def test_Feature():
a = '\t'.join(['Chr1', 'TAIR10', 'gene', '2', '20', '.', '+',
'.', 'ID=Gene1;Parent=p'])
f = Feature.from_string(a)
eq_('Gene1', f.ID)
eq_(['p'], f.parents)
b = '\t'.join(['Chr1', 'TAIR10', 'gene', '2', '20', '.', '+',
'.', 'ID=Gene1;Parent=p,q'])
f = Feature.from_string(b)
eq_(['p', 'q'], f.parents)
c = '\t'.join(['Chr1', 'TAIR10', 'gene', '2', '20', '.', '+',
'.', ''])
f = Feature.from_string(c)
eq_('', f.ID)
eq_([], f.parents)
def test_transcript_splice_junctions():
f = dummy('g.gff')
features = Feature.from_file(f.name)
juncs = transcript_splice_junctions(features)
eq_({
'Chr1_40.1': [220, 302],
'Chr1_364.1': [83],
'Chr1_366.1': [90, 196, 326, 535],
}, juncs)
def test_Feature():
a = '\t'.join([
'Chr1', 'TAIR10', 'gene', '2', '20', '.', '+', '.', 'ID=Gene1;Parent=p'
])
f = Feature.from_string(a)
eq_('Gene1', f.ID)
eq_(['p'], f.parents)
b = '\t'.join([
'Chr1', 'TAIR10', 'gene', '2', '20', '.', '+', '.',
'ID=Gene1;Parent=p,q'
])
f = Feature.from_string(b)
eq_(['p', 'q'], f.parents)
c = '\t'.join(['Chr1', 'TAIR10', 'gene', '2', '20', '.', '+', '.', ''])
f = Feature.from_string(c)
eq_('', f.ID)
eq_([], f.parents)
def test_transcript_splice_junctions():
f = dummy('g.gff')
features = Feature.from_file(f.name)
juncs = transcript_splice_junctions(features)
eq_(
{
'Chr1_40.1': [220, 302],
'Chr1_364.1': [83],
'Chr1_366.1': [90, 196, 326, 535],
}, juncs)
return (end - start + 1) / b.length
def overlap(db, feature):
overlaps = db.overlaps(feature)
if len(overlaps) >= args.min_overlap_count and \
len(overlaps) <= args.max_overlap_count:
valid = []
for o in overlaps:
amt = calc_overlap(o, feature)
if amt >= args.min_overlap and amt <= args.max_overlap:
valid.append(o.ID)
if len(valid) > 0:
feature.attributes['overlaps'] = ','.join(valid)
if __name__ == '__main__':
args = parser.parse_args()
db = PositionDatabase(Feature.from_file(args.reference))
chromosomes, genes, transcripts = build_tree(Feature.from_file(args.gff))
for t in transcripts.values():
overlap(db, t)
flat = flatten_tree(chromosomes)
print '\n'.join([str(f) for f in flat])
def mapper_init(self):
"""TODO"""
features = Feature.from_file(self.options.reference)
self.junctions = transcript_splice_junctions(features)
return (end - start + 1) / b.length
def overlap(db, feature):
overlaps = db.overlaps(feature)
if len(overlaps) >= args.min_overlap_count and \
len(overlaps) <= args.max_overlap_count:
valid = []
for o in overlaps:
amt = calc_overlap(o, feature)
if amt >= args.min_overlap and amt <= args.max_overlap:
valid.append(o.ID)
if len(valid) > 0:
feature.attributes['overlaps'] = ','.join(valid)
if __name__ == '__main__':
args = parser.parse_args()
db = PositionDatabase(Feature.from_file(args.reference))
chromosomes, genes, transcripts = build_tree(Feature.from_file(args.gff))
for t in transcripts.values():
overlap(db, t)
flat = flatten_tree(chromosomes)
print '\n'.join([str(f) for f in flat])
def mapper_init(self):
"""TODO"""
features = Feature.from_file(self.options.reference)
self.junctions = transcript_splice_junctions(features)
hits = sum(counts[feature.ID].values())
except KeyError:
hits = 0
# coverage is RPKM, reads per kilobase of reference per million mapped reads
# http://www.clcbio.com/manual/genomics/Definition_RPKM.html
try:
return (math.pow(10, 9) * hits) / (self.total * feature.length)
except ZeroDivisionError:
return 0
if __name__ == '__main__':
args = parser.parse_args()
chromosomes, genes, transcripts = build_tree(Feature.from_file(args.gff))
# TODO would be nice to split filters out into predicate functions
for transcript in transcripts.values():
exons = len([x for x in transcript.children if x.type == 'exon'])
if args.counts:
counts = Counts.from_file(args.counts)
coverage = counts.coverage(transcript)
else:
coverage = 0
if transcript.length < args.min_length or transcript.length > args.max_length \
or exons < args.min_exons or exons > args.max_exons \
or coverage < args.min_coverage or coverage > args.max_coverage:
hits = sum(counts[feature.ID].values())
except KeyError:
hits = 0
# coverage is RPKM, reads per kilobase of reference per million mapped reads
# http://www.clcbio.com/manual/genomics/Definition_RPKM.html
try:
return (math.pow(10, 9) * hits) / (self.total * feature.length)
except ZeroDivisionError:
return 0
if __name__ == '__main__':
args = parser.parse_args()
chromosomes, genes, transcripts = build_tree(Feature.from_file(args.gff))
# TODO would be nice to split filters out into predicate functions
for transcript in transcripts.values():
exons = len([x for x in transcript.children if x.type == 'exon'])
if args.counts:
counts = Counts.from_file(args.counts)
coverage = counts.coverage(transcript)
else:
coverage = 0
if transcript.length < args.min_length or transcript.length > args.max_length \
or exons < args.min_exons or exons > args.max_exons \
or coverage < args.min_coverage or coverage > args.max_coverage:
