skani updates

assemble/skani.py

@@ -76,6 +76,18 @@ def _is_fasta_basically_empty(self, inFasta, min_length=500):
                     return False
         return True
 
+    def _sort_skani_table_by_product(self, in_tsv, out_tsv):
+        ''' Sort the skani output tsv by the product of ANI and Total_bases_covered
+        '''
+        with open(in_tsv, 'r') as inf:
+            reader = csv.DictReader(inf, delimiter='\t')
+            sorted_rows = sorted(reader, key=lambda row: float(row['ANI']) * float(row['Total_bases_covered']), reverse=True)
+
+        with open(out_tsv, 'w') as outf:
+            writer = csv.DictWriter(outf, fieldnames=reader.fieldnames, delimiter='\t', dialect=csv.unix_dialect, quoting=csv.QUOTE_MINIMAL)
+            writer.writeheader()
+            writer.writerows(sorted_rows)
+
     def execute(self, subcommand, args, outfile, threads=None):
         ''' generic execution of skani
         '''
@@ -107,7 +119,7 @@ def dist(self, query_fasta, ref_fastas, outfile, other_args = (), threads=None):
             self.execute('dist', ['-q', query_fasta, '-r'] + list(ref_fastas) + list(other_args), outfile, threads=threads)
 
     def find_reference_clusters(self, ref_fastas,
-                                m=50, s=50, c=20, min_af=15,
+                                m=15, s=50, c=10, min_af=15,
                                 other_args = ('--no-learned-ani', '--robust', '--detailed', '--ci', '--sparse'),
                                 threads=None):
         ''' use skani triangle to define clusters of highly-related genomes
@@ -136,8 +148,12 @@ def find_closest_reference(self, contigs_fasta, ref_fastas, out_file,
         ''' use skani dist to find the closest reference genome for each contig
             (default settings here are for viral genomes)
         '''
-        self.dist(contigs_fasta, ref_fastas, out_file,
-                  ['-m', m, '-c', c, '-s', s, '--min-af', min_af] + list(other_args), threads=threads)
+
+        with util.file.tempfname('.skani_dist.tsv') as tmp_tsv:
+            self.dist(contigs_fasta, ref_fastas, tmp_tsv,
+                    ['-m', m, '-c', c, '-s', s, '--min-af', min_af] + list(other_args), threads=threads)
+            self._sort_skani_table_by_product(tmp_tsv, out_file)
+
         with open(out_file, 'r') as inf:
             top_row = None
             for row in csv.DictReader(inf, delimiter='\t'):

assembly.py

@@ -379,7 +379,7 @@ def parser_gapfill_gap2seq(parser=argparse.ArgumentParser(description='Close gap
 __commands__.append(('gapfill_gap2seq', parser_gapfill_gap2seq))
 
 
-def cluster_references_ani(inRefs, outClusters, m=50, s=50, c=20, min_af=15, threads=None):
+def cluster_references_ani(inRefs, outClusters, m=15, s=50, c=10, min_af=15, threads=None):
     ''' This step uses the skani triangle tool to define clusters of highly-related genomes.
     '''
     skani = assemble.skani.SkaniTool()
@@ -391,9 +391,9 @@ def cluster_references_ani(inRefs, outClusters, m=50, s=50, c=20, min_af=15, thr
 def parser_cluster_references_ani(parser=argparse.ArgumentParser(description='Cluster references')):
     parser.add_argument('inRefs', nargs='+', help='FASTA files containing reference genomes')
     parser.add_argument('outClusters', help='Output file containing clusters of highly-related genomes. Each line contains the filenames of the genomes in one cluster.')
-    parser.add_argument('-m', type=int, default=50, help='marker k-mer compression factor (default: %(default)s)')
+    parser.add_argument('-m', type=int, default=15, help='marker k-mer compression factor (default: %(default)s)')
     parser.add_argument('-s', type=int, default=50, help='screen out pairs with < percent identity using k-mer sketching (default: %(default)s)')
-    parser.add_argument('-c', type=int, default=20, help='compression factor (k-mer subsampling ratio) (default: %(default)s)')
+    parser.add_argument('-c', type=int, default=10, help='compression factor (k-mer subsampling ratio) (default: %(default)s)')
     parser.add_argument('--min_af', dest='min_af', type=int, default=15, help='minimum alignment fraction (default: %(default)s)')
     util.cmd.common_args(parser, (('threads', None), ('loglevel', None), ('version', None), ('tmp_dir', None)))
     util.cmd.attach_main(parser, cluster_references_ani, split_args=True)
@@ -402,7 +402,7 @@ def parser_cluster_references_ani(parser=argparse.ArgumentParser(description='Cl
 __commands__.append(('cluster_references_ani', parser_cluster_references_ani))
 
 
-def skani_contigs_to_refs(inContigs, inRefs, out_skani_dist, out_skani_dist_filtered, out_clusters_filtered, m=50, s=50, c=20, min_af=15, threads=None):
+def skani_contigs_to_refs(inContigs, inRefs, out_skani_dist, out_skani_dist_filtered, out_clusters_filtered, m=15, s=50, c=10, min_af=15, threads=None):
 
     skani = assemble.skani.SkaniTool()
     clusters = skani.find_reference_clusters(inRefs, m=m, s=s, c=c, min_af=min_af, threads=threads)
@@ -436,9 +436,9 @@ def parser_skani_contigs_to_refs(parser=argparse.ArgumentParser(description='Fin
     parser.add_argument('out_skani_dist', help='Output file containing ANI distances between contigs and references')
     parser.add_argument('out_skani_dist_filtered', help='Output file containing ANI distances between contigs and references, with only the top reference hit per cluster')
     parser.add_argument('out_clusters_filtered', help='Output file containing clusters of highly-related genomes, with only clusters that have a hit to the contigs')
-    parser.add_argument('-m', type=int, default=50, help='marker k-mer compression factor (default: %(default)s)')
+    parser.add_argument('-m', type=int, default=15, help='marker k-mer compression factor (default: %(default)s)')
     parser.add_argument('-s', type=int, default=50, help='screen out pairs with < percent identity using k-mer sketching (default: %(default)s)')
-    parser.add_argument('-c', type=int, default=20, help='compression factor (k-mer subsampling ratio) (default: %(default)s)')
+    parser.add_argument('-c', type=int, default=10, help='compression factor (k-mer subsampling ratio) (default: %(default)s)')
     parser.add_argument('--min_af', dest='min_af', type=int, default=15, help='minimum alignment fraction (default: %(default)s)')
     util.cmd.common_args(parser, (('threads', None), ('loglevel', None), ('version', None), ('tmp_dir', None)))
     util.cmd.attach_main(parser, skani_contigs_to_refs, split_args=True)

test/input/TestSkaniReferenceSelection/RVA_DQ473496.1_Rhinovirus_A49.fa

@@ -0,0 +1,90 @@
+>RVA_DQ473496.1_Rhinovirus_A49  (7106 bp)
+TTAAAACTGGATCCAGGTTGTTCCCACCTGGATCTCCCAAAGGGAGTGGAACTCTGTTATTACGGTAACTTTGTACGCCA
+GTTTTATCCTCCCCCACCCCATGTAACTTAGAAGCTTTTCACAAAGACCAATAGTTGGTAATCATCCAGATTACTTAAGG
+TCAAGCACTTCTGTTTCCCCGGTCAACGTTGATATGCTCTAACAGGGCAAAAACAACTGCGATCGTTATCCGCAAAGCGC
+CTACGCAAAGCCTAGTAACACCTTTGAAATTGTTTGGTTGGTCGTTCCGCCATTTTCCCTGGTAGACCTGGCAGATGAGG
+CTAGAAACACCCCACTGGCGACAGTGTTCTAGCCTGCGTGGCTGCCTGCACACCCTGTGGGTGTGAAGCCAAATAATGGA
+CAAGGTGTGAAGAGCCCCGTGTGCTCGCTTTGAGTCCTCCGGCCCCTGAATGCGGCTAACCTTAACCCTGCAGCTAGAGC
+ACACAAGCCAGTGTGTATCTAGTCGTAATGAGTAATTGCGGGACGGGACCGACTACTTTGGGTGTCCGTGTTTCACTTTT
+TACTTATAATTGCTTATGGTGACAATATATATATAGTATATATATTGACACCATGGGCGCACAGGTATCTAGGCAAAATG
+TTGGAACTCATTCTACACAAAACTCCGTATCAAATGGATCTAGTTTGAACTATTTCAACATCAACTACTTTAAAGATGCT
+GCTTCTAATGGCGCATCCAAATTGGAGTTTACTCAAGACCCTAGCAAATTTACAGACCCTGTTAAAGATGTATTAGAGAA
+AGGAATACCAACACTACAATCACCCACAGTCGAGGCTTGTGGATATTCGGATAGAATCATACAAATCACTAGAGGAGATT
+CAACAATAACTTCACAAGATGTGGCAAATGCTGTTGTTGCATATGGTGTTTGGCCACATTATCTATCCTCCAAAGATGCT
+TCTGCAATTGACAAACCTTCTCGTCCGGATACATCTTCTAATAGATTTTATACTTTGAAGAGTGTGACTTGGACCAACTC
+TTCAAAAGGTTGGTGGTGGAAATTGCCAGATGCACTGAGAGAAATGGGCATCTTTGGTGAAAACATGTTTTATCACTATT
+TGGGCAGGAGTGGTTACACAATACATGTACAGTGCAATGCTAGCAAATTCCATCAAGGCACGTTAATCGTTGCACTAATA
+CCTGAACACCAAATTGCAAGCGCCCTTCATGGTGATGTGAATGTTGGTTATAACTTTACCCATCCAGGAGAAACGGGAAG
+GGAGGTCAAAAGTGACAGGAGCATGAACCTTAATTTGCAACCCACTGAAGAGTATTGGCTTAATTTTGATGGAACACTAC
+TCGGAAATATTACTATATTTCCCCATCAATTTATTAACTTGAGAAGCAACAACTCTGCTACAATAATTGCTCCATATGTC
+AATGCAGTCCCTATGGACTCAATGCGCAGCCACAACAACTGGAGTCTGGTGATAATCCCAATATGCCCTCTTGAAACATC
+AAGTACCGTTAACACAATACCTATCACAATATCAATAAGTCCCATGTGTGCAGAGTTCTCTGGTGCGCGTGCTAAGAGTC
+AGGGTTTACCGGTTTTCATCACGCCAGGGTCAGGCCAGTTTCTGACAACAGATGACTTTCAATCCCCATGTGCACTCCCT
+TGGTATCACCCAACCAAAGAAATCTCTATTCCAGGTGAGGTAAAAAATTTAGTTGAAATATGTCAAGTAGATAGTTTAGT
+GCCAATAAATAATACTGAAAATAACATCACTAATGAAAACATGTATTCGGTTGTGCTGCAATCAACAGTTGGAGAACCAG
+ATAAAATCTTTTCTATCAAAACAGATGTAGCCTCTCAGCCATTAGCTACTACTCTAATTGGTGAAATATCCAGTTATTAT
+ACTCACTGGACAGGAAGTCTCCGCTTCAGCTTCATGTTCTGTGGCACAGCCAATACCACTGTTAAACTCTTGTTAGCCTA
+TACGCCACCTGGCATCGCAGAACCTACTACAAGAAAGGAAGCAATGTTAGGTACTCATGTAATATGGGATGTAGGTCTTC
+AATCTACAATATCAATGGTGGTACCATGGATCAGTGCTAGTCACTACAGGAACACATCACCAGGTACATCTACATCTGGA
+TACATAACATGCTGGTATCAAACTAGGCTAGTAATTCCACCTCAAACTCCAGCCACAGCTAGGTTATTATGTTTTGTATC
+AGGCTGTAAAGATTTTTGTTTACGCATGGCTCGGGACACTAATTTGCATTTGCAGAGTGGGGTAATAGAACAAAATCCTG
+TTGAACACTACATAGATGAGGTCCTTAATGAGGTTTTAGTTGTTCCAAATATTAATAGTAGTCACCCCACGACATCAAAC
+TCTGCTCCAGCATTAGATGCTGCAGAAACAGGGCACACTAGCAATGTGCAACCTGAAGATGTCATTGAAACCAGATATGT
+ACAGACATCACAAACAAGAGATGAAATGAGTTTAGAGAGTTTTCTTGGTAGGTCAGGGTGTATACATGAATCCAAACTAG
+AGGTCACACTTACAAATTACAATGAAAATAATTTCAAAGTATGGAACATCAATTTGCAAGAAATGGCCCAGATCAGAAGA
+AAATTTGAACTGTTTACTTACACTAGATTCGATTCTGAAATAACCTTGGTTCCATGCATTTCTGCACTTAGCAAGGATAT
+TGGACACATTACAATGCAATACATGTATGTGCCGCCAGGTGCACCTGTACCAAAGAGCAGAGATGATTATGCATGGCAGT
+CTGGCACTAATGCATCTGTTTTCTGGCAACATGGGCAAGCATACCCAAGATTTTCTCTACCCTTTTTGAGTGTAGCTTCA
+GCTTACTACATGTTTTATGATGGATATAATGAACAGGGCCAAAATTATGGTACGGTAAGTACAAACAACATGGGATCATT
+ATGCTCTAGGATAGTAACAGAGAAACACATTCACAGTATGCATATCATGACAAGAATCTATCATAAAGCTAAACACGTCA
+AAGCATGGTGTCCGCGCCCACCCAGAGCACTTGAATATACTCGCGCTCACCGTACTAATTTCAAAGTTGAAGACAGAGAC
+ATTAAAACAGGAATCACATCCAGAGCAATTATTACAACAGCTGGTCCTAGTGATATGTATGTTCATGTTGGTAATCTTAT
+CTATAGAAATTTACATCTCTTCAATTCTGAAATGCATGAATCTATACTGGTATCTTATTCATCAGATTTAATCATTTACC
+GAACAAACACCATAGGTGATGATTATATCCCCTCTTGTGATTGCACCCAGGCTACATATTACTGTAAACATAAAAATAGA
+TATTTCCCAATCACAGTGACAAGCCATGACTGGTATGAAATACAGGAAAGTGAATACTACCCCAAGCATATACAATACAA
+TTTACTAATTGGTGAGGGCCCTTGTGAACCAGGTGATTGTGGTGGGAAATTGTTATGTAAACATGGTGTTATTGGTATAG
+TAACAGCTGGTGGTGATAATCATGTGGCTTTTATTGATCTTAGACACTTTCACTGTGCTGAGGAGCAAGGAATTACAGAT
+TACATACACATGTTAGGTGAAGCATTTGGAAATGGATTTGTAGATAGTGTAAAAGAACATATACATGCCATTAATCCAGT
+AGGAAATGTTAGTAAAAAGATCATCAAATGGATGCTGAGGATAATATCAGCAATGGTTATAATAATAAGGAATTCCTCTG
+ATCCACAAACTATATTGGCAACACTCACGCTAATTGGATGTTCTGGTTCACCATGGAGATTTTTAAAGGAGAAATTTTGC
+AAATGGACACAACTCAATTATATACATAAAGAATCAGACTCATGGTTGAAAAAATTTACTGAAGCATGCAATGCAGCCAG
+AGGACTTGAGTGGATAGGGAATAAAATATCTAAATTCATTGAATGGATGAAATCAATGCTCCCACAAGCACAACTTAAGG
+TCAAGTACTTAAATGAACTCAAAAAATTGAGCTTGTATGAAAAACAAGTTGAAAGTCTTAGAGTAGCAGATGTAAGAACA
+CAAGAAAAGATTAAAATGGAAATTGATACCCTACATGACTTATCATGCAAATTTTTACCATTGTATGCAGGTGAAGCAAA
+AAGGATAAAGACGTTACATATTAAGTGTGATAACATTATCAAACAAAAGAAAAGGTGTGAACCAGTGGCTATAGTTATCC
+ACGGGCCACCTGGTGCTGGAAAATCAATAACAACAAATTTTTTAGCTAGGATGATAACTAATGATAGTGATATATACTCT
+TTACCCCCAGATCCAAAATATTTTGATGGCTACGATCAACAAAGTGTGGTGATAATGGATGATATCATGCAAAATCCAGC
+TGGGGATGACATGACTCTATTCTGTCAAATGGTTTCCAGTGTCACGTTCATACCACCAATGGCTGATTTACCAGATAAGG
+GCAAGGCCTTTGACTCTAGGTTTGTATTATGCAGCACAAATCACTCTCTTTTAGCACCGCCTACAATAACCTCACTGCCT
+GCTATGAACAGAAGATTCTTTTTAGACTTAGACATAATAGTACATGATAATTTCAAGGATTCACAGGGTAAACTTAATGT
+AGCAGCAGCCTTCCAACCATGTAATGTGGACAATAAAATTGGGAATGCTCGTTGTTGCCCATTTGTGTGTGGAAAGGCTT
+TTTCTTTCAAAGATCGCAATTCTTGCAATAAATACACCCTAGCACAAGTGTACAACATAATGCTTGAAGAAGACAAACGG
+AGGAGACAAGTGGTTGACGTCATGTCGGCCATATTCCAAGGACCAATTGACATGAAAAACCCACCACCACCTGCAATTAC
+TGATTTACTCCAGTCAGTTAGGACTCCTGAGGTTATCAAGTATTGTGAAGTTAATAGATGGATAATTCCAGCAGAATGTA
+AAATAGAGAAGGAATTAAATTTAGCCAATACAATCATAACAATCATTGCCAATGTTATTAGTATAGCAGGTATTATATAC
+GTCATCTACAAACTCTTCTGTACACTACAAGGACCATATTCAGGGGAACCTAAACCTAAAACTAAAGTCCCAGAAAGACG
+TGTTGTGGCACAGGGGCCAGAGGAAGAATTTGGAATGTCTTTGATTAAGCACAACTCATGTGTGATTACAACAGAGAATG
+GAAAGTTTACAGGCCTTGGGATATATGACAAGTTTGTGGTTGTACCAACACATGCAGATCCTGGGAAAGAAATTCAGGTT
+GACGGCATAACTACAAAAGTTGCTGACTCATATGATTTATATAATAAAGATGGAATAAAGTTAGAAATAACAGTCTTAAA
+ATTAGATAGGAATGAAAAGTTCAGAGACATTAGAAAATACATACCAAATAATGAGGATGACTACCCTGACTGCAACTTAG
+CCTTGTTAGCAAATCAACCTGAACCCACTATAATCAATGTTGGAGATGTTATATCCTATGGCAACATACTGCTTAGTGGT
+AACCAGACAGCTAGAATGCTCAAATACAGCTATCCAACTAAATCTGGGTACTGTGGGGGTGTTTTGTATAAAATTGGGCA
+AGTGCTTGGAATACACGTAGGAGGAAATGGTAGAGATGGATTTTCAGCTATGTTGCTTAGATCTTACTTTACTGATGTGC
+AAGGCCAAATAATCTCATCAAAGAAAACTAGTGAATGTAATCTTCCCAGTATACATAATCCATCCAAAACAAAATTGCAG
+CCCAGTGTCTTCTATGATGTTTTCCCTGGTTCAAAAGAACCAGCTGTGTTGTCTGAGAAAGATACACGGCTTCAGGTTGA
+CTTTAATGAAGCATTATTTTCTAAATACAAAGGAAACACAAACTGCTTTATGAATGATCACATAAGGATTGCATCTTCAC
+ACTATGCAGCTCAATTAATCACTTTAGATATTGACCCAAGTCCTATCACCCTTGAAGATAGTGTTTTTGGTACTGATGGA
+TTAGAGGCTCTTGACCTAAATACTAGTGCAGGATTTCCTTACATTACAATGGGGATAAAGAAGAGAGATTTAATAAATAA
+CAAAACCAAAGACATAAGTAAACTTAGGGAAGCAATTGACAAGTATGGAGTTGACTTGCCTATGGTTACTTTCTTAAAAG
+ATGAACTTAGAAAACATGAGAAGATAATCAAAGGCAAAACTAGAGTTATTGAAGCTAGTAGTGTGAATGATACTTTGCTG
+TTCAGAACAACTTTTGGGAATCTTTTTTCAAAATTTCATCTAAATCCTGGAATTATCACTGGATCAGCAGTCGGGTGTGA
+CCCAGAAACTTTCTGGTCAAAGATACCAGCAATGTTAGATGATAAGTGTATTATGGCTTTTGATTATACAAATTATGATG
+GTAGTATACACCCTATTTGGTTTGAAGCCCTTAAACAAGTATTGATGGATTTATCCTTTGATCCAATGTTAATAGATAGA
+TTATGCAAATCCAAACACATCTTTAGAAACACATACTATGAGGTTGAAGGAGGTGTTCCATCTGGATGTTCAGGTACTAG
+TATTTTCAACACTATGATTAATAATATTATTATAAGGACCTTAGTGTTAGATGCATATAAAAATATAGATTTAGACAAAC
+TTAAGATAATTGCCTATGGTGATGATGTTATATTTTCATACACACATGAATTGGACATGGAAGCTATAGCAATAGAGGGT
+ATTAAATATGGTCTAACCATAACCCCTGCTGACAAATCTACCACTTTTGTAAAGTTAAATTACAATAATGTCACCTTTTT
+GAAAAGAGGATTCAAACAAGATGAGAAATACAACTTCTTGATACACCCCACCTTTCCTGAGAGTGAAATATTTGAATCTA
+TCAGGTGGACAAAGAAACCTTCACAGATGCACGAGCACGTACTATCTCTGTGTCGCTTAATGTGGCACAATGGGCGTGAT
+GCGTATGAAAAGTTTGTGGAGAAGATACGCAGTGTGAGCGCTGGTCGCGCACTGTACATCCCTCCATATAACTTGCTTTT
+GCATGAGTGGTATGAAAAATTTTAAACTGATATAGAAATATTAAACTGTTAGTTTATTAGTTTTAC