def annotate_indel_on_db(row, fasta, dbsnp, clnvr, chr_prefixed):
"""Check if there are equivalent indels on dbSNP and ClinVar
for each indel. If exists, annotate with SNP info.
Args:
row (pandas.Series): with 'chr', 'pos', 'is_ins', 'indel_seq' lables
fasta (str): path to .fa
dbsnp (str): path to 00-All.151.indel.vcf.gz
clnvr (str): path to clinvar.indel.vcf.gz
chr_prefixed (bool): True if chromosome names in BAM are "chr"-prefixed
Returns:
report (IndelSnpFeatures): idl object reporting SNP info
"""
chr = row["chr"]
pos = row["pos"]
idl_type = row["is_ins"]
idl_seq = row["indel_seq"]
# obj representing the indel in reference genome
idl = curate_indel_in_genome(fasta, chr, pos, idl_type, idl_seq,
chr_prefixed)
# obj representing report of the indel
report = IndelSnpFeatures(chr, pos, idl_type, idl_seq)
# search for equivalent indels over pos +/- search_window nt
search_window = 50
start, end = pos - search_window, pos + search_window
chr_vcf = row["chr"].replace("chr", "")
for record in dbsnp.fetch(chr_vcf, start, end, parser=pysam.asTuple()):
bambinos = vcf2bambino(record)
for bb in bambinos:
if idl_type == bb.idl_type and len(idl_seq) == len(bb.idl_seq):
# indel on db representing in reference genome
db_idl = curate_indel_in_genome(fasta, chr, bb.pos,
bb.idl_type, bb.idl_seq,
chr_prefixed)
if idl == db_idl:
rs = record[2]
report.add_dbsnp_id(rs)
report.add_dbsnp_freq(dbsnp_freq(record))
# report.add_dbsnp_origin(dbsnp_origin(record))
report.add_dbsnp_common(dbsnp_common(record))
for record in clnvr.fetch(chr_vcf, start, end, parser=pysam.asTuple()):
bambinos = vcf2bambino(record)
for bb in bambinos:
if idl_type == bb.idl_type and len(idl_seq) == len(bb.idl_seq):
db_idl = curate_indel_in_genome(fasta, chr, bb.pos,
bb.idl_type, bb.idl_seq,
chr_prefixed)
if idl == db_idl:
id = record[2]
report.add_clnvr_id(id)
report.add_clnvr_freq(clnvr_freq(record))
# report.add_clnvr_origin(clnvr_origin(record))
report.add_clnvr_info(cln_info(record))
return report
def test_tabix_multi_ps_open(self):
with open(self.tabix_ref,"rb") as fh1:
with open(self.tabix_ref,"rb") as fh2:
ps1 = pysam.tabix_file_iterator(fh1,pysam.asTuple())
ps2 = pysam.tabix_file_iterator(fh2,pysam.asTuple())
reader = MockReader(ps1,ps2,self.tabix_ref,tabix=True)
for expected, found in zip(reader,self.bed_lines+self.bed_lines):
self.assertEqual(expected.strip("\n"),found.strip("\n"))
def test_tabix_multi_ps_open(self):
with open(self.tabix_ref, "rb") as fh1:
with open(self.tabix_ref, "rb") as fh2:
ps1 = pysam.tabix_file_iterator(fh1, pysam.asTuple())
ps2 = pysam.tabix_file_iterator(fh2, pysam.asTuple())
reader = MockReader(ps1, ps2, self.tabix_ref, tabix=True)
for expected, found in zip(reader,
self.bed_lines + self.bed_lines):
self.assertEqual(expected.strip("\n"), found.strip("\n"))
def get_coverage(self, chrom, start, stop):
if not self._binned:
for row in self._tabixfile.fetch(chrom, start, stop+1, parser=pysam.asTuple()):
yield json.loads(row[2])
else:
# Right now we don't include the region_end column in our coverage files,
# so there's no way to make sure we get the bin overlapping the start of our query region.
# To deal with it for now, we'll just use start-50
# TODO: include region_end in coverage files.
for row in self._tabixfile.fetch(chrom, max(1, start-50), stop+1, parser=pysam.asTuple()):
d = json.loads(row[2])
if d['end'] < start or d['start'] > stop: continue
d['start'] = max(d['start'], start)
d['end'] = min(d['end'], stop)
yield d
def get(self, chrom, position, ref, alt):
if self.has_chr_prefix and not chrom.startswith('chr'):
chrom = 'chr' + chrom
elif not self.has_chr_prefix and chrom.startswith('chr'):
chrom = chrom[3:]
if not self.overlaps(chrom, position):
self.chrom = chrom
self.start = position
self.stop = position + self.step_bp
self.data = dict()
for f in self.files:
with pysam.Tabixfile(f, 'r') as tabix:
for row in tabix.fetch(self.chrom,
self.start - 1,
self.stop + 1,
parser=pysam.asTuple()):
name = ':'.join(row[:4])
cadd_raw, cadd_phred = map(float, row[4:6])
if name in self.data:
if self.data[name][1] < cadd_phred:
self.data[name] = (cadd_raw, cadd_phred)
else:
self.data[name] = (cadd_raw, cadd_phred)
return self.data.get(':'.join((chrom, str(position), ref, alt)),
(None, None))
def _get_schema(self):
if self._dataset is None:
self._open_dataset()
self._chroms = list(self._dataset.contigs)
rec = next(self._dataset.fetch(self._chroms[0], parser=asTuple()))
num_fields = len(rec)
chrom_coord_dtype = np.int64
dtypes = {
"chrom": pd.CategorialDtype(self._chroms + ["NULL"], ordered=True),
"start": chrom_coord_dtype,
"end": chrom_coord_dtype,
"name": str,
"score": np.float32,
"strand": bool,
}
self._dtype = {
key: dtypes[key]
for key in list(dtypes.keys())[:num_fields]
}
return Schema(
datashape=None,
dtype=self._dtype,
shape=(None, len(self._dtype)),
npartitions=len(self._chroms),
extra_metadata={},
)
def getphastscores(phastconsbed, gcoords):
scores = [] #all scores
scoresd = {} #{geneid : [scores]}
tbx = pysam.Tabixfile(phastconsbed)
nt = 0
ntwithscores = 0
for gene in gcoords:
pcscores = [] #list of all pc scores for coords in this gene
chrm = gcoords[gene][0]
coords = gcoords[gene][2]
for coord in coords:
nt += 1
for row in tbx.fetch(chrm,
coord,
coord + 1,
parser=pysam.asTuple()):
score = row[4]
if score:
ntwithscores += 1
scores.append(score)
pcscores.append(score)
if pcscores:
scoresd[gene] = pcscores
print 'Interrogated {0} nucleotides. Found phastcons scores for {1} ({2}%) of them.'.format(
nt, ntwithscores,
round((ntwithscores / float(nt)), 4) * 100)
return scores, scoresd
def parse_annotations(chrom, pos):
AF_supAFR = CSQ = 'NA'
if chrom == 'X':
if pos <= 2699520:
replace = 'X_PAR1'
elif pos >= 154931044:
replace = 'X_PAR2'
else:
replace = 'X_nonPAR'
else:
replace = chrom
path_vcf = '../../../SHAPEIT/out_annotate_2016Dec28/{}.minAC1.no_mask.without_related.vcf.gz'.format(replace)
tbx = pysam.TabixFile(path_vcf)
for row in tbx.fetch(chrom, pos - 1, pos, parser=pysam.asTuple()):
for _ in row[7].split(';'):
if _ == 'DB':
continue
k, v = _.split('=')
if k == 'AF_supAFR':
AF_supAFR = v
elif k == 'CSQ':
CSQ = v
return AF_supAFR, CSQ
def testIteratorUncompressed(self):
'''test iteration from uncompressed file.'''
tmpfilename = 'tmp_testIteratorUncompressed'
infile = gzip.open(self.filename, "rb")
outfile = open(tmpfilename, "wb")
outfile.write(infile.read())
outfile.close()
infile.close()
with open(tmpfilename) as infile:
for x, r in enumerate(pysam.tabix_iterator(infile,
pysam.asTuple())):
self.assertEqual(self.compare[x], list(r))
self.assertEqual(len(self.compare[x]), len(r))
# test indexing
for c in range(0, len(r)):
self.assertEqual(self.compare[x][c], r[c])
# test slicing access
for c in range(0, len(r) - 1):
for cc in range(c + 1, len(r)):
self.assertEqual(self.compare[x][c:cc], r[c:cc])
os.unlink(tmpfilename)
def annotate(self, bedline, genome):
c = bedline.rstrip().rsplit("\t")
chr = c[0]
start = c[1]
end = c[2]
if not re.search('chr', chr):
raise LookupError("chromosome names must start with chr: " + chr)
return []
if (self.genome != genome):
raise LookupError(
"tried to compare a %s bedfile to a %s annotation." %
(genome, self.genome))
return []
else:
annotations = []
if (chr and start and end):
if self.tabixContigs.has_key(chr):
tabixTupleParse = self.tabix.fetch(reference=chr,
start=int(start),
end=int(end),
parser=pysam.asTuple())
for tabixTuple in tabixTupleParse:
annotations.append(tabixTuple[3])
return uniqann(annotations)
else:
return []
else:
raise LookupError(
"can't find chr,start,end. File must be tab-delimited")
return []
def load_segmented_data(filepath, interval): res = genomic_interval_set() tabix = pysam.TabixFile(filepath) for row in tabix.fetch(interval.chrom, interval.start, interval.end, parser = pysam.asTuple()): chrom=row[0] start = int(row[1]) end = int(row[2]) try: name=row[3] except: name='.' try: score=float(row[4]) except: score=-np.inf try: strand=row[5] except: strand='+' res += genomic_interval(chrom, start, end, name=name, score=score, strand=strand) tabix.close() return res
def snp_cal(chromo, window_start, window_end):
rows = tuple(
parsevcf.fetch(region="%s:%s-%s" % (chromo, window_start, window_end),
parser=pysam.asTuple()))
sites_total, sites_unmasked, sites_passing, sites_variant = 0, 0, 0, 0
calls = [0] * len(samples)
hets = [0] * len(samples)
for line in rows:
sites_total += 1
if "CpGRep" in line[6]: continue
sites_unmasked += 1
if "FAIL" in line[6]: continue
if "WARN" in line[6]: continue
sites_passing += 1
if line[4] != '.': sites_variant += 1
for i in range(0, len(samples)):
GT = line[i + 9]
if GT[:1] != '.': calls[i] += 1
if GT[:3] == '0/1': hets[i] += 1
if GT[:3] == '0|1': hets[i] += 1
output.write(
'%s\t%s\t%s\t%s\t%s\t%s\t%s\t%s\n' %
(chromo, window_start, sites_total, sites_unmasked, sites_passing,
sites_variant, '\t'.join(map(str, calls)), '\t'.join(map(str, hets))))
def annotate_variants_list(args, select_cursor, update_cursor):
"""
Populate a new, user-defined column in the variants
table with a INTEGER indicating the count of overlaps
between the variant and the
annotation file.
"""
add_requested_column(args.col_name, update_cursor)
# For each, use Tabix to count overlaps with the user-defined
# annotation file. Update the variant row with the count.
annos = pysam.Tabixfile(args.anno_file)
select_cursor.execute("SELECT chrom, start, end, variant_id FROM variants")
for row in select_cursor:
hit_list = []
for hit in annos.fetch(str(row['chrom']), int(row['start']), int(row['end']),
parser=pysam.asTuple()):
try:
hit_list.append(hit[int(args.col_extract) - 1])
except IndexError:
sys.exit("Column " + args.col_extract + " exceeds \
the number of columns in your \
annotation file. Exiting.")
hits = ",".join(hit_list)
if len(hit_list):
update_qry = "UPDATE variants SET " + args.col_name + " = '" + hits + \
"' WHERE variant_id = " + str(row['variant_id'])
else:
update_qry = "UPDATE variants SET " + args.col_name + " = NULL" + \
" WHERE variant_id = " + str(row['variant_id'])
update_cursor.execute(update_qry)
def load_counts(self, discfile, window_in, window_out):
reg = '{0}:{1}-{2}'
def _get_coords(pos, strand):
if strand == '+':
start, end = pos - window_out, pos + window_in
else:
start, end = pos - window_in, pos + window_out
return start, end
startA, endA = _get_coords(self.posA, self.strandA)
startB, endB = _get_coords(self.posB, self.strandB)
region = reg.format(self.chrA, startA, endA)
counts = defaultdict(int)
pairs = discfile.fetch(region=region, parser=pysam.asTuple())
for pair in pairs:
pair = _DiscPair(*pair)
# Pairs were selected based on window around chrA; check chrB
if pair.chrB != self.chrB:
continue
if not (startB <= pair.posB < endB):
continue
# Require pairs match breakpoint strand
if pair.strandA != self.strandA or pair.strandB != self.strandB:
continue
counts[pair.sample] += 1
self.pair_counts = pd.DataFrame.from_dict({'count': counts})
def testTabixIndexedTsvCreation(self):
inFile = "testdata/ESP6500SI-V2.chr1.snps_indels.head.25.txt"
destDir = "out"
# chr, startPos, endPos
resultIndexedFile = TabixIndexer.index(destDir=destDir, inputFilename=inFile, fileColumnNumList=[0, 1, 1])
self.assertTrue(os.path.exists(resultIndexedFile), "No index file was generated.")
chrom = "1"
start = "69594"
end = "69594"
tsvRecords = None
tsvReader = pysam.Tabixfile(filename=resultIndexedFile) # initialize the tsv reader
try:
tsvRecords = tsvReader.fetch(chrom, int(start)-1, int(end), parser=pysam.asTuple())
except ValueError:
pass
tsvRecord = None
for tsvRecord in tsvRecords:
self.assertEqual(tsvRecord[5], "2,6190", "Value in column sixth does not match the expected value.")
self.assertIsNotNone(tsvRecord, "No record for %s:%s-%s was found." % (chrom, start, end))
os.remove(resultIndexedFile)
def __iter__(self):
from pysam import Tabixfile, asTuple
f = Tabixfile(self.filename, mode='r')
try:
# header row
if self.header is not None:
yield self.header
else:
# assume last header line has fields
h = list(f.header)
if len(h) > 0:
header_line = text_type(h[-1], encoding='ascii')
yield tuple(header_line.split('\t'))
# data rows
for row in f.fetch(reference=self.reference,
start=self.start,
end=self.stop,
region=self.region,
parser=asTuple()):
yield tuple(row)
except:
raise
finally:
f.close()
def get_snp_data(*args, **kwargs):
'''
proxy for TabixFile.fetch
'''
kwargs['multiple_iterators'] = True
return TabixFile(SNP_FILE, parser=asTuple()).\
fetch(*args, **kwargs)
def testIteratorUncompressed(self):
'''test iteration from uncompressed file.'''
tmpfilename = 'tmp_testIteratorUncompressed'
infile = gzip.open(self.filename, "rb")
outfile = open(tmpfilename, "wb")
outfile.write(infile.read())
outfile.close()
infile.close()
with open(tmpfilename) as infile:
for x, r in enumerate(pysam.tabix_iterator(
infile, pysam.asTuple())):
self.assertEqual(self.compare[x], list(r))
self.assertEqual(len(self.compare[x]), len(r))
# test indexing
for c in range(0, len(r)):
self.assertEqual(self.compare[x][c], r[c])
# test slicing access
for c in range(0, len(r) - 1):
for cc in range(c + 1, len(r)):
self.assertEqual(self.compare[x][c:cc],
r[c:cc])
os.unlink(tmpfilename)
def vci_query(vci_file, region, fasta_file):
# ./bin/g2gtools vciquery -v data/mm/REF2CAST.vci.gz -r "1:13009000-13009800" -d
start = time.time()
vci_file = g2g_utils.check_file(vci_file, 'r')
LOG.info("VCI File: {}".format(vci_file))
LOG.info("Region: {}".format(region))
vci_f = VCIFile(vci_file, seq_ids=[region.seq_id])
vci_f.parse(False)
mappings = vci_f.find_mappings(region.seq_id, region.start, region.end)
for m in mappings:
LOG.debug(m)
start_pos = mappings[0].to_start
end_pos = mappings[-1].to_end
LOG.debug("Converted Region: {}:{}-{}".format(region.seq_id, start_pos + 1,
end_pos + 1))
for line in vci_f.fetch(reference=region.seq_id,
start=start_pos,
end=end_pos,
parser=pysam.asTuple()):
print(str(line))
LOG.info("VCI parsed: {0}".format(g2g_utils.format_time(
start, time.time())))
def fetch_highest_scores(self, chrom, pos_begin, pos_end):
result = dict()
stripped_chrom = handle_chrom_prefix(self.chr_prefix, chrom)
try:
for line in self.accessor.direct_infile.fetch(
stripped_chrom, pos_begin - 1, pos_end,
parser=pysam.asTuple()):
line = LineAdapter(self.accessor.score_file, line)
for column in self.score_names:
score_index = self.schema.col_names.index(column)
score_value = float(line[score_index]) \
if str.lower(line[score_index]) \
!= self.config.general.no_score_value else np.nan
result[column] = max(score_value,
result.get(column, np.nan))
return result
except ValueError as ex:
print(
f"could not find region {chrom}:{pos_begin}-{pos_end} "
f"in {self.score_filename}: ",
ex,
file=sys.stderr,
)
return result
def getMinMaxPositions(depthFile, contig):
with closing(pysam.TabixFile(depthFile)) as tabix:
first_entry = None
for first_entry in tabix.fetch(contig, 0, parser=pysam.asTuple()):
break
last_Mbp = 0
while any(True for _ in tabix.fetch(
contig, last_Mbp, parser=pysam.asTuple())):
last_Mbp += 5000000
last_entry = None
for last_entry in tabix.fetch(contig,
last_Mbp - 5000000,
parser=pysam.asTuple()):
pass
return (long(first_entry[1]) if first_entry is not None else None,
long(last_entry[1]) if last_entry is not None else None)
def bed_regions(bed_file, chromo):
open_bed = pysam.TabixFile(bed_file)
for line in open_bed.fetch(chromo, parser=pysam.asTuple()):
start = int(line[1])
end = int(line[2])
yield start, end
def _create_column_dict_from_tabix_index(self, mutation):
mut_start = int(mutation.start)
mut_end = int(mutation.end)
chrom = mutation.chr
vals = {}
try:
# tabix needs position - 1
tsv_records = self.tsv_reader.fetch(chrom,
mut_start - 1,
mut_end,
parser=pysam.asTuple())
i = -1
for i, tsv_record in enumerate(tsv_records):
if not tsv_record: # skip in case no records are found
continue
logging.getLogger(__name__).debug("Got a record.")
# Determine whether the new tsv record matches mutation or not
if self._is_matching(mutation, tsv_record):
for colName in self.output_tsv_headers:
if colName.strip() == "":
continue
val = tsv_record[self.tsv_headers[colName]]
if colName not in vals:
vals[colName] = [val]
else:
vals[colName] += [val]
logging.getLogger(__name__).debug("Processed %d records." %
(i + 1))
except ValueError as ve:
msg = "Exception when looking for tsv records. Empty set of records being returned: " + repr(
ve)
logging.getLogger(__name__).debug(msg)
return vals
def get_snp_data(*args, **kwargs):
'''
proxy for TabixFile.fetch
'''
kwargs['multiple_iterators'] = True
return TabixFile(SNP_FILE, parser=asTuple()).\
fetch(*args, **kwargs)
def get_info_from_variants(chrom, start, stop, field):
"""
Check the format of the CHROM field in VCF file
Arguments:
chrom (str): chromosome of search region
start (str): start position of search region
stop (str): stop position of search region
field (str): field to extract from VCF file
Returns:
list (tuple): list of tuples by (chromosome, field value)
"""
tuples = []
format_index = get_format_index()
field_index = get_format_field_index(field)
try:
for entry in tbx.fetch(chrom, int(start), int(stop), parser=asTuple()):
if float(entry[5]) > 20:
tuples.append(
(chrom, get_field_value(entry, format_index, field_index)))
except ValueError:
print("No variants found in region {}:{}:{}".format(
chrom, start, stop))
pass
return tuples
def annotate(self,bedline,genome):
c = bedline.rstrip().rsplit("\t")
chr = c[0]
start = c[1]
end = c[2]
if not re.search('chr',chr):
raise LookupError("chromosome names must start with chr: " + chr)
return []
if (self.genome != genome):
raise LookupError("tried to compare a %s bedfile to a %s annotation." % (genome,self.genome))
return []
else:
annotations = []
if (chr and start and end):
if self.tabixContigs.has_key(chr):
tabixTupleParse = self.tabix.fetch(reference=chr,
start=int(start),
end=int(end),
parser=pysam.asTuple())
for tabixTuple in tabixTupleParse:
annotations.append(tabixTuple[3])
return uniqann(annotations)
else:
return []
else:
raise LookupError("can't find chr,start,end. File must be tab-delimited")
return []
def annotate_indel_on_db(idl, idl_report, db, genome, chr_prefixed,
vcf_chr_prefixed, preset):
chr, pos, idl_type, idl_seq = idl.chr, idl.pos, idl.idl_type, idl.idl_seq
# search for equivalent indels over pos +/- search_window nt
search_window = 50
start, end = pos - search_window, pos + search_window
chr_vcf = chr.replace("chr", "")
chr_vcf = "chr" + chr_vcf if vcf_chr_prefixed else chr_vcf
for record in db.fetch(chr_vcf, start, end, parser=pysam.asTuple()):
bambinos = vcf2bambino(record)
for bb in bambinos:
if idl_type == bb.idl_type and len(idl_seq) == len(bb.idl_seq):
# indel on db representing in reference genome
db_idl = curate_indel_in_genome(genome, chr, bb.pos,
bb.idl_type, bb.idl_seq,
chr_prefixed)
if idl == db_idl:
if preset == "dbsnp":
idl_report.add_dbsnp_id(record[2])
idl_report.add_dbsnp_freq(dbsnp_freq(record))
# idl_report.add_dbsnp_origin(dbsnp_origin(record))
idl_report.add_dbsnp_common(dbsnp_common(record))
elif preset == "clinvar":
idl_report.add_clnvr_id(id)
idl_report.add_clnvr_freq(clnvr_freq(record))
# idl_report.add_clnvr_origin(clnvr_origin(record))
idl_report.add_clnvr_info(cln_info(record))
else:
idl_report.add_germline_id(record[2])
return idl_report
def testCopy(self):
a = self.tabix.fetch(parser=pysam.asTuple()).next()
b = copy.copy(a)
self.assertEqual(a, b)
a = self.tabix.fetch(parser=pysam.asGTF()).next()
b = copy.copy(a)
self.assertEqual(a, b)
def readDepthChunk(depthFile, contig, start, end):
chunk = dict()
if start > 0:
start -= 1
with closing(pysam.Tabixfile(depthFile)) as tabix:
for row in tabix.fetch(contig, start, end, parser=pysam.asTuple()):
chunk[long(row[1])] = int(row[3])
return chunk
def testCopy(self):
a = self.tabix.fetch(parser=pysam.asTuple()).next()
b = copy.copy(a)
self.assertEqual(a, b)
a = self.tabix.fetch(parser=pysam.asGTF()).next()
b = copy.copy(a)
self.assertEqual(a, b)
def testTuple( self ):
for x, r in enumerate(self.tabix.fetch( parser = pysam.asTuple() )):
self.assertEqual( self.compare[x], list(r) )
self.assertEqual( len(self.compare[x]), len(r) )
for c in range(0,len(r)):
self.assertEqual( self.compare[x][c], r[c] )
def aggregate(self, chrom):
import pysam
filepath = self.filepath
binsize = self.gs.binsize
idmap = self.gs.idmap
chromsizes = self.gs.chromsizes
chrom_binoffset = self.gs.chrom_binoffset
chrom_abspos = self.gs.chrom_abspos
start_abspos = self.gs.start_abspos
C2, P2 = self.C2, self.P2
these_bins = self.gs.fetch(chrom)
rows = []
with pysam.TabixFile(filepath, 'r', encoding='ascii') as f:
parser = pysam.asTuple()
accumulator = Counter()
for bin1_id, bin1 in these_bins.iterrows():
for line in f.fetch(chrom, bin1.start, bin1.end,
parser=parser):
chrom2 = line[C2]
pos2 = int(line[P2])
try:
cid2 = idmap[chrom2]
except KeyError:
# this chrom2 is not requested
continue
if binsize is None:
lo = chrom_binoffset[cid2]
hi = chrom_binoffset[cid2 + 1]
bin2_id = lo + np.searchsorted(
start_abspos[lo:hi],
chrom_abspos[cid2] + pos2,
side='right') - 1
else:
bin2_id = chrom_binoffset[cid2] + (pos2 // binsize)
accumulator[bin2_id] += 1
if not accumulator:
continue
rows.append(
pandas.DataFrame(
{
'bin1_id': bin1_id,
'bin2_id': list(accumulator.keys()),
'count': list(accumulator.values())
},
columns=['bin1_id', 'bin2_id',
'count']).sort_values('bin2_id'))
accumulator.clear()
logger.info(chrom)
return pandas.concat(rows, axis=0) if len(rows) else None
def bed_regions(bed_file, chromo):
open_bed = pysam.TabixFile(bed_file)
coords = []
for line in open_bed.fetch(chromo, parser=pysam.asTuple()):
start = int(line[1])
end = int(line[2])
coords += range(start, end)
return coords
def by_region(region, version, species, limit=None):
"""Perform the search by region.
Args:
region (str): The region to look for SNPs.
version (int): The Ensembl version number.
species (str): The Ensembl species identifier.
limit (int, optional): Maximum number of SNPs to return, ``None`` for
all.
Returns:
list: All the SNPs in `region`. Each element is another ``list`` with
the following values:
* chromosome
* position
* SNP identifier
* reference allele
* alternate allele
Raises:
ValueError: When `region` is empty.
"""
LOG = utils.get_logger()
LOG.debug(sqlite3.version_info)
LOG.debug(sqlite3.version)
LOG.debug('range={}'.format(region))
LOG.debug('version={}'.format(version))
LOG.debug('species_id={}'.format(species))
LOG.debug('limit={}'.format(limit))
try:
if not region:
raise ValueError('no ids were passed in')
new_region = fetch_utils.str_to_region(region)
tabix_file = fetch_utils.get_tabix_file(version, species)
tbx = pysam.TabixFile(tabix_file)
start_time = time.time()
snps = []
for row in tbx.fetch('{}'.format(new_region.chromosome),
new_region.start_position,
new_region.end_position,
parser=pysam.asTuple()):
snps.append(list(row[:5]))
LOG.info('Done: {}'.format(utils.format_time(start_time, time.time())))
return snps
except Exception as e:
LOG.error('Error: {}'.format(e))
return None
def testUnset(self):
for x, r in enumerate(self.tabix.fetch(parser=pysam.asTuple())):
self.assertEqual(self.compare[x], list(r))
c = list(r)
e = list(r)
for y in range(len(r)):
r[y] = c[y] = None
e[y] = ""
self.assertEqual(c, list(r))
self.assertEqual("\t".join(e), str(r))
def match(self, chrm, position, indels=True):
''' match single position in CADD file; default indels to true
b/c will be using this more with the indel file'''
tbxFh = self._indel if indels else self._snv
caddChr = 'MT' if chrm == 'M' else xstr(chrm)
try:
return tbxFh.fetch(caddChr, int(position) - 1, int(position), parser=pysam.asTuple())
except ValueError as e: # happens sometimes on chrm M/MT
warning("WARNING", e)
return []
def _region_reset(self, region):
region = handle_chrom_prefix(self._has_chrom_prefix, region)
try:
self.lines_iterator = self.infile.fetch(region=region,
parser=pysam.asTuple())
except ValueError as ex:
print(f"could not find region {region} in {self.filename}:",
ex,
file=sys.stderr)
self.lines_iterator = None
def testUnset( self ):
for x, r in enumerate(self.tabix.fetch( parser = pysam.asTuple() )):
self.assertEqual( self.compare[x], list(r) )
c = list(r)
e = list(r)
for y in range(len(r)):
r[y] = c[y] = None
e[y] = ""
self.assertEqual( c, list(r) )
self.assertEqual( "\t".join(e), str(r) )
def main():
parser = argparse.ArgumentParser(
description=__doc__,
formatter_class=argparse.RawDescriptionHelpFormatter)
parser.add_argument('vcf')
parser.add_argument('famfile', type=argparse.FileType('r'))
parser.add_argument('-c', '--countfile', required=True)
parser.add_argument('-d', '--discfile', required=True)
parser.add_argument('--discfile-index')
parser.add_argument('--countfile-index')
parser.add_argument('--background', type=int, default=160)
parser.add_argument('--max-parents', type=float, default=10)
parser.add_argument('petest', type=argparse.FileType('w'), help='fout')
parser.add_argument('srtest', type=argparse.FileType('w'), help='fout')
args = parser.parse_args()
vcf = pysam.VariantFile(args.vcf)
fam = parse_famfile(args.famfile)
if args.discfile_index is None:
discfile = pysam.TabixFile(args.discfile, parser=pysam.asTuple())
else:
discfile = pysam.TabixFile(args.discfile,
index=args.discfile_index,
parser=pysam.asTuple())
if args.countfile_index is None:
countfile = pysam.TabixFile(args.countfile, parser=pysam.asTuple())
else:
countfile = pysam.TabixFile(args.countfile,
index=args.countfile_index,
parser=pysam.asTuple())
header = 'name sample log_pval called_median bg_median'.split()
args.petest.write('\t'.join(header) + '\n')
header = 'name sample coord pos log_pval called_median bg_median'.split()
args.srtest.write('\t'.join(header) + '\n')
runner = DenovoTestRunner(vcf, fam, countfile, discfile, args.petest,
args.srtest, args.background, args.max_parents)
runner.run()
def get_gapped_wnds(self,chr,tbx_gaps):
gapped_wnds = []
for t in tbx_gaps.fetch(chr,parser=pysam.asTuple()):
_chr,start,end = t
wnd_start = np.searchsorted(self.starts,start)
wnd_end = np.searchsorted(self.starts,end)
gapped_wnds.append(tuple([wnd_start,wnd_end]))
return gapped_wnds
def get_gapped_wnds(self, chr, tbx_gaps):
gapped_wnds = []
for t in tbx_gaps.fetch(chr, parser=pysam.asTuple()):
_chr, start, end = t
wnd_start = np.searchsorted(self.starts, start)
wnd_end = np.searchsorted(self.starts, end)
gapped_wnds.append(tuple([wnd_start, wnd_end]))
return gapped_wnds
def get_ref_alt_from_dbSNP(chrom, pos, path_vcf):
tbx = pysam.TabixFile(path_vcf)
for row in tbx.fetch(chrom, pos - 1, pos, parser=pysam.asTuple()):
if len(row[3]) == 1 and 1 in map(len, row[4].split(',')):
break
else:
stop_not_found_in_dbSNP
assert ',' not in row[4], row
return row[3], row[4]
def __init__(self, task_queue, results_queue, family, args):
multiprocessing.Process.__init__(self)
self.task_queue = task_queue
self.family = family
self.results_queue = results_queue
self.verbosity = args.verbose
self.phased = args.phased
self.cadd_file = args.cadd_file[0]
self.chr_prefix = args.chr_prefix
if self.cadd_file:
self.cadd_file = Tabixfile(self.cadd_file, parser = asTuple())
def get_overlapping_wnds(self,chr,tbx):
wnd_starts, wnd_ends = self.get_wnds_by_chr(chr)
bnds = np.array([ [int(l[1]),int(l[2])]
for l in tbx.fetch(chr,parser=pysam.asTuple()) ])
start_idxs = np.searchsorted(wnd_starts,bnds[:,0])
end_idxs = np.searchsorted(wnd_starts,bnds[:,1])
#print start_idxs
#print end_idxs
ret = np.c_[start_idxs,end_idxs]
return ret
def parse_dbSNP(args, chrom, pos, ref, alt):
## todo: check this function... I might miss variants in dbSNP...
tbx = pysam.TabixFile(args.dbSNP)
row = None
for row in tbx.fetch(chrom, pos - 1 - 1, pos, parser=pysam.asTuple()):
print(row, file=sys.stderr)
if any([
## SNP.
row[3] == ref and alt in row[4].split(','),
## SNP in dbSNP and MNP in preselected.txt
all([
row[3] == ref[0],
len(set(alt[0].split(',')) & set(row[4].split(','))) > 0,
len(ref) in map(len, alt.split(',')),
len(row[3]) == 1,
]),
## Insertion.
all([
int(row[1]) == pos,
len(row[3]) == 1,
ref == '-',
## One or more ALTs overlap (e.g. rs3835252).
len(set(x[1:] for x in row[4].split(',')) & set(alt.split(','))) >= 1,
]),
## Deletion.
all([
int(row[1]) == pos,
len(row[4]) == 1,
alt == '-',
row[3][:1] == ref,
len(row[3]) > 1,
len(row[4]) == 1,
]),
## Deletion.
all([
int(row[1]) + 1 == pos,
len(row[3]) == len(ref) + 1,
set(map(len, row[4].split(','))) == set([1]),
alt == '-',
row[3][1:] == ref,
]),
]):
rsID = row[2]
break
## Not found in dbSNP.
else:
rsID = ''
return rsID
def testRead(self):
for x, r in enumerate(self.tabix.fetch(parser=pysam.asTuple())):
self.assertEqual(self.compare[x], list(r))
self.assertEqual(len(self.compare[x]), len(r))
# test indexing
for c in range(0, len(r)):
self.assertEqual(self.compare[x][c], r[c])
# test slicing access
for c in range(0, len(r) - 1):
for cc in range(c + 1, len(r)):
self.assertEqual(self.compare[x][c:cc], r[c:cc])
def get_dup_overlap(self, tbx_dups):
#assuming dup file is collapsed
min_s = min(self.all_starts)
max_e = max(self.all_ends)
t = 0
for l in tbx_dups.fetch(self.chr,min_s,max_e,parser=pysam.asTuple()):
c,s,e = l
s,e = int(s), int(e)
curr_s = s>min_s and s or min_s
curr_e = e<max_e and e or max_e
t += curr_e-curr_s
if t==0: return 0.0
return float(t) / float(max_e - min_s)
def testWrite(self):
for x, r in enumerate(self.tabix.fetch(parser=pysam.asTuple())):
self.assertEqual(self.compare[x], list(r))
c = list(r)
for y in range(len(r)):
r[y] = "test_%05i" % y
c[y] = "test_%05i" % y
self.assertEqual([x for x in c], list(r))
self.assertEqual("\t".join(c), str(r))
# check second assignment
for y in range(len(r)):
r[y] = "test_%05i" % y
self.assertEqual([x for x in c], list(r))
self.assertEqual("\t".join(c), str(r))
def __init__(self, inFile, parser=pysam.asTuple()):
# inFile is passed in but is never used, and yet the TabixReader works. How?!
# This inFile magic is because Tabixfile is a Cython object that uses def __cinit__
# rather than def __init__; the former is called automatically exactly once for the
# base class prior to any use of __init__. Use of subsequent __init__ should obey
# normal inheritance rules (assuming inheritance from object and it's not an old-style class).
# So __cinit__ sets the input file, and then our __init__ (which doesn't override a base method)
# is called and sets the parser.
# This could all break in the future if pysam moves away from __cinit__, but doing so would
# reduce performance and so it seems unlikely.
# See:
# https://github.com/cython/cython/blob/master/docs/src/userguide/special_methods.rst#id19
# https://github.com/pysam-developers/pysam/blob/master/pysam/libctabix.pyx#L331
#super(TabixReader, self).__init__(inFile, parser=parser)
self.parser = parser
def testIteratorCompressed(self):
"""test iteration from compressed file."""
with gzip.open(self.filename) as infile:
for x, r in enumerate(pysam.tabix_iterator(infile, pysam.asTuple())):
self.assertEqual(self.compare[x], list(r))
self.assertEqual(len(self.compare[x]), len(r))
# test indexing
for c in range(0, len(r)):
self.assertEqual(self.compare[x][c], r[c])
# test slicing access
for c in range(0, len(r) - 1):
for cc in range(c + 1, len(r)):
self.assertEqual(self.compare[x][c:cc], r[c:cc])
def plot_GC(chr,tbx_gc,cp_vect,starts,ends):
F=open("GC.txt",'w')
cp_vect = cp_vect.astype(np.float64)
var = get_windowed_variance(cp_vect,50)
F.write("var\tgc\n")
for i in xrange(50,starts.shape[0],101):
s = starts[i-50]
e = ends[i+50]
gc = np.mean(np.array([float(l[3]) for l in tbx_gc.fetch(chr,s,e,parser=pysam.asTuple())]))
if var[i] != 0:
print >>F,"%f\t%f"%( var[i],gc )
exit(1)
def load_data(filepath, interval, data_columns=[5], dtype=np.float): """ Loads numeric data columns from a BED-format TABIX file """ res = np.zeros((len(interval), len(data_columns)), dtype = dtype) tabix = pysam.TabixFile(filepath) for row in tabix.fetch(interval.chrom, interval.start, interval.end, parser = pysam.asTuple()): i = int(row[1])- interval.start for j, col in enumerate(data_columns): res[i, j] = dtype(row[col-1]) tabix.close() return res
def get_snps(pid):
'''
return sequences mentioned in SNPData.csv
'''
coords = map(make_coord_string, snps.COORDINATES.values())
search_args = {
'coordinate': ','.join(coords),
'patient': pid,
'_count': 100000
}
seq_bundle = call_api('/Sequence', search_args)
seqs = (entry['content'] for entry in seq_bundle['entry'])
translation_f = TabixFile(SNP_TRANSLATION_FNAME, parser=asTuple())
return jsonify({
get_rsid(translation_f, seq): seq['observedSeq']
for seq in seqs
})
def get_callset(self, exclude_tbxs=[], min_exclude_ratio=0.3, min_exclude_len=20000):
"""
return segments and their copies in genome
coordinates
adding subtraction of gaps
"""
c=callset()
wnd_starts,wnd_ends,cps = self.segment_edges
wnd_starts,wnd_ends,cps = np.array(wnd_starts), np.array(wnd_ends), np.array(cps)
for i in xrange(len(wnd_starts)-1):
start, end = self.starts[wnd_starts[i]], self.ends[wnd_ends[i]]
wnd_start, wnd_end = wnd_starts[i], wnd_ends[i]
#exclude totally anything in these tbxs
for exclude_tbx in exclude_tbxs:
ex_starts, ex_ends = [], []
for l in exclude_tbx.fetch(self.chr,start,end,parser=pysam.asTuple()):
_chr,_s,_e = l
_s, _e = int(_s), int(_e)
if _e-_s > min_exclude_len:
ex_starts.append(_s)
ex_ends.append(_e)
n_exclude = len(ex_starts)
if n_exclude:
ex_coords = self.get_exclude_coords(ex_starts, ex_ends)
wnd_start_ends = self.subtract_excluded(wnd_start, wnd_end, ex_coords)
else:
wnd_start_ends = [tuple([wnd_start, wnd_end])]
for i in xrange(len(wnd_start_ends)):
wnd_start = wnd_start_ends[i][0]
wnd_end = wnd_start_ends[i][1]
c.add_call(self.chr,
self.starts[wnd_start],
self.ends[wnd_end],
np.mean(self.cp_data[wnd_start:wnd_end]),
wnd_start,
wnd_end,
self.cp_data[wnd_start:wnd_end])
return c
def testRead(self):
for x, r in enumerate(self.tabix.fetch(parser=pysam.asTuple())):
c = self.compare[x]
self.assertEqual(c, list(r))
self.assertEqual(len(c), len(r))
# test indexing
for y in range(0, len(r)):
self.assertEqual(c[y], r[y])
# test slicing access
for y in range(0, len(r) - 1):
for cc in range(y + 1, len(r)):
self.assertEqual(c[y:cc],
r[y:cc])
self.assertEqual("\t".join(map(str, c)),
str(r))
def _tabix_iteradaptor(stream):
"""Open `stream` as an iterator over a `tabix`_ file, returning raw strings from tabix data.
Parameters
----------
streams : open file-like, :class:`pysam.ctabix.tabix_file_iterator`
Returns
-------
generator
Generator of tab-delimited string records in `tabix`_ file
"""
if not isinstance(stream,(pysam.ctabix.tabix_generic_iterator,
pysam.ctabix.tabix_file_iterator)
):
stream = pysam.tabix_file_iterator(stream,pysam.asTuple())
return (str(X) for X in stream)
def process(self):
if self.genelist:
gene_list = pysam.TabixFile(self.genelist)
with open(self.input, 'r') as fin:
with open(self.output, 'w') as fout:
for line in fin:
line = line.strip()
if line.startswith('#chr'):
header = line.split('\t')
if self.genelist:
header += ['PIDD_GENE', 'Inheritance', 'Phenotype']
fout.write('\t'.join(header) + '\n')
continue
elif line.startswith('##'):
continue
try:
row = OrderedDict(zip(header, line.split('\t')))
except:
continue
if self.genelist:
PIDD_GENE = []
Inheritance = []
Phenotype = []
try:
for genepanel_line in gene_list.fetch(row['#chr'],
int(row['start']),
int(row['end']),
parser=pysam.asTuple()):
PIDD_GENE += [genepanel_line[3]]
Inheritance += [genepanel_line[4]]
Phenotype += [genepanel_line[5]]
row['PIDD_GENE'] = "|".join(PIDD_GENE) if PIDD_GENE else 'NA'
row['Inheritance'] = "|".join(Inheritance) if Inheritance else 'NA'
row['Phenotype'] ="|".join(Phenotype) if Phenotype else 'NA'
except ValueError:
pass
if self.filter_line(row):
fout.write('\t'.join(row.values()) + '\n')
def _get_hits(coords, annotation, parser_type):
"""Retrieve BED information, recovering if BED annotation file does have a chromosome.
"""
if parser_type == "bed":
parser = pysam.asBed()
elif parser_type == "vcf":
parser = pysam.asVCF()
elif parser_type == "tuple":
parser = pysam.asTuple()
elif parser_type is None:
parser = None
else:
raise ValueError("Unexpected parser type: %s" % parser)
chrom, start, end = coords
try:
hit_iter = annotation.fetch(str(chrom), start, end, parser=parser)
# catch invalid region errors raised by ctabix
except ValueError:
hit_iter = []
return hit_iter
def __init__(self, filepath, chromsizes, bins):
try:
import pysam
except ImportError:
raise ImportError("pysam is required to read tabix files")
n_bins = len(bins)
self.idmap = pandas.Series(index=chromsizes.keys(), data=range(len(chromsizes)))
self.bins = bins
self.binsize = get_binsize(bins)
self.pairsfile = pysam.TabixFile(filepath, 'r', encoding='ascii')
self.parser = pysam.asTuple()
# number of lines in file
p1 = subprocess.Popen(['pigz', '-p', '8', '-dc', filepath], stdout=subprocess.PIPE)
p2 = subprocess.Popen(['wc', '-l'], stdin=p1.stdout, stdout=subprocess.PIPE)
self.n_records = int(p2.communicate()[0])
# convert genomic coords of bin starts to absolute
self.idmap = pandas.Series(index=chromsizes.keys(), data=range(len(chromsizes)))
bin_chrom_ids = self.idmap[bins['chrom']].values
self.cumul_length = np.r_[0, np.cumsum(chromsizes)]
self.abs_start_coords = self.cumul_length[bin_chrom_ids] + bins['start']
# chrom offset index: chrom_id -> offset in bins
chrom_nbins = bins.groupby(bin_chrom_ids, sort=False).size()
self.chrom_offset = np.r_[0, np.cumsum(chrom_nbins)]