def annotateCpGIslands( infiles, outfile ):
'''annotate transcript by absence/presence of CpG islands
'''
cpgfile, tssfile = infiles
cpg = Bed.readAndIndex( IOTools.openFile( cpgfile ) )
extension_upstream = PARAMS["cpg_search_upstream"]
extension_downstream = PARAMS["cpg_search_downstream"]
c = E.Counter()
outf = IOTools.openFile( outfile, "w" )
outf.write("transcript_id\tstrand\tstart\tend\trelative_start\trelative_end\n" )
for tss in Bed.iterator(IOTools.openFile( tssfile ) ):
c.tss_total += 1
if tss.strand == "+":
start, end = tss.start - extension_upstream, tss.start + extension_downstream
else:
start, end = tss.end - extension_downstream, tss.end + extension_upstream
try:
matches = list(cpg[tss.contig].find( start, end ))
except KeyError:
c.promotor_without_matches += 1
continue
if len(matches) == 0:
c.promotor_without_matches += 1
continue
c.promotor_output += 1
for match in matches:
c.matches_total += 1
genome_start, genome_end, x = match
l = genome_end - genome_start
# get relative location of match
if tss.strand == "+":
relative_start = genome_start - tss.start
else:
relative_start = tss.end - genome_end
relative_end = relative_start + l
outf.write( "\t".join( map(str, (
tss.name, tss.strand,
genome_start, genome_end,
relative_start, relative_end ))) + "\n" )
c.matches_output += 1
outf.close()
with IOTools.openFile( outfile + ".summary", "w" ) as outf:
outf.write ("category\tcounts\n" )
outf.write( c.asTable() + "\n" )
E.info( c )
def annotateCpGIslands(infiles, outfile):
'''annotate transcript by absence/presence of CpG islands
'''
cpgfile, tssfile = infiles
cpg = Bed.readAndIndex(IOTools.openFile(cpgfile))
extension_upstream = PARAMS["cpg_search_upstream"]
extension_downstream = PARAMS["cpg_search_downstream"]
c = E.Counter()
outf = IOTools.openFile(outfile, "w")
outf.write(
"transcript_id\tstrand\tstart\tend\trelative_start\trelative_end\n")
for tss in Bed.iterator(IOTools.openFile(tssfile)):
c.tss_total += 1
if tss.strand == "+":
start, end = tss.start - extension_upstream, tss.start + extension_downstream
else:
start, end = tss.end - extension_downstream, tss.end + extension_upstream
try:
matches = list(cpg[tss.contig].find(start, end))
except KeyError:
c.promotor_without_matches += 1
continue
if len(matches) == 0:
c.promotor_without_matches += 1
continue
c.promotor_output += 1
for match in matches:
c.matches_total += 1
genome_start, genome_end, x = match
l = genome_end - genome_start
# get relative location of match
if tss.strand == "+":
relative_start = genome_start - tss.start
else:
relative_start = tss.end - genome_end
relative_end = relative_start + l
outf.write("\t".join(
map(str, (tss.name, tss.strand, genome_start, genome_end,
relative_start, relative_end))) + "\n")
c.matches_output += 1
outf.close()
with IOTools.openFile(outfile + ".summary", "w") as outf:
outf.write("category\tcounts\n")
outf.write(c.asTable() + "\n")
E.info(c)
def main(argv=None):
"""script main.
parses command line options in sys.argv, unless *argv* is given.
"""
if not argv:
argv = sys.argv
# setup command line parser
parser = E.OptionParser(
version="%prog version: $Id: bed2graph.py 2861 2010-02-23 17:36:32Z andreas $", usage=globals()["__doc__"])
parser.add_option("-o", "--output-section", dest="output", type="choice",
choices=("full", "name"),
help="output either ``full`` overlapping entries, only the ``name``s. [default=%default].")
parser.set_defaults(
output="full",
)
# add common options (-h/--help, ...) and parse command line
(options, args) = E.Start(parser, argv=argv)
if len(args) != 2:
raise ValueError("two arguments required")
if args[0] == "-":
infile1 = options.stdin
else:
infile1 = IOTools.openFile(args[0], "r")
infile2 = IOTools.openFile(args[1], "r")
idx = Bed.readAndIndex(infile2, with_values=True)
output = options.output
outfile = options.stdout
if output == "name":
outfile.write("name1\tname2\n")
outf = lambda x: x.fields[0]
else:
outf = str
for bed in Bed.iterator(infile1):
try:
overlaps = idx[bed.contig].find(bed.start, bed.end)
except (KeyError, IndexError):
# ignore missing contig and zero length intervals
continue
for o in overlaps:
outfile.write("\t".join((outf(bed), outf(o[2]))) + "\n")
E.Stop()
def main(argv=None):
"""script main.
parses command line options in sys.argv, unless *argv* is given.
"""
if not argv:
argv = sys.argv
# setup command line parser
parser = E.OptionParser(
version="%prog version: $Id: bed2graph.py 2861 2010-02-23 17:36:32Z andreas $", usage=globals()["__doc__"])
parser.add_option("-o", "--output", dest="output", type="choice",
choices=("full", "name"),
help="output either ``full`` overlapping entries, only the ``name``s. [default=%default].")
parser.set_defaults(
output="full",
)
# add common options (-h/--help, ...) and parse command line
(options, args) = E.Start(parser, argv=argv)
if len(args) != 2:
raise ValueError("two arguments required")
if args[0] == "-":
infile1 = options.stdin
else:
infile1 = IOTools.openFile(args[0], "r")
infile2 = IOTools.openFile(args[1], "r")
idx = Bed.readAndIndex(infile2, with_values=True)
output = options.output
outfile = options.stdout
if output == "name":
outfile.write("name1\tname2\n")
outf = lambda x: x.fields[0]
else:
outf = str
for bed in Bed.iterator(infile1):
try:
overlaps = idx[bed.contig].find(bed.start, bed.end)
except (KeyError, IndexError):
# ignore missing contig and zero length intervals
continue
for o in overlaps:
outfile.write("\t".join((outf(bed), outf(o[2]))) + "\n")
E.Stop()
def __init__(self, filename, *args, **kwargs ):
assert filename != None, "please supply filename for CounterOverlap"
Counter.__init__(self, *args, **kwargs )
self.filename = filename
E.info( "reading intervals from %s" % self.filename )
self.index = Bed.readAndIndex( IOTools.openFile( self.filename, "r"),
per_track = True )
E.info( "read intervals for %s tracks" % len(self.index) )
self.tracks = self.index.keys()
self.headers = []
for track in self.tracks:
self.headers.extend( ["%s_nover" % track, "%s_bases" % track] )
def __init__(self, filename, *args, **kwargs ):
assert filename != None, "please supply filename for CounterOverlap"
Counter.__init__(self, *args, **kwargs )
self.filename = filename
E.info( "reading intervals from %s" % self.filename )
self.index = Bed.readAndIndex( IOTools.openFile( self.filename, "r"),
per_track = True )
E.info( "read intervals for %s tracks" % len(self.index) )
self.tracks = self.index.keys()
self.headers = []
for track in self.tracks:
self.headers.extend( ["%s_nover" % track, "%s_bases" % track] )
def main(argv=None):
"""script main.
parses command line options in sys.argv, unless *argv* is given.
"""
if argv is None:
argv = sys.argv
# setup command line parser
parser = E.OptionParser(version="%prog version: $Id$",
usage=globals()["__doc__"])
parser.add_option("-m",
"--method",
dest="method",
type="choice",
choices=["ave_dist", "min_dist", "corr"],
default="min_dist",
help="Method for calcuating similarity between profiles")
parser.add_option("-s",
"--spread",
dest="spread",
type="int",
default=10,
help="Amount to spread each tag by")
parser.add_option("-k",
"--keep-dist",
dest="keep_dist",
action="store_true",
help="Keep the distribution of tag depths")
parser.add_option("-r",
"--rands",
dest="rands",
default=100,
help="Number of randomisations to use for calculating"
" mean and stdev of distance")
# add common options (-h/--help, ...) and parse command line
(options, args) = E.Start(parser, argv=argv)
profile1_file, profile2_file = args
profile1_file = pysam.AlignmentFile(profile1_file)
if profile2_file.endswith("bed") or profile2_file.endswith("bed.gz"):
profile2_file = Bed.readAndIndex(profile2_file, with_values=True)
profile2_counter = bed_counter
else:
profile2_file = pysam.AlignmentFile(profile2_file)
profile2_counter = iCLIP.count_intervals
if options.method == "min_dist":
distance_func = iCLIP.findMinDistance
elif options.method == "ave_dist":
distance_func = iCLIP.calcAverageDistance
else:
def distance_func(profile1, profile2):
return iCLIP.corr_profile(profile1,
profile2,
options.spread,
profile2_ready=True)
for exon in GTF.iterator(options.stdin):
if exon.feature != "exon":
continue
contig = exon.contig
strand = exon.strand
transcript_id = exon.transcript_id
start = exon.start
end = exon.end
profile1 = iCLIP.count_intervals(profile1_file, [(start, end)],
contig=contig,
strand=strand)
profile2 = profile2_counter(profile2_file, [(start, end)],
contig=contig,
strand=strand)
if profile1.sum() == 0 or profile2.sum() == 0:
z = "NA"
distance = "NA"
options.stdout.write(
"%(contig)s\t%(start)i\t%(end)i\t%(transcript_id)s\t%(strand)s\t%(distance)s\t%(z)s\n"
% locals())
continue
if options.method == "corr":
profile2 = iCLIP.spread(profile2, options.spread)
distance = distance_func(profile1, profile2)
rands = iCLIP.rand_apply(profile=profile1,
exon=exon,
n=options.rands,
func=distance_func,
keep_dist=options.keep_dist,
profile2=profile2)
z = (distance - rands.mean()) / rands.std()
options.stdout.write(
"%(contig)s\t%(start)i\t%(end)i\t%(transcript_id)s\t%(strand)s\t%(distance).3f\t%(z).2f\n"
% locals())
# write footer and output benchmark information.
E.Stop()
def buildIndex(self, filename):
return Bed.readAndIndex(IOTools.openFile(filename, "r"))
def __init__(self, filename):
self.mIndices = Bed.readAndIndex(IOTools.openFile(filename, "r"),
per_track=True)
def buildIndex(self, filename):
return Bed.readAndIndex(IOTools.openFile(filename, "r"))
def __init__(self, filename):
self.mIndices = Bed.readAndIndex(IOTools.openFile(filename, "r"),
per_track=True)
def main(argv=None):
"""script main.
parses command line options in sys.argv, unless *argv* is given.
"""
if argv is None:
argv = sys.argv
# setup command line parser
parser = E.OptionParser(version="%prog version: $Id$",
usage=globals()["__doc__"])
parser.add_option(
"-b",
"--bamfile",
dest="bam",
type="string",
help=
"BAM formated alignment file to test. Should have MD and NH tags set")
parser.add_option(
"-t",
"--quality-threshold",
dest="threshold",
type="int",
default=30,
help="minimum quality threshold for a mismatched base to count")
parser.add_option("-f",
"--fasta-path",
dest="fastapath",
type="string",
help="path to indexed fasta file for genome of choice")
parser.add_option("-p",
"--vcf-path",
dest="vcfpath",
type="string",
help="path to indexed vcf file for dataset of choice")
parser.add_option(
"-d",
"--sample",
dest="samppattern",
type="string",
help=
"pattern to match and extract the donor name from the bam file, for use in parsing the vcf file"
)
parser.add_option(
"-n",
"--REDI-path",
dest="redipath",
type="string",
help=
"path to Bed format REDIportal table containing RNA editing positions")
(options, args) = E.Start(parser, argv=argv)
bamfile = pysam.AlignmentFile(options.bam)
fastafile = IndexedFasta(options.fastapath)
vcffile = vcf.Reader(open(options.vcfpath, "r"))
BEDREDI = Bed.readAndIndex(IOTools.openFile(options.redipath),
with_values=True)
options.stdout.write("\t".join([
"gene_id", "strand", "mismatches", "bases", "low_qual", "a", "t", "c",
"g", "a_to_t", "a_to_g", "a_to_c", "t_to_a", "t_to_g", "t_to_c",
"g_to_a", "g_to_t", "g_to_c", "c_to_a", "c_to_t", "c_to_g",
"indel_count", "RNA_editing_events"
]) + "\n")
samplepattern = options.samppattern
(not_reverse_g_to_t, reverse_g_to_t) = 0, 0
donorfrombam = re.search(r"%s" % (samplepattern), options.bam,
flags=0).group(1)
# find the donarid:
vcf_record = vcffile.next()
samples = vcf_record.samples
donors = [dnr.sample for dnr in samples]
donorid = None
for samp in donors:
if donorfrombam in samp:
donorid = samp
if donorid is None:
raise ValueError("Donor %s not found in VCF" % donorfrombam)
reversecomplement = {"a": "t", "t": "a", "c": "g", "g": "c"}
for gene in GTF.flat_gene_iterator(GTF.iterator(options.stdin)):
exontuple = GTF.asRanges(gene, "exon")
start = min(e.start for e in gene)
end = max(e.end for e in gene)
strand = gene[0].strand
seq = fastafile.getSequence(gene[0].contig, "+", start, end)
thischr = gene[0].contig.replace("chr", "")
reads = bamfile.fetch(gene[0].contig, start, end)
if all("chr" in c for c in vcffile.contigs.keys()) == False:
contig = (gene[0].contig).replace("chr", "")
if contig == "M":
contig = contig + "T"
else:
contig = gene[0].contig
vcfregion = vcffile.fetch(contig, start, end)
regionchecker = list(vcfregion)
BEDREDIregion = BEDREDI[gene[0].contig].find(start, end + 1)
editpositions = {
edit_pos: edit_pos_field
for edit_pos, edit_pos_plus, edit_pos_field in BEDREDIregion
if edit_pos_field.fields[2] == strand
}
gene_id = gene[0].gene_id
mm_count = 0
base_count = 0
skipped = 0
indel_count = 0
RNA_edits = 0
matched_bases = defaultdict(int)
transition = {
"a_to_t": 0,
"a_to_g": 0,
"a_to_c": 0,
"t_to_a": 0,
"t_to_g": 0,
"t_to_c": 0,
"g_to_a": 0,
"g_to_t": 0,
"g_to_c": 0,
"c_to_a": 0,
"c_to_t": 0,
"c_to_g": 0
}
snp_dict = {}
for snp in regionchecker:
if snp.genotype(donorid)["GT"] != "0/0":
snp_dict[snp.POS - 1] = snp.ALT
for read in reads:
if read.is_unmapped:
continue
if read.is_duplicate:
continue
if read.mate_is_unmapped:
continue
if read.get_tag("NH") > 1:
continue
qualities = read.query_qualities
alignmentcigar = read.cigarstring
indel_count += (alignmentcigar.count("I") +
alignmentcigar.count("D"))
alignment = read.get_aligned_pairs(with_seq=True)
# list[:] is weird syntax for copying the list
testalignment = alignment[:]
def _is_exon_range(base):
result_ranges = []
for exonrange in exontuple:
result_ranges.append(
exonrange[0] <= base[1] < exonrange[1])
if True in result_ranges:
return True
else:
return False
alignment = [
base for base in alignment if not base[0] is None
and not base[1] is None and _is_exon_range(base)
]
# base_count += sum(1 for base in alignment
# if start <= base[1] < end and
# base[2].lower() != "n")
total_alignment = [
base for base in alignment
if start <= base[1] < end and base[2].lower() != "n"
]
base_count += len(total_alignment)
for base in total_alignment:
if seq[(base[1]) - start].lower() != base[2].lower():
if (testalignment[0][1] is None) or (testalignment[-1][1]
is None):
E.debug("first or last base of read is None")
raise ValueError
else:
E.debug(
"identity of error causing base from read sequence: %s"
% (read.query_alignment_sequence)[base[0]].lower())
E.debug("read sequence: %s" %
(read.query_alignment_sequence))
E.debug(
"identity start and end of read as calculated from start and end as described in gtffile and extracted from fasta: %s"
% (seq[(testalignment[0][1] -
start):(testalignment[-1][1] - start)]))
E.debug(
"section of the read 10 bp downstream and upstream of the sequence containing the error extracted from the fasta: %s"
% (seq[((base[1] - 10) - start):(
(base[1] + 10) - start)].lower()))
E.debug("filename?: %s" % (read.tostring(bamfile)))
E.debug(
"positions of start and end of the gene based on the gtf: %s,%s"
% (start, end))
E.debug(
"identity of start of gene extratced from gtf: %s"
% (seq[(base[1]) - start]))
E.debug(
"identity of error causing base from reference genome: %s"
% base[2])
E.debug("position of base in read: %s" % base[0])
E.debug("position of base in genome: %s" % base[1])
E.debug(
"position of base in read as calculated from position of base in genome and and start from gtf: %s"
% (base[1] - start))
E.debug(
"identity of error causing base (reference), calculated from fasta and testalignment info: %s"
% (seq[(testalignment[0][1] -
start):(testalignment[-1][1] -
start)].upper()[base[0]]))
E.debug(
"position of base in read from first alignment genome base minus start plus position of base in in read, should equal position of base in read: %s"
% ((testalignment[0][1] - start) + base[0]))
E.debug(
"identity of error causing base (reference), calculated from fasta and position of base in genome from aligned pairs: %s"
% (seq[(base[1]) - start]))
E.debug(
"position of start base in genome from the alignment minus position of start base in genome from the gtf, should be zero: %s"
% (alignment[0][1] - start))
E.debug("complete aligned pairs, unfiltered: %s" %
(testalignment))
E.debug("full fasta sequence of read: %s" %
(textwrap.fill(seq, 50)))
raise ValueError
else:
matched_bases[base[2].lower()] += 1
if read.get_tag("NM") == 0:
continue
# mismatches
readseq = read.query_sequence
def _is_snp(base):
global got_snp_pos
global wrong_base
if snp_dict.has_key(base[1]):
read_base = readseq[base[0]].lower()
alt_base = snp_dict[base[1]][0].sequence.lower()
got_snp_pos += 1
if read_base != alt_base:
wrong_base += 1
return True
else:
return False
else:
return True
def _is_indel(base):
if (len(readseq) >= (base[0] + 5)):
if (len(seq) < (((base[1]) - start) + 5)):
upperrange = len(seq) - (base[1] - start)
lowerrange = 5 - upperrange
readindelwindow = readseq[(base[0] -
lowerrange):(base[0] +
upperrange)]
seqindelwindow = seq[(
((base[1]) - start) -
lowerrange):(((base[1]) - start) + upperrange)]
matchwindows = []
for i in range(len(readindelwindow)):
try:
matchwindows.append(
(readindelwindow[i].lower() ==
seqindelwindow[i].lower()))
except IndexError:
print i
print readindelwindow
print seqindelwindow
print start
print lowerrange
print upperrange
print base[0]
print(base[0] - lowerrange)
print(base[0] + upperrange)
print base[1]
print(base[1] - start)
print((((base[1]) - start) - lowerrange) - 1)
print((((base[1]) - start) + upperrange) - 1)
print readseq
print seq
print gene_id
print gene[0].contig
raise
elif (len(seq) >= (((base[1]) - start) + 5)):
readindelwindow = readseq[base[0]:(base[0] + 5)]
seqindelwindow = seq[((base[1]) -
start):(((base[1]) - start) + 5)]
matchwindows = []
for i in range(len(readindelwindow)):
try:
matchwindows.append(readindelwindow[i].lower(
) == seqindelwindow[i].lower())
except IndexError:
print i
print readindelwindow
print seqindelwindow
print start
print base[0]
print base[1]
print(base[1] - start) - 1
print((base[1] - start) + 5) - 1
print readseq
print seq
print gene_id
print gene[0].contig
raise
if matchwindows.count(False) >= 4:
return False
else:
return True
elif (len(readseq) < (base[0] + 5)):
if len(seq) < (((base[1]) - start) + 5):
readsequpperrange = len(readseq) - base[0]
readseqlowerrange = 5 - readsequpperrange
sequpperrange = len(seq) - (base[1] - start)
seqlowerrange = 5 - sequpperrange
if readsequpperrange < sequpperrange:
upperrange = readsequpperrange
lowerrange = readseqlowerrange
elif sequpperrange < readsequpperrange:
upperrange = sequpperrange
lowerrange = seqlowerrange
elif sequpperrange == readsequpperrange:
upperrange = sequpperrange
lowerrange = seqlowerrange
elif ((base[1] - start) - 4) < 0:
return True
else:
upperrange = len(readseq) - base[0]
lowerrange = 5 - upperrange
readindelwindow = readseq[(base[0] -
lowerrange):(base[0] +
upperrange)]
seqindelwindow = seq[(((base[1]) - start) -
lowerrange):(((base[1]) - start) +
upperrange)]
matchwindows = []
for i in range(len(readindelwindow)):
try:
matchwindows.append((readindelwindow[i].lower() ==
seqindelwindow[i].lower()))
except IndexError:
print i
print readindelwindow
print seqindelwindow
print start
print lowerrange
print upperrange
print base[0]
print(base[0] - lowerrange)
print(base[0] + upperrange)
print base[1]
print(base[1] - start)
print((((base[1]) - start) - lowerrange))
print((((base[1]) - start) + upperrange))
print readseq
print seq
print gene_id
print gene[0].contig
raise
if matchwindows.count(False) >= 4:
return False
else:
return True
def _is_RNA_edit(base, editpositions):
global got_edit_pos
global wrong_edit_base
genomebase = base[2]
readbase = readseq[base[0]].lower()
if not base[1] in editpositions.keys() or \
genomebase == "n" or \
readbase == "n" or \
not genomebase.islower():
return True
else:
got_edit_pos += 1
if genomebase == editpositions[base[1]].fields[0].lower() and \
readbase == editpositions[base[1]].fields[1].lower():
return False
else:
wrong_edit_base += 1
return True
for base in total_alignment:
if _is_RNA_edit(base, editpositions) == False:
RNA_edits += 1
mismatches = [
base for base in total_alignment if base[2].islower()
and qualities[base[0]] >= options.threshold and _is_snp(base)
and _is_indel(base) and _is_RNA_edit(base, editpositions)
and readseq[base[0]].lower() != "n"
]
total_mm = sum(1 for base in total_alignment
if base[2].islower() and _is_snp(base)
and readseq[base[0]].lower() != "n")
hq_mm = len(mismatches)
for base in mismatches:
genomebase = base[2].lower()
readbase = readseq[base[0]].lower()
try:
if strand == "-":
revgenomebase = reversecomplement[genomebase]
revreadbase = reversecomplement[readbase]
if revgenomebase == "g" and revreadbase == "a":
if read.is_reverse:
reverse_g_to_t += 1
else:
not_reverse_g_to_t += 1
transition["%s_to_%s" %
(revgenomebase, revreadbase)] += 1
else:
transition["%s_to_%s" % (genomebase, readbase)] += 1
except KeyError:
print transition
print read.query_alignment_sequence.upper()
print seq[(alignment[0][1] - start):(alignment[-1][1] -
start)].upper()
print read.tostring(bamfile)
raise
mm_count += hq_mm
skipped += total_mm - hq_mm
outline = "\t".join(
map(str, [
gene_id, strand, mm_count, base_count, skipped,
matched_bases['a'], matched_bases['t'], matched_bases['c'],
matched_bases['g'], transition['a_to_t'], transition['a_to_g'],
transition['a_to_c'], transition['t_to_a'],
transition['t_to_g'], transition['t_to_c'],
transition['g_to_a'], transition['g_to_t'],
transition['g_to_c'], transition['c_to_a'],
transition['c_to_t'], transition['c_to_g'], indel_count,
RNA_edits
]))
options.stdout.write(outline + "\n")
# write footer and output benchmark information.
E.info("Out of %i mismatches at snp positions %i were the wrong base" %
(got_snp_pos, wrong_base))
E.info(
"Out of %i mismatches at RNA edit positions %i were the wrong base" %
(got_edit_pos, wrong_edit_base))
E.info(
"Out of %i g_to_c transitions on - strand genes, the read was on the + strand %i times"
% (not_reverse_g_to_t, reverse_g_to_t))
E.Stop()
def main(argv=None):
"""script main.
parses command line options in sys.argv, unless *argv* is given.
"""
if argv is None:
argv = sys.argv
# setup command line parser
parser = E.OptionParser(version="%prog version: $Id$",
usage=globals()["__doc__"])
parser.add_option("-m","--method", dest="method", type="choice",
choices=["ave_dist","min_dist","corr"],
default="min_dist",
help="Method for calcuating similarity between profiles")
parser.add_option("-s", "--spread", dest="spread", type="int",
default=10,
help="Amount to spread each tag by")
parser.add_option("-k", "--keep-dist", dest="keep_dist",
action="store_true",
help="Keep the distribution of tag depths")
parser.add_option("-r", "--rands", dest="rands",
default=100,
help="Number of randomisations to use for calculating"
" mean and stdev of distance")
# add common options (-h/--help, ...) and parse command line
(options, args) = E.Start(parser, argv=argv)
profile1_file, profile2_file = args
profile1_file = pysam.AlignmentFile(profile1_file)
if profile2_file.endswith("bed") or profile2_file.endswith("bed.gz"):
profile2_file = Bed.readAndIndex(profile2_file, with_values=True)
profile2_counter = bed_counter
else:
profile2_file = pysam.AlignmentFile(profile2_file)
profile2_counter = iCLIP.count_intervals
if options.method=="min_dist":
distance_func = iCLIP.findMinDistance
elif options.method=="ave_dist":
distance_func = iCLIP.calcAverageDistance
else:
def distance_func(profile1,profile2):
return iCLIP.corr_profile(profile1,profile2, options.spread, profile2_ready=True)
for exon in GTF.iterator(options.stdin):
if exon.feature != "exon":
continue
contig = exon.contig
strand = exon.strand
transcript_id = exon.transcript_id
start = exon.start
end = exon.end
profile1 = iCLIP.count_intervals(profile1_file,
[(start, end)],
contig=contig,
strand=strand)
profile2 = profile2_counter(profile2_file,
[(start, end)],
contig=contig,
strand=strand)
if profile1.sum() == 0 or profile2.sum() == 0:
z = "NA"
distance = "NA"
options.stdout.write(
"%(contig)s\t%(start)i\t%(end)i\t%(transcript_id)s\t%(strand)s\t%(distance)s\t%(z)s\n" % locals())
continue
if options.method=="corr":
profile2 = iCLIP.spread(profile2, options.spread)
distance = distance_func(profile1, profile2)
rands = iCLIP.rand_apply(profile=profile1,
exon=exon,
n=options.rands,
func=distance_func,
keep_dist=options.keep_dist,
profile2=profile2)
z = (distance - rands.mean())/rands.std()
options.stdout.write(
"%(contig)s\t%(start)i\t%(end)i\t%(transcript_id)s\t%(strand)s\t%(distance).3f\t%(z).2f\n" % locals())
# write footer and output benchmark information.
E.Stop()
def countInteractions(reads, digest, probe_fragments, outfile, metrics_file):
reads = pysam.AlignmentFile(reads)
digest_intervals = Bed.readAndIndex(IOTools.openFile(digest),
with_values="name")
probe_fragments = Bed.readAndIndex(IOTools.openFile(probe_fragments),
with_values="name")
c = collections.Counter()
results = collections.defaultdict(int)
contigs = digest_intervals.keys()
rejects = pysam.AlignmentFile(outfile + ".rejects.bam",
"wb",
template=reads)
E.debug("Starting Counting Run...")
for fragment in readBundle(reads):
if c["fragments"] % 1000 == 0:
E.debug("\t".join(["%s:\t%s" % x for x in c.iteritems()]))
c["fragments"] += 1
c["reads"] += len(fragment)
def _get_read_position(aln):
if aln.is_reverse:
c = aln.cigartuples[::-1]
else:
c = aln.cigartuples
pos = 0
for op, length in c:
if op == 0:
return pos
else:
pos += length
return pos
def _get_first(alignments):
'''find alignment that maps earliest part of read'''
slist = sorted(alignments, key=lambda x: _get_read_position(x))
return (slist[0], slist[1:])
def _get_probes(read):
try:
m = list(probe_fragments[reads.getrname(
read.reference_id)].find(read.pos, read.aend))
except KeyError:
return []
return m
first_read = [aln for aln in fragment if aln.is_read1]
second_read = [aln for aln in fragment if aln.is_read2]
if len(first_read) == 0 or len(second_read) == 0:
c["Unpaired"] += 1
continue
assert len(first_read) + len(second_read) == len(fragment)
primary_aln, other_aln = zip(_get_first(first_read),
_get_first(second_read))
other_aln = sum(other_aln, [])
probe_matches = [_get_probes(read) for read in primary_aln]
if len(sum(probe_matches, [])) == 0:
c["no_probe"] += 1
for read in fragment:
rejects.write(read)
continue
other_matches = set(
sum([
list(digest_intervals[reads.getrname(read.reference_id)].find(
read.pos, read.aend)) for read in other_aln
if reads.getrname(read.reference_id) in contigs
], []))
primary_matches = set(
sum([
list(digest_intervals[reads.getrname(read.reference_id)].find(
read.pos, read.aend)) for read in primary_aln
if reads.getrname(read.reference_id) in contigs
], []))
if len(primary_matches) > 2:
c["multi-hit"] += 1
continue
if not all([match in primary_matches for match in other_matches]):
c["multi-hit"] += 1
continue
primary_matches = list(primary_matches)
if len(primary_matches) == 2:
results[(primary_matches[0][2], primary_matches[1][2])] += 1
results[(primary_matches[1][2], primary_matches[0][2])] += 1
elif len(primary_matches) == 1:
results[(primary_matches[0][2], primary_matches[0][2])] += 1
else:
raise ValueError("Matches not found")
with IOTools.openFile(outfile, "w") as outf:
outf.write("Frag1\tFrag2\tCount\n")
for pair in results:
outf.write("%s\t%s\t%s\n" % (pair + (results[pair], )))
with IOTools.openFile(metrics_file, "w") as outf:
outf.write("\t".join(c.keys()) + "\n")
outf.write("\t".join(map(str, c.values())) + "\n")
rejects.close()
def countInteractions(reads, digest, probe_fragments, outfile,
metrics_file):
reads = pysam.AlignmentFile(reads)
digest_intervals = Bed.readAndIndex(
IOTools.openFile(digest), with_values="name")
probe_fragments = Bed.readAndIndex(
IOTools.openFile(probe_fragments), with_values="name")
c = collections.Counter()
results = collections.defaultdict(int)
contigs = digest_intervals.keys()
rejects=pysam.AlignmentFile(outfile + ".rejects.bam", "wb", template=reads)
E.debug("Starting Counting Run...")
for fragment in readBundle(reads):
if c["fragments"] % 1000 == 0:
E.debug("\t".join(["%s:\t%s" % x for x in c.iteritems()]))
c["fragments"] += 1
c["reads"] += len(fragment)
def _get_read_position(aln):
if aln.is_reverse:
c = aln.cigartuples[::-1]
else:
c = aln.cigartuples
pos = 0
for op, length in c:
if op == 0:
return pos
else:
pos += length
return pos
def _get_first(alignments):
'''find alignment that maps earliest part of read'''
slist = sorted(alignments, key=lambda x: _get_read_position(x))
return (slist[0], slist[1:])
def _get_probes(read):
try:
m = list(
probe_fragments[reads.getrname(read.reference_id)].find(
read.pos, read.aend))
except KeyError:
return []
return m
first_read = [aln for aln in fragment if aln.is_read1]
second_read = [aln for aln in fragment if aln.is_read2]
if len(first_read) == 0 or len(second_read) == 0:
c["Unpaired"] += 1
continue
assert len(first_read) + len(second_read) == len(fragment)
primary_aln, other_aln = zip(_get_first(first_read),
_get_first(second_read))
other_aln = sum(other_aln, [])
probe_matches = [_get_probes(read) for read in primary_aln]
if len(sum(probe_matches, [])) == 0:
c["no_probe"] += 1
for read in fragment:
rejects.write(read)
continue
other_matches = set(sum([
list(digest_intervals[reads.getrname(read.reference_id)].find(
read.pos, read.aend))
for read in other_aln
if reads.getrname(read.reference_id) in contigs],[]))
primary_matches = set(sum([
list(digest_intervals[reads.getrname(read.reference_id)].find(
read.pos, read.aend))
for read in primary_aln
if reads.getrname(read.reference_id) in contigs],[]))
if len(primary_matches) > 2:
c["multi-hit"] += 1
continue
if not all([match in primary_matches for match in other_matches]):
c["multi-hit"] += 1
continue
primary_matches = list(primary_matches)
if len(primary_matches) == 2:
results[(primary_matches[0][2], primary_matches[1][2])] += 1
results[(primary_matches[1][2], primary_matches[0][2])] += 1
elif len(primary_matches) == 1:
results[(primary_matches[0][2], primary_matches[0][2])] += 1
else:
raise ValueError("Matches not found")
with IOTools.openFile(outfile, "w") as outf:
outf.write("Frag1\tFrag2\tCount\n")
for pair in results:
outf.write("%s\t%s\t%s\n" % (pair+(results[pair],)))
with IOTools.openFile(metrics_file, "w") as outf:
outf.write("\t".join(c.keys())+"\n")
outf.write("\t".join(map(str,c.values())) + "\n")
rejects.close()
