# HG changeset patch
# User greg
# Date 1587477622 14400
# Node ID 38a38babcb31b1fe807e37be9becd94b42f9b4c8
Uploaded
diff -r 000000000000 -r 38a38babcb31 .shed.yml
--- /dev/null Thu Jan 01 00:00:00 1970 +0000
+++ b/.shed.yml Tue Apr 21 10:00:22 2020 -0400
@@ -0,0 +1,11 @@
+name: vsnp_build_tables
+owner: greg
+description: |
+ Contains a tool that produces Excel spreadsheets from outputs produced by the vsnp_get_snps tool.
+homepage_url: https://github.com/USDA-VS/vSNP
+long_description: |
+ Contains a tool that produces Excel spreadsheets from outputs produced by the vsnp_get_snps tool.
+remote_repository_url: https://github.com/gregvonkuster/galaxy_tools/tree/master/tools/sequence_analysis/vsnp/vsnp_build_tables
+type: unrestricted
+categories:
+ - Sequence Analysis
diff -r 000000000000 -r 38a38babcb31 static/images/._table_description.png
Binary file static/images/._table_description.png has changed
diff -r 000000000000 -r 38a38babcb31 static/images/table_description.png
Binary file static/images/table_description.png has changed
diff -r 000000000000 -r 38a38babcb31 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 Tue Apr 21 10:00:22 2020 -0400
@@ -0,0 +1,1 @@
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\ No newline at end of file
diff -r 000000000000 -r 38a38babcb31 test-data/Mbovis-01D6_cascade_table.xlsx
Binary file test-data/Mbovis-01D6_cascade_table.xlsx has changed
diff -r 000000000000 -r 38a38babcb31 test-data/Mbovis-01D6_snps.fasta
--- /dev/null Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/Mbovis-01D6_snps.fasta Tue Apr 21 10:00:22 2020 -0400
@@ -0,0 +1,1 @@
+(root,((((SRR1792271_zc,SRR1792272_zc),SRR1791772_zc),SRR8073662_zc),SRR1791698_zc_vcf),SRR1792265_zc);
diff -r 000000000000 -r 38a38babcb31 test-data/Mbovis-01D6_snps.json
--- /dev/null Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/Mbovis-01D6_snps.json Tue Apr 21 10:00:22 2020 -0400
@@ -0,0 +1,1 @@
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diff -r 000000000000 -r 38a38babcb31 test-data/Mbovis-01D6_sort_table.xlsx
Binary file test-data/Mbovis-01D6_sort_table.xlsx has changed
diff -r 000000000000 -r 38a38babcb31 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 Tue Apr 21 10:00:22 2020 -0400
@@ -0,0 +1,1 @@
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\ No newline at end of file
diff -r 000000000000 -r 38a38babcb31 test-data/Mbovis-01D_cascade_table.xlsx
Binary file test-data/Mbovis-01D_cascade_table.xlsx has changed
diff -r 000000000000 -r 38a38babcb31 test-data/Mbovis-01D_snps.fasta
--- /dev/null Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/Mbovis-01D_snps.fasta Tue Apr 21 10:00:22 2020 -0400
@@ -0,0 +1,1 @@
+(root,((((SRR1792271_zc,SRR1792272_zc),SRR1791772_zc),SRR8073662_zc),SRR1791698_zc_vcf),SRR1792265_zc);
diff -r 000000000000 -r 38a38babcb31 test-data/Mbovis-01D_snps.json
--- /dev/null Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/Mbovis-01D_snps.json Tue Apr 21 10:00:22 2020 -0400
@@ -0,0 +1,1 @@
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diff -r 000000000000 -r 38a38babcb31 test-data/Mbovis-01D_sort_table.xlsx
Binary file test-data/Mbovis-01D_sort_table.xlsx has changed
diff -r 000000000000 -r 38a38babcb31 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 Tue Apr 21 10:00:22 2020 -0400
@@ -0,0 +1,1 @@
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\ No newline at end of file
diff -r 000000000000 -r 38a38babcb31 test-data/Mbovis-01_cascade_table.xlsx
Binary file test-data/Mbovis-01_cascade_table.xlsx has changed
diff -r 000000000000 -r 38a38babcb31 test-data/Mbovis-01_snps.fasta
--- /dev/null Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/Mbovis-01_snps.fasta Tue Apr 21 10:00:22 2020 -0400
@@ -0,0 +1,1 @@
+(root,((((SRR1792271_zc,SRR1792272_zc),SRR1791772_zc),SRR8073662_zc),SRR1791698_zc_vcf),SRR1792265_zc);
diff -r 000000000000 -r 38a38babcb31 test-data/Mbovis-01_snps.json
--- /dev/null Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/Mbovis-01_snps.json Tue Apr 21 10:00:22 2020 -0400
@@ -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 38a38babcb31 test-data/Mbovis-01_sort_table.xlsx
Binary file test-data/Mbovis-01_sort_table.xlsx has changed
diff -r 000000000000 -r 38a38babcb31 test-data/cascade_table.xlsx
Binary file test-data/cascade_table.xlsx has changed
diff -r 000000000000 -r 38a38babcb31 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 Tue Apr 21 10:00:22 2020 -0400
@@ -0,0 +1,1 @@
+{"name":null,"index":["NC_002945.4:1005705","NC_002945.4:1018313","NC_002945.4:1021422","NC_002945.4:1034434","NC_002945.4:1036102","NC_002945.4:1036530","NC_002945.4:1057","NC_002945.4:1096802","NC_002945.4:110198","NC_002945.4:1104019","NC_002945.4:1104291","NC_002945.4:1124266","NC_002945.4:1137800","NC_002945.4:1139489","NC_002945.4:114965","NC_002945.4:1159390","NC_002945.4:1160992","NC_002945.4:1168458","NC_002945.4:117800","NC_002945.4:1186381","NC_002945.4:1190076","NC_002945.4:1190080","NC_002945.4:1190084","NC_002945.4:1191092","NC_002945.4:1199529","NC_002945.4:1199530","NC_002945.4:1199951","NC_002945.4:1206896","NC_002945.4:1212203","NC_002945.4:1213847","NC_002945.4:1214540","NC_002945.4:1224899","NC_002945.4:1230875","NC_002945.4:1244746","NC_002945.4:1259250","NC_002945.4:1264712","NC_002945.4:127447","NC_002945.4:1277988","NC_002945.4:1295457","NC_002945.4:130166","NC_002945.4:130237","NC_002945.4:1312836","NC_002945.4:1314197","NC_002945.4:1333537","NC_002945.4:1335092","NC_002945.4:1341613","NC_002945.4:1348342","NC_002945.4:1382465","NC_002945.4:1383731","NC_002945.4:1405922","NC_002945.4:140686","NC_002945.4:1412824","NC_002945.4:1412828","NC_002945.4:1412885","NC_002945.4:1412893","NC_002945.4:1421904","NC_002945.4:143799","NC_002945.4:1442194","NC_002945.4:144992","NC_002945.4:1463503","NC_002945.4:1467394","NC_002945.4:1470606","NC_002945.4:1479827","NC_002945.4:1481327","NC_002945.4:1484942","NC_002945.4:148871","NC_002945.4:1492328","NC_002945.4:1498639","NC_002945.4:1501932","NC_002945.4:1509487","NC_002945.4:1517866","NC_002945.4:1524526","NC_002945.4:1529147","NC_002945.4:1533175","NC_002945.4:1535299","NC_002945.4:1535303","NC_002945.4:1535366","NC_002945.4:1536267","NC_002945.4:1547426","NC_002945.4:1568090","NC_002945.4:1584881","NC_002945.4:1591357","NC_002945.4:159370","NC_002945.4:1594398","NC_002945.4:1597464","NC_002945.4:1597847","NC_002945.4:1600443","NC_002945.4:160535","NC_002945.4:1619153","NC_002945.4:1619361","NC_002945.4:1625561","NC_002945.4:1628068","NC_002945.4:1632869","NC_002945.4:165799","NC_002945.4:1659174","NC_002945.4:166696","NC_002945.4:1682044","NC_002945.4:1701507","NC_002945.4:1704859","NC_002945.4:1711760","NC_002945.4:1716413","NC_002945.4:1717086","NC_002945.4:1720220","NC_002945.4:1723583","NC_002945.4:1741553","NC_002945.4:1762390","NC_002945.4:1790296","NC_002945.4:179885","NC_002945.4:1799442","NC_002945.4:1803035","NC_002945.4:1806623","NC_002945.4:1817260","NC_002945.4:1828312","NC_002945.4:1833330","NC_002945.4:1863248","NC_002945.4:1871114","NC_002945.4:1880430","NC_002945.4:189083","NC_002945.4:1894922","NC_002945.4:1896107","NC_002945.4:1911237","NC_002945.4:1915461","NC_002945.4:1915936","NC_002945.4:1920100","NC_002945.4:192177","NC_002945.4:1932972","NC_002945.4:1941781","NC_002945.4:1954048","NC_002945.4:1957978","NC_002945.4:1958977","NC_002945.4:1961656","NC_002945.4:1961826","NC_002945.4:1974665","NC_002945.4:198890","NC_002945.4:1989922","NC_002945.4:1996251","NC_002945.4:2002061","NC_002945.4:2007303","NC_002945.4:2010421","NC_002945.4:2020061","NC_002945.4:2021640","NC_002945.4:2024890","NC_002945.4:2027869","NC_002945.4:2035774","NC_002945.4:2036697","NC_002945.4:2049171","NC_002945.4:2057553","NC_002945.4:2059249","NC_002945.4:2059920","NC_002945.4:2075405","NC_002945.4:2078648","NC_002945.4:2093479","NC_002945.4:2096812","NC_002945.4:2099043","NC_002945.4:2118096","NC_002945.4:2121160","NC_002945.4:2137049","NC_002945.4:2138896","NC_002945.4:2145868","NC_002945.4:2163576","NC_002945.4:2204661","NC_002945.4:2210027","NC_002945.4:2239061","NC_002945.4:223919","NC_002945.4:2257546","NC_002945.4:2267557","NC_002945.4:2268821","NC_002945.4:228109","NC_002945.4:2283200","NC_002945.4:2283218","NC_002945.4:2283220","NC_002945.4:2283227","NC_002945.4:2283235","NC_002945.4:2283236","NC_002945.4:2283350","NC_002945.4:2283353","NC_002945.4:2283355","NC_002945.4:2283362","NC_002945.4:2283366","NC_002945.4:2283367","NC_002945.4:2283368","NC_002945.4:2283371","NC_002945.4:230661","NC_002945.4:2308525","NC_002945.4:2310215","NC_002945.4:232188","NC_002945.4:2333994","NC_002945.4:2339770","NC_002945.4:2358298","NC_002945.4:2360219","NC_002945.4:2368982","NC_002945.4:2369407","NC_002945.4:2378324","NC_002945.4:2381437","NC_002945.4:2384647","NC_002945.4:2410761","NC_002945.4:2412437","NC_002945.4:2413021","NC_002945.4:2418267","NC_002945.4:2428397","NC_002945.4:2433602","NC_002945.4:2479007","NC_002945.4:2492067","NC_002945.4:2497022","NC_002945.4:2499336","NC_002945.4:2506199","NC_002945.4:2508626","NC_002945.4:2513801","NC_002945.4:2515130","NC_002945.4:2520576","NC_002945.4:2524942","NC_002945.4:2528517","NC_002945.4:2529413","NC_002945.4:2532958","NC_002945.4:2538021","NC_002945.4:2539896","NC_002945.4:2549198","NC_002945.4:2573831","NC_002945.4:2615591","NC_002945.4:2631265","NC_002945.4:2656304","NC_002945.4:2656651","NC_002945.4:2662768","NC_002945.4:2663582","NC_002945.4:2667489","NC_002945.4:2683485","NC_002945.4:2688315","NC_0029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\ No newline at end of file
diff -r 000000000000 -r 38a38babcb31 test-data/input_newick.newick
--- /dev/null Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/input_newick.newick Tue Apr 21 10:00:22 2020 -0400
@@ -0,0 +1,1 @@
+(root,((((SRR1792271_zc,SRR1792272_zc),SRR1791772_zc),SRR8073662_zc),SRR1791698_zc_vcf),SRR1792265_zc);
diff -r 000000000000 -r 38a38babcb31 test-data/input_snps_json.json
--- /dev/null Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/input_snps_json.json Tue Apr 21 10:00:22 2020 -0400
@@ -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 38a38babcb31 test-data/sort_table.xlsx
Binary file test-data/sort_table.xlsx has changed
diff -r 000000000000 -r 38a38babcb31 tool-data/vsnp_genbank.loc.sample
--- /dev/null Thu Jan 01 00:00:00 1970 +0000
+++ b/tool-data/vsnp_genbank.loc.sample Tue Apr 21 10:00:22 2020 -0400
@@ -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 38a38babcb31 tool_data_table_conf.xml.sample
--- /dev/null Thu Jan 01 00:00:00 1970 +0000
+++ b/tool_data_table_conf.xml.sample Tue Apr 21 10:00:22 2020 -0400
@@ -0,0 +1,7 @@
+
+
+ value, name, path, description
+
+
+
+
diff -r 000000000000 -r 38a38babcb31 vsnp_build_tables.py
--- /dev/null Thu Jan 01 00:00:00 1970 +0000
+++ b/vsnp_build_tables.py Tue Apr 21 10:00:22 2020 -0400
@@ -0,0 +1,380 @@
+#!/usr/bin/env python
+
+import argparse
+import multiprocessing
+import os
+import pandas
+import queue
+import pandas.io.formats.excel
+import re
+from Bio import SeqIO
+
+INPUT_JSON_AVG_MQ_DIR = 'input_json_avg_mq_dir'
+INPUT_JSON_DIR = 'input_json_dir'
+INPUT_NEWICK_DIR = 'input_newick_dir'
+# Maximum columns allowed in a LibreOffice
+# spreadsheet is 1024. Excel allows for
+# 16,384 columns, but we'll set the lower
+# number as the maximum since Galaxy is
+# mostly run on Linux.
+MAXCOLS = 10000
+OUTPUT_EXCEL_DIR = 'output_excel_dir'
+
+
+def annotate_table(table_df, group, annotation_dict):
+ for gbk_chrome, pro in list(annotation_dict.items()):
+ ref_pos = list(table_df)
+ ref_series = pandas.Series(ref_pos)
+ ref_df = pandas.DataFrame(ref_series.str.split(':', expand=True).values, columns=['reference', 'position'])
+ all_ref = ref_df[ref_df['reference'] == gbk_chrome]
+ positions = all_ref.position.to_frame()
+ # Create an annotation file.
+ annotation_file = "%s_annotations.csv" % group
+ with open(annotation_file, "a") as fh:
+ for index, row in positions.iterrows():
+ pos = row.position
+ try:
+ aaa = pro.iloc[pro.index.get_loc(int(pos))][['chrom', 'locus', 'product', 'gene']]
+ try:
+ chrom, name, locus, tag = aaa.values[0]
+ print("{}:{}\t{}, {}, {}".format(chrom, pos, locus, tag, name), file=fh)
+ except ValueError:
+ # If only one annotation for the entire
+ # chromosome (e.g., flu) then having [0] fails
+ chrom, name, locus, tag = aaa.values
+ print("{}:{}\t{}, {}, {}".format(chrom, pos, locus, tag, name), file=fh)
+ except KeyError:
+ print("{}:{}\tNo annotated product".format(gbk_chrome, pos), file=fh)
+ # Read the annotation file into a data frame.
+ annotations_df = pandas.read_csv(annotation_file, sep='\t', header=None, names=['index', 'annotations'], index_col='index')
+ # Remove the annotation_file from disk since both
+ # cascade and sort tables are built using the file,
+ # and it is opened for writing in append mode.
+ os.remove(annotation_file)
+ # Process the data.
+ table_df_transposed = table_df.T
+ table_df_transposed.index = table_df_transposed.index.rename('index')
+ table_df_transposed = table_df_transposed.merge(annotations_df, left_index=True, right_index=True)
+ table_df = table_df_transposed.T
+ return table_df
+
+
+def excel_formatter(json_file_name, excel_file_name, group, annotation_dict):
+ pandas.io.formats.excel.header_style = None
+ table_df = pandas.read_json(json_file_name, orient='split')
+ if annotation_dict is not None:
+ table_df = annotate_table(table_df, group, annotation_dict)
+ else:
+ table_df = table_df.append(pandas.Series(name='no annotations'))
+ writer = pandas.ExcelWriter(excel_file_name, engine='xlsxwriter')
+ table_df.to_excel(writer, sheet_name='Sheet1')
+ writer_book = writer.book
+ ws = writer.sheets['Sheet1']
+ format_a = writer_book.add_format({'bg_color': '#58FA82'})
+ format_g = writer_book.add_format({'bg_color': '#F7FE2E'})
+ format_c = writer_book.add_format({'bg_color': '#0000FF'})
+ format_t = writer_book.add_format({'bg_color': '#FF0000'})
+ format_normal = writer_book.add_format({'bg_color': '#FDFEFE'})
+ formatlowqual = writer_book.add_format({'font_color': '#C70039', 'bg_color': '#E2CFDD'})
+ format_ambigous = writer_book.add_format({'font_color': '#C70039', 'bg_color': '#E2CFDD'})
+ format_n = writer_book.add_format({'bg_color': '#E2CFDD'})
+ rows, cols = table_df.shape
+ ws.set_column(0, 0, 30)
+ ws.set_column(1, cols, 2.1)
+ ws.freeze_panes(2, 1)
+ format_annotation = writer_book.add_format({'font_color': '#0A028C', 'rotation': '-90', 'align': 'top'})
+ # Set last row.
+ ws.set_row(rows + 1, cols + 1, format_annotation)
+ # Make sure that row/column locations don't overlap.
+ ws.conditional_format(rows - 2, 1, rows - 1, cols, {'type': 'cell', 'criteria': '<', 'value': 55, 'format': formatlowqual})
+ ws.conditional_format(2, 1, rows - 2, cols, {'type': 'cell', 'criteria': '==', 'value': 'B$2', 'format': format_normal})
+ ws.conditional_format(2, 1, rows - 2, cols, {'type': 'text', 'criteria': 'containing', 'value': 'A', 'format': format_a})
+ ws.conditional_format(2, 1, rows - 2, cols, {'type': 'text', 'criteria': 'containing', 'value': 'G', 'format': format_g})
+ ws.conditional_format(2, 1, rows - 2, cols, {'type': 'text', 'criteria': 'containing', 'value': 'C', 'format': format_c})
+ ws.conditional_format(2, 1, rows - 2, cols, {'type': 'text', 'criteria': 'containing', 'value': 'T', 'format': format_t})
+ ws.conditional_format(2, 1, rows - 2, cols, {'type': 'text', 'criteria': 'containing', 'value': 'S', 'format': format_ambigous})
+ ws.conditional_format(2, 1, rows - 2, cols, {'type': 'text', 'criteria': 'containing', 'value': 'Y', 'format': format_ambigous})
+ ws.conditional_format(2, 1, rows - 2, cols, {'type': 'text', 'criteria': 'containing', 'value': 'R', 'format': format_ambigous})
+ ws.conditional_format(2, 1, rows - 2, cols, {'type': 'text', 'criteria': 'containing', 'value': 'W', 'format': format_ambigous})
+ ws.conditional_format(2, 1, rows - 2, cols, {'type': 'text', 'criteria': 'containing', 'value': 'K', 'format': format_ambigous})
+ ws.conditional_format(2, 1, rows - 2, cols, {'type': 'text', 'criteria': 'containing', 'value': 'M', 'format': format_ambigous})
+ ws.conditional_format(2, 1, rows - 2, cols, {'type': 'text', 'criteria': 'containing', 'value': 'N', 'format': format_n})
+ ws.conditional_format(2, 1, rows - 2, cols, {'type': 'text', 'criteria': 'containing', 'value': '-', 'format': format_n})
+ format_rotation = writer_book.add_format({})
+ format_rotation.set_rotation(90)
+ for column_num, column_name in enumerate(list(table_df.columns)):
+ ws.write(0, column_num + 1, column_name, format_rotation)
+ format_annotation = writer_book.add_format({'font_color': '#0A028C', 'rotation': '-90', 'align': 'top'})
+ # Set last row.
+ ws.set_row(rows, 400, format_annotation)
+ writer.save()
+
+
+def get_annotation_dict(gbk_file):
+ gbk_dict = SeqIO.to_dict(SeqIO.parse(gbk_file, "genbank"))
+ annotation_dict = {}
+ tmp_file = "features.csv"
+ # Create a file of chromosomes and features.
+ for chromosome in list(gbk_dict.keys()):
+ with open(tmp_file, 'w+') as fh:
+ for feature in gbk_dict[chromosome].features:
+ if "CDS" in feature.type or "rRNA" in feature.type:
+ try:
+ product = feature.qualifiers['product'][0]
+ except KeyError:
+ product = None
+ try:
+ locus = feature.qualifiers['locus_tag'][0]
+ except KeyError:
+ locus = None
+ try:
+ gene = feature.qualifiers['gene'][0]
+ except KeyError:
+ gene = None
+ fh.write("%s\t%d\t%d\t%s\t%s\t%s\n" % (chromosome, int(feature.location.start), int(feature.location.end), locus, product, gene))
+ # Read the chromosomes and features file into a data frame.
+ df = pandas.read_csv(tmp_file, sep='\t', names=["chrom", "start", "stop", "locus", "product", "gene"])
+ # Process the data.
+ df = df.sort_values(['start', 'gene'], ascending=[True, False])
+ df = df.drop_duplicates('start')
+ pro = df.reset_index(drop=True)
+ pro.index = pandas.IntervalIndex.from_arrays(pro['start'], pro['stop'], closed='both')
+ annotation_dict[chromosome] = pro
+ return annotation_dict
+
+
+def get_base_file_name(file_path):
+ base_file_name = os.path.basename(file_path)
+ if base_file_name.find(".") > 0:
+ # Eliminate the extension.
+ return os.path.splitext(base_file_name)[0]
+ elif base_file_name.find("_") > 0:
+ # The dot extension was likely changed to
+ # the " character.
+ items = base_file_name.split("_")
+ return "_".join(items[0:-1])
+ else:
+ return base_file_name
+
+
+def output_cascade_table(cascade_order, mqdf, group, annotation_dict):
+ cascade_order_mq = pandas.concat([cascade_order, mqdf], join='inner')
+ output_table(cascade_order_mq, "cascade", group, annotation_dict)
+
+
+def output_excel(df, type_str, group, annotation_dict, count=None):
+ # Output the temporary json file that
+ # is used by the excel_formatter.
+ if count is None:
+ if group is None:
+ json_file_name = "%s_order_mq.json" % type_str
+ excel_file_name = os.path.join(OUTPUT_EXCEL_DIR, "%s_table.xlsx" % type_str)
+ else:
+ json_file_name = "%s_%s_order_mq.json" % (group, type_str)
+ excel_file_name = os.path.join(OUTPUT_EXCEL_DIR, "%s_%s_table.xlsx" % (group, type_str))
+ else:
+ if group is None:
+ json_file_name = "%s_order_mq_%d.json" % (type_str, count)
+ excel_file_name = os.path.join(OUTPUT_EXCEL_DIR, "%s_table_%d.xlsx" % (type_str, count))
+ else:
+ json_file_name = "%s_%s_order_mq_%d.json" % (group, type_str, count)
+ excel_file_name = os.path.join(OUTPUT_EXCEL_DIR, "%s_%s_table_%d.xlsx" % (group, type_str, count))
+ df.to_json(json_file_name, orient='split')
+ # Output the Excel file.
+ excel_formatter(json_file_name, excel_file_name, group, annotation_dict)
+
+
+def output_sort_table(cascade_order, mqdf, group, annotation_dict):
+ sort_df = cascade_order.T
+ sort_df['abs_value'] = sort_df.index
+ sort_df[['chrom', 'pos']] = sort_df['abs_value'].str.split(':', expand=True)
+ sort_df = sort_df.drop(['abs_value', 'chrom'], axis=1)
+ sort_df.pos = sort_df.pos.astype(int)
+ sort_df = sort_df.sort_values(by=['pos'])
+ sort_df = sort_df.drop(['pos'], axis=1)
+ sort_df = sort_df.T
+ sort_order_mq = pandas.concat([sort_df, mqdf], join='inner')
+ output_table(sort_order_mq, "sort", group, annotation_dict)
+
+
+def output_table(df, type_str, group, annotation_dict):
+ if isinstance(group, str) and group.startswith("dataset"):
+ # Inputs are single files, not collections,
+ # so input file names are not useful for naming
+ # output files.
+ group_str = None
+ else:
+ group_str = group
+ count = 0
+ chunk_start = 0
+ chunk_end = 0
+ column_count = df.shape[1]
+ if column_count >= MAXCOLS:
+ # Here the number of columns is greater than
+ # the maximum allowed by Excel, so multiple
+ # outputs will be produced.
+ while column_count >= MAXCOLS:
+ count += 1
+ chunk_end += MAXCOLS
+ df_of_type = df.iloc[:, chunk_start:chunk_end]
+ output_excel(df_of_type, type_str, group_str, annotation_dict, count=count)
+ chunk_start += MAXCOLS
+ column_count -= MAXCOLS
+ count += 1
+ df_of_type = df.iloc[:, chunk_start:]
+ output_excel(df_of_type, type_str, group_str, annotation_dict, count=count)
+ else:
+ output_excel(df, type_str, group_str, annotation_dict)
+
+
+def preprocess_tables(task_queue, annotation_dict, timeout):
+ while True:
+ try:
+ tup = task_queue.get(block=True, timeout=timeout)
+ except queue.Empty:
+ break
+ newick_file, json_file, json_avg_mq_file = tup
+ avg_mq_series = pandas.read_json(json_avg_mq_file, typ='series', orient='split')
+ # Map quality to dataframe.
+ mqdf = avg_mq_series.to_frame(name='MQ')
+ mqdf = mqdf.T
+ # Get the group.
+ group = get_base_file_name(newick_file)
+ snps_df = pandas.read_json(json_file, orient='split')
+ with open(newick_file, 'r') as fh:
+ for line in fh:
+ line = re.sub('[:,]', '\n', line)
+ line = re.sub('[)(]', '', line)
+ line = re.sub('[0-9].*\.[0-9].*\n', '', line)
+ line = re.sub('root\n', '', line)
+ sample_order = line.split('\n')
+ sample_order = list([_f for _f in sample_order if _f])
+ sample_order.insert(0, 'root')
+ tree_order = snps_df.loc[sample_order]
+ # Count number of SNPs in each column.
+ snp_per_column = []
+ for column_header in tree_order:
+ count = 0
+ column = tree_order[column_header]
+ for element in column:
+ if element != column[0]:
+ count = count + 1
+ snp_per_column.append(count)
+ row1 = pandas.Series(snp_per_column, tree_order.columns, name="snp_per_column")
+ # Count number of SNPS from the
+ # top of each column in the table.
+ snp_from_top = []
+ for column_header in tree_order:
+ count = 0
+ column = tree_order[column_header]
+ # for each element in the column
+ # skip the first element
+ for element in column[1:]:
+ if element == column[0]:
+ count = count + 1
+ else:
+ break
+ snp_from_top.append(count)
+ row2 = pandas.Series(snp_from_top, tree_order.columns, name="snp_from_top")
+ tree_order = tree_order.append([row1])
+ tree_order = tree_order.append([row2])
+ # In pandas=0.18.1 even this does not work:
+ # abc = row1.to_frame()
+ # abc = abc.T --> tree_order.shape (5, 18), abc.shape (1, 18)
+ # tree_order.append(abc)
+ # Continue to get error: "*** ValueError: all the input arrays must have same number of dimensions"
+ tree_order = tree_order.T
+ tree_order = tree_order.sort_values(['snp_from_top', 'snp_per_column'], ascending=[True, False])
+ tree_order = tree_order.T
+ # Remove snp_per_column and snp_from_top rows.
+ cascade_order = tree_order[:-2]
+ # Output the cascade table.
+ output_cascade_table(cascade_order, mqdf, group, annotation_dict)
+ # Output the sorted table.
+ output_sort_table(cascade_order, mqdf, group, annotation_dict)
+ task_queue.task_done()
+
+
+def set_num_cpus(num_files, processes):
+ num_cpus = int(multiprocessing.cpu_count())
+ if num_files < num_cpus and num_files < processes:
+ return num_files
+ if num_cpus < processes:
+ half_cpus = int(num_cpus / 2)
+ if num_files < half_cpus:
+ return num_files
+ return half_cpus
+ return processes
+
+
+if __name__ == '__main__':
+ parser = argparse.ArgumentParser()
+
+ parser.add_argument('--input_avg_mq_json', action='store', dest='input_avg_mq_json', required=False, default=None, help='Average MQ json file')
+ parser.add_argument('--input_newick', action='store', dest='input_newick', required=False, default=None, help='Newick file')
+ parser.add_argument('--input_snps_json', action='store', dest='input_snps_json', required=False, default=None, help='SNPs json file')
+ parser.add_argument('--gbk_file', action='store', dest='gbk_file', required=False, default=None, help='Optional gbk file'),
+ parser.add_argument('--processes', action='store', dest='processes', type=int, help='User-selected number of processes to use for job splitting')
+
+ args = parser.parse_args()
+
+ if args.gbk_file is not None:
+ # Create the annotation_dict for annotating
+ # the Excel tables.
+ annotation_dict = get_annotation_dict(args.gbk_file)
+ else:
+ annotation_dict = None
+
+ # The assumption here is that the list of files
+ # in both INPUT_NEWICK_DIR and INPUT_JSON_DIR are
+ # named such that they are properly matched if
+ # the directories contain more than 1 file (i.e.,
+ # hopefully the newick file names and json file names
+ # will be something like Mbovis-01D6_* so they can be
+ # sorted and properly associated with each other).
+ if args.input_newick is not None:
+ newick_files = [args.input_newick]
+ else:
+ newick_files = []
+ for file_name in sorted(os.listdir(INPUT_NEWICK_DIR)):
+ file_path = os.path.abspath(os.path.join(INPUT_NEWICK_DIR, file_name))
+ newick_files.append(file_path)
+ if args.input_snps_json is not None:
+ json_files = [args.input_snps_json]
+ else:
+ json_files = []
+ for file_name in sorted(os.listdir(INPUT_JSON_DIR)):
+ file_path = os.path.abspath(os.path.join(INPUT_JSON_DIR, file_name))
+ json_files.append(file_path)
+ if args.input_avg_mq_json is not None:
+ json_avg_mq_files = [args.input_avg_mq_json]
+ else:
+ json_avg_mq_files = []
+ for file_name in sorted(os.listdir(INPUT_JSON_AVG_MQ_DIR)):
+ file_path = os.path.abspath(os.path.join(INPUT_JSON_AVG_MQ_DIR, file_name))
+ json_avg_mq_files.append(file_path)
+
+ multiprocessing.set_start_method('spawn')
+ queue1 = multiprocessing.JoinableQueue()
+ queue2 = multiprocessing.JoinableQueue()
+ num_files = len(newick_files)
+ cpus = set_num_cpus(num_files, args.processes)
+ # Set a timeout for get()s in the queue.
+ timeout = 0.05
+
+ for i, newick_file in enumerate(newick_files):
+ json_file = json_files[i]
+ json_avg_mq_file = json_avg_mq_files[i]
+ queue1.put((newick_file, json_file, json_avg_mq_file))
+
+ # Complete the preprocess_tables task.
+ processes = [multiprocessing.Process(target=preprocess_tables, args=(queue1, annotation_dict, timeout, )) for _ in range(cpus)]
+ for p in processes:
+ p.start()
+ for p in processes:
+ p.join()
+ queue1.join()
+
+ if queue1.empty():
+ queue1.close()
+ queue1.join_thread()
diff -r 000000000000 -r 38a38babcb31 vsnp_build_tables.xml
--- /dev/null Thu Jan 01 00:00:00 1970 +0000
+++ b/vsnp_build_tables.xml Tue Apr 21 10:00:22 2020 -0400
@@ -0,0 +1,206 @@
+
+
+
+ biopython
+ pandas
+ xlsxwriter
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+**What it does**
+
+Accepts a combination of single SNPs json, average MQ json and newick files (or associated collections of
+each) to produce annotated SNPs tables in the form of Excel spreadsheets. The SNPs json and average MQ json
+files are typically produced by the **vSNP: get SNPs** tool and the newick files are typically produced by
+the **Phyogenetic reconstruction with RaXML** tool.
+
+The SNPs tables display closely related isolates and enables identification of mixed SNPs when multiple
+bacterial strains are infecting an organism. The table structure is shown below. The columns identify the
+genome location of the SNP calls and the isolates are contained within the rows. The reference (or ancestral
+strain if the reference is an outgroup) is listed across the top, identified as the "reference call". SNPs
+that are not highlighted will match the reference. The map-quality row values are the average of the map
+quality scores of each isolate in that position. These scores measure the confidence that the read has been
+mapped to the correct location on the genome. The maximum score possible is 60, and lower scores lessen the
+confidence that the SNP was correctly identified. The annotation of the position is provided at the bottom
+of the table.
+
+.. image:: table_description.png
+
+SNPs are sorted according to their evolutionary age within the table. The oldest SNPs (encompassing the most
+isolates) are furthest to the left. This sorting is somewhat crude - the intent is to improve readibility or
+more easily match a related tree.
+
+For a more detailed discussion, see the **Validating and correcting SNP calls** section of
+[the vSNP document here](https://github.com/USDA-VS/vSNP/blob/master/docs/detailed_usage.md).
+
+**Required Options**
+
+ * **Choose the category for the files to be analyzed** - select "Single files" or "Collections of files", then select the appropriate history items (single SNPs json, average MQ json and newick files, or collections of each) based on the selected option.
+ * **Use Genbank file** - Select "yes" to annotate the tables using the information in the Genbank file. Locally cached files, if available, provide the most widely used annotations, but more custom Genbank files can be chosen from the current history.
+ * **Number of processes for job splitting** - Select the number of processes for splitting the job to shorten execution time.
+
+
+
+ @misc{None,
+ journal = {None},
+ author = {1. Stuber T},
+ title = {Manuscript in preparation},
+ year = {None},
+ url = {https://github.com/USDA-VS/vSNP},}
+
+
+
+