def add_sample(self, sample_id, cnv_file):
"""
"""
print "Adding CNVs for %s" % sample_id
self._db.samples.remove({"sample_id": sample_id})
self._db.cnvs.remove({"sample_id": sample_id})
self._db.cnvs.ensure_index([("sample_id", 1), ("genes", 1)])
self._db.samples.insert({"sample_id": str(sample_id), "status": "loading"})
reader = csv.reader(cnv_file)
reader.next()
for row in reader:
chrom = "chr" + row[7]
start = int(row[5])
stop = int(row[6])
xstart = genomeloc.get_single_location(chrom, start)
xstop = genomeloc.get_single_location(chrom, stop)
cnv = {
"sample_id": sample_id,
"type": row[3],
"nexons": int(row[4]),
"xstart": xstart,
"xstop": xstop,
"genes": self.reference.get_genes_in_region(xstart, xstop),
"reads": [int(row[10]), int(row[11])],
"read_ratio": float(row[12]),
}
self._db.cnvs.insert(cnv)
def add_sample(self, sample_id, cnv_file):
"""
"""
print "Adding CNVs for %s" % sample_id
self.remove_sample(sample_id)
self._db.cnvs.ensure_index([('sample_id', 1), ('genes', 1)]) # silly to have this here
self._db.samples.insert({
'sample_id': str(sample_id),
'status': 'loading',
})
reader = csv.reader(cnv_file)
reader.next()
for row in reader:
chrom = 'chr' + row[7]
start = int(row[5])
stop = int(row[6])
xstart = genomeloc.get_single_location(chrom, start)
xstop = genomeloc.get_single_location(chrom, stop)
cnv = {
'sample_id': sample_id,
'type': row[3],
'nexons': int(row[4]),
'xstart': xstart,
'xstop': xstop,
'genes': self.reference.get_genes_in_region(chrom, start, stop),
'reads': [int(row[10]), int(row[11])],
'read_ratio': float(row[12]),
}
self._db.cnvs.insert(cnv)
def add_sample(self, sample_id, cnv_file):
"""
"""
print "Adding CNVs for %s" % sample_id
self.remove_sample(sample_id)
self._db.cnvs.ensure_index([('sample_id', 1), ('genes', 1)]) # silly to have this here
self._db.samples.insert({
'sample_id': str(sample_id),
'status': 'loading',
})
reader = csv.reader(cnv_file)
reader.next()
for row in reader:
chrom = 'chr' + row[7]
start = int(row[5])
stop = int(row[6])
xstart = genomeloc.get_single_location(chrom, start)
xstop = genomeloc.get_single_location(chrom, stop)
cnv = {
'sample_id': sample_id,
'type': row[3],
'nexons': int(row[4]),
'xstart': xstart,
'xstop': xstop,
'genes': self.reference.get_genes_in_region(xstart, xstop),
'reads': [int(row[10]), int(row[11])],
'read_ratio': float(row[12]),
}
self._db.cnvs.insert(cnv)
def create_genome_subset_from_interval_list(interval_list_file):
"""
Creates a genome subset from interval list file
This is a file with cols chr, start, stop, strand, name
Strand and name are ignored, and actually it could have extra cols too and won't complain
Coordinates are 1-indexed and inclusive
"""
intervals = []
for line in interval_list_file:
fields = line.strip('\n').split('\t')
chrom = 'chr'+fields[0]
start = int(fields[1])
end = int(fields[2])
intervals.append((genomeloc.get_single_location(chrom, start), genomeloc.get_single_location(chrom, end)))
return GenomeSubsetFilter(intervals)
def create_genome_subset_from_interval_list(interval_list_file):
"""
Creates a genome subset from interval list file
This is a file with cols chr, start, stop, strand, name
Strand and name are ignored, and actually it could have extra cols too and won't complain
Coordinates are 1-indexed and inclusive
"""
intervals = []
for line in interval_list_file:
fields = line.strip('\n').split('\t')
chrom = 'chr' + fields[0]
start = int(fields[1])
end = int(fields[2])
intervals.append((genomeloc.get_single_location(chrom, start),
genomeloc.get_single_location(chrom, end)))
return GenomeSubsetFilter(intervals)
def load_dbnsfp():
polyphen_map = {
'D': 'probably_damaging',
'P': 'possibly_damaging',
'B': 'benign',
}
sift_map = {
'D': 'damaging',
'T': 'tolerated',
}
fathmm_map = {
'D': 'damaging',
'T': 'tolerated',
}
muttaster_map = {
'A': 'disease_causing',
'D': 'disease_causing',
'N': 'polymorphism',
'P': 'polymorphism',
}
nsfp_file = open(settings.INTERMEDIATE_FILE_DIR + 'dbnsfp.tsv', 'w')
for chrom in CHROMOSOMES:
print "Reading dbNSFP data for {}".format(chrom)
single_chrom_file = open(settings.DBNSFP_DIR + 'dbNSFP2.1_variant.' + chrom)
for i, line in enumerate(single_chrom_file):
if i == 0: continue
fields = line.strip('\n').split('\t')
chrom, pos, ref, alt = fields[:4]
chrom = 'chr' + chrom
pos = int(pos)
xpos = genomeloc.get_single_location(chrom, pos)
if not xpos:
continue
polyphen = polyphen_map.get(fields[25], '.')
sift = sift_map.get(fields[23], '.')
fathmm = fathmm_map.get(fields[39], '.')
muttaster = muttaster_map.get(fields[33], '.')
fields = [
str(xpos),
ref,
alt,
polyphen,
sift,
fathmm,
muttaster
]
nsfp_file.write('\t'.join(fields)+'\n')
def load_population(self, population):
"""
Take a population and a data source; extract and load it into annotator
Data source can be VCF file, VCF Counts file, or a counts dir (in the case of ESP data)
"""
if population['file_type'] == 'vcf':
if population['file_path'].endswith('.gz'):
vcf_file = gzip.open(population['file_path'])
size = os.path.getsize(population['file_path'])
progress_file = vcf_file.fileobj
else:
vcf_file = open(population['file_path'])
size = os.path.getsize(population['file_path'])
progress_file = vcf_file
progress = get_progressbar(
size, 'Loading vcf: {}'.format(population['slug']))
for variant in vcf_stuff.iterate_vcf(vcf_file,
genotypes=True,
genotype_meta=False):
progress.update(progress_file.tell())
freq = get_aaf(variant)
self._add_population_frequency(variant.xpos, variant.ref,
variant.alt, population['slug'],
freq)
vcf_file.close()
elif population['file_type'] == 'sites_vcf':
if population['file_path'].endswith('.gz'):
vcf_file = gzip.open(population['file_path'])
size = os.path.getsize(population['file_path'])
progress_file = vcf_file.fileobj
else:
vcf_file = open(population['file_path'])
size = os.path.getsize(population['file_path'])
progress_file = vcf_file
meta_key = population.get('vcf_info_key', 'AF')
progress = get_progressbar(
size, 'Loading sites vcf: {}'.format(population['slug']))
is_1kg_popmax = "popmax" in meta_key.lower() and (
"1000 Genomes" in population["name"])
if is_1kg_popmax:
meta_fields = [
"EAS_AF", "EUR_AF", "AFR_AF", "AMR_AF", "SAS_AF"
]
else:
meta_fields = [
meta_key,
]
for variant in vcf_stuff.iterate_vcf(vcf_file,
meta_fields=meta_fields):
progress.update(progress_file.tell())
if "popmax" in meta_key.lower() and ("1000 Genomes"
in population["name"]):
allele_idx = variant.extras['alt_allele_pos']
freq = 0
for meta_key in meta_fields:
freq = max(
freq,
float(
variant.extras.get(meta_key,
0).split(',')[allele_idx]))
##INFO=<ID=EAS_AF,Number=A,Type=Float,Description="Allele frequency in the EAS populations calculated from AC and AN, in the range (0,1)">
##INFO=<ID=EUR_AF,Number=A,Type=Float,Description="Allele frequency in the EUR populations calculated from AC and AN, in the range (0,1)">
##INFO=<ID=AFR_AF,Number=A,Type=Float,Description="Allele frequency in the AFR populations calculated from AC and AN, in the range (0,1)">
##INFO=<ID=AMR_AF,Number=A,Type=Float,Description="Allele frequency in the AMR populations calculated from AC and AN, in the range (0,1)">
##INFO=<ID=SAS_AF,Number=A,Type=Float,Description="Allele frequency in the SAS populations calculated from AC and AN, in the range (0,1)">
else:
freq = float(
variant.extras.get(
meta_key,
0).split(',')[variant.extras['alt_allele_pos']])
self._add_population_frequency(variant.xpos, variant.ref,
variant.alt, population['slug'],
freq)
vcf_file.close()
#
# Directory of per-chromosome VCFs that ESP publishes
#
elif population['file_type'] == 'esp_vcf_dir':
for filename in os.listdir(population['dir_path']):
file_path = os.path.abspath(
os.path.join(population['dir_path'], filename))
f = open(file_path)
file_size = os.path.getsize(file_path)
progress = get_progressbar(
file_size, 'Loading ESP file: {}'.format(filename))
for variant in get_variants_from_esp_file(f):
progress.update(f.tell())
self._add_population_frequency(
variant['xpos'], variant['ref'], variant['alt'],
population['slug'], variant[population['counts_key']])
f.close()
#
# text file of allele counts, as Monkol has been using for the joint calling data
#
elif population['file_type'] == 'counts_file':
if population['file_path'].endswith('.gz'):
counts_file = gzip.open(population['file_path'])
size = os.path.getsize(population['file_path'])
progress_file = counts_file.fileobj
else:
counts_file = open(population['file_path'])
size = os.path.getsize(population['file_path'])
progress_file = counts_file
progress = get_progressbar(
size, 'Loading population: {}'.format(population['slug']))
for line in counts_file:
progress.update(progress_file.tell())
fields = line.strip('\n').split('\t')
chrom = 'chr' + fields[0]
pos = int(fields[1])
xpos = genomeloc.get_single_location(chrom, pos)
ref = fields[2]
alt = fields[3]
if int(fields[5]) == 0:
continue
freq = float(fields[4]) / float(fields[5])
self._add_population_frequency(xpos, ref, alt,
population['slug'], freq)
counts_file.close()
# this is now the canonical allele frequency file -
# tab separated file with xpos / ref / alt / freq
elif population['file_type'] == 'xbrowse_freq_file':
if population['file_path'].endswith('.gz'):
counts_file = gzip.open(population['file_path'])
progress_file = counts_file.fileobj
else:
counts_file = open(population['file_path'])
progress_file = counts_file
size = os.path.getsize(population['file_path'])
progress = get_progressbar(
size, 'Loading population: {}'.format(population['slug']))
for line in counts_file:
progress.update(progress_file.tell())
fields = line.strip('\n').split('\t')
xpos = int(fields[0])
ref = fields[1]
alt = fields[2]
freq = float(fields[3])
self._add_population_frequency(xpos, ref, alt,
population['slug'], freq)
counts_file.close()
elif population['file_type'] == 'tsv_file':
if population['file_path'].endswith('.gz'):
freq_file = gzip.open(population['file_path'])
progress_file = freq_file.fileobj
else:
freq_file = open(population['file_path'])
progress_file = freq_file
size = os.path.getsize(population['file_path'])
progress = get_progressbar(
size, 'Loading population: {}'.format(population['slug']))
header = next(freq_file)
print("Header: " + header)
for line in freq_file:
progress.update(progress_file.tell())
fields = line.strip('\n').split('\t')
chrom = fields[0]
pos = int(fields[1])
ref = fields[2]
alt = fields[3]
freq = float(fields[4])
xpos = genomeloc.get_single_location(chrom, pos)
self._add_population_frequency(xpos, ref, alt,
population['slug'], freq)
freq_file.close()
elif population['file_type'] == 'sites_vcf_with_counts':
if population['file_path'].endswith(
'.gz') or population['file_path'].endswith('.bgz'):
vcf_file = gzip.open(population['file_path'])
size = os.path.getsize(population['file_path'])
progress_file = vcf_file.fileobj
else:
vcf_file = open(population['file_path'])
size = os.path.getsize(population['file_path'])
progress_file = vcf_file
ac_info_key = population['ac_info_key']
an_info_key = population['an_info_key']
progress = get_progressbar(
size, 'Loading sites vcf: {}'.format(population['slug']))
for variant in vcf_stuff.iterate_vcf(
vcf_file, meta_fields=[ac_info_key, an_info_key]):
progress.update(progress_file.tell())
alt_allele_pos = variant.extras['alt_allele_pos']
try:
ac = int(
variant.extras.get(ac_info_key).split(',')
[alt_allele_pos].replace("NA", "0"))
except Exception, e:
print(
"Couldn't parse AC value %s from %s: %s" %
(alt_allele_pos, ac_info_key, variant.extras), e)
continue
try:
if "popmax" in ac_info_key.lower():
AN_index = alt_allele_pos # each allele may have a different AN value from a different population
else:
AN_index = 0
an = int(
variant.extras.get(an_info_key).split(',')
[AN_index].replace("NA", "0"))
except Exception, e:
print(
"Couldn't parse AN value %s from %s: %s" %
(alt_allele_pos, an_info_key, variant.extras), e)
continue
if an == 0:
freq = 0.0
else:
freq = float(ac) / an
self._add_population_frequency(variant.xpos, variant.ref,
variant.alt, population['slug'],
freq)
def load(self):
self._db.drop_collection('variants')
self._db.variants.ensure_index([('xpos', 1), ('ref', 1), ('alt', 1)])
# load dbnsfp info
polyphen_map = {
'D': 'probably_damaging',
'P': 'possibly_damaging',
'B': 'benign',
'.': None
}
sift_map = {'D': 'damaging', 'T': 'tolerated', '.': None}
fathmm_map = {'D': 'damaging', 'T': 'tolerated', '.': None}
muttaster_map = {
'A': 'disease_causing',
'D': 'disease_causing',
'N': 'polymorphism',
'P': 'polymorphism',
'.': None
}
#interesting_fields = "rs_dbSNP141 Ancestral_allele SIFT_score SIFT_converted_rankscore SIFT_pred Polyphen2_HDIV_pred Polyphen2_HVAR_pred MutationTaster_pred MutationAssessor_pred FATHMM_pred MetaSVM_pred CADD_phred"
# LRT_pred MetaLR_pred VEST3_rankscore PROVEAN_converted_rankscore PROVEAN_pred CADD_raw CADD_raw_rankscore GERP++_NR GERP++_RS GERP++_RS_rankscore ESP6500_AA_AF ESP6500_EA_AF ARIC5606_AA_AC ARIC5606_AA_AF ARIC5606_EA_AC ARIC5606_EA_AF ExAC_AC ExAC_AF ExAC_Adj_AC ExAC_Adj_AF ExAC_AFR_AC ExAC_AFR_AF ExAC_AMR_AC ExAC_AMR_AF ExAC_EAS_AC ExAC_EAS_AF ExAC_FIN_AC ExAC_FIN_AF ExAC_NFE_AC ExAC_NFE_AF ExAC_SAS_AC ExAC_SAS_AF clinvar_rs clinvar_clnsig clinvar_trait"
#interesting_fields = interesting_fields.split()
def collapse(scores):
s = set(scores.split(";"))
if len(s) > 1:
raise ValueError("Couldn't collapse %s" % str(scores))
return list(s)[0]
pred_rank = ['D', 'A', 'T', 'N', 'P', 'B', '.']
def select_worst(pred_value):
i = len(pred_rank) - 1
for pred in pred_value.split(";"):
r = pred_rank.index(pred)
if r < i:
i = r
return pred_rank[i]
for chrom in CHROMOSOMES:
if chrom == "chrM":
continue # no dbNSFP data for chrM
print "Reading dbNSFP data for {}".format(chrom)
single_chrom_file = open(self._settings.dbnsfp_dir +
'dbNSFP2.9_variant.' + chrom)
header = single_chrom_file.readline()
header_fields = header.strip("\n").split()
field_index = {
name: header_fields.index(name)
for name in header_fields
}
for i, line in enumerate(single_chrom_file):
if i == 0:
continue
if not i % 100000:
print i
fields = line.strip('\n').split('\t')
chrom, pos, ref, alt = fields[:4]
chrom = 'chr' + chrom
pos = int(pos)
xpos = genomeloc.get_single_location(chrom, pos)
if not xpos:
raise ValueError(
"Unexpected chr, pos: %(chrom)s, %(pos)s" %
(chrom, pos))
rsid = fields[field_index["rs_dbSNP141"]]
annotations_dict = {
'rsid':
rsid if rsid != '.' else None,
'polyphen':
polyphen_map[select_worst(
fields[field_index["Polyphen2_HVAR_pred"]])],
'sift':
sift_map[select_worst(fields[field_index["SIFT_pred"]])],
'fathmm':
fathmm_map[select_worst(
fields[field_index["FATHMM_pred"]])],
'muttaster':
muttaster_map[select_worst(
fields[field_index["MutationTaster_pred"]])],
'metasvm':
collapse(fields[field_index["MetaSVM_pred"]]),
#'cadd_phred': collapse(fields[field_index["CADD_phred"]]),
}
#extras_to_add_now = ["clinvar_rs", "clinvar_clnsig", "clinvar_trait"]
#for name in extras_to_add_now:
# annotations_dict[name] = fields[field_index[name]]
self._db.variants.update({
'xpos': xpos,
'ref': ref,
'alt': alt
}, {'$set': annotations_dict},
upsert=True)
def load(self):
self._db.drop_collection('variants')
self._db.variants.ensure_index([('xpos', 1), ('ref', 1), ('alt', 1)])
# load dbsnp info
for i, variant in enumerate(vcf_stuff.iterate_vcf(open(self._settings.dbsnp_vcf_file))):
if not i % 100000:
print i
self._db.variants.update(
{'xpos': variant.xpos, 'ref': variant.ref, 'alt': variant.alt},
{'$set': {'rsid': variant.vcf_id}},
upsert=True
)
# load dbnsfp info
polyphen_map = {
'D': 'probably_damaging',
'P': 'possibly_damaging',
'B': 'benign',
}
sift_map = {
'D': 'damaging',
'T': 'tolerated',
}
fathmm_map = {
'D': 'damaging',
'T': 'tolerated',
}
muttaster_map = {
'A': 'disease_causing',
'D': 'disease_causing',
'N': 'polymorphism',
'P': 'polymorphism',
}
for chrom in CHROMOSOMES:
print "Reading dbNSFP data for {}".format(chrom)
single_chrom_file = open(self._settings.dbnsfp_dir + 'dbNSFP2.1_variant.' + chrom)
for i, line in enumerate(single_chrom_file):
if i == 0:
continue
if not i%100000:
print i
fields = line.strip('\n').split('\t')
chrom, pos, ref, alt = fields[:4]
chrom = 'chr' + chrom
pos = int(pos)
xpos = genomeloc.get_single_location(chrom, pos)
if not xpos:
continue
polyphen = polyphen_map.get(fields[25])
sift = sift_map.get(fields[23])
fathmm = fathmm_map.get(fields[39])
muttaster = muttaster_map.get(fields[33])
self._db.variants.update(
{'xpos': xpos, 'ref': ref, 'alt': alt},
{'$set': {
'polyphen': polyphen,
'sift': sift,
'fathmm': fathmm,
'muttaster': muttaster,
}},
upsert=True
)
def load_population(self, population):
"""
Take a population and a data source; extract and load it into annotator
Data source can be VCF file, VCF Counts file, or a counts dir (in the case of ESP data)
"""
if population['file_type'] == 'vcf':
if population['file_path'].endswith('.gz'):
vcf_file = gzip.open(population['file_path'])
size = os.path.getsize(population['file_path'])
progress_file = vcf_file.fileobj
else:
vcf_file = open(population['file_path'])
size = os.path.getsize(population['file_path'])
progress_file = vcf_file
progress = get_progressbar(size, 'Loading vcf: {}'.format(population['slug']))
for variant in vcf_stuff.iterate_vcf(vcf_file, genotypes=True, genotype_meta=False):
progress.update(progress_file.tell())
freq = get_aaf(variant)
self._add_population_frequency(variant.xpos, variant.ref, variant.alt, population['slug'], freq)
vcf_file.close()
elif population['file_type'] == 'sites_vcf':
if population['file_path'].endswith('.gz'):
vcf_file = gzip.open(population['file_path'])
size = os.path.getsize(population['file_path'])
progress_file = vcf_file.fileobj
else:
vcf_file = open(population['file_path'])
size = os.path.getsize(population['file_path'])
progress_file = vcf_file
meta_key = population.get('vcf_info_key', 'AF')
progress = get_progressbar(size, 'Loading sites vcf: {}'.format(population['slug']))
is_1kg_popmax = "popmax" in meta_key.lower() and ("1000 Genomes" in population["name"])
if is_1kg_popmax:
meta_fields = ["EAS_AF", "EUR_AF", "AFR_AF", "AMR_AF", "SAS_AF"]
else:
meta_fields = [meta_key,]
for variant in vcf_stuff.iterate_vcf(vcf_file, meta_fields=meta_fields):
progress.update(progress_file.tell())
if "popmax" in meta_key.lower() and ("1000 Genomes" in population["name"]):
allele_idx = variant.extras['alt_allele_pos']
freq = 0
for meta_key in meta_fields:
freq = max(freq, float(variant.extras.get(meta_key, 0).split(',')[allele_idx]))
##INFO=<ID=EAS_AF,Number=A,Type=Float,Description="Allele frequency in the EAS populations calculated from AC and AN, in the range (0,1)">
##INFO=<ID=EUR_AF,Number=A,Type=Float,Description="Allele frequency in the EUR populations calculated from AC and AN, in the range (0,1)">
##INFO=<ID=AFR_AF,Number=A,Type=Float,Description="Allele frequency in the AFR populations calculated from AC and AN, in the range (0,1)">
##INFO=<ID=AMR_AF,Number=A,Type=Float,Description="Allele frequency in the AMR populations calculated from AC and AN, in the range (0,1)">
##INFO=<ID=SAS_AF,Number=A,Type=Float,Description="Allele frequency in the SAS populations calculated from AC and AN, in the range (0,1)">
else:
freq = float(variant.extras.get(meta_key, 0).split(',')[variant.extras['alt_allele_pos']])
self._add_population_frequency(
variant.xpos,
variant.ref,
variant.alt,
population['slug'],
freq
)
vcf_file.close()
#
# Directory of per-chromosome VCFs that ESP publishes
#
elif population['file_type'] == 'esp_vcf_dir':
for filename in os.listdir(population['dir_path']):
file_path = os.path.abspath(os.path.join(population['dir_path'], filename))
f = open(file_path)
file_size = os.path.getsize(file_path)
progress = get_progressbar(file_size, 'Loading ESP file: {}'.format(filename))
for variant in get_variants_from_esp_file(f):
progress.update(f.tell())
self._add_population_frequency(
variant['xpos'],
variant['ref'],
variant['alt'],
population['slug'],
variant[population['counts_key']]
)
f.close()
#
# text file of allele counts, as Monkol has been using for the joint calling data
#
elif population['file_type'] == 'counts_file':
if population['file_path'].endswith('.gz'):
counts_file = gzip.open(population['file_path'])
size = os.path.getsize(population['file_path'])
progress_file = counts_file.fileobj
else:
counts_file = open(population['file_path'])
size = os.path.getsize(population['file_path'])
progress_file = counts_file
progress = get_progressbar(size, 'Loading population: {}'.format(population['slug']))
for line in counts_file:
progress.update(progress_file.tell())
fields = line.strip('\n').split('\t')
chrom = 'chr' + fields[0]
pos = int(fields[1])
xpos = genomeloc.get_single_location(chrom, pos)
ref = fields[2]
alt = fields[3]
if int(fields[5]) == 0:
continue
freq = float(fields[4]) / float(fields[5])
self._add_population_frequency(
xpos,
ref,
alt,
population['slug'],
freq
)
counts_file.close()
# this is now the canonical allele frequency file -
# tab separated file with xpos / ref / alt / freq
elif population['file_type'] == 'xbrowse_freq_file':
if population['file_path'].endswith('.gz'):
counts_file = gzip.open(population['file_path'])
progress_file = counts_file.fileobj
else:
counts_file = open(population['file_path'])
progress_file = counts_file
size = os.path.getsize(population['file_path'])
progress = get_progressbar(size, 'Loading population: {}'.format(population['slug']))
for line in counts_file:
progress.update(progress_file.tell())
fields = line.strip('\n').split('\t')
xpos = int(fields[0])
ref = fields[1]
alt = fields[2]
freq = float(fields[3])
self._add_population_frequency(
xpos,
ref,
alt,
population['slug'],
freq
)
counts_file.close()
elif population['file_type'] == 'tsv_file':
if population['file_path'].endswith('.gz'):
freq_file = gzip.open(population['file_path'])
progress_file = freq_file.fileobj
else:
freq_file = open(population['file_path'])
progress_file = freq_file
size = os.path.getsize(population['file_path'])
progress = get_progressbar(size, 'Loading population: {}'.format(population['slug']))
header = next(freq_file)
print("Header: " + header)
for line in freq_file:
progress.update(progress_file.tell())
fields = line.strip('\n').split('\t')
chrom = fields[0]
pos = int(fields[1])
ref = fields[2]
alt = fields[3]
freq = float(fields[4])
xpos = genomeloc.get_single_location(chrom, pos)
self._add_population_frequency(
xpos,
ref,
alt,
population['slug'],
freq
)
freq_file.close()
elif population['file_type'] == 'sites_vcf_with_counts':
if population['file_path'].endswith('.gz') or population['file_path'].endswith('.bgz'):
vcf_file = gzip.open(population['file_path'])
size = os.path.getsize(population['file_path'])
progress_file = vcf_file.fileobj
else:
vcf_file = open(population['file_path'])
size = os.path.getsize(population['file_path'])
progress_file = vcf_file
ac_info_key = population['ac_info_key']
an_info_key = population['an_info_key']
progress = get_progressbar(size, 'Loading sites vcf: {}'.format(population['slug']))
for variant in vcf_stuff.iterate_vcf(vcf_file, meta_fields=[ac_info_key, an_info_key]):
progress.update(progress_file.tell())
alt_allele_pos = variant.extras['alt_allele_pos']
try:
ac = int(variant.extras.get(ac_info_key).split(',')[alt_allele_pos].replace("NA", "0"))
except Exception, e:
print("Couldn't parse AC value %s from %s: %s" % (alt_allele_pos, ac_info_key, variant.extras), e)
continue
try:
if "popmax" in ac_info_key.lower():
AN_index = alt_allele_pos # each allele may have a different AN value from a different population
else:
AN_index = 0
an = int(variant.extras.get(an_info_key).split(',')[AN_index].replace("NA", "0"))
except Exception, e:
print("Couldn't parse AN value %s from %s: %s" % (alt_allele_pos, an_info_key, variant.extras), e)
continue
if an == 0:
freq = 0.0
else:
freq = float(ac)/an
self._add_population_frequency(
variant.xpos,
variant.ref,
variant.alt,
population['slug'],
freq
)
def load_population_to_annotator(self, population):
"""
Take a population and a data source; extract and load it into annotator
Data source can be VCF file, VCF Counts file, or a counts dir (in the case of ESP data)
"""
if population['file_type'] == 'vcf':
if population['file_path'].endswith('.gz'):
vcf_file = gzip.open(population['file_path'])
else:
vcf_file = open(population['file_path'])
for i, variant in enumerate(vcf_stuff.iterate_vcf(vcf_file, genotypes=True, genotype_meta=False)):
if i % 10000 == 0:
print i
freq = get_aaf(variant)
self._add_population_frequency(variant.xpos, variant.ref, variant.alt, population['slug'], freq)
elif population['file_type'] == 'sites_vcf':
if population['file_path'].endswith('.gz'):
vcf_file = gzip.open(population['file_path'])
else:
vcf_file = open(population['file_path'])
meta_key = population['vcf_info_key']
for i, variant in enumerate(vcf_stuff.iterate_vcf(vcf_file, meta_fields=[meta_key,])):
if i % 10000 == 0:
print i
freq = float(variant.extras.get(meta_key, 0))
self._add_population_frequency(
variant.xpos,
variant.ref,
variant.alt,
population['slug'],
freq
)
#
# Directory of per-chromosome VCFs that ESP publishes
#
elif population['file_type'] == 'esp_vcf_dir':
for filename in os.listdir(population['dir_path']):
print "Adding %s" % filename
file_path = os.path.abspath(os.path.join(population['dir_path'], filename))
f = open(file_path)
for i, variant in enumerate(get_variants_from_esp_file(f)):
if i % 10000 == 0:
print i
self._add_population_frequency(
variant['xpos'],
variant['ref'],
variant['alt'],
population['slug'],
variant[population['counts_key']]
)
#
# text file of allele counts, as Monkol has been using for the joint calling data
#
elif population['file_type'] == 'counts_file':
if population['file_path'].endswith('.gz'):
counts_file = gzip.open(population['file_path'])
else:
counts_file = open(population['file_path'])
for i, line in enumerate(counts_file):
if i % 10000 == 0:
print i
fields = line.strip('\n').split('\t')
chrom = 'chr' + fields[0]
pos = int(fields[1])
xpos = genomeloc.get_single_location(chrom, pos)
ref = fields[2]
alt = fields[3]
if int(fields[5]) == 0:
continue
freq = float(fields[4]) / float(fields[5])
self._add_population_frequency(
xpos,
ref,
alt,
population['slug'],
freq
)
# this is now the canonical allele frequency file -
# tab separated file with xpos / ref / alt / freq
elif population['file_type'] == 'xbrowse_freq_file':
if population['file_path'].endswith('.gz'):
counts_file = gzip.open(population['file_path'])
else:
counts_file = open(population['file_path'])
for i, line in enumerate(counts_file):
if i % 10000 == 0:
print i
fields = line.strip('\n').split('\t')
xpos = int(fields[0])
ref = fields[1]
alt = fields[2]
freq = float(fields[3])
self._add_population_frequency(
xpos,
ref,
alt,
population['slug'],
freq
)
def load(self):
self._db.drop_collection('variants')
self._db.variants.ensure_index([('xpos', 1), ('ref', 1), ('alt', 1)])
# load dbnsfp info
polyphen_map = {
'D': 'probably_damaging',
'P': 'possibly_damaging',
'B': 'benign',
'.': None
}
sift_map = {
'D': 'damaging',
'T': 'tolerated',
'.': None
}
fathmm_map = {
'D': 'damaging',
'T': 'tolerated',
'.': None
}
muttaster_map = {
'A': 'disease_causing',
'D': 'disease_causing',
'N': 'polymorphism',
'P': 'polymorphism',
'.': None
}
#interesting_fields = "rs_dbSNP141 Ancestral_allele SIFT_score SIFT_converted_rankscore SIFT_pred Polyphen2_HDIV_pred Polyphen2_HVAR_pred MutationTaster_pred MutationAssessor_pred FATHMM_pred MetaSVM_pred CADD_phred"
# LRT_pred MetaLR_pred VEST3_rankscore PROVEAN_converted_rankscore PROVEAN_pred CADD_raw CADD_raw_rankscore GERP++_NR GERP++_RS GERP++_RS_rankscore ESP6500_AA_AF ESP6500_EA_AF ARIC5606_AA_AC ARIC5606_AA_AF ARIC5606_EA_AC ARIC5606_EA_AF ExAC_AC ExAC_AF ExAC_Adj_AC ExAC_Adj_AF ExAC_AFR_AC ExAC_AFR_AF ExAC_AMR_AC ExAC_AMR_AF ExAC_EAS_AC ExAC_EAS_AF ExAC_FIN_AC ExAC_FIN_AF ExAC_NFE_AC ExAC_NFE_AF ExAC_SAS_AC ExAC_SAS_AF clinvar_rs clinvar_clnsig clinvar_trait"
#interesting_fields = interesting_fields.split()
def collapse(scores):
s = set(scores.split(";"))
if len(s) > 1:
raise ValueError("Couldn't collapse %s" % str(scores))
return list(s)[0]
pred_rank = ['D', 'A', 'T', 'N', 'P', 'B', '.']
def select_worst(pred_value):
i = len(pred_rank) - 1
for pred in pred_value.split(";"):
r = pred_rank.index(pred)
if r < i:
i = r
return pred_rank[i]
for chrom in CHROMOSOMES:
if chrom == "chrM":
continue # no dbNSFP data for chrM
print "Reading dbNSFP data for {}".format(chrom)
single_chrom_file = open(self._settings.dbnsfp_dir + 'dbNSFP2.9_variant.' + chrom)
header = single_chrom_file.readline()
header_fields = header.strip("\n").split()
field_index = {name: header_fields.index(name) for name in header_fields}
for i, line in enumerate(single_chrom_file):
if i == 0:
continue
if not i%100000:
print i
fields = line.strip('\n').split('\t')
chrom, pos, ref, alt = fields[:4]
chrom = 'chr' + chrom
pos = int(pos)
xpos = genomeloc.get_single_location(chrom, pos)
if not xpos:
raise ValueError("Unexpected chr, pos: %(chrom)s, %(pos)s" % (chrom, pos))
rsid = fields[field_index["rs_dbSNP141"]]
annotations_dict = {
'rsid': rsid if rsid != '.' else None,
'polyphen': polyphen_map[select_worst(fields[field_index["Polyphen2_HVAR_pred"]])],
'sift': sift_map[select_worst(fields[field_index["SIFT_pred"]])],
'fathmm': fathmm_map[select_worst(fields[field_index["FATHMM_pred"]])],
'muttaster': muttaster_map[select_worst(fields[field_index["MutationTaster_pred"]])],
'metasvm': collapse(fields[field_index["MetaSVM_pred"]]),
#'cadd_phred': collapse(fields[field_index["CADD_phred"]]),
}
#extras_to_add_now = ["clinvar_rs", "clinvar_clnsig", "clinvar_trait"]
#for name in extras_to_add_now:
# annotations_dict[name] = fields[field_index[name]]
self._db.variants.update(
{'xpos': xpos, 'ref': ref, 'alt': alt},
{'$set': annotations_dict},
upsert=True
)
def load_dbnsfp(self):
self._db.drop_collection('variants')
self._db.variants.ensure_index([('xpos', 1), ('ref', 1), ('alt', 1)])
# load dbnsfp info
polyphen_map = {
'D': 'probably_damaging',
'P': 'possibly_damaging',
'B': 'benign',
'.': None
}
sift_map = {'D': 'damaging', 'T': 'tolerated', '.': None}
fathmm_map = {'D': 'damaging', 'T': 'tolerated', '.': None}
muttaster_map = {
'A': 'disease_causing',
'D': 'disease_causing',
'N': 'polymorphism',
'P': 'polymorphism',
'.': None
}
def collapse(scores):
s = set(scores.split(";"))
if len(s) > 1:
raise ValueError("Couldn't collapse %s" % str(scores))
return list(s)[0]
pred_rank = ['D', 'A', 'T', 'N', 'P', 'B', '.']
def select_worst(pred_value):
i = len(pred_rank) - 1
for pred in pred_value.split(";"):
r = pred_rank.index(pred)
if r < i:
i = r
return pred_rank[i]
for chrom in CHROMOSOMES:
if chrom == "chrM":
continue # no dbNSFP data for chrM
print "Reading dbNSFP data for {}".format(chrom)
single_chrom_file = open(
self._settings.dbnsfp_dir[self._genome_version] +
'dbNSFP2.9_variant.' + chrom)
header = single_chrom_file.readline()
header_fields = header.strip("\n").split()
field_index = {
name: header_fields.index(name)
for name in header_fields
}
for i, line in tqdm(enumerate(single_chrom_file)):
if i == 0:
continue
fields = line.strip('\n').split('\t')
chrom, pos, ref, alt = fields[:4]
chrom = 'chr' + chrom
pos = int(pos)
xpos = genomeloc.get_single_location(chrom, pos)
if not xpos:
raise ValueError(
"Unexpected chr, pos: %(chrom)s, %(pos)s" %
(chrom, pos))
rsid = fields[field_index["rs_dbSNP141"]]
annotations_dict = {
'rsid':
rsid if rsid != '.' else None,
'polyphen':
polyphen_map[select_worst(
fields[field_index["Polyphen2_HVAR_pred"]])],
'sift':
sift_map[select_worst(fields[field_index["SIFT_pred"]])],
'fathmm':
fathmm_map[select_worst(
fields[field_index["FATHMM_pred"]])],
'muttaster':
muttaster_map[select_worst(
fields[field_index["MutationTaster_pred"]])],
'metasvm':
collapse(fields[field_index["MetaSVM_pred"]]),
#'cadd_phred': collapse(fields[field_index["CADD_phred"]]),
}
self._db.variants.update({
'xpos': xpos,
'ref': ref,
'alt': alt
}, {'$set': annotations_dict},
upsert=True)
def load_dbnsfp(self):
settings.CUSTOM_ANNOTATOR_SETTINGS.db[self._genome_version].drop_collection('variants')
settings.CUSTOM_ANNOTATOR_SETTINGS.db[self._genome_version].variants.ensure_index([('xpos', 1), ('ref', 1), ('alt', 1)])
# load dbnsfp info
polyphen_map = {
'D': 'probably_damaging',
'P': 'possibly_damaging',
'B': 'benign',
'.': None
}
sift_map = {
'D': 'damaging',
'T': 'tolerated',
'.': None
}
fathmm_map = {
'D': 'damaging',
'T': 'tolerated',
'.': None
}
muttaster_map = {
'A': 'disease_causing',
'D': 'disease_causing',
'N': 'polymorphism',
'P': 'polymorphism',
'.': None
}
def collapse(scores):
s = set(scores.split(";"))
if len(s) > 1:
raise ValueError("Couldn't collapse %s" % str(scores))
return list(s)[0]
pred_rank = ['D', 'A', 'T', 'N', 'P', 'B', '.']
def select_worst(pred_value):
i = len(pred_rank) - 1
for pred in pred_value.split(";"):
r = pred_rank.index(pred)
if r < i:
i = r
return pred_rank[i]
for chrom in CHROMOSOMES:
if chrom == "chrM":
continue # no dbNSFP data for chrM
print "Reading dbNSFP data for {}".format(chrom)
single_chrom_file = open(settings.CUSTOM_ANNOTATOR_SETTINGS.dbnsfp_dir[self._genome_version] + 'dbNSFP2.9_variant.' + chrom)
header = single_chrom_file.readline()
header_fields = header.strip("\n").split()
field_index = {name: header_fields.index(name) for name in header_fields}
for i, line in tqdm(enumerate(single_chrom_file)):
if i == 0:
continue
fields = line.strip('\n').split('\t')
chrom, pos, ref, alt = fields[:4]
chrom = 'chr' + chrom
pos = int(pos)
xpos = genomeloc.get_single_location(chrom, pos)
if not xpos:
raise ValueError("Unexpected chr, pos: %(chrom)s, %(pos)s" % (chrom, pos))
rsid = fields[field_index["rs_dbSNP141"]]
annotations_dict = {
'rsid': rsid if rsid != '.' else None,
'polyphen': polyphen_map[select_worst(fields[field_index["Polyphen2_HVAR_pred"]])],
'sift': sift_map[select_worst(fields[field_index["SIFT_pred"]])],
'fathmm': fathmm_map[select_worst(fields[field_index["FATHMM_pred"]])],
'muttaster': muttaster_map[select_worst(fields[field_index["MutationTaster_pred"]])],
'metasvm': collapse(fields[field_index["MetaSVM_pred"]]),
#'cadd_phred': collapse(fields[field_index["CADD_phred"]]),
}
settings.CUSTOM_ANNOTATOR_SETTINGS.db[self._genome_version].variants.update(
{'xpos': xpos, 'ref': ref, 'alt': alt},
{'$set': annotations_dict},
upsert=True
)