def main():
"""Main application"""
# parse csv files for each species
recarrays = [hmmer.parse_csv(x) for x in sys.argv[1:]]
# protein domain counts csv
write_counts_csv(recarrays, '../csv/domains/tap_domain_frequencies.csv')
def classify_species(filepath, results, domains, protein_families):
"""Classifies a species using the results from hmmsearch"""
recarray = hmmer.parse_csv(filepath)
# output base filename
target = os.path.splitext(os.path.basename(filepath))[0]
# Load contigs with matching domains
contigs = load_contigs(target, recarray, domains)
# open csv file for output
filename = "classification_%s.csv" % target
fp = open(os.path.join("../csv/classifications", filename), 'wt')
csv_writer = csv.writer(fp)
csv_writer.writerow(['contig', 'family', 'type'])
# classify contigs
classifications = classify_contigs(contigs, protein_families)
# collapse related contigs and write classification to csv
collapsed = collapse_contig_classifications(target, classifications,
csv_writer)
# convert to recarray and add to master dict
datatypes = [('contig', '|S32'), ('family', '|S64'), ('type', '|S2')]
results[target] = np.array(collapsed, dtype=datatypes)
def main():
"""Main application"""
# parse csv files for each species
recarrays = [hmmer.parse_csv(x) for x in sys.argv[1:]]
# protein domain counts csv
write_counts_csv(recarrays, '../csv/domains/tap_domain_frequencies.csv')
def main():
"""Main"""
recarrays = [hmmer.parse_csv(x) for x in sys.argv[1:]]
for i, filepath in enumerate(sys.argv[1:]):
filename, ext = os.path.splitext(os.path.basename(filepath))
title = "%s TAP domain" % filename
hmmer.plot_evalue_histogram(recarrays[i], filename, title)
def analyze_hmmer_table(go_terms, pfam2go, hmmer_table, go_level=1):
"""Analyzes a given HMMER3 search table for Pfam/GO term matches.
Arguments
---------
go_terms : goatools.obo_parser.GODag
A dictionary of GO terms
pfam2go : dict
Pfam/GO mapping
hmmer_table : str
filepath to a HMMER3 search output table
go_level : int
(Optional) the GO level to summarize
"""
summary = {
"unknown": 0
}
# parse HMMER output and store in data dict
contigs = hmmer.parse_csv(hmmer_table)
# iterate through HMMER output
for contig in contigs:
pfam_domain = contig[2]
# check to see if domain is in Pfam2go
if pfam_domain not in pfam2go:
summary['unknown'] += 1
continue
# otherwise get a list of the associated GO terms
terms = pfam2go[pfam_domain]
# iterate through GO terms for the contig
for t in terms:
go_term = go_terms[t[0]]
# find GO category at the desired level
node = go_term
for i in range(go_term.level - go_level):
node = node.parents[0]
# add to summary table
category = node.name
if not category in summary:
summary[category] = 0
summary[category] += 1
return summary
def main():
"""Main"""
targets = {}
# read in files
for filepath in sys.argv[1:]:
# species name
name = os.path.splitext(os.path.basename(filepath))[0]
# parse HMMER output and store in data dict
recarray = hmmer.parse_csv(filepath)
targets[name] = set(recarray['query_name'])
# output basic statistics
print("(%s) # Domains: %d (%d unique)" %
(name, recarray.shape[0], len(targets[name])))
# pairwise comparisons
write_correlation_csv(targets)
write_correlation_csv(targets, True)
def main():
"""Main"""
targets = {}
# read in files
for filepath in sys.argv[1:]:
# species name
name = os.path.splitext(os.path.basename(filepath))[0]
# parse HMMER output and store in data dict
recarray = hmmer.parse_csv(filepath)
targets[name] = set(recarray['query_name'])
# output basic statistics
print("(%s) # Domains: %d (%d unique)" % (name, recarray.shape[0],
len(targets[name])))
# pairwise comparisons
write_correlation_csv(targets)
write_correlation_csv(targets, True)
