def __init__(self, fname):
self.fname = fname
Model.__init__(self)
self.gtf = GTF(self.fname)
gene_gen = self.gtf.genes
gene = gene_gen.next()
self.has_isoform = 'isoform_id' in gene.attributes
self.has_biotype = 'gene_biotype' in gene.attributes
def testGTFIso(self):
src = StringIO.StringIO('''\
chr1|test|exon|1001|1100|0|+|.|gene_id "foo1"; transcript_id "bar1"; isoform_id "iso1"
chr1|test|exon|1201|1300|0|+|.|gene_id "foo1"; transcript_id "bar1"; isoform_id "iso1"
chr1|test|exon|1401|1500|0|+|.|gene_id "foo1"; transcript_id "bar1"; isoform_id "iso1"
chr1|test|CDS|1051|1447|0|+|1|gene_id "foo1"; transcript_id "bar1"; isoform_id "iso1"
chr1|test|start_codon|1051|1053|0|+|.|gene_id "foo1"; transcript_id "bar1"; isoform_id "iso1"
chr1|test|stop_codon|1448|1450|0|+|.|gene_id "foo1"; transcript_id "bar1"; isoform_id "iso1"
chr1|test|exon|1001|1100|0|+|.|gene_id "foo2"; transcript_id "bar2"; isoform_id "iso1"
chr1|test|exon|1401|1500|0|+|.|gene_id "foo2"; transcript_id "bar2"; isoform_id "iso1"
chr1|test|CDS|1051|1447|0|+|1|gene_id "foo2"; transcript_id "bar2"; isoform_id "iso1"
chr1|test|start_codon|1051|1053|0|+|.|gene_id "foo2"; transcript_id "bar2"; isoform_id "iso1"
chr1|test|stop_codon|1448|1450|0|+|.|gene_id "foo2"; transcript_id "bar2"; isoform_id "iso1"
'''.replace('|', '\t'))
gtf = GTF(fileobj=src, quiet=True)
genes = list(gtf.genes)
self.assertEqual(len(genes), 1)
self.assertEqual(['foo1'], [g.gene_id for g in genes])
self.assertEqual(['iso1'], [
g.gid for g in genes
]) # gid is the actual unique id (should be constant for isoforms)
transcripts = list(genes[0].transcripts)
self.assertEqual(['bar1', 'bar2'],
[t.transcript_id for t in transcripts])
self.assertEqual(len(transcripts), 2)
self.assertEqual(list(genes[0].regions),
[(1, 1000, 1100, True, 'bar1,bar2'),
(2, 1200, 1300, False, 'bar1'),
(3, 1400, 1500, True, 'bar1,bar2')])
def testFind(self):
src = StringIO.StringIO('''\
chr1|test|gene|1001|1100|0|+|.|gene_id "foo1"; transcript_id "bar1";
chr1|test|gene|2001|2100|0|+|.|gene_id "foo2"; transcript_id "bar2";
chr1|test|gene|3001|3100|0|+|.|gene_id "foo3"; transcript_id "bar3";
'''.replace('|', '\t'))
gtf = GTF(fileobj=src, quiet=True)
genes = list(gtf.genes)
self.assertEqual(['foo1', 'foo2', 'foo3'], [x.gene_id for x in genes])
found = False
for gene in gtf.find('chr1', 1900, 2200):
found = True
self.assertEqual(gene.gid, 'foo2')
self.assertTrue(found)
found = False
for gene in gtf.find('chr1', 1900, 2000):
found = True
self.assertEqual(gene.gid, 'foo2')
self.assertTrue(found)
found = False
for gene in gtf.find('chr1', 2050, 2200):
found = True
self.assertEqual(gene.gid, 'foo2')
self.assertTrue(found)
def testJunctionsIsoforms(self):
gtf = GTF(fileobj=StringIO.StringIO('''\
test1|test|exon|10|20|0|+|.|gene_id "foo1"; transcript_id "bar1"; isoform_id "iso1"
test1|test|exon|30|40|0|+|.|gene_id "foo1"; transcript_id "bar1"; isoform_id "iso1"
test1|test|exon|90|100|0|+|.|gene_id "foo1"; transcript_id "bar1"; isoform_id "iso1"
test1|test|exon|10|20|0|+|.|gene_id "foo1"; transcript_id "bar2"; isoform_id "iso1"
test1|test|exon|50|70|0|+|.|gene_id "foo1"; transcript_id "bar2"; isoform_id "iso1"
test1|test|exon|90|100|0|+|.|gene_id "foo1"; transcript_id "bar2"; isoform_id "iso1"
'''.replace('|', '\t')),
quiet=True)
valid = '''\
>test1:16-20,29-33
ATGCGCGC
>test1:16-20,49-53
ATGCCTGA
>test1:16-20,89-93
ATGCTCGA
>test1:36-40,49-53
GATCCTGA
>test1:36-40,89-93
GATCTCGA
>test1:66-70,89-93
ATCGTCGA
'''
out = StringIO.StringIO('')
ngsutils.gtf.junctions.gtf_junctions(gtf,
fa,
fragment_size=4,
min_size=8,
out=out,
quiet=True)
self.assertEqual(out.getvalue(), valid)
class GTFModel(Model):
def __init__(self, fname):
self.fname = fname
Model.__init__(self)
# test for what attributes we need to return
self.gtf = GTF(self.fname)
gene_gen = self.gtf.genes
gene = gene_gen.next()
self.has_isoform = 'isoform_id' in gene.attributes
self.has_biotype = 'gene_biotype' in gene.attributes
def get_source(self):
return self.fname
def get_name(self):
return 'gtf'
def get_headers(self):
out = [
'gene_id',
'gene_name',
]
if self.has_isoform:
out.append('isoform_id')
if self.has_biotype:
out.append('gene_biotype')
out.extend('chrom strand txstart txend'.split())
return out
def get_regions(self):
eta = ETA(self.gtf.fsize(), fileobj=self.gtf)
for gene in self.gtf.genes:
eta.print_status(extra=gene.gene_name)
starts = []
ends = []
# just include all regions - don't worry about transcripts and exons
# the regions encompass all exons anyway...
for num, start, end, const, names in gene.regions:
starts.append(start)
ends.append(end)
out = [
gene.gene_id,
gene.gene_name,
]
if self.has_isoform:
out.append(gene.attributes['isoform_id'] if 'isoform_id' in
gene.attributes else '')
if self.has_biotype:
out.append(gene.attributes['gene_biotype'] if 'gene_biotype' in
gene.attributes else '')
out.extend([gene.chrom, gene.strand, gene.start, gene.end])
yield (gene.chrom, starts, ends, gene.strand, out, None)
eta.done()
def __init__(self, fname):
self.fname = fname
Model.__init__(self)
self.gtf = GTF(self.fname)
gene_gen = self.gtf.genes
gene = gene_gen.next()
self.has_isoform = 'isoform_id' in gene.attributes
self.has_biotype = 'gene_biotype' in gene.attributes
class GTFModel(Model):
def __init__(self, fname):
self.fname = fname
Model.__init__(self)
# test for what attributes we need to return
self.gtf = GTF(self.fname)
gene_gen = self.gtf.genes
gene = gene_gen.next()
self.has_isoform = 'isoform_id' in gene.attributes
self.has_biotype = 'gene_biotype' in gene.attributes
def get_source(self):
return self.fname
def get_name(self):
return 'gtf'
def get_headers(self):
out = ['gene_id', 'gene_name', ]
if self.has_isoform:
out.append('isoform_id')
if self.has_biotype:
out.append('gene_biotype')
out.extend('chrom strand txstart txend'.split())
return out
def get_regions(self):
eta = ETA(self.gtf.fsize(), fileobj=self.gtf)
for gene in self.gtf.genes:
eta.print_status(extra=gene.gene_name)
starts = []
ends = []
# just include all regions - don't worry about transcripts and exons
# the regions encompass all exons anyway...
for num, start, end, const, names in gene.regions:
starts.append(start)
ends.append(end)
out = [gene.gene_id, gene.gene_name, ]
if self.has_isoform:
out.append(gene.attributes['isoform_id'] if 'isoform_id' in gene.attributes else '')
if self.has_biotype:
out.append(gene.attributes['gene_biotype'] if 'gene_biotype' in gene.attributes else '')
out.extend([gene.chrom, gene.strand, gene.start, gene.end])
yield (gene.chrom, starts, ends, gene.strand, out, None)
eta.done()
def testGTF(self):
src = StringIO.StringIO('''\
chr1|test|exon|1001|1100|0|+|.|gene_id "foo"; transcript_id "bar";
chr1|test|exon|1201|1300|0|+|.|gene_id "foo"; transcript_id "bar";
chr1|test|CDS|1051|1247|0|+|1|gene_id "foo"; transcript_id "bar";
chr1|test|start_codon|1051|1053|0|+|.|gene_id "foo"; transcript_id "bar";
chr1|test|stop_codon|1248|1250|0|+|.|gene_id "foo"; transcript_id "bar";
'''.replace('|', '\t'))
gtf = GTF(fileobj=src, quiet=True)
for gene in gtf.genes:
self.assertEqual(gene.gene_id, 'foo')
for transcript in gene.transcripts:
self.assertEqual(transcript.transcript_id, 'bar')
self.assertEqual(list(transcript.exons), [(1000, 1100),
(1200, 1300)])
def testGeneOnly(self):
src = StringIO.StringIO('''\
chr1|test|gene|1001|1100|0|+|.|gene_id "foo1"; transcript_id "bar1";
chr1|test|gene|2001|2100|0|+|.|gene_id "foo2"; transcript_id "bar2";
'''.replace('|', '\t'))
gtf = GTF(fileobj=src, quiet=True)
genes = list(gtf.genes)
self.assertEqual(['foo1', 'foo2'], [x.gene_id for x in genes])
# gene start / end
self.assertEqual((1000, 1100), (genes[0].start, genes[0].end))
t = list(genes[0].transcripts)[0]
# apply start / end to transcript and as an exon
self.assertEqual((1000, 1100), (t.start, t.end))
self.assertEqual((1000, 1100), (t.exons[0]))
t = list(genes[1].transcripts)[0]
# apply start / end to transcript and as an exon
self.assertEqual((2000, 2100), (t.start, t.end))
self.assertEqual((2000, 2100), (t.exons[0]))
def testGTFNoIso(self):
src = StringIO.StringIO('''\
chr1|test|exon|1001|1100|0|+|.|gene_id "foo"; transcript_id "bar1";
chr1|test|exon|1201|1300|0|+|.|gene_id "foo"; transcript_id "bar1";
chr1|test|exon|1401|1500|0|+|.|gene_id "foo"; transcript_id "bar1";
chr1|test|CDS|1051|1447|0|+|1|gene_id "foo"; transcript_id "bar1";
chr1|test|start_codon|1051|1053|0|+|.|gene_id "foo"; transcript_id "bar1";
chr1|test|stop_codon|1448|1450|0|+|.|gene_id "foo"; transcript_id "bar1";
chr1|test|exon|1001|1100|0|+|.|gene_id "foo"; transcript_id "bar2";
chr1|test|exon|1401|1500|0|+|.|gene_id "foo"; transcript_id "bar2";
chr1|test|CDS|1051|1447|0|+|1|gene_id "foo"; transcript_id "bar2";
chr1|test|start_codon|1051|1053|0|+|.|gene_id "foo"; transcript_id "bar2";
chr1|test|stop_codon|1448|1450|0|+|.|gene_id "foo"; transcript_id "bar2";
'''.replace('|', '\t'))
gtf = GTF(fileobj=src, quiet=True)
genes = list(gtf.genes)
self.assertEqual('foo', genes[0].gene_id)
transcripts = list(genes[0].transcripts)
self.assertEqual(len(transcripts), 2)
self.assertEqual(list(genes[0].regions),
[(1, 1000, 1100, True, 'bar1,bar2'),
(2, 1200, 1300, False, 'bar1'),
(3, 1400, 1500, True, 'bar1,bar2')])
elif arg in ['-frag', '-min']:
last = arg
elif arg == '-scramble':
scramble = True
elif arg == '-retain-introns':
retain_introns = True
elif arg == '-known':
known = True
elif arg == '-h':
usage()
elif gtf is None and os.path.exists(arg):
gtf = arg
elif fasta is None and os.path.exists(arg):
if not os.path.exists('%s.fai' % arg):
usage('Missing .fai FASTA index for file: %s' % arg)
fasta = arg
if not gtf or not fasta:
usage()
if known and scramble:
usage("You can not use both -known and -scramble at the same time!")
gtf_junctions(GTF(gtf),
fasta,
frag_size,
min_size,
known=known,
scramble=scramble,
retain_introns=retain_introns)
last = None
elif arg == '-noheader':
header = False
elif arg == '-gene_id':
gene_id = True
elif arg == '-transcript_id':
transcript_id = True
elif arg == '-gene_name':
gene_name = True
elif arg == '-gene_location':
gene_location = True
elif arg in ['-ref', '-pos']:
last = arg
elif not gtffile and os.path.exists(arg):
gtffile = arg
elif not infile and os.path.exists(arg):
infile = arg
else:
print 'Unknown argument: %s' % arg
if not gtffile:
usage('Missing GTF file')
if not infile:
usage('Missing input file')
if not (gene_name or gene_location or gene_id or transcript_id):
usage('Missing outputs - nothing to annotate')
gtf = GTF(gtffile)
gtf_annotate(gtf, infile, ref_col, pos_col, gene_name, gene_location,
gene_id, transcript_id, header)
maxtx = max(size, maxtx)
maxintron = max(intron_size, maxintron)
maxcoding = max(cds, maxcoding)
maxutr5 = max(utr5, maxutr5)
maxutr3 = max(utr3, maxutr3)
cols.append(maxtx)
cols.append(maxintron)
cols.append(maxcoding)
cols.append(maxutr5)
cols.append(maxutr3)
out.write('%s\n' % '\t'.join([str(x) for x in cols]))
if __name__ == '__main__':
fname = None
for arg in sys.argv[1:]:
if arg == '-h':
usage()
if not fname and os.path.exists(arg):
fname = arg
else:
usage()
if not fname:
usage()
gtf = GTF(fname)
gtf_genesize(gtf)
genes, exons, introns, regions, tss, tlss, txs, tlxs, utr_5, utr_3,
junc_5, junc_3, promoter
]:
if arg:
i += 1
if i == 0:
usage('You must select one [type] to export.')
elif i > 1:
usage('You must select *only one* [type] to export.')
elif not filename:
usage('Missing input file')
elif promoter and not (promoter_down or promoter_up):
usage('You must specify a valid promoter length!')
gtf = GTF(filename)
if genes:
gtf_genes_tobed(gtf)
elif exons:
gtf_exons_tobed(gtf)
elif introns:
gtf_introns_tobed(gtf)
elif regions:
gtf_regions_tobed(gtf)
elif tss:
gtf_tss_tobed(gtf)
elif tlss:
gtf_tlss_tobed(gtf)
elif txs:
gtf_txs_tobed(gtf)
def testQuery(self):
genes = list(ngsutils.gtf.query.gtf_query(GTF(fname, cache_enabled=False), 'chr1', 1000, 2000))
self.assertEquals(str(genes[0]), 'foo1(iso1) chr1:1000-2500[+]')
import unittest
import StringIO
import ngsutils.gtf.genesize
from ngsutils.gtf import GTF
gtf = GTF(fileobj=StringIO.StringIO('''\
chr1|test|exon|1001|1100|0|+|.|gene_id "foo1"; transcript_id "bar1"; isoform_id "iso1"
chr1|test|exon|1201|1300|0|+|.|gene_id "foo1"; transcript_id "bar1"; isoform_id "iso1"
chr1|test|exon|1401|1500|0|+|.|gene_id "foo1"; transcript_id "bar1"; isoform_id "iso1"
chr1|test|CDS|1051|1447|0|+|1|gene_id "foo1"; transcript_id "bar1"; isoform_id "iso1"
chr1|test|start_codon|1051|1053|0|+|.|gene_id "foo1"; transcript_id "bar1"; isoform_id "iso1"
chr1|test|stop_codon|1448|1450|0|+|.|gene_id "foo1"; transcript_id "bar1"; isoform_id "iso1"
chr1|test|exon|1001|1100|0|+|.|gene_id "foo2"; transcript_id "bar2"; isoform_id "iso1"
chr1|test|exon|1401|1500|0|+|.|gene_id "foo2"; transcript_id "bar2"; isoform_id "iso1"
chr1|test|CDS|1051|1447|0|+|1|gene_id "foo2"; transcript_id "bar2"; isoform_id "iso1"
chr1|test|start_codon|1051|1053|0|+|.|gene_id "foo2"; transcript_id "bar2"; isoform_id "iso1"
chr1|test|stop_codon|1448|1450|0|+|.|gene_id "foo2"; transcript_id "bar2"; isoform_id "iso1"
chr1|test|exon|2001|2100|0|+|.|gene_id "foo3"; transcript_id "bar1"; isoform_id "iso2"
chr1|test|exon|2201|2300|0|+|.|gene_id "foo3"; transcript_id "bar1"; isoform_id "iso2"
chr1|test|exon|2401|2500|0|+|.|gene_id "foo3"; transcript_id "bar1"; isoform_id "iso2"
chr1|test|CDS|2051|2447|0|+|1|gene_id "foo3"; transcript_id "bar1"; isoform_id "iso2"
chr1|test|start_codon|2051|2053|0|+|.|gene_id "foo3"; transcript_id "bar1"; isoform_id "iso2"
chr1|test|stop_codon|2448|2450|0|+|.|gene_id "foo3"; transcript_id "bar1"; isoform_id "iso2"
'''.replace('|', '\t')),
quiet=True)
class GTFGeneSizeTest(unittest.TestCase):
def testGeneSize(self):
out = StringIO.StringIO('')
ngsutils.gtf.genesize.gtf_genesize(gtf, out=out)
def get_regions(self):
gtf = GTF(self.fname)
eta = ETA(gtf.fsize(), fileobj=gtf)
for gene in gtf.genes:
eta.print_status(extra=gene.gene_name)
starts = []
ends = []
const_spans = []
was_last_const = False
for num, start, end, const, names in gene.regions:
starts.append(start)
ends.append(end)
# assemble a list of lists with contiguous spans of constant regions
# this will let us count junction-spanning reads that are cover two
# constant exons/regions
if const:
if not was_last_const:
const_spans.append([])
const_spans[-1].append((start, end))
was_last_const = True
else:
was_last_const = False
def callback(bam, common_count, common_reads, common_cols):
# gather constant reads
const_count = 0
for span in const_spans:
starts = []
ends = []
for start, end in span:
starts.append(start)
ends.append(end)
count, reads = _fetch_reads(bam, gene.chrom, gene.strand if self.stranded else None, starts, ends, self.multiple, False, self.whitelist, self.blacklist, self.uniq_only, self.library_type)
const_count += count
#find counts for each region
for num, start, end, const, names in gene.regions:
count, reads = _fetch_reads(bam, gene.chrom, gene.strand if self.stranded else None, [start], [end], self.multiple, False, self.whitelist, self.blacklist, self.uniq_only, self.library_type)
excl_count, excl_reads = _fetch_reads_excluding(bam, gene.chrom, gene.strand if self.stranded else None, start, end, self.multiple, self.whitelist, self.blacklist, self.library_type)
# remove reads that exclude this region
for read in excl_reads:
if read in reads:
reads.remove(read)
count = count - 1
# find reads that *arent'* in this region
other_reads = 0
for read in common_reads:
if not read in reads and not read in excl_reads:
other_reads += 1
if other_reads > 0:
altindex = float(count - excl_count) / other_reads
else:
altindex = ''
if len(common_reads) > 0:
incl_pct = float(count) / len(common_reads)
excl_pct = float(excl_count) / len(common_reads)
else:
incl_pct = ''
excl_pct = ''
cols = common_cols[:]
cols.append(start)
cols.append(end)
cols.append(const_count)
cols.append(num)
cols.append('const' if const else 'alt')
cols.append(count)
cols.append(excl_count)
cols.append(incl_pct)
cols.append(excl_pct)
cols.append(altindex)
yield cols
yield (gene.chrom, starts, ends, gene.strand, [gene.gene_name, gene.gene_id, gene.isoform_id, gene.chrom, gene.strand, gene.start, gene.end], callback)
eta.done()
class ExonModel(Model):
def __init__(self, fname):
self.fname = fname
Model.__init__(self)
self.gtf = GTF(self.fname)
gene_gen = self.gtf.genes
gene = gene_gen.next()
self.has_isoform = 'isoform_id' in gene.attributes
self.has_biotype = 'gene_biotype' in gene.attributes
def get_source(self):
return self.fname
def get_name(self):
return 'exon'
def get_headers(self):
out = [
'gene_id',
'gene_name',
]
if self.has_isoform:
out.append('isoform_id')
if self.has_biotype:
out.append('gene_biotype')
out.extend('chrom strand txstart txend'.split())
return out
def get_postheaders(self):
return 'regionstart regionend const_count region_num const_alt count excl_count incl_pct excl_pct alt-index'.split(
)
def get_regions(self):
eta = ETA(self.gtf.fsize(), fileobj=self.gtf)
for gene in self.gtf.genes:
eta.print_status(extra=gene.gene_name)
starts = []
ends = []
const_spans = []
geneout = [
gene.gene_id,
gene.gene_name,
]
if self.has_isoform:
geneout.append(gene.attributes['isoform_id'] if 'isoform_id' in
gene.attributes else '')
if self.has_biotype:
geneout.append(gene.attributes['gene_biotype']
if 'gene_biotype' in gene.attributes else '')
geneout.extend([gene.chrom, gene.strand, gene.start, gene.end])
was_last_const = False
for num, start, end, const, names in gene.regions:
starts.append(start)
ends.append(end)
# assemble a list of lists with contiguous spans of constant regions
# this will let us count junction-spanning reads that are cover two
# constant exons/regions
if const:
if not was_last_const:
const_spans.append([])
const_spans[-1].append((start, end))
was_last_const = True
else:
was_last_const = False
def callback(bam, common_count, common_reads, common_cols):
# gather constant reads
const_count = 0
for span in const_spans:
starts = []
ends = []
for start, end in span:
starts.append(start)
ends.append(end)
count, reads = _fetch_reads(
bam, gene.chrom,
gene.strand if self.stranded else None, starts, ends,
self.multiple, False, self.whitelist, self.blacklist,
self.uniq_only, self.library_type)
const_count += count
#find counts for each region
for num, start, end, const, names in gene.regions:
count, reads = _fetch_reads(
bam, gene.chrom,
gene.strand if self.stranded else None, [start], [end],
self.multiple, False, self.whitelist, self.blacklist,
self.uniq_only, self.library_type)
excl_count, excl_reads = _fetch_reads_excluding(
bam, gene.chrom,
gene.strand if self.stranded else None, start, end,
self.multiple, self.whitelist, self.blacklist,
self.library_type)
# remove reads that exclude this region
for read in excl_reads:
if read in reads:
reads.remove(read)
count = count - 1
# find reads that *arent'* in this region
other_reads = 0
for read in common_reads:
if not read in reads and not read in excl_reads:
other_reads += 1
if other_reads > 0:
altindex = float(count - excl_count) / other_reads
else:
altindex = ''
if len(common_reads) > 0:
incl_pct = float(count) / len(common_reads)
excl_pct = float(excl_count) / len(common_reads)
else:
incl_pct = ''
excl_pct = ''
cols = common_cols[:]
cols.append(start)
cols.append(end)
cols.append(const_count)
cols.append(num)
cols.append('const' if const else 'alt')
cols.append(count)
cols.append(excl_count)
cols.append(incl_pct)
cols.append(excl_pct)
cols.append(altindex)
yield cols
yield (gene.chrom, starts, ends, gene.strand, geneout, callback)
eta.done()
def count(self,
bam,
library_type,
coverage=False,
uniq_only=False,
fpkm=False,
norm='',
multiple='complete',
whitelist=None,
blacklist=None,
out=sys.stdout,
quiet=False,
start_only=False):
self.uniq_only = uniq_only
self.multiple = multiple
self.whitelist = whitelist
self.blacklist = blacklist
self.library_type = library_type
self.stranded = library_type in ['FR', 'RF']
Model.count(self, bam, library_type, coverage, uniq_only, fpkm, norm,
multiple, whitelist, blacklist, out, quiet, start_only)
def bam_stats(infiles, gtf_file=None, region=None, delim=None, tags=[], show_all=False, fillin_stats=True):
if gtf_file:
gtf = GTF(gtf_file)
else:
gtf = None
sys.stderr.write('Calculating Read stats...\n')
stats = [BamStats(bam_open(x), gtf, region, delim, tags, show_all=show_all) for x in infiles]
sys.stdout.write('\t')
for fname, stat in zip(infiles, stats):
sys.stdout.write('%s\t\t' % fname)
sys.stdout.write('\n')
sys.stdout.write('Reads:\t')
for stat in stats:
sys.stdout.write('%s\t\t' % stat.total)
sys.stdout.write('\n')
sys.stdout.write('Mapped:\t')
for stat in stats:
sys.stdout.write('%s\t\t' % stat.mapped)
sys.stdout.write('\n')
sys.stdout.write('Unmapped:\t')
for stat in stats:
sys.stdout.write('%s\t\t' % stat.unmapped)
sys.stdout.write('\n')
sys.stdout.write('\nFlag distribution\n')
validflags = set()
maxsize = 0
for flag in flag_descriptions:
for stat in stats:
if stat.flag_counts.counts[flag] > 0:
validflags.add(flag)
maxsize = max(maxsize, len(flag_descriptions[flag]))
for flag in sorted(validflags):
sys.stdout.write("[0x%03x] %-*s" % (flag, maxsize, flag_descriptions[flag]))
for stat in stats:
sys.stdout.write('\t%s\t%0.2f%%' % (stat.flag_counts.counts[flag], (float(stat.flag_counts.counts[flag]) * 100 / stat.total)))
sys.stdout.write('\n')
sys.stdout.write('\n')
if stats[0].tlen_counts:
sys.stdout.write('Template length:')
for stat in stats:
mean, stdev = counts_mean_stdev(stat.tlen_counts)
sys.stdout.write('\t%0.2f\t+/- %0.2f' % (mean, stdev))
sys.stdout.write('\n')
sys.stdout.write('\n')
stat_tags = {}
for tag in stats[0].tagbins:
stat_tags[tag] = []
for stat in stats:
stat_tags[tag].append(stat.tagbins[tag])
for tag in stat_tags:
asc = stats[0].tagbins[tag].asc
sys.stdout.write("Ave %s:" % tag)
for i, tagbin in enumerate(stat_tags[tag]):
sys.stdout.write('\t%s' % tagbin.mean)
if i != len(stats):
sys.stdout.write('\t')
sys.stdout.write('\n')
sys.stdout.write("Max %s:" % tag)
for i, tagbin in enumerate(stat_tags[tag]):
sys.stdout.write('\t%s' % tagbin.max)
if i != len(stats):
sys.stdout.write('\t')
sys.stdout.write('\n')
sys.stdout.write('%s distribution:\n' % tag)
gens = []
gen_vals = []
last_pcts = []
for stat in stats:
gens.append(stat.distribution_gen(tag))
gen_vals.append(None)
last_pcts.append(0.0)
good = True
last = None
while good:
good = False
for i, stat in enumerate(stats):
if not gen_vals[i]:
try:
gen_vals[i] = gens[i].next()
except StopIteration:
pass
vals = [tup[0] for tup in gen_vals if tup]
if not vals:
continue
if asc:
minval = min(vals)
else:
minval = max(vals)
if last and type(last) == int and fillin_stats:
if asc:
last += 1
# fill in missing values
while last < minval:
sys.stdout.write('%s' % last)
for i, stat in enumerate(stats):
sys.stdout.write('\t0\t%s' % last_pcts[i])
sys.stdout.write('\n')
last += 1
else:
last -= 1
# fill in missing values
while last > minval:
sys.stdout.write('%s' % last)
for i, stat in enumerate(stats):
sys.stdout.write('\t0\t%s' % last_pcts[i])
sys.stdout.write('\n')
last -= 1
last = minval
sys.stdout.write(str(minval))
for i, tup in enumerate(gen_vals):
if tup and tup[0] == minval:
sys.stdout.write('\t%s\t%s' % (tup[1], tup[2]))
last_pcts[i] = tup[2]
gen_vals[i] = None
good = True
else:
sys.stdout.write('\t0\t%s' % (last_pcts[i]))
sys.stdout.write('\n')
sys.stdout.write('\n')
sys.stdout.write('Reference counts')
for stat in stats:
sys.stdout.write('\tcount\t')
sys.stdout.write('\n')
for k in sorted([x for x in stats[0].refs]):
sys.stdout.write('%s' % k)
for stat in stats:
sys.stdout.write('\t%s\t' % stat.refs[k])
sys.stdout.write('\n')
if gtf_file:
sys.stdout.write('Mapping regions')
for stat in stats:
sys.stdout.write('\tcount\tCPM')
sys.stdout.write('\n')
sorted_keys = [x for x in stats[0].regiontagger.counts]
sorted_keys.sort()
for k in sorted_keys:
sys.stdout.write('%s' % k)
for stat in stats:
sys.stdout.write('\t%s\t%s' % (stat.regiontagger.counts[k], float(stat.regiontagger.counts[k]) / stat.mapped / 1000000))
sys.stdout.write('\n')
class ExonModel(Model):
def __init__(self, fname):
self.fname = fname
Model.__init__(self)
self.gtf = GTF(self.fname)
gene_gen = self.gtf.genes
gene = gene_gen.next()
self.has_isoform = 'isoform_id' in gene.attributes
self.has_biotype = 'gene_biotype' in gene.attributes
def get_source(self):
return self.fname
def get_name(self):
return 'exon'
def get_headers(self):
out = ['gene_id', 'gene_name', ]
if self.has_isoform:
out.append('isoform_id')
if self.has_biotype:
out.append('gene_biotype')
out.extend('chrom strand txstart txend'.split())
return out
def get_postheaders(self):
return 'regionstart regionend const_count region_num const_alt count excl_count incl_pct excl_pct alt-index'.split()
def get_regions(self):
eta = ETA(self.gtf.fsize(), fileobj=self.gtf)
for gene in self.gtf.genes:
eta.print_status(extra=gene.gene_name)
starts = []
ends = []
const_spans = []
geneout = [gene.gene_id, gene.gene_name, ]
if self.has_isoform:
geneout.append(gene.attributes['isoform_id'] if 'isoform_id' in gene.attributes else '')
if self.has_biotype:
geneout.append(gene.attributes['gene_biotype'] if 'gene_biotype' in gene.attributes else '')
geneout.extend([gene.chrom, gene.strand, gene.start, gene.end])
was_last_const = False
for num, start, end, const, names in gene.regions:
starts.append(start)
ends.append(end)
# assemble a list of lists with contiguous spans of constant regions
# this will let us count junction-spanning reads that are cover two
# constant exons/regions
if const:
if not was_last_const:
const_spans.append([])
const_spans[-1].append((start, end))
was_last_const = True
else:
was_last_const = False
def callback(bam, common_count, common_reads, common_cols):
# gather constant reads
const_count = 0
for span in const_spans:
starts = []
ends = []
for start, end in span:
starts.append(start)
ends.append(end)
count, reads = _fetch_reads(bam, gene.chrom, gene.strand if self.stranded else None, starts, ends, self.multiple, False, self.whitelist, self.blacklist, self.uniq_only, self.library_type)
const_count += count
#find counts for each region
for num, start, end, const, names in gene.regions:
count, reads = _fetch_reads(bam, gene.chrom, gene.strand if self.stranded else None, [start], [end], self.multiple, False, self.whitelist, self.blacklist, self.uniq_only, self.library_type)
excl_count, excl_reads = _fetch_reads_excluding(bam, gene.chrom, gene.strand if self.stranded else None, start, end, self.multiple, self.whitelist, self.blacklist, self.library_type)
# remove reads that exclude this region
for read in excl_reads:
if read in reads:
reads.remove(read)
count = count - 1
# find reads that *arent'* in this region
other_reads = 0
for read in common_reads:
if not read in reads and not read in excl_reads:
other_reads += 1
if other_reads > 0:
altindex = float(count - excl_count) / other_reads
else:
altindex = ''
if len(common_reads) > 0:
incl_pct = float(count) / len(common_reads)
excl_pct = float(excl_count) / len(common_reads)
else:
incl_pct = ''
excl_pct = ''
cols = common_cols[:]
cols.append(start)
cols.append(end)
cols.append(const_count)
cols.append(num)
cols.append('const' if const else 'alt')
cols.append(count)
cols.append(excl_count)
cols.append(incl_pct)
cols.append(excl_pct)
cols.append(altindex)
yield cols
yield (gene.chrom, starts, ends, gene.strand, geneout, callback)
eta.done()
def count(self, bam, library_type, coverage=False, uniq_only=False, fpkm=False, norm='', multiple='complete', whitelist=None, blacklist=None, out=sys.stdout, quiet=False, start_only=False):
self.uniq_only = uniq_only
self.multiple = multiple
self.whitelist = whitelist
self.blacklist = blacklist
self.library_type = library_type
self.stranded = library_type in ['FR', 'RF']
Model.count(self, bam, library_type, coverage, uniq_only, fpkm, norm, multiple, whitelist, blacklist, out, quiet, start_only)
