+    <table name="fasta_indexes" comment_char="#">
+        <columns>value, dbkey, name, path</columns>
+        <file path="${__HERE__}/test-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="${__HERE__}/test-data/vsnp_genbank.loc" />
+    </table>
+    <table name="vsnp_dnaprints" comment_char="#">
+        <columns>value, name, path, description</columns>
+        <file path="${__HERE__}/test-data/vsnp_dnaprints.loc" />
+    </table>
+</tables>
diff -r 000000000000 -r 50f539302bf4 vsnp_add_zero_coverage.py
--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/vsnp_add_zero_coverage.py	Fri Aug 27 11:46:52 2021 +0000
@@ -0,0 +1,132 @@
+#!/usr/bin/env python
+
+import argparse
+import os
+import re
+import shutil
+
+import pandas
+import pysam
+from Bio import SeqIO
+
+
+def get_sample_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]
+    return base_file_name
+
+
+def get_coverage_df(bam_file):
+    # 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"])
+    return coverage_df
+
+
+def get_zero_df(reference):
+    # 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"])
+    return zero_df
+
+
+def output_zc_vcf_file(base_file_name, vcf_file, zero_df, total_zero_coverage, output_vcf):
+    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)])
+    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)
+        with open(output_vcf, "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:
+        shutil.move(vcf_file, output_vcf)
+    return good_snp_count
+
+
+def output_metrics_file(base_file_name, average_coverage, genome_coverage, good_snp_count, output_metrics):
+    bam_metrics = [base_file_name, "", "%4f" % average_coverage, genome_coverage]
+    vcf_metrics = [base_file_name, str(good_snp_count), "", ""]
+    metrics_columns = ["File", "Number of Good SNPs", "Average Coverage", "Genome Coverage"]
+    with open(output_metrics, "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))
+
+
+def output_files(vcf_file, total_zero_coverage, zero_df, output_vcf, average_coverage, genome_coverage, output_metrics):
+    base_file_name = get_sample_name(vcf_file)
+    good_snp_count = output_zc_vcf_file(base_file_name, vcf_file, zero_df, total_zero_coverage, output_vcf)
+    output_metrics_file(base_file_name, average_coverage, genome_coverage, good_snp_count, output_metrics)
+
+
+def get_coverage_and_snp_count(bam_file, vcf_file, reference, output_metrics, output_vcf):
+    coverage_df = get_coverage_df(bam_file)
+    zero_df = get_zero_df(reference)
+    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)
+    # Output a zero-coverage vcf fil and the metrics file.
+    output_files(vcf_file, total_zero_coverage, zero_df, output_vcf, average_coverage, genome_coverage, output_metrics)
+
+
+if __name__ == '__main__':
+    parser = argparse.ArgumentParser()
+
+    parser.add_argument('--bam_input', action='store', dest='bam_input', help='bam input file')
+    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('--vcf_input', action='store', dest='vcf_input', help='vcf input file')
+
+    args = parser.parse_args()
+
+    get_coverage_and_snp_count(args.bam_input, args.vcf_input, args.reference, args.output_metrics, args.output_vcf)
diff -r 000000000000 -r 50f539302bf4 vsnp_build_tables.py
--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/vsnp_build_tables.py	Fri Aug 27 11:46:52 2021 +0000
@@ -0,0 +1,302 @@
+#!/usr/bin/env python
+
+import argparse
+import os
+import re
+
+import pandas
+import pandas.io.formats.excel
+from Bio import SeqIO
+
+# 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 _, 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_sample_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]
+    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 = os.path.join(OUTPUT_EXCEL_DIR, "%s_order_mq.json" % type_str)
+            excel_file_name = os.path.join(OUTPUT_EXCEL_DIR, "%s_table.xlsx" % type_str)
+        else:
+            json_file_name = os.path.join(OUTPUT_EXCEL_DIR, "%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:
+        # The table has more columns than is allowed by the
+        # MAXCOLS setting, so multiple files will be produced
+        # as an output collection.
+        if group is None:
+            json_file_name = os.path.join(OUTPUT_EXCEL_DIR, "%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 = os.path.join(OUTPUT_EXCEL_DIR, "%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(newick_file, json_file, json_avg_mq_file, annotation_dict):
+    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_sample_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)
+
+
+if __name__ == '__main__':
+    parser = argparse.ArgumentParser()
+
+    parser.add_argument('--gbk_file', action='store', dest='gbk_file', required=False, default=None, help='Optional gbk file'),
+    parser.add_argument('--input_avg_mq_json', action='store', dest='input_avg_mq_json', help='Average MQ json file')
+    parser.add_argument('--input_newick', action='store', dest='input_newick', help='Newick file')
+    parser.add_argument('--input_snps_json', action='store', dest='input_snps_json', help='SNPs json file')
+
+    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
+
+    preprocess_tables(args.input_newick, args.input_snps_json, args.input_avg_mq_json, annotation_dict)
diff -r 000000000000 -r 50f539302bf4 vsnp_determine_ref_from_data.py
--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/vsnp_determine_ref_from_data.py	Fri Aug 27 11:46:52 2021 +0000
@@ -0,0 +1,225 @@
+#!/usr/bin/env python
+
+import argparse
+import gzip
+import os
+from collections import OrderedDict
+
+import yaml
+from Bio.SeqIO.QualityIO import FastqGeneralIterator
+
+OUTPUT_DBKEY_DIR = 'output_dbkey'
+OUTPUT_METRICS_DIR = 'output_metrics'
+
+
+def get_sample_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]
+    return base_file_name
+
+
+def get_dbkey(dnaprints_dict, key, s):
+    # dnaprints_dict looks something like this:
+    # {'brucella': {'NC_002945v4': ['11001110', '11011110', '11001100']}
+    # {'bovis': {'NC_006895': ['11111110', '00010010', '01111011']}}
+    d = dnaprints_dict.get(key, {})
+    for data_table_value, v_list in d.items():
+        if s in v_list:
+            return data_table_value
+    return ""
+
+
+def get_dnaprints_dict(dnaprint_fields):
+    # A dndprint_fields entry looks something liek this.
+    # [['AF2122', '/galaxy/tool-data/vsnp/AF2122/dnaprints/NC_002945v4.yml']]
+    dnaprints_dict = {}
+    for item in dnaprint_fields:
+        # Here item is a 2-element list of data
+        # table components, # value and path.
+        value = item[0]
+        path = item[1].strip()
+        with open(path, "rt") as fh:
+            # The format of all dnaprints yaml
+            # files is something like this:
+            # brucella:
+            #  - 0111111111111111
+            print_dict = yaml.load(fh, Loader=yaml.Loader)
+        for print_dict_k, print_dict_v in print_dict.items():
+            dnaprints_v_dict = dnaprints_dict.get(print_dict_k, {})
+            if len(dnaprints_v_dict) > 0:
+                # dnaprints_dict already contains k (e.g., 'brucella',
+                # and dnaprints_v_dict will be a dictionary # that
+                # looks something like this:
+                # {'NC_002945v4': ['11001110', '11011110', '11001100']}
+                value_list = dnaprints_v_dict.get(value, [])
+                value_list = value_list + print_dict_v
+                dnaprints_v_dict[value] = value_list
+            else:
+                # dnaprints_v_dict is an empty dictionary.
+                dnaprints_v_dict[value] = print_dict_v
+            dnaprints_dict[print_dict_k] = dnaprints_v_dict
+    # dnaprints_dict looks something like this:
+    # {'brucella': {'NC_002945v4': ['11001110', '11011110', '11001100']}
+    # {'bovis': {'NC_006895': ['11111110', '00010010', '01111011']}}
+    return dnaprints_dict
+
+
+def get_group_and_dbkey(dnaprints_dict, brucella_string, brucella_sum, bovis_string, bovis_sum, para_string, para_sum):
+    if brucella_sum > 3:
+        group = "Brucella"
+        dbkey = get_dbkey(dnaprints_dict, "brucella", brucella_string)
+    elif bovis_sum > 3:
+        group = "TB"
+        dbkey = get_dbkey(dnaprints_dict, "bovis", bovis_string)
+    elif para_sum >= 1:
+        group = "paraTB"
+        dbkey = get_dbkey(dnaprints_dict, "para", para_string)
+    else:
+        group = ""
+        dbkey = ""
+    return group, dbkey
+
+
+def get_oligo_dict():
+    oligo_dict = {}
+    oligo_dict["01_ab1"] = "AATTGTCGGATAGCCTGGCGATAACGACGC"
+    oligo_dict["02_ab3"] = "CACACGCGGGCCGGAACTGCCGCAAATGAC"
+    oligo_dict["03_ab5"] = "GCTGAAGCGGCAGACCGGCAGAACGAATAT"
+    oligo_dict["04_mel"] = "TGTCGCGCGTCAAGCGGCGTGAAATCTCTG"
+    oligo_dict["05_suis1"] = "TGCGTTGCCGTGAAGCTTAATTCGGCTGAT"
+    oligo_dict["06_suis2"] = "GGCAATCATGCGCAGGGCTTTGCATTCGTC"
+    oligo_dict["07_suis3"] = "CAAGGCAGATGCACATAATCCGGCGACCCG"
+    oligo_dict["08_ceti1"] = "GTGAATATAGGGTGAATTGATCTTCAGCCG"
+    oligo_dict["09_ceti2"] = "TTACAAGCAGGCCTATGAGCGCGGCGTGAA"
+    oligo_dict["10_canis4"] = "CTGCTACATAAAGCACCCGGCGACCGAGTT"
+    oligo_dict["11_canis"] = "ATCGTTTTGCGGCATATCGCTGACCACAGC"
+    oligo_dict["12_ovis"] = "CACTCAATCTTCTCTACGGGCGTGGTATCC"
+    oligo_dict["13_ether2"] = "CGAAATCGTGGTGAAGGACGGGACCGAACC"
+    oligo_dict["14_63B1"] = "CCTGTTTAAAAGAATCGTCGGAACCGCTCT"
+    oligo_dict["15_16M0"] = "TCCCGCCGCCATGCCGCCGAAAGTCGCCGT"
+    oligo_dict["16_mel1b"] = "TCTGTCCAAACCCCGTGACCGAACAATAGA"
+    oligo_dict["17_tb157"] = "CTCTTCGTATACCGTTCCGTCGTCACCATGGTCCT"
+    oligo_dict["18_tb7"] = "TCACGCAGCCAACGATATTCGTGTACCGCGACGGT"
+    oligo_dict["19_tbbov"] = "CTGGGCGACCCGGCCGACCTGCACACCGCGCATCA"
+    oligo_dict["20_tb5"] = "CCGTGGTGGCGTATCGGGCCCCTGGATCGCGCCCT"
+    oligo_dict["21_tb2"] = "ATGTCTGCGTAAAGAAGTTCCATGTCCGGGAAGTA"
+    oligo_dict["22_tb3"] = "GAAGACCTTGATGCCGATCTGGGTGTCGATCTTGA"
+    oligo_dict["23_tb4"] = "CGGTGTTGAAGGGTCCCCCGTTCCAGAAGCCGGTG"
+    oligo_dict["24_tb6"] = "ACGGTGATTCGGGTGGTCGACACCGATGGTTCAGA"
+    oligo_dict["25_para"] = "CCTTTCTTGAAGGGTGTTCG"
+    oligo_dict["26_para_sheep"] = "CGTGGTGGCGACGGCGGCGGGCCTGTCTAT"
+    oligo_dict["27_para_cattle"] = "TCTCCTCGGTCGGTGATTCGGGGGCGCGGT"
+    return oligo_dict
+
+
+def get_seq_counts(value, fastq_list, gzipped):
+    count = 0
+    for fastq_file in fastq_list:
+        if gzipped:
+            with gzip.open(fastq_file, 'rt') as fh:
+                for title, seq, qual in FastqGeneralIterator(fh):
+                    count += seq.count(value)
+        else:
+            with open(fastq_file, 'r') as fh:
+                for title, seq, qual in FastqGeneralIterator(fh):
+                    count += seq.count(value)
+    return(value, count)
+
+
+def get_species_counts(fastq_list, gzipped):
+    count_summary = {}
+    oligo_dict = get_oligo_dict()
+    for v1 in oligo_dict.values():
+        returned_value, count = get_seq_counts(v1, fastq_list, gzipped)
+        for key, v2 in oligo_dict.items():
+            if returned_value == v2:
+                count_summary.update({key: count})
+    count_list = []
+    for v in count_summary.values():
+        count_list.append(v)
+    brucella_sum = sum(count_list[:16])
+    bovis_sum = sum(count_list[16:24])
+    para_sum = sum(count_list[24:])
+    return count_summary, count_list, brucella_sum, bovis_sum, para_sum
+
+
+def get_species_strings(count_summary):
+    binary_dictionary = {}
+    for k, v in count_summary.items():
+        if v > 1:
+            binary_dictionary.update({k: 1})
+        else:
+            binary_dictionary.update({k: 0})
+    binary_dictionary = OrderedDict(sorted(binary_dictionary.items()))
+    binary_list = []
+    for v in binary_dictionary.values():
+        binary_list.append(v)
+    brucella_binary = binary_list[:16]
+    brucella_string = ''.join(str(e) for e in brucella_binary)
+    bovis_binary = binary_list[16:24]
+    bovis_string = ''.join(str(e) for e in bovis_binary)
+    para_binary = binary_list[24:]
+    para_string = ''.join(str(e) for e in para_binary)
+    return brucella_string, bovis_string, para_string
+
+
+def output_dbkey(file_name, dbkey, output_file):
+    # Output the dbkey.
+    with open(output_file, "w") as fh:
+        fh.write("%s" % dbkey)
+
+
+def output_files(fastq_file, count_list, group, dbkey, dbkey_file, metrics_file):
+    base_file_name = get_sample_name(fastq_file)
+    output_dbkey(base_file_name, dbkey, dbkey_file)
+    output_metrics(base_file_name, count_list, group, dbkey, metrics_file)
+
+
+def output_metrics(file_name, count_list, group, dbkey, output_file):
+    # Output the metrics.
+    with open(output_file, "w") as fh:
+        fh.write("Sample: %s\n" % file_name)
+        fh.write("Brucella counts: ")
+        for i in count_list[:16]:
+            fh.write("%d," % i)
+        fh.write("\nTB counts: ")
+        for i in count_list[16:24]:
+            fh.write("%d," % i)
+        fh.write("\nPara counts: ")
+        for i in count_list[24:]:
+            fh.write("%d," % i)
+        fh.write("\nGroup: %s" % group)
+        fh.write("\ndbkey: %s\n" % dbkey)
+
+
+if __name__ == '__main__':
+    parser = argparse.ArgumentParser()
+
+    parser.add_argument('--dnaprint_fields', action='append', dest='dnaprint_fields', nargs=2, help="List of dnaprints data table value, name and path fields")
+    parser.add_argument('--read1', action='store', dest='read1', help='Required: single read')
+    parser.add_argument('--read2', action='store', dest='read2', required=False, default=None, help='Optional: paired read')
+    parser.add_argument('--gzipped', action='store_true', dest='gzipped', help='Input files are gzipped')
+    parser.add_argument('--output_dbkey', action='store', dest='output_dbkey', help='Output reference file')
+    parser.add_argument('--output_metrics', action='store', dest='output_metrics', help='Output metrics file')
+
+    args = parser.parse_args()
+
+    fastq_list = [args.read1]
+    if args.read2 is not None:
+        fastq_list.append(args.read2)
+
+    # The value of dnaprint_fields is a list of lists, where each list is
+    # the [value, name, path] components of the vsnp_dnaprints data table.
+    # The data_manager_vsnp_dnaprints tool assigns the dbkey column from the
+    # all_fasta data table to the value column in the vsnp_dnaprints data
+    # table to ensure a proper mapping for discovering the dbkey.
+    dnaprints_dict = get_dnaprints_dict(args.dnaprint_fields)
+
+    # Here fastq_list consists of either a single read
+    # or a set of paired reads, producing single outputs.
+    count_summary, count_list, brucella_sum, bovis_sum, para_sum = get_species_counts(fastq_list, args.gzipped)
+    brucella_string, bovis_string, para_string = get_species_strings(count_summary)
+    group, dbkey = get_group_and_dbkey(dnaprints_dict, brucella_string, brucella_sum, bovis_string, bovis_sum, para_string, para_sum)
+    output_files(args.read1, count_list, group, dbkey, dbkey_file=args.output_dbkey, metrics_file=args.output_metrics)
diff -r 000000000000 -r 50f539302bf4 vsnp_statistics.py
--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/vsnp_statistics.py	Fri Aug 27 11:46:52 2021 +0000
@@ -0,0 +1,193 @@
+#!/usr/bin/env python
+
+import argparse
+import csv
+import gzip
+import os
+from functools import partial
+
+import numpy
+import pandas
+from Bio import SeqIO
+
+
+def nice_size(size):
+    # Returns a readably formatted string with the size
+    words = ['bytes', 'KB', 'MB', 'GB', 'TB', 'PB', 'EB']
+    prefix = ''
+    try:
+        size = float(size)
+        if size < 0:
+            size = abs(size)
+            prefix = '-'
+    except Exception:
+        return '??? bytes'
+    for ind, word in enumerate(words):
+        step = 1024 ** (ind + 1)
+        if step > size:
+            size = size / float(1024 ** ind)
+            if word == 'bytes':  # No decimals for bytes
+                return "%s%d bytes" % (prefix, size)
+            return "%s%.1f %s" % (prefix, size, word)
+    return '??? bytes'
+
+
+def output_statistics(fastq_files, idxstats_files, metrics_files, output_file, gzipped, dbkey):
+    # Produce an Excel spreadsheet that
+    # contains a row for each sample.
+    columns = ['Reference', 'File Size', 'Mean Read Length', 'Mean Read Quality', 'Reads Passing Q30',
+               'Total Reads', 'All Mapped Reads', 'Unmapped Reads', 'Unmapped Reads Percentage of Total',
+               'Reference with Coverage', 'Average Depth of Coverage', 'Good SNP Count']
+    data_frames = []
+    for i, fastq_file in enumerate(fastq_files):
+        idxstats_file = idxstats_files[i]
+        metrics_file = metrics_files[i]
+        file_name_base = os.path.basename(fastq_file)
+        # Read fastq_file into a data frame.
+        _open = partial(gzip.open, mode='rt') if gzipped else open
+        with _open(fastq_file) as fh:
+            identifiers = []
+            seqs = []
+            letter_annotations = []
+            for seq_record in SeqIO.parse(fh, "fastq"):
+                identifiers.append(seq_record.id)
+                seqs.append(seq_record.seq)
+                letter_annotations.append(seq_record.letter_annotations["phred_quality"])
+        # Convert lists to Pandas series.
+        s1 = pandas.Series(identifiers, name='id')
+        s2 = pandas.Series(seqs, name='seq')
+        # Gather Series into a data frame.
+        fastq_df = pandas.DataFrame(dict(id=s1, seq=s2)).set_index(['id'])
+        total_reads = int(len(fastq_df.index) / 4)
+        current_sample_df = pandas.DataFrame(index=[file_name_base], columns=columns)
+        # Reference
+        current_sample_df.at[file_name_base, 'Reference'] = dbkey
+        # File Size
+        current_sample_df.at[file_name_base, 'File Size'] = nice_size(os.path.getsize(fastq_file))
+        # Mean Read Length
+        sampling_size = 10000
+        if sampling_size > total_reads:
+            sampling_size = total_reads
+        fastq_df = fastq_df.iloc[3::4].sample(sampling_size)
+        dict_mean = {}
+        list_length = []
+        i = 0
+        for id, seq, in fastq_df.iterrows():
+            dict_mean[id] = numpy.mean(letter_annotations[i])
+            list_length.append(len(seq.array[0]))
+            i += 1
+        current_sample_df.at[file_name_base, 'Mean Read Length'] = '%.1f' % numpy.mean(list_length)
+        # Mean Read Quality
+        df_mean = pandas.DataFrame.from_dict(dict_mean, orient='index', columns=['ave'])
+        current_sample_df.at[file_name_base, 'Mean Read Quality'] = '%.1f' % df_mean['ave'].mean()
+        # Reads Passing Q30
+        reads_gt_q30 = len(df_mean[df_mean['ave'] >= 30])
+        reads_passing_q30 = '{:10.2f}'.format(reads_gt_q30 / sampling_size)
+        current_sample_df.at[file_name_base, 'Reads Passing Q30'] = reads_passing_q30
+        # Total Reads
+        current_sample_df.at[file_name_base, 'Total Reads'] = total_reads
+        # All Mapped Reads
+        all_mapped_reads, unmapped_reads = process_idxstats_file(idxstats_file)
+        current_sample_df.at[file_name_base, 'All Mapped Reads'] = all_mapped_reads
+        # Unmapped Reads
+        current_sample_df.at[file_name_base, 'Unmapped Reads'] = unmapped_reads
+        # Unmapped Reads Percentage of Total
+        if unmapped_reads > 0:
+            unmapped_reads_percentage = '{:10.2f}'.format(unmapped_reads / total_reads)
+        else:
+            unmapped_reads_percentage = 0
+        current_sample_df.at[file_name_base, 'Unmapped Reads Percentage of Total'] = unmapped_reads_percentage
+        # Reference with Coverage
+        ref_with_coverage, avg_depth_of_coverage, good_snp_count = process_metrics_file(metrics_file)
+        current_sample_df.at[file_name_base, 'Reference with Coverage'] = ref_with_coverage
+        # Average Depth of Coverage
+        current_sample_df.at[file_name_base, 'Average Depth of Coverage'] = avg_depth_of_coverage
+        # Good SNP Count
+        current_sample_df.at[file_name_base, 'Good SNP Count'] = good_snp_count
+        data_frames.append(current_sample_df)
+    output_df = pandas.concat(data_frames)
+    output_df.to_csv(output_file, sep='\t', quoting=csv.QUOTE_NONE, escapechar='\\')
+
+
+def process_idxstats_file(idxstats_file):
+    all_mapped_reads = 0
+    unmapped_reads = 0
+    with open(idxstats_file, "r") as fh:
+        for i, line in enumerate(fh):
+            line = line.rstrip('\r\n')
+            items = line.split("\t")
+            if i == 0:
+                # NC_002945.4 4349904 213570 4047
+                all_mapped_reads = int(items[2])
+            elif i == 1:
+                # * 0 0 82774
+                unmapped_reads = int(items[3])
+    return all_mapped_reads, unmapped_reads
+
+
+def process_metrics_file(metrics_file):
+    ref_with_coverage = '0%'
+    avg_depth_of_coverage = 0
+    good_snp_count = 0
+    with open(metrics_file, "r") as ifh:
+        for i, line in enumerate(ifh):
+            if i == 0:
+                # Skip comments.
+                continue
+            line = line.rstrip('\r\n')
+            items = line.split("\t")
+            if i == 1:
+                # MarkDuplicates 10.338671 98.74%
+                ref_with_coverage = items[3]
+                avg_depth_of_coverage = items[2]
+            elif i == 2:
+                # VCFfilter 611
+                good_snp_count = items[1]
+    return ref_with_coverage, avg_depth_of_coverage, good_snp_count
+
+
+parser = argparse.ArgumentParser()
+
+parser.add_argument('--dbkey', action='store', dest='dbkey', help='Reference dbkey')
+parser.add_argument('--gzipped', action='store_true', dest='gzipped', required=False, default=False, help='Input files are gzipped')
+parser.add_argument('--input_idxstats_dir', action='store', dest='input_idxstats_dir', required=False, default=None, help='Samtools idxstats input directory')
+parser.add_argument('--input_metrics_dir', action='store', dest='input_metrics_dir', required=False, default=None, help='vSNP add zero coverage metrics input directory')
+parser.add_argument('--input_reads_dir', action='store', dest='input_reads_dir', required=False, default=None, help='Samples input directory')
+parser.add_argument('--list_paired', action='store_true', dest='list_paired', required=False, default=False, help='Input samples is a list of paired reads')
+parser.add_argument('--output', action='store', dest='output', help='Output Excel statistics file')
+parser.add_argument('--read1', action='store', dest='read1', help='Required: single read')
+parser.add_argument('--read2', action='store', dest='read2', required=False, default=None, help='Optional: paired read')
+parser.add_argument('--samtools_idxstats', action='store', dest='samtools_idxstats', help='Output of samtools_idxstats')
+parser.add_argument('--vsnp_azc', action='store', dest='vsnp_azc', help='Output of vsnp_add_zero_coverage')
+
+args = parser.parse_args()
+
+fastq_files = []
+idxstats_files = []
+metrics_files = []
+# Accumulate inputs.
+if args.read1 is not None:
+    # The inputs are not dataset collections, so
+    # read1, read2 (possibly) and vsnp_azc will also
+    # not be None.
+    fastq_files.append(args.read1)
+    idxstats_files.append(args.samtools_idxstats)
+    metrics_files.append(args.vsnp_azc)
+    if args.read2 is not None:
+        fastq_files.append(args.read2)
+        idxstats_files.append(args.samtools_idxstats)
+        metrics_files.append(args.vsnp_azc)
+else:
+    for file_name in sorted(os.listdir(args.input_reads_dir)):
+        fastq_files.append(os.path.join(args.input_reads_dir, file_name))
+    for file_name in sorted(os.listdir(args.input_idxstats_dir)):
+        idxstats_files.append(os.path.join(args.input_idxstats_dir, file_name))
+        if args.list_paired:
+            # Add the idxstats file for reverse.
+            idxstats_files.append(os.path.join(args.input_idxstats_dir, file_name))
+    for file_name in sorted(os.listdir(args.input_metrics_dir)):
+        metrics_files.append(os.path.join(args.input_metrics_dir, file_name))
+        if args.list_paired:
+            # Add the metrics file for reverse.
+            metrics_files.append(os.path.join(args.input_metrics_dir, file_name))
+output_statistics(fastq_files, idxstats_files, metrics_files, args.output, args.gzipped, args.dbkey)
diff -r 000000000000 -r 50f539302bf4 vsnp_statistics.xml
--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/vsnp_statistics.xml	Fri Aug 27 11:46:52 2021 +0000
@@ -0,0 +1,205 @@
+<tool id="vsnp_statistics" name="vSNP: statistics" version="@WRAPPER_VERSION@.1+galaxy0" profile="@PROFILE@">
+    <description></description>
+    <macros>
+        <import>macros.xml</import>
+    </macros>
+    <requirements>
+        <requirement type="package" version="1.79">biopython</requirement>
+        <requirement type="package" version="1.21.1">numpy</requirement>
+        <requirement type="package" version="3.0.7">openpyxl</requirement>
+        <requirement type="package" version="1.3.0">pandas</requirement>
+        <requirement type="package" version="2.0.1">xlrd</requirement>
+    </requirements>
+    <command detect_errors="exit_code"><![CDATA[
+#import re
+#set input_idxstats_dir = 'input_idxstats'
+#set input_metrics_dir = 'input_metrics'
+#set input_reads_dir = 'input_reads'
+mkdir -p $input_idxstats_dir &&
+mkdir -p $input_metrics_dir &&
+mkdir -p $input_reads_dir &&
+
+#if $input_type_cond.input_type  == 'single_files':
+    #set read1 = $input_type_cond.read_type_cond.read1
+    #set read1_identifier = re.sub('[^\s\w\-]', '_', str($read1.element_identifier))
+    ln -s '${read1}' '${read1_identifier}' &&
+    #if $input_type_cond.read_type_cond.read_type == 'pair':
+        #set read2 = $input_type_cond.read_type_cond.read2
+        #set read2_identifier = re.sub('[^\s\w\-]', '_', str($read2.element_identifier))
+        ln -s '${read2}' '${read2_identifier}' &&
+    #else:
+        #set read2 = None
+    #end if
+#else:
+    #if $input_type_cond.collection_type_cond.collection_type == 'single':
+        #for $i in $input_type_cond.collection_type_cond.reads_collection:
+            #set identifier = re.sub('[^\s\w\-]', '_', str($i.element_identifier))
+            ln -s '${i.file_name}' '$input_reads_dir/${identifier}' &&
+        #end for
+    #else:
+        #set read1 = $input_type_cond.collection_type_cond.reads_collection['forward']
+        #set read1_identifier = re.sub('[^\s\w\-]', '_', str($read1.name))
+        ln -s '${read1}' '$input_reads_dir/${read1_identifier}' &&
+        #set read2 = $input_type_cond.collection_type_cond.reads_collection['reverse']
+        #set read2_identifier = re.sub('[^\s\w\-]', '_', str($read2.name))
+        ln -s '${read2}' '$input_reads_dir/${read2_identifier}' &&
+    #end if
+    #for $i in $input_type_cond.samtools_idxstats:
+        #set identifier = re.sub('[^\s\w\-]', '_', str($i.element_identifier))
+        ln -s '${i.file_name}' '$input_idxstats_dir/${identifier}' &&
+    #end for
+    #for $i in $input_type_cond.vsnp_azc:
+        #set identifier = re.sub('[^\s\w\-]', '_', str($i.element_identifier))
+        ln -s '${i.file_name}' '$input_metrics_dir/${identifier}' &&
+    #end for
+#end if
+
+python '$__tool_directory__/vsnp_statistics.py'
+#if $input_type_cond.input_type == 'single_files':
+    --dbkey '$input_type_cond.samtools_idxstats.metadata.dbkey'
+    #if $input_type_cond.read_type_cond.read1.is_of_type('fastqsanger.gz'):
+        --gzipped
+    #end if
+    --read1 '${read1_identifier}'
+    #if $input_type_cond.read_type_cond.read_type == 'pair':
+      --read2 '${read2_identifier}'
+    #end if
+    --samtools_idxstats '$input_type_cond.samtools_idxstats'
+    --vsnp_azc '$input_type_cond.vsnp_azc'
+#else:
+    --dbkey '$input_type_cond.samtools_idxstats[0].metadata.dbkey'
+    #if $input_type_cond.collection_type_cond.reads_collection[0].is_of_type('fastqsanger.gz'):
+        --gzipped
+    #end if
+    #if $input_type_cond.collection_type_cond.collection_type == 'paired':
+        --list_paired
+    #end if
+    --input_idxstats_dir '$input_idxstats_dir'
+    --input_metrics_dir '$input_metrics_dir'
+    --input_reads_dir '$input_reads_dir'
+#end if
+--output '$output'
+]]></command>
+    <inputs>
+        <conditional name="input_type_cond">
+            <param name="input_type" type="select" label="Choose the category of the files to be analyzed">
+                <option value="single_files" selected="true">Single files</option>
+                <option value="collections">Collections of files</option>
+            </param>
+            <when value="single_files">
+                <conditional name="read_type_cond">
+                    <param name="read_type" type="select" label="Choose the read type">
+                        <option value="single" selected="true">Single reads</option>
+                         <option value="pair">Paired reads</option>
+                    </param>
+                     <when value="single">
+                        <param name="read1" type="data" format="fastqsanger.gz,fastqsanger" label="Read1 fastq file"/>
+                    </when>
+                    <when value="pair">
+                        <param name="read1" type="data" format="fastqsanger.gz,fastqsanger" label="Read1 fastq file"/>
+                        <param name="read2" type="data" format="fastqsanger.gz,fastqsanger" label="Read2 fastq file"/>
+                    </when>
+                </conditional>
+                <param name="samtools_idxstats" type="data" format="tabular" label="Samtools idxstats file"/>
+                <param name="vsnp_azc" type="data" format="tabular" label="vSNP: add zero coverage metrics file"/>
+            </when>
+            <when value="collections">
+                <conditional name="collection_type_cond">
+                    <param name="collection_type" type="select" label="Collections of single reads or paired reads?">
+                        <option value="single" selected="true">Single reads</option>
+                        <option value="paired">Paired reads in separate datasets</option>
+                    </param>
+                    <when value="single">
+                        <param name="reads_collection" type="data_collection" format="fastqsanger,fastqsanger.gz" collection_type="list" label="Collection of fastqsanger files"/>
+                    </when>
+                    <when value="paired">
+                        <param name="reads_collection" type="data_collection" format="fastqsanger,fastqsanger.gz" collection_type="paired" label="Collection of fastqsanger paired read files"/>
+                    </when>
+                </conditional>
+                <param name="samtools_idxstats" type="data_collection" format="tabular" collection_type="list" label="Collection of samtools idxstats files"/>
+                <param name="vsnp_azc" type="data_collection" format="tabular" collection_type="list" label="Collection of vSNP: add zero coverage metrics files"/>
+            </when>
+        </conditional>
+    </inputs>
+    <outputs>
+        <data name="output" format="tabular"/>
+    </outputs>
+    <tests>
+        <!-- A single fastq file -->
+        <test expect_num_outputs="1">
+            <param name="input_type" value="single_files"/>
+            <param name="read_type" value="single"/>
+            <param name="read1" value="Mcap_Deer_DE_SRR650221.fastq.gz" ftype="fastqsanger.gz" dbkey="89"/>
+            <param name="samtools_idxstats" value="samtools_idxstats1.tabular" ftype="tabular" dbkey="89"/>
+            <param name="vsnp_azc" value="add_zc_metrics1.tabular" ftype="tabular" dbkey="89"/>
+            <output name="output" file="vsnp_statistics1.tabular" ftype="tabular"/>
+        </test>
+        <!-- A set of paired fastq files -->
+        <test expect_num_outputs="1">
+            <param name="input_type" value="single_files"/>
+            <param name="read_type" value="pair"/>
+            <param name="read1" value="13-1941-6_S4_L001_R1_600000.fastq.gz" ftype="fastqsanger.gz" dbkey="89"/>
+            <param name="read2" value="13-1941-6_S4_L001_R2_600000.fastq.gz" ftype="fastqsanger.gz" dbkey="89"/>
+            <param name="samtools_idxstats" value="samtools_idxstats2.tabular" ftype="tabular" dbkey="89"/>
+            <param name="vsnp_azc" value="add_zc_metrics2.tabular" ftype="tabular" dbkey="89"/>
+            <output name="output" file="vsnp_statistics2.tabular" ftype="tabular"/>
+        </test>
+        <!-- A collection of SE fastq files -->
+        <test expect_num_outputs="1">
+            <param name="input_type" value="collections"/>
+            <param name="read_type" value="single"/>
+            <param name="reads_collection">
+                <collection type="list">
+                    <element name="Mcap_Deer_DE_SRR650221.fastq.gz" value="Mcap_Deer_DE_SRR650221.fastq.gz" dbkey="89"/>
+                    <element name="13-1941-6_S4_L001_R1_600000.fastq.gz" value="13-1941-6_S4_L001_R1_600000.fastq.gz" dbkey="89"/>
+                </collection>
+            </param>
+            <param name="samtools_idxstats">
+                <collection type="list">
+                    <element name="13-1941-6_S4_L001_R1_600000.fastq.gz" value="samtools_idxstats3.tabular" dbkey="89"/>
+                    <element name="Mcap_Deer_DE_SRR650221.fastq.gz" value="samtools_idxstats4.tabular" dbkey="89"/>
+                </collection>
+            </param>
+            <param name="vsnp_azc">
+                <collection type="list">
+                    <element name="13-1941-6_S4_L001_R1_600000.fastq.gz" value="add_zc_metrics3.tabular" dbkey="89"/>
+                    <element name="Mcap_Deer_DE_SRR650221.fastq.gz" value="add_zc_metrics4.tabular" dbkey="89"/>
+                </collection>
+            </param>
+            <output name="output" file="vsnp_statistics3.tabular" ftype="tabular"/>
+        </test>
+        <!-- A collection of PE fastq files -->
+        <test expect_num_outputs="1">
+            <param name="input_type" value="collections"/>
+            <param name="collection_type" value="paired"/>
+            <param name="reads_collection">
+                <collection type="paired">
+                    <element name="forward" value="13-1941-6_S4_L001_R1_600000.fastq.gz" ftype="fastqsanger.gz"/>
+                    <element name="reverse" value="13-1941-6_S4_L001_R2_600000.fastq.gz" ftype="fastqsanger.gz"/>
+                </collection>
+            </param>
+            <param name="samtools_idxstats">
+                <collection type="list">
+                    <element name="13-1941-6_S4_L001_R1_600000.fastq" value="samtools_idxstats5.tabular" dbkey="89"/>
+                </collection>
+            </param>
+            <param name="vsnp_azc">
+                <collection type="list">
+                    <element name="13-1941-6_S4_L001_R1_600000.fastq" value="add_zc_metrics5.tabular" dbkey="89"/>
+                </collection>
+            </param>
+            <output name="output" file="vsnp_statistics4.tabular" ftype="tabular"/>
+        </test>
+    </tests>
+    <help>
+**What it does**
+
+Accepts associated fastq files, SAMtools idxstats files and **vSNP: add zero coverage** metrics files and extracts information from them
+to produce an Excel spreadsheet containing statistics for each sample.  The samples can be single or paired reads, and all associated inputs
+can be either single files or collections of files.  The output statistics include reference, file size, mean read length, mean read quality,
+reads passing Q30, total reads, all mapped reads, unmapped reads, unmapped reads percentage of total, reference with coverage, average depth
+of coverage and good SNP count.
+    </help>
+    <expand macro="citations"/>
+</tool>
+