writer = fasta.MfaWriter('peptides.fa')
annotFile = open('annot.txt', 'w')
annotFilenames = glob.glob('gsAnnotations/*.txt')
for annotFilename in annotFilenames:
name = extractRootName(annotFilename)
print '>>>', name + '\n'
annotFilename = 'gsAnnotations/%s.txt' % name
annotation = open(annotFilename).readlines()
predictions = parseGenscan(annotation)
pepFilename = 'gsPeptides/%s.fa' % name
peptides = fasta.load_mfa(pepFilename)
peptides = [(h.split()[0],s) for h,s in peptides]
peptides = dict(peptides)
print 'Lengths'
for h in peptides:
print h, len(peptides[h])
print
i = 1
for prediction in predictions.values():
for exon in prediction:
print exon
print
isCoding,warnings,errors = testAnnotation(prediction)
def load_full(iFileHandle):
"""Load genscan predictions.
Arguments:
iFileHandle -- Input file or filename.
Return values:
data -- Annotation data (a list of lists, each list in one gene)
proteins -- Predicted proteins (a list of tuples (header, sequence))
meta -- Meta-data in first 8 lines of genscan output
"""
iFile = smartopen(iFileHandle)
data = {}
proteins = []
meta = []
startPredState = '----- ---- - ------ ------ ---- -- -- ---- ---- ----- ----- ------'
endPredState = 'Predicted peptide sequence(s):'
skipState = 'Slice no. '
metaState = 'GENSCAN 1.0'
state = None
for line in iFile:
line = line.strip()
if metaState in line:
state = 'meta'
if line==startPredState:
state = 'pred'
elif line=='NO EXONS/GENES PREDICTED IN SEQUENCE':
state = 'fail'
elif line==endPredState:
state = 'prot'
elif skipState in line:
state = 'skip'
else:
if state=='meta':
if line:
meta.append(line)
elif state=='pred':
if line:
tokens = line.split()
d = Predicted(tokens)
gene = int(d.gene_exon.split('.')[0])
try:
data[gene].append(d)
except KeyError:
data[gene] = [d]
elif state=='prot':
break
elif state=='fail':
return [], [], ''
if state=='prot':
proteins = fasta.load_mfa(iFile)
data = data.items()
data.sort()
data = [x[1] for x in data]
return data, proteins, meta
def load_full(iFileHandle):
"""Load genscan predictions.
Arguments:
iFileHandle -- Input file or filename.
Return values:
data -- Annotation data (a list of lists, each list in one gene)
proteins -- Predicted proteins (a list of tuples (header, sequence))
meta -- Meta-data in first 8 lines of genscan output
"""
iFile = smartopen(iFileHandle)
data = {}
proteins = []
meta = []
startPredState = '----- ---- - ------ ------ ---- -- -- ---- ---- ----- ----- ------'
endPredState = 'Predicted peptide sequence(s):'
skipState = 'Slice no. '
metaState = 'GENSCAN 1.0'
state = None
for line in iFile:
line = line.strip()
if metaState in line:
state = 'meta'
if line == startPredState:
state = 'pred'
elif line == 'NO EXONS/GENES PREDICTED IN SEQUENCE':
state = 'fail'
elif line == endPredState:
state = 'prot'
elif skipState in line:
state = 'skip'
else:
if state == 'meta':
if line:
meta.append(line)
elif state == 'pred':
if line:
tokens = line.split()
d = Predicted(tokens)
gene = int(d.gene_exon.split('.')[0])
try:
data[gene].append(d)
except KeyError:
data[gene] = [d]
elif state == 'prot':
break
elif state == 'fail':
return [], [], ''
if state == 'prot':
proteins = fasta.load_mfa(iFile)
data = data.items()
data.sort()
data = [x[1] for x in data]
return data, proteins, meta
}[ioDir]
writer = fasta.MfaWriter('peptides.fa')
annotFile = open('annot.txt', 'w')
annotFilenames = glob.glob('gsAnnotations/*.txt')
for annotFilename in annotFilenames:
name = extractRootName(annotFilename)
print '>>>', name + '\n'
annotFilename = 'gsAnnotations/%s.txt' % name
annotation = open(annotFilename).readlines()
predictions = parseGenscan(annotation)
pepFilename = 'gsPeptides/%s.fa' % name
peptides = fasta.load_mfa(pepFilename)
peptides = [(h.split()[0], s) for h, s in peptides]
peptides = dict(peptides)
print 'Lengths'
for h in peptides:
print h, len(peptides[h])
print
i = 1
for prediction in predictions.values():
for exon in prediction:
print exon
print
isCoding, warnings, errors = testAnnotation(prediction)
