def find_orthologs_per_hit(arguments):
annota.connect()
line, args = arguments
if not line.strip() or line.startswith('#'):
return None
r = map(str.strip, line.split('\t'))
query_name = r[0]
best_hit_name = r[1]
if best_hit_name == '-' or best_hit_name == 'ERROR':
return None
best_hit_evalue = float(r[2])
best_hit_score = float(r[3])
# dp we need this?
#if best_hit_score < args.seed_ortholog_score or best_hit_evalue > args.seed_ortholog_evalue:
# return None
all_orthologies = annota.get_member_orthologs(best_hit_name)
orthologs = sorted(all_orthologies[args.orthology_type])
taxid = query_name.split(".")[0]
# target species and taxid to be added
return (query_name, [], taxid, orthologs)
def annotate_hit_line(arguments):
annota.connect()
line, args = arguments
if not line.strip() or line.startswith('#'):
return None
r = map(str.strip, line.split('\t'))
query_name = r[0]
best_hit_name = r[1]
if best_hit_name == '-' or best_hit_name == 'ERROR':
return None
best_hit_evalue = float(r[2])
best_hit_score = float(r[3])
if best_hit_score < args.seed_ortholog_score or best_hit_evalue > args.seed_ortholog_evalue:
return None
match_nogs = annota.get_member_ogs(best_hit_name)
if not match_nogs:
return None
match_levels = set([nog.split("@")[1] for nog in match_nogs])
if args.tax_scope == "auto":
for level in TAXONOMIC_RESOLUTION:
if level in match_levels:
annot_levels = set(LEVEL_CONTENT.get(level, [level]))
annot_levels.add(level)
annot_level_max = "%s[%d]" %(level, len(annot_levels))
break
else:
annot_levels = set(LEVEL_CONTENT.get(args.tax_scope, [args.tax_scope]))
annot_levels.add(args.tax_scope)
annot_level_max = "%s[%d]" %(args.tax_scope, len(annot_levels))
all_orthologies = annota.get_member_orthologs(best_hit_name, target_levels=annot_levels)
orthologs = sorted(all_orthologies[args.target_orthologs])
if args.excluded_taxa:
orthologs = [o for o in orthologs if not o.startswith("%s." %args.excluded_taxa)]
if orthologs:
pname, gos, kegg, bigg = annota.summarize_annotations(orthologs,
target_go_ev=args.go_evidence,
excluded_go_ev=args.go_excluded)
best_name = ''
if pname:
name_candidate, freq = pname.most_common(1)[0]
if freq >= 2:
best_name = name_candidate
else:
pname = []
best_name = ''
gos = set()
kegg = set()
bigg = set()
return (query_name, best_hit_name, best_hit_evalue, best_hit_score,
best_name, gos, kegg, bigg, annot_level_max, match_nogs, orthologs)
def _annotate_hit_line(arguments):
annota.connect()
line, args = arguments
if not line.strip() or line.startswith('#'):
return None
r = map(str.strip, line.split('\t'))
query_name = r[0]
best_hit_name = r[1]
if best_hit_name == '-' or best_hit_name == 'ERROR':
return None
best_hit_evalue = float(r[2])
best_hit_score = float(r[3])
if best_hit_score < args.seed_ortholog_score or best_hit_evalue > args.seed_ortholog_evalue:
return None
match_nogs = annota.get_member_ogs(best_hit_name)
if not match_nogs:
return None
match_levels = set()
for nog in match_nogs:
match_levels.update(LEVEL_PARENTS[nog.split("@")[1]])
swallowest_level = sorted(match_levels & set(LEVEL_DEPTH.keys()),
key=lambda x: LEVEL_DEPTH[x],
reverse=True)[0]
annot_levels = set()
if args.tax_scope == "auto":
for level in TAXONOMIC_RESOLUTION:
if level in match_levels:
annot_levels.add(level)
annot_level_max = LEVEL_NAMES.get(level, level)
break
else:
annot_levels.add(args.tax_scope)
annot_level_max = LEVEL_NAMES.get(args.tax_scope, args.tax_scope)
if args.target_taxa != 'all':
target_taxa = orthology.normalize_target_taxa(args.target_taxa)
else:
target_taxa = None
try:
all_orthologies = annota.get_member_orthologs(
best_hit_name, target_taxa=target_taxa, target_levels=annot_levels)
except Exception:
orthologs = None
status = 'Error'
else:
orthologs = sorted(all_orthologies[args.target_orthologs])
if args.excluded_taxa:
orthologs = [
o for o in orthologs
if not o.startswith("%s." % args.excluded_taxa)
]
status = 'OK'
if orthologs:
annotations = annota.summarize_annotations(
orthologs,
target_go_ev=args.go_evidence,
excluded_go_ev=args.go_excluded)
else:
annotations = {}
return (query_name, best_hit_name, best_hit_evalue, best_hit_score,
annotations, annot_level_max, swallowest_level, match_nogs,
orthologs)
def annotate_hits_file_sequential(seed_orthologs_file, annot_file,
hmm_hits_file, args):
annot_header = (
"#query_name",
"seed_eggNOG_ortholog",
"seed_ortholog_evalue",
"seed_ortholog_score",
"predicted_gene_name",
"GO_terms",
"KEGG_pathways",
"Annotation_tax_scope",
"OGs",
"bestOG|evalue|score",
"COG cat",
"eggNOG annot",
)
start_time = time.time()
seq2bestOG = {}
if pexists(hmm_hits_file):
seq2bestOG = get_seq_hmm_matches(hmm_hits_file)
seq2annotOG = annota.get_ogs_annotations(
set([v[0] for v in seq2bestOG.itervalues()]))
print colorify("Functional annotation of refined hits starts now", 'green')
OUT = open(annot_file, "w")
if not args.no_file_comments:
print >> OUT, '# ' + time.ctime()
print >> OUT, '# ' + ' '.join(sys.argv)
print >> OUT, '\t'.join(annot_header)
qn = 0
for line in open(seed_orthologs_file):
if not line.strip() or line.startswith('#'):
continue
if qn and (qn % 500 == 0):
total_time = time.time() - start_time
print >> sys.stderr, qn + 1, total_time, "%0.2f q/s (refinement)" % (
(float(qn + 1) / total_time))
sys.stderr.flush()
qn += 1
r = map(str.strip, line.split('\t'))
query_name = r[0]
best_hit_name = r[1]
if best_hit_name == '-' or best_hit_name == 'ERROR':
continue
best_hit_evalue = float(r[2])
best_hit_score = float(r[3])
if best_hit_score < args.seed_ortholog_score or best_hit_evalue > args.seed_ortholog_evalue:
continue
match_nogs = annota.get_member_ogs(best_hit_name)
if not match_nogs:
continue
match_levels = set([nog.split("@")[1] for nog in match_nogs])
if args.tax_scope == "auto":
for level in TAXONOMIC_RESOLUTION:
if level in match_levels:
annot_levels = set(LEVEL_CONTENT.get(level, [level]))
annot_levels.add(level)
annot_level_max = "%s[%d]" % (level, len(annot_levels))
break
else:
annot_levels = set(
LEVEL_CONTENT.get(args.tax_scope, [args.tax_scope]))
annot_levels.add(args.tax_scope)
annot_level_max = "%s[%d]" % (args.tax_scope, len(annot_levels))
all_orthologies = annota.get_member_orthologs(
best_hit_name, target_levels=annot_levels)
orthologs = sorted(all_orthologies[args.target_orthologs])
if args.excluded_taxa:
orthologs = [
o for o in orthologs
if not o.startswith("%s." % args.excluded_taxa)
]
if orthologs:
pname, gos, keggs = annota.get_member_annotations(
orthologs,
target_go_ev=args.go_evidence,
excluded_go_ev=args.go_excluded)
best_name = ''
if pname:
name_candidate, freq = pname.most_common(1)[0]
if freq >= 2:
best_name = name_candidate
else:
pname = []
best_name = ''
gos = set()
keggs = set()
if query_name in seq2bestOG:
(hitname, evalue, score, qlength, hmmfrom, hmmto, seqfrom, seqto,
q_coverage) = seq2bestOG[query_name]
bestOG = '%s|%s|%s' % (hitname, evalue, score)
og_cat, og_desc = seq2annotOG.get(hitname, ['', ''])
else:
bestOG = 'NA|NA|NA'
og_cat, og_desc = '', ''
print >> OUT, '\t'.join(
map(str, (
query_name,
best_hit_name,
best_hit_evalue,
best_hit_score,
best_name,
','.join(sorted(gos)),
','.join(sorted(map(lambda x: "map%05d" % x, map(int,
keggs)))),
annot_level_max,
','.join(match_nogs),
bestOG,
og_cat.replace('\n', ''),
og_desc.replace('\n', ' '),
)))
OUT.flush()
elapsed_time = time.time() - start_time
if not args.no_file_comments:
print >> OUT, '# %d queries scanned' % (qn + 1)
print >> OUT, '# Total time (seconds):', elapsed_time
print >> OUT, '# Rate:', "%0.2f q/s" % ((float(qn + 1) / elapsed_time))
OUT.close()
print colorify(" Processed queries:%s total_time:%s rate:%s" %\
(qn+1, elapsed_time, "%0.2f q/s" % ((float(qn+1) / elapsed_time))), 'lblue')
def annotate_hits_file(seed_orthologs_file, annot_file, hmm_hits_file, args):
annot_header = ("#query_name",
"seed_eggNOG_ortholog",
"seed_ortholog_evalue",
"seed_ortholog_score",
"predicted_gene_name",
"GO_terms",
"KEGG_pathways",
"Annotation_tax_scope",
"OGs",
"bestOG|evalue|score",
"COG cat",
"eggNOG annot",
)
start_time = time.time()
seq2bestOG = {}
if pexists(hmm_hits_file):
seq2bestOG = get_seq_hmm_matches(hmm_hits_file)
seq2annotOG = annota.get_ogs_annotations(set([v[0] for v in seq2bestOG.itervalues()]))
print colorify("Functional annotation of refined hits starts now", 'green')
OUT = open(annot_file, "w")
if not args.no_file_comments:
print >>OUT, '# ' + time.ctime()
print >>OUT, '# ' + ' '.join(sys.argv)
print >>OUT, '\t'.join(annot_header)
qn = 0
for line in open(seed_orthologs_file):
if not line.strip() or line.startswith('#'):
continue
if qn and (qn % 500 == 0):
total_time = time.time() - start_time
print >>sys.stderr, qn+1, total_time, "%0.2f q/s (refinement)" % (
(float(qn + 1) / total_time))
sys.stderr.flush()
qn += 1
r = map(str.strip, line.split('\t'))
query_name = r[0]
best_hit_name = r[1]
if best_hit_name == '-' or best_hit_name == 'ERROR':
continue
best_hit_evalue = float(r[2])
best_hit_score = float(r[3])
if best_hit_score < args.seed_ortholog_score or best_hit_evalue > args.seed_ortholog_evalue:
continue
match_nogs = annota.get_member_ogs(best_hit_name)
if not match_nogs:
continue
match_levels = set([nog.split("@")[1] for nog in match_nogs])
if args.tax_scope == "auto":
for level in TAXONOMIC_RESOLUTION:
if level in match_levels:
annot_levels = set(LEVEL_CONTENT.get(level, [level]))
annot_levels.add(level)
annot_level_max = "%s[%d]" %(level, len(annot_levels))
break
else:
annot_levels = set(LEVEL_CONTENT.get(args.tax_scope, [args.tax_scope]))
annot_levels.add(args.tax_scope)
annot_level_max = "%s[%d]" %(args.tax_scope, len(annot_levels))
all_orthologies = annota.get_member_orthologs(best_hit_name, target_levels=annot_levels)
orthologs = sorted(all_orthologies[args.target_orthologs])
if args.excluded_taxa:
orthologs = [o for o in orthologs if not o.startswith("%s." %args.excluded_taxa)]
if orthologs:
pname, gos, keggs = annota.get_member_annotations(orthologs,
excluded_gos=set(["IEA", "ND"]))
best_name = ''
if pname:
name_candidate, freq = pname.most_common(1)[0]
if freq >= 2:
best_name = name_candidate
else:
pname = []
best_name = ''
gos = set()
keggs = set()
if query_name in seq2bestOG:
(hitname, evalue, score, qlength, hmmfrom, hmmto, seqfrom,
seqto, q_coverage) = seq2bestOG[query_name]
bestOG = '%s|%s|%s' %(hitname, evalue, score)
og_cat, og_desc = seq2annotOG.get(hitname, ['', ''])
else:
bestOG = 'NA|NA|NA'
og_cat, og_desc = '', ''
print >>OUT, '\t'.join(map(str, (query_name,
best_hit_name,
best_hit_evalue,
best_hit_score,
best_name,
','.join(sorted(gos)),
','.join(sorted(map(lambda x: "map%05d"%x, map(int, keggs)))),
annot_level_max,
','.join(match_nogs),
bestOG,
og_cat.replace('\n', ''),
og_desc.replace('\n', ' '),
)))
OUT.flush()
elapsed_time = time.time() - start_time
if not args.no_file_comments:
print >>OUT, '# %d queries scanned' % (qn + 1)
print >>OUT, '# Total time (seconds):', elapsed_time
print >>OUT, '# Rate:', "%0.2f q/s" % ((float(qn + 1) / elapsed_time))
OUT.close()
print colorify(" Processed queries:%s total_time:%s rate:%s" %\
(qn+1, elapsed_time, "%0.2f q/s" % ((float(qn+1) / elapsed_time))), 'lblue')
