def annotate(params, proteins):
"""
Runs THMHMM and parses the output files. Takes a standard 'inmembrane'
params dictionary and a global proteins dictionary which it populates with
results.
In the current implementation, this function extracts and feeds sequences to tmhmm
one by one via a temporary file.
These keys are added to the proteins dictionary:
- 'tmhmm_helices', a list of tuples describing the first and last residue
number of each transmembrane segment;
- 'tmhmm_scores', a list of confidence scores (floats) for each predicted
tm segment;
- 'tmhmm_inner_loops', a list of tuples describing the first and last residue
number of each predicted internal loop segment;
- 'tmhmm_outer_loops', a list of tuples describing the first and last residue
number of each predicted outer loop segment;
"""
tmhmm_out = 'tmhmm.out'
run('%(tmhmm_bin)s %(fasta)s' % params, tmhmm_out)
return parse_tmhmm(open('tmhmm.out').read(), proteins)
def annotate(params, proteins):
"""
Runs THMHMM and parses the output files. Takes a standard 'inmembrane'
params dictionary and a global proteins dictionary which it populates with
results.
In the current implementation, this function extracts and feeds sequences to tmhmm
one by one via a temporary file.
These keys are added to the proteins dictionary:
- 'tmhmm_helices', a list of tuples describing the first and last residue
number of each transmembrane segment;
- 'tmhmm_scores', a list of confidence scores (floats) for each predicted
tm segment;
- 'tmhmm_inner_loops', a list of tuples describing the first and last residue
number of each predicted internal loop segment;
- 'tmhmm_outer_loops', a list of tuples describing the first and last residue
number of each predicted outer loop segment;
"""
tmhmm_out = 'tmhmm.out'
run('%(tmhmm_bin)s %(fasta)s' % params, tmhmm_out)
return parse_tmhmm(open('tmhmm.out').read(), proteins)
def annotate(params, proteins):
for seqid in proteins:
proteins[seqid]['is_signalp'] = False
proteins[seqid]['signalp_cleave_position'] = None
signalp4_out = 'signalp.out'
cmd = '%(signalp4_bin)s -t %(signalp4_organism)s %(fasta)s' % \
params
run(cmd, signalp4_out)
with open(signalp4_out) as signalp4_text:
proteins = parse_signalp(signalp4_text, proteins)
return proteins
def annotate(params, proteins):
"""
Returns a reference to the proteins data structure.
Uses HMMER to identify sequence motifs in proteins. This function
annotates the proteins with:
- 'hmmsearch': a list of motifs that are found in the protein. The
motifs correspond to the basename of the .hmm files found in the directory
indicated by the 'hmm_profiles_dir' field of 'params'.
"""
log_stderr("# Searching for HMMER profiles in " +
params['hmm_profiles_dir'])
file_tag = os.path.join(params['hmm_profiles_dir'], '*.hmm')
for hmm_profile in glob.glob(file_tag):
params['hmm_profile'] = hmm_profile
hmm_profile = os.path.basename(params['hmm_profile'])
hmm_name = hmm_profile.replace('.hmm', '')
hmmsearch3_out = 'hmm.%s.out' % hmm_name
cmd = '%(hmmsearch3_bin)s -Z 2000 -E 10 %(hmm_profile)s %(fasta)s' % params
run(cmd, hmmsearch3_out)
# init proteins data structure with blank hmmsearch field first
for seqid in proteins:
if 'hmmsearch' not in proteins[seqid]:
proteins[seqid]['hmmsearch'] = []
# parse the hmmsearch output file
seqid = None
for l in open(hmmsearch3_out):
words = l.split()
if l.startswith(">>"):
seqid = parse_fasta_header(l[3:])[0]
continue
if seqid is None:
continue
if 'conditional E-value' in l:
evalue = float(words[-1])
score = float(words[-5])
if evalue <= params['hmm_evalue_max'] and \
score >= params['hmm_score_min']:
proteins[seqid]['hmmsearch'].append(hmm_name)
return proteins
def annotate(params, proteins):
"""
Returns a reference to the proteins data structure.
Uses HMMER to identify sequence motifs in proteins. This function
annotates the proteins with:
- 'hmmsearch': a list of motifs that are found in the protein. The
motifs correspond to the basename of the .hmm files found in the directory
indicated by the 'hmm_profiles_dir' field of 'params'.
"""
log_stderr(
"# Searching for HMMER profiles in " + params['hmm_profiles_dir'])
file_tag = os.path.join(params['hmm_profiles_dir'], '*.hmm')
for hmm_profile in glob.glob(file_tag):
params['hmm_profile'] = hmm_profile
hmm_profile = os.path.basename(params['hmm_profile'])
hmm_name = hmm_profile.replace('.hmm', '')
hmmsearch3_out = 'hmm.%s.out' % hmm_name
cmd = '%(hmmsearch3_bin)s -Z 2000 -E 10 %(hmm_profile)s %(fasta)s' % params
run(cmd, hmmsearch3_out)
# init proteins data structure with blank hmmsearch field first
for seqid in proteins:
if 'hmmsearch' not in proteins[seqid]:
proteins[seqid]['hmmsearch'] = []
# parse the hmmsearch output file
seqid = None
for l in open(hmmsearch3_out):
words = l.split()
if l.startswith(">>"):
seqid = parse_fasta_header(l[3:])[0]
continue
if seqid is None:
continue
if 'conditional E-value' in l:
evalue = float(words[-1])
score = float(words[-5])
if evalue <= params['hmm_evalue_max'] and \
score >= params['hmm_score_min']:
proteins[seqid]['hmmsearch'].append(hmm_name)
return proteins
def annotate(params, proteins):
for seqid in proteins:
proteins[seqid]['is_signalp'] = False
proteins[seqid]['signalp_cleave_position'] = None
signalp4_out = 'signalp.out'
cmd = '%(signalp4_bin)s -t %(signalp4_organism)s %(fasta)s' % \
params
run(cmd, signalp4_out)
with open(signalp4_out) as signalp4_text:
proteins = parse_signalp(signalp4_text, proteins)
return proteins
def annotate(params, proteins):
"""
Uses LipoP to identify lipo-attachment signals in the protein.
The 'proteins' dictionary is annotated by adding two fields:
- 'is_lipop' is a boolean indicating whether a signal is found or not
- 'lipop_cleave_position' gives the position where the signal
is cleaved and the protein is attached to a lipid
Returns a reference to the proteins data structure.
"""
lipop1_out = 'lipop.out'
run('%(lipop1_bin)s %(fasta)s' % params, lipop1_out)
proteins = parse_lipop(open(lipop1_out).read(), proteins)
return proteins
def annotate(params, proteins):
"""
Runs MEMSAT3 and parses the output files. Takes a standard 'inmembrane'
params dictionary and a global proteins dictionary which it populates with
results.
In the current implementation, this function extracts and feeds sequences to MEMSAT3
one by one via a temporary file.
These keys are added to the proteins dictionary:
- 'memsat3_helices', a list of tuples describing the first and last residue
number of each transmembrane segment;
- 'memsat3_scores', a list of confidence scores (floats) for each predicted
tm segment;
- 'memsat3_inner_loops', a list of tuples describing the first and last residue
number of each predicted internal loop segment;
- 'memsat3_outer_loops', a list of tuples describing the first and last residue
number of each predicted outer loop segment;
"""
for seqid in proteins:
protein = proteins[seqid]
# initialize the protein data structure
protein.update({
'memsat3_scores': [],
'memsat3_helices': [],
'memsat3_inner_loops': [],
'memsat3_outer_loops': []
})
# write seq to single fasta file
single_fasta = seqid_to_filename(seqid) + '.fasta'
if not os.path.isfile(single_fasta):
write_proteins_fasta(single_fasta, proteins, [seqid])
memsat_out = single_fasta.replace('fasta', 'memsat')
run('%s %s' % (params['memsat3_bin'], single_fasta), memsat_out)
globmem_out = single_fasta.replace('fasta', 'globmem')
if has_transmembrane_in_globmem(globmem_out):
parse_memsat(protein, memsat_out)
def annotate(params, proteins):
"""
Runs MEMSAT3 and parses the output files. Takes a standard 'inmembrane'
params dictionary and a global proteins dictionary which it populates with
results.
In the current implementation, this function extracts and feeds sequences to MEMSAT3
one by one via a temporary file.
These keys are added to the proteins dictionary:
- 'memsat3_helices', a list of tuples describing the first and last residue
number of each transmembrane segment;
- 'memsat3_scores', a list of confidence scores (floats) for each predicted
tm segment;
- 'memsat3_inner_loops', a list of tuples describing the first and last residue
number of each predicted internal loop segment;
- 'memsat3_outer_loops', a list of tuples describing the first and last residue
number of each predicted outer loop segment;
"""
for seqid in proteins:
protein = proteins[seqid]
# initialize the protein data structure
protein.update({
'memsat3_scores':[],
'memsat3_helices':[],
'memsat3_inner_loops':[],
'memsat3_outer_loops':[]
})
# write seq to single fasta file
single_fasta = seqid_to_filename(seqid) + '.fasta'
if not os.path.isfile(single_fasta):
write_proteins_fasta(single_fasta, proteins, [seqid])
memsat_out = single_fasta.replace('fasta', 'memsat')
run('%s %s' % (params['memsat3_bin'], single_fasta), memsat_out)
globmem_out = single_fasta.replace('fasta', 'globmem')
if has_transmembrane_in_globmem(globmem_out):
parse_memsat(protein, memsat_out)
def annotate(params, proteins):
for seqid in proteins:
proteins[seqid]['is_signalp'] = False
proteins[seqid]['signalp_cleave_position'] = None
signalp4_out = 'signalp.out'
cmd = '%(signalp4_bin)s -t %(signalp4_organism)s %(fasta)s' % \
params
run(cmd, signalp4_out)
for line in open(signalp4_out):
if line.startswith("#"):
continue
words = line.split()
seqid = parse_fasta_header(words[0])[0]
proteins[seqid]['signalp_cleave_position'] = int(words[4])
if (words[9] == "Y"):
proteins[seqid]['is_signalp'] = True
return proteins
