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hmmer2go.py
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hmmer2go.py
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#!/usr/bin/env python2
#-*- coding:utf-8 -*-
"""
HMMER2GO
keith hughitt <khughitt@umd.edu>
2012/10/07
Parses HMMER3 output tables and maps matched Pfam domains to GO terms.
Usage:
hmmer2go.py gene_ontology.1_2.obo hmmer_results.csv
"""
import os
import re
import sys
import csv
import hmmer
from goatools import obo_parser
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 load_pfam2go(source=None):
"""Map HMMER3 results to Gene Ontology (GO) terms"""
# download pfam2go mapping if none specified
if source is None:
import urllib2
url = "http://www.geneontology.org/external2go/pfam2go"
raw = urllib2.urlopen(url).read().split("\n")
else:
# otherwise read in local file
fp = open(source, 'r')
raw = fp.readlines()
fp.close()
# parse and store in a python dict
mapping = {}
# pfam2go entry regex
regex = r"Pfam:(\w+) ([\w\-]+) > (GO:[\w\- \(\)\[\]\/\+>,':]+) ; (GO:\d+)"
for line in raw:
# skip comments
if line.startswith("!") or line == '':
continue
# parse a single entry in pfam2go
# ex: Pfam:PF00060 Lig_chan > GO:membrane ; GO:0016020
match = re.match(regex, line)
pfam_id, pfam_name, go_name, go_id = match.groups()
if pfam_name not in mapping:
mapping[pfam_name] = []
mapping[pfam_name].append((go_id, go_name))
return mapping
def write_csv(results, filepath, norm=None):
"""Write GO analysis results to CSV"""
# get a list of all GO categories used
categories = set([])
for data in results.values():
categories = categories.union(data.keys())
# if no normalization requested do not scale results
if norm is None:
scale_factors = {k: 1 for k in results.keys()}
else:
# otherwise divide norm value by the number of Pfam domains used
scale_factors = {}
for species, data in results.items():
# get total number of domains
total = 0
for term, value in data.items():
total += value
# scale factor = NORM / NUM DOMAINS
scale_factors[species] = norm / float(total)
# write results to CSV
writer = csv.writer(open(filepath, 'wt'))
writer.writerow(["category"] + results.keys())
for category in categories:
row = [category]
for species, data in results.items():
# add category count if it exists for the species
if category in data:
row.append(int(round(data[category] * scale_factors[species])))
else:
# otherwise add 0
row.append(0)
# add row to CSV
writer.writerow(row)
def parse_hmmer_table(filepath):
"""Parses a hmmer table and outputs a summary of the Pfam GO associations"""
if __name__ == "__main__":
go_input = "../input/gene_ontology.1_2.obo"
pfam2go_file = "../input/pfam2go.txt"
# load Gene ontology
print("Loading Gene Ontology...")
go_terms = obo_parser.GODag(go_input)
#go_terms = obo_parser.GODag(sys.argv[1])
# load pfam2go
print("Loading Pfam2go...")
pfam2go = load_pfam2go(pfam2go_file)
# map GO terms
results = {}
for filepath in sys.argv[1:]:
name = os.path.splitext(os.path.basename(filepath))[0]
results[name] = analyze_hmmer_table(go_terms, pfam2go, filepath)
# output results to CSV
write_csv(results, "../csv/go_analysis.csv")
write_csv(results, "../csv/go_analysis_normed.csv", norm=5e4)