def get_hgvs_name(record, as_list=False):
"""construct the valid HGVS name as the _id field"""
chrom = record.CHROM
chromStart = record.INFO['RSPOS']
ref = record.REF
_alt_list = []
_id_list = []
_pos_list = []
for alt in record.ALT:
# should not make alt a string if alt is None
if alt:
alt = str(alt)
_alt_list.append(alt)
try:
# NOTE: current get_pos_start_end doesn't handle ALT=None case
# TODO: need to remove str(alt) when get_pos_start_end can
# handle ALT=None case
(start, end) = get_pos_start_end(chrom, chromStart, ref, alt)
_pos_list.append(OrderedDict(start=start, end=end))
# handle cases where start & end position could not be
# inferred from VCF
except ValueError:
_pos_list.append(OrderedDict(start=None, end=None))
try:
HGVS = get_hgvs_from_vcf(chrom,
chromStart,
ref,
str(alt),
mutant_type=False)
_id_list.append(HGVS)
# handle cases where hgvs id could not be inferred from vcf
except ValueError:
pass
return _id_list, _alt_list, _pos_list
def load_data():
# number of civic ids with ref, alt, chrom
no_case1 = 0
# number of civic ids with chrom, ref, but no alt
no_case2 = 0
# number of civic ids with chrom, alt, but no ref
no_case3 = 0
# number of civic ids with no alt and ref
no_case4 = 0
for variant_id in range(MAX_VARIANT_NUMBER):
if variant_id % 200 == 0:
print("scanned {} variants".format(variant_id))
civic_url = 'https://civic.genome.wustl.edu/api/variants/'
url = civic_url + str(variant_id)
doc = requests.get(url).json()
# time delay for 0.5s
time.sleep(0.5)
if set(['error', 'status']) != set(doc.keys()):
[chrom, pos, ref, alt] = [doc['coordinates'][x] for x in ['chromosome', 'start', 'reference_bases', 'variant_bases']]
doc.pop("id")
new_doc = {}
doc['variant_id'] = variant_id
if chrom and ref and alt:
no_case1 += 1
try:
new_doc['_id'] = get_hgvs_from_vcf(chrom, pos, ref, alt)
except ValueError:
print("id has ref,alt, but coudn't be converted to hgvs id: {}".format(variant_id))
continue
# handle cases of deletions where only ref info is provided
elif chrom and ref and not alt:
no_case2 += 1
start = int(pos)
end = int(pos) + len(ref) - 1
if start == end:
new_doc['_id'] = 'chr{0}:g.{1}del'.format(chrom, start)
else:
new_doc['_id'] = 'chr{0}:g.{1}_{2}del'.format(chrom, start, end)
# handle cases of insertions where only alt info is provided
elif chrom and alt and not ref:
no_case3 += 1
new_doc['_id'] = 'chr{0}:g.{1}_{2}ins{3}'.format(chrom, start, end, alt)
# handle cases where no ref or alt info provided,
# in this case, use CIVIC internal ID as the primary id for MyVariant.info, e.g. CIVIC_VARIANT:1
else:
no_case4 += 1
new_doc['_id'] = 'CIVIC_VARIANT:' + str(variant_id)
for _evidence in doc['evidence_items']:
if 'disease' in _evidence and 'doid' in _evidence['disease'] and _evidence['disease']['doid']:
_evidence['disease']['doid'] = 'DOID:' + _evidence['disease']['doid']
new_doc['civic'] = doc
yield dict_sweep(unlist(new_doc),['','null', 'N/A', None, [], {}])
# change doid into its formal representation, which should be sth like DOID:1
else:
continue
print("number of ids with ref, alt, chrom: {}".format(no_case1))
print("number of ids with chrom, ref but no alt: {}".format(no_case2))
print("number of ids with chrom, alt but no ref: {}".format(no_case3))
print("number of ids with no ref and alt: {}".format(no_case4))
def fetch_generator(tabix, contig):
dbfile_path = 'home/kevinxin/cadd/' + 'cadd_id' + contig
db = dbm.open(dbfile_path)
ids = db.keys()
set_ids = set(ids)
print(len(ids))
fetch = tabix.fetch(contig)
rows = map(lambda x: x.split('\t'), fetch)
# looking for annotype as 'codingtranscript', 'noncodingtranscript'
annos = (row for row in rows if "CodingTranscript" in row[9] or
get_hgvs_from_vcf(row[0], row[1], row[2], row[4]) in set_ids)
json_rows = map(_map_line_to_json, annos)
json_rows = (row for row in json_rows if row)
row_groups = (it for (key, it) in groupby(json_rows, lambda row: row["_id"]))
return (merge_duplicate_rows(rg, "cadd") for rg in row_groups)
def fetch_generator(tabix, contig):
dbfile_path = 'home/kevinxin/cadd/' + 'cadd_id' + contig
db = dbm.open(dbfile_path)
ids = db.keys()
set_ids = set(ids)
print(len(ids))
fetch = tabix.fetch(contig)
rows = map(lambda x: x.split('\t'), fetch)
# looking for annotype as 'codingtranscript', 'noncodingtranscript'
annos = (row for row in rows if "CodingTranscript" in row[9]
or get_hgvs_from_vcf(row[0], row[1], row[2], row[4]) in set_ids)
json_rows = map(_map_line_to_json, annos)
json_rows = (row for row in json_rows if row)
row_groups = (it
for (key, it) in groupby(json_rows, lambda row: row["_id"]))
return (merge_duplicate_rows(rg, "cadd") for rg in row_groups)
def _map_line_to_json(item):
chrom = item.CHROM
chromStart = item.POS
ref = item.REF
info = item.INFO
hpo_count=item.INFO['HPO_CT']
for alt in item.ALT:
alt = str(alt)
(HGVS, var_type) = get_hgvs_from_vcf(chrom, chromStart, ref, alt, mutant_type=True)
if HGVS is None:
return
one_snp_json = {
"_id": HGVS,
"geno2mp": {
"hpo_count": hpo_count,
}
}
obj = (dict_sweep(unlist(value_convert(one_snp_json)), [None]))
yield obj
def _map_line_to_json(item):
chrom = item.CHROM
chromStart = item.POS
ref = item.REF
info = item.INFO
hpo_count=item.INFO['HPO_CT']
for alt in item.ALT:
alt = str(alt)
(HGVS, var_type) = get_hgvs_from_vcf(chrom, chromStart, ref, alt, mutant_type=True)
if HGVS is None:
return
one_snp_json = {
"_id": HGVS,
"geno2mp": {
"hpo_count": hpo_count,
}
}
obj = (dict_sweep(unlist(value_convert_to_number(one_snp_json)), [None]))
yield obj
def load_data (data_file):
d = {}
with open(data_file, "r+") as f:
for line in f:
try:
y = re.split("[\t \n]", line)
if y[0] != "Chrom":
_id = hgvs.get_hgvs_from_vcf(y[0], y[1],y[2], y[3])
d = {"_id":_id, "fire": {}}
d["fire"]["chr"] = y[0]
d["fire"]["pos"] = y[1]
d["fire"]["ref"] = y[2]
d["fire"]["alt"] = y[3]
d["fire"]["score"] = float(y[4])
yield d
d = {}
except Exception as e:
logging.error("Pb with %s: %s" % (line,e))
continue
def parse(cls, record: vcf.model._Record) -> list:
"""
Read the profile data from a VCF record. Note that there is no such "profile" section shown in the gnomAD
browser. These fields, i.e. "chrom", "pos", "filter", "multi-allelic", "ref", "alt", "alleles", "type", and
"rsid", are named as profile fields simply for the convenience of implementation.
Each ALT has its own profile (which will be wrapped into a dict) and this function will return a list of tuples
(<hgvs_id>, <profile_dict>).
It's feasible to return a dict of {<hgvs_id>: <profile_dict>} instead of a list of tuples, but the order of
<hgvs_id> should be preserved (to the order of ALTs). It's easier to just use an index to iterate over the list
of tuples, considering the implementation of `PopulationFrequencyParser.parse()` method.
"""
# although each ALT looks exactly like a string, it is a special type
alt_list = [str(alt) for alt in record.ALT]
# for each ALT, get its (hgvs_id, var_type) tuple
# Here I assume that the "chr" prefix of `record.CHROM`, if any, has already been removed
hgvs_list = [get_hgvs_from_vcf(record.CHROM, record.POS, record.REF, alt, mutant_type=True) for alt in alt_list]
# if multi-allelic, put all variants' HGVS ids as a list in multi-allelic field
multi_allelic = [t[0] for t in hgvs_list] if len(hgvs_list) > 1 else None
def generate_profiles():
for alt, (hgvs_id, var_type) in zip(alt_list, hgvs_list):
profile_dict = {
"chrom": record.CHROM,
"pos": record.POS,
"filter": record.FILTER,
"multi-allelic": multi_allelic,
"ref": record.REF,
"alt": alt,
"alleles": alt_list,
"type": var_type,
"rsid": record.ID
}
yield hgvs_id, profile_dict
return list(generate_profiles())
def _map_line_to_json(item, keys):
key_start = ["AC", "AF", "AN", "Hom", "GC", "Hemi"]
chrom = str(item.CHROM)
if chrom not in CHROM_VALID_VALUES:
return
chromStart = item.POS
ref = item.REF
info = item.INFO
_filter = item.FILTER
rsid = item.ID
# the following value could be missing in the vcf record
# check first if the key exists in the vcf record
# if not, return None
vqslod = info[
'VQSLOD'] if 'VQSLOD' in info and info['VQSLOD'] != math.inf else None
vqsr_culprit = info['VQSR_culprit'] if 'VQSR_culprit' in info else None
baseqranksum = info['BaseQRankSum'] if 'BaseQRankSum' in info else None
clippingranksum = info[
'ClippingRankSum'] if 'ClippingRankSum' in info else None
mqranksum = info['MQRankSum'] if 'MQRankSum' in info else None
readposranksum = info[
'ReadPosRankSum'] if 'ReadPosRankSum' in info else None
qd = info['QD'] if 'QD' in info else None
inbreedingcoeff = info[
'InbreedingCoeff'] if 'InbreedingCoeff' in info else None
# convert vcf object to string
item.ALT = [str(alt) for alt in item.ALT]
# if multiallelic, put all variants as a list in multi-allelic field
hgvs_list = None
if len(item.ALT) > 1:
hgvs_list = [
get_hgvs_from_vcf(chrom, chromStart, ref, alt, mutant_type=False)
for alt in item.ALT
]
for i, alt in enumerate(item.ALT):
(HGVS, var_type) = get_hgvs_from_vcf(chrom,
chromStart,
ref,
alt,
mutant_type=True)
if HGVS is None:
return
assert len(item.ALT) == len(
info['AC']
), "Expecting length of item.ALT= length of info.AC, but not for %s" % (
HGVS)
assert len(item.ALT) == len(
info['AF']
), "Expecting length of item.ALT= length of info.AF, but not for %s" % (
HGVS)
one_snp_json = {
"_id": HGVS,
"gnomad_genome": {
"chrom": chrom,
"pos": chromStart,
"filter": _filter,
"multi-allelic": hgvs_list,
"ref": ref,
"alt": alt,
"alleles": item.ALT,
"type": var_type,
"rsid": rsid,
"baseqranksum": baseqranksum,
"clippingranksum": clippingranksum,
"fs": info['FS'],
"inbreedingcoeff": inbreedingcoeff,
"mq": {
"mq": info['MQ'],
"mqranksum": mqranksum
},
"qd": qd,
"readposranksum": readposranksum,
"vqslod": vqslod,
"vqsr_culprit": vqsr_culprit
}
}
# create a holder in one_snp_json for each _start, e.g. 'ac', 'af', 'gc'
for _start in key_start:
one_snp_json['gnomad_genome'][_start.lower()] = {}
# loop through each available key
for _key in keys:
if _key in info:
# loop through each prefix
for _start in key_start:
# "ac", "af" value is related to multi-allelic, need to deal with separately
if _key.startswith(_start) and _start in [
'AC', 'AF', 'Hom', 'Hemi'
]:
one_snp_json['gnomad_genome'][_start.lower()][
_key.lower()] = info[_key][i]
elif _key.startswith(_start) and _start not in [
'AC', 'AF', 'Hom', 'Hemi'
]:
one_snp_json['gnomad_genome'][_start.lower()][
_key.lower()] = info[_key]
obj = (dict_sweep(
unlist(
value_convert_to_number(one_snp_json, skipped_keys=['chrom'])),
[None]))
yield obj
def annotate_by_snpeff(self, varobj_list):
'''load data'''
# title of vcf
vcf_stdin = '#CHROM\tPOS\tID\tREF\tALT\tQUAL\tFILTER\tINFO\n'
# extract each item from list, transform into vcf format
snpeff_valid_id = []
for item in varobj_list:
if '>' in item:
hgvs_info = self.snp_hgvs_id_parser(item)
try:
vcf_stdin += self.snp_vcf_constructer(hgvs_info)
except TypeError:
print(item)
continue
snpeff_valid_id.append(item)
elif item.endswith('del'):
hgvs_info = self.del_hgvs_id_parser(item)
try:
vcf_stdin += self.del_vcf_constructor(hgvs_info)
except TypeError:
print(item)
continue
snpeff_valid_id.append(item)
elif 'ins' in item and 'del' not in item:
hgvs_info = self.ins_hgvs_id_parser(item)
try:
vcf_stdin += self.ins_vcf_constructor(hgvs_info)
except TypeError:
print(item)
continue
snpeff_valid_id.append(item)
elif 'delins' in item:
hgvs_info = self.delins_hgvs_id_parser(item)
try:
vcf_stdin += self.delins_vcf_constructor(hgvs_info)
except TypeError:
print(item)
continue
else:
print(item)
print('beyond current capacity')
proc = subprocess.Popen(SNPEFF_CMD, stdin=subprocess.PIPE, stdout=subprocess.PIPE, stderr=subprocess.PIPE)
(stdout, stderr) = proc.communicate(vcf_stdin)
assert stderr == '', stderr
vcf_stdout_raw = stdout.split('\n')
for vcf_line in vcf_stdout_raw:
if vcf_line.startswith('#'):
continue
elif vcf_line == '':
continue
else:
# assume the first item is 'ANN'
ann_info = vcf_line.split(';')[0]
ann = []
# Multiple annotations per VCF line
for item in ann_info.split(','):
if len(item.split('|')) > 1:
(effect, putative_impact, gene_name, gene_id, feature_type, feature_id) = item.split('|')[1:7]
(transcript_biotype, exon, hgvs_coding, hgvs_protein, cdna, cds, protein, distance_to_feature) = item.split('|')[7:15]
print(effect)
if cdna:
(cdna_position, cdna_len) = cdna.split('/')
else:
cdna_position = None
cdna_len = None
if cds:
(cds_position, cds_len) = cds.split('/')
else:
cds_position = None
cds_len = None
if protein:
(protein_position, protein_len) = protein.split('/')
else:
protein_position = None
protein_len = None
if exon:
(rank, total) = exon.split('/')
else:
rank = None
total = None
ann.append({
"effect": effect,
"putative_impact": putative_impact,
"genename": gene_name,
"gene_id": gene_id,
"feature_type": feature_type,
"feature_id": feature_id,
"transcript_biotype": transcript_biotype,
"rank": rank,
"total": total,
"hgvs.c": hgvs_coding,
"hgvs.p": hgvs_protein,
"cdna": {
"position": cdna_position,
"length": cdna_len
},
"cds": {
"position": cds_position,
"length": cds_len
},
"protein": {
"position": protein_position,
"length": protein_len
},
"distance_to_feature": distance_to_feature
})
print(ann)
# not all annotations include lof & nmd information. Set them to 'None' as default
lof = None
nmd = None
# the case that annotation include 'ann' & 'lof' & 'nmd'
if len(vcf_line.split(';')) == 3:
(lof_info, nmd_info) = vcf_line.split(';')[1:3]
# assume the second item is 'lof'
assert lof_info.startswith('LOF')
# the information to be parsed is like this: 'LOF=(PTEN|PTEN|1|1.00)'
lof_info = lof_info.split('(')[1].split(')')[0]
nmd_info = nmd_info.split('(')[1].split(')')[0]
(id_lof, name_lof, nt_lof, pt_lof) = lof_info.split('|')
(id_nmd, name_nmd, nt_nmd, pt_nmd) = nmd_info.split('|')
lof = {
"gene_id": id_lof,
"genename": name_lof,
"number_of_transcripts_in_gene": nt_lof,
"percent_of_transcripts_affected": pt_lof
}
nmd = {
"gene_id": id_nmd,
"genename": name_nmd,
"number_of_transcripts_in_gene": nt_nmd,
"percent_of_transcripts_affected": pt_nmd
}
# the case that annotation include 'ann' & 'lof or nmd'
elif len(vcf_line.split(';')) == 2:
(ann_info, idk_info) = vcf_line.split(';')
if idk_info.startswith('LOF'):
lof_info = idk_info.split('(')[1].split(')')[0]
(id_lof, name_lof, nt_lof, pt_lof) = lof_info.split('|')
lof = {
"gene_id": id_lof,
"genename": name_lof,
"number_of_transcripts_in_gene": nt_lof,
"percent_of_transcripts_affected": pt_lof
}
else:
nmd_info = idk_info.split('(')[1].split(')')[0]
(id_nmd, name_nmd, nt_nmd, pt_nmd) = nmd_info.split('|')
nmd = {
"gene_id": id_nmd,
"genename": name_nmd,
"number_of_transcripts_in_gene": nt_nmd,
"percent_of_transcripts_affected": pt_nmd
}
(chrom, pos, _id, ref, alt) = ann_info.split('\t')[0:5]
hgvs_id = get_hgvs_from_vcf(chrom, pos, ref, alt)
one_snp_json = {
"id": hgvs_id,
"snpeff": {
"ann": ann,
"lof": lof,
"nmd": nmd,
"vcf": {
"position": pos,
"ref": ref,
"alt": alt
}
}
}
snpeff_json = dict_sweep(unlist(one_snp_json), vals=['', None])
yield snpeff_json
def _map_line_to_json(fields):
assert len(fields) == VALID_COLUMN_NO
chrom = fields[0]
chromStart = fields[1]
ref = fields[2]
alt = fields[4]
HGVS = get_hgvs_from_vcf(chrom, chromStart, ref, alt)
# load as json data
if HGVS is None:
return
one_snp_json = {
"_id": HGVS,
"cadd": {
'chrom': fields[0],
'pos': fields[1],
'ref': fields[2],
'anc': fields[3],
'alt': fields[4],
'type': fields[5],
'length': fields[6],
'istv': fields[7],
'isderived': fields[8],
'annotype': fields[9],
'consequence': fields[10],
'consscore': fields[11],
'consdetail': fields[12],
'gc': fields[13],
'cpg': fields[14],
'mapability': {
'20bp': fields[15],
'35bp': fields[16]
},
'scoresegdup': fields[17],
'phast_cons': {
'primate': fields[18],
'mammalian': fields[19],
'vertebrate': fields[20]
},
'phylop': {
'primate': fields[21],
'mammalian': fields[22],
'vertebrate': fields[23]
},
'gerp': {
'n': fields[24],
's': fields[25],
'rs': fields[26],
'rs_pval': fields[27]
},
'bstatistic': fields[28],
'mutindex': fields[29],
'dna': {
'helt': fields[30],
'mgw': fields[31],
'prot': fields[32],
'roll': fields[33]
},
'mirsvr': {
'score': fields[34],
'e': fields[35],
'aln': fields[36]
},
'targetscans': fields[37],
'fitcons': fields[38],
'chmm': {
'tssa': fields[39],
'tssaflnk': fields[40],
'txflnk': fields[41],
'tx': fields[42],
'txwk': fields[43],
'enh': fields[44],
# 'enh': fields[45],
'znfrpts': fields[46],
'het': fields[47],
'tssbiv': fields[48],
'bivflnk': fields[49],
'enhbiv': fields[50],
'reprpc': fields[51],
'reprpcwk': fields[52],
'quies': fields[53],
},
'encode': {
'exp': fields[54],
'h3k27ac': fields[55],
'h3k4me1': fields[56],
'h3k4me3': fields[57],
'nucleo': fields[58],
'occ': fields[59],
'p_val': {
'comb': fields[60],
'dnas': fields[61],
'faire': fields[62],
'polii': fields[63],
'ctcf': fields[64],
'mycp': fields[65]
},
'sig': {
'dnase': fields[66],
'faire': fields[67],
'polii': fields[68],
'ctcf': fields[69],
'myc': fields[70]
},
},
'segway': fields[71],
'motif': {
'toverlap': fields[72],
'dist': fields[73],
'ecount': fields[74],
'ename': fields[75],
'ehipos': fields[76],
'escorechng': fields[77]
},
'tf': {
'bs': fields[78],
'bs_peaks': fields[79],
'bs_peaks_max': fields[80]
},
'isknownvariant': fields[81],
'esp': {
'af': fields[82],
'afr': fields[83],
'eur': fields[84]
},
'1000g': {
'af': fields[85],
'asn': fields[86],
'amr': fields[87],
'afr': fields[88],
'eur': fields[89]
},
'min_dist_tss': fields[90],
'min_dist_tse': fields[91],
'gene': {
'gene_id': fields[92],
'feature_id': fields[93],
'ccds_id': fields[94],
'genename': fields[95],
'cds': {
'cdna_pos': fields[96],
'rel_cdna_pos': fields[97],
'cds_pos': fields[98],
'rel_cds_pos': fields[99]
},
'prot': {
'protpos': fields[100],
'rel_prot_pos': fields[101],
'domain': fields[102]
}
},
'dst2splice': fields[103],
'dst2spltype': fields[104],
'exon': fields[105],
'intron': fields[106],
'oaa': fields[107], # ref aa
'naa': fields[108], # alt aa
'grantham': fields[109],
'polyphen': {
'cat': fields[110],
'val': fields[111]
},
'sift': {
'cat': fields[112],
'val': fields[113]
},
'rawscore': fields[114], # raw CADD score
'phred': fields[115] # log-percentile of raw CADD score
}
}
obj = dict_sweep(unlist(value_convert(one_snp_json)), ["NA"])
yield obj
def _map_line_to_json(item):
chrom = item.CHROM
chromStart = item.POS
ref = item.REF
info = item.INFO
try:
baseqranksum = info['BaseQRankSum']
except:
baseqranksum = None
try:
clippingranksum = info['ClippingRankSum']
except:
clippingranksum = None
try:
mqranksum = info['MQRankSum']
except:
mqranksum = None
try:
readposranksum = info['ReadPosRankSum']
except:
readposranksum = None
try:
qd = info['QD']
except:
qd = None
try:
inbreedingcoeff = info['InbreedingCoeff']
except:
inbreedingcoeff = None
for i in range(0, len(item.ALT)):
item.ALT[i] = str(item.ALT[i])
for alt in item.ALT:
alt = str(alt)
(HGVS, var_type) = get_hgvs_from_vcf(chrom, chromStart, ref, alt, mutant_type=True)
if HGVS is None:
return
one_snp_json = {
"_id": HGVS,
"exac": {
"chrom": chrom,
"pos": chromStart,
"ref": ref,
"alt": alt,
"alleles": item.ALT,
"type": var_type,
"ac": {
"ac": info['AC'],
"ac_afr": info['AC_AFR'],
"ac_amr": info['AC_AMR'],
"ac_adj": info['AC_Adj'],
"ac_eas": info['AC_EAS'],
"ac_fin": info['AC_FIN'],
"ac_het": info['AC_Het'],
"ac_hom": info['AC_Hom'],
"ac_nfe": info['AC_NFE'],
"ac_oth": info['AC_OTH'],
"ac_sas": info['AC_SAS']
},
"af": info['AF'],
"an": {
"an": info['AN'],
"an_afr": info['AN_AFR'],
"an_amr": info['AN_AMR'],
"an_adj": info['AN_Adj'],
"an_eas": info['AN_EAS'],
"an_fin": info['AN_FIN'],
"an_nfe": info['AN_NFE'],
"an_oth": info['AN_OTH'],
"an_sas": info['AN_SAS']
},
"baseqranksum": baseqranksum,
"clippingranksum": clippingranksum,
"fs": info['FS'],
"het": {
"het_afr": info['Het_AFR'],
"het_amr": info['Het_AMR'],
"het_eas": info['Het_EAS'],
"het_fin": info['Het_FIN'],
"het_nfe": info['Het_NFE'],
"het_oth": info['Het_OTH'],
"het_sas": info['Het_SAS']
},
"hom": {
"hom_afr": info['Hom_AFR'],
"hom_amr": info['Hom_AMR'],
"hom_eas": info['Hom_EAS'],
"hom_fin": info['Hom_FIN'],
"hom_nfe": info['Hom_NFE'],
"hom_oth": info['Hom_OTH'],
"hom_sas": info['Hom_SAS']
},
"inbreedingcoeff": inbreedingcoeff,
"mq": {
"mq": info['MQ'],
"mq0": info['MQ0'],
"mqranksum": mqranksum
},
"ncc": info['NCC'],
"qd": qd,
"readposranksum": readposranksum,
"vqslod": info['VQSLOD'],
"culprit": info['culprit']
}
}
obj = (dict_sweep(unlist(value_convert(one_snp_json)), [None]))
yield obj
def _map_line_to_json(doc_key, item):
chrom = item.CHROM
chromStart = item.POS
ref = item.REF
info = item.INFO
try:
baseqranksum = info['BaseQRankSum']
except:
baseqranksum = None
try:
clippingranksum = info['ClippingRankSum']
except:
clippingranksum = None
try:
mqranksum = info['MQRankSum']
except:
mqranksum = None
try:
readposranksum = info['ReadPosRankSum']
except:
readposranksum = None
try:
qd = info['QD']
except:
qd = None
try:
inbreedingcoeff = info['InbreedingCoeff']
except:
inbreedingcoeff = None
for i in range(0, len(item.ALT)):
item.ALT[i] = str(item.ALT[i])
for alt in item.ALT:
alt = str(alt)
(HGVS, var_type) = get_hgvs_from_vcf(chrom,
chromStart,
ref,
alt,
mutant_type=True)
if HGVS is None:
return
one_snp_json = {
"_id": HGVS,
doc_key: {
"chrom": chrom,
"pos": chromStart,
"ref": ref,
"alt": alt,
"alleles": item.ALT,
"type": var_type,
"ac": {
"ac": info['AC'],
"ac_afr": info['AC_AFR'],
"ac_amr": info['AC_AMR'],
"ac_adj": info['AC_Adj'],
"ac_eas": info['AC_EAS'],
"ac_fin": info['AC_FIN'],
"ac_het": info['AC_Het'],
"ac_hom": info['AC_Hom'],
"ac_nfe": info['AC_NFE'],
"ac_oth": info['AC_OTH'],
"ac_sas": info['AC_SAS'],
"ac_female": info['AC_FEMALE'],
"ac_male": info['AC_MALE']
},
"af": info['AF'],
"an": {
"an": info['AN'],
"an_afr": info['AN_AFR'],
"an_amr": info['AN_AMR'],
"an_adj": info['AN_Adj'],
"an_eas": info['AN_EAS'],
"an_fin": info['AN_FIN'],
"an_nfe": info['AN_NFE'],
"an_oth": info['AN_OTH'],
"an_sas": info['AN_SAS'],
"an_female": info['AN_FEMALE'],
"an_male": info['AN_MALE']
},
"baseqranksum": baseqranksum,
"clippingranksum": clippingranksum,
"fs": info['FS'],
"het": {
"het_afr": info['Het_AFR'],
"het_amr": info['Het_AMR'],
"het_eas": info['Het_EAS'],
"het_fin": info['Het_FIN'],
"het_nfe": info['Het_NFE'],
"het_oth": info['Het_OTH'],
"het_sas": info['Het_SAS']
},
"hom": {
"hom_afr": info['Hom_AFR'],
"hom_amr": info['Hom_AMR'],
"hom_eas": info['Hom_EAS'],
"hom_fin": info['Hom_FIN'],
"hom_nfe": info['Hom_NFE'],
"hom_oth": info['Hom_OTH'],
"hom_sas": info['Hom_SAS']
},
"inbreedingcoeff": inbreedingcoeff,
"mq": {
"mq": info['MQ'],
"mq0": info['MQ0'],
"mqranksum": mqranksum
},
"ncc": info['NCC'],
"qd": qd,
"readposranksum": readposranksum,
"vqslod": info['VQSLOD'],
"culprit": info['culprit']
}
}
obj = (dict_sweep(unlist(value_convert_to_number(one_snp_json)),
[None]))
yield obj
def _map_line_to_json(df):
# specific variable treatment
chrom = df["chr_id"]
pos = df["chr_pos"]
if chrom == 'M':
chrom = 'MT'
ref = df["ref_nt"]
alt = df["alt_nt"]
HGVS = get_hgvs_from_vcf(chrom, int(pos), ref, alt, mutant_type=False)
transcript_id = clean_data(df["transcript_id"], ("-",))
peptide_id = clean_data(df["peptide_id"], ("-",))
uniprot_ac = clean_data(df["uniprot_ac"], ("-",))
refseq_ac = clean_data(df["refseq_ac"], ("-",))
cds_pos = clean_data(df["cds_pos"], ("-",))
pep_pos = clean_data(df["pep_pos"], ("-",))
uniprot_pos = clean_data(df["uniprot_pos"], ("-",))
ref_aa = clean_data(df["ref_aa"], ("-",))
alt_aa = clean_data(df["alt_aa"], ("-",))
mut_freq = clean_data(df["mut_freq"], ("-",))
data_src = clean_data(df["data_src"], ("-",))
do_id = clean_data(df["do_id"], ("-",))
do_name_id, do_name = do_name_split(df["do_name"])
if do_id and do_name_id:
assert do_id == do_name_id, "do_id mismatch!"
uberon_id = to_list(df["uberon_id"])
gene_name = clean_data(df["gene_name"], ("-",))
pmid_list = to_list(df["pmid_list"])
site_prd = site_prd_parser(clean_data(df["site_prd"], ("-",)))
site_ann = site_ann_parser(df["site_ann"])
# load as json data
one_snp_json = {
"_id": HGVS,
"biomuta": {
'chrom': chrom,
'pos': pos,
'ref': ref,
'alt': alt,
'transcript_id': transcript_id,
'peptide_id': peptide_id,
'uniprot_ac': uniprot_ac,
'refseq_ac': refseq_ac,
'cds_pos': cds_pos,
'pep_pos': pep_pos,
'uniprot_pos': uniprot_pos,
'ref_aa': ref_aa,
'alt_aa': alt_aa,
'mut_freq': mut_freq,
'data_src': data_src,
'do_id': {
"do_id" : do_id,
"do_name" : do_name
},
'uberon_id': uberon_id,
'gene_name': gene_name,
'pmid': pmid_list,
}
}
if site_ann:
for dic in site_ann:
one_snp_json["biomuta"].update(dic)
if site_prd:
one_snp_json["biomuta"].update(site_prd)
one_snp_json = value_convert_to_number(one_snp_json)
one_snp_json['biomuta']['chrom'] = str(one_snp_json['biomuta']['chrom'])
one_snp_json['biomuta']['do_id']['do_id'] = str(one_snp_json['biomuta']['do_id']['do_id'])
return one_snp_json
def load_data(data_folder):
# number of civic ids with ref, alt, chrom
no_case1 = 0
# number of civic ids with chrom, ref, but no alt
no_case2 = 0
# number of civic ids with chrom, alt, but no ref
no_case3 = 0
# number of civic ids with no alt and ref
no_case4 = 0
for infile in glob.glob(os.path.join(data_folder,"variant_*.json")):
doc = json.load(open(infile))
if set(['error', 'status']) != set(doc.keys()):
[chrom, pos, ref, alt] = [doc['coordinates'][x] for x in ['chromosome', 'start', 'reference_bases', 'variant_bases']]
variant_id = doc.pop("id")
new_doc = {}
doc['variant_id'] = variant_id
if chrom and ref and alt:
no_case1 += 1
try:
new_doc['_id'] = get_hgvs_from_vcf(chrom, pos, ref, alt)
except ValueError:
logging.warning("id has ref,alt, but coudn't be converted to hgvs id: {}".format(variant_id))
continue
# handle cases of deletions where only ref info is provided
elif chrom and ref and not alt:
no_case2 += 1
start = int(pos)
end = int(pos) + len(ref) - 1
if start == end:
new_doc['_id'] = 'chr{0}:g.{1}del'.format(chrom, start)
else:
new_doc['_id'] = 'chr{0}:g.{1}_{2}del'.format(chrom, start, end)
# handle cases of insertions where only alt info is provided
elif chrom and alt and not ref:
no_case3 += 1
new_doc['_id'] = 'chr{0}:g.{1}_{2}ins{3}'.format(chrom, start, end, alt)
# handle cases where no ref or alt info provided,
# in this case, use CIVIC internal ID as the primary id for MyVariant.info, e.g. CIVIC_VARIANT:1
else:
no_case4 += 1
new_doc['_id'] = 'CIVIC_VARIANT:' + str(variant_id)
for _evidence in doc['evidence_items']:
if 'disease' in _evidence and 'doid' in _evidence['disease'] and _evidence['disease']['doid']:
_evidence['disease']['doid'] = 'DOID:' + _evidence['disease']['doid']
if 'source' in _evidence and 'citation_id' in _evidence['source']:
if _evidence['source']['source_type'] == "PubMed":
_evidence['source']['pubmed'] = to_int(_evidence['source']['citation_id'])
_evidence['source'].pop('source_type')
_evidence['source'].pop('citation_id')
elif _evidence['source']['source_type'] == "ASCO":
_evidence['source']['asco'] = to_int(_evidence['source']['citation_id'])
_evidence['source'].pop('source_type')
_evidence['source'].pop('citation_id')
else:
raise ValueError("The value of source_type is not one of PubMed or ASCO, it's {}, need to restructure parser".format(_evidence['source']['source_type']))
new_doc['civic'] = doc
yield dict_sweep(unlist(new_doc),['','null', 'N/A', None, [], {}])
# change doid into its formal representation, which should be sth like DOID:1
else:
continue
logging.info("number of ids with ref, alt, chrom: {}".format(no_case1))
logging.info("number of ids with chrom, ref but no alt: {}".format(no_case2))
logging.info("number of ids with chrom, alt but no ref: {}".format(no_case3))
logging.info("number of ids with no ref and alt: {}".format(no_case4))
def _map_line_to_json(doc_key, item):
chrom = item.CHROM
chromStart = item.POS
ref = item.REF
info = item.INFO
try:
baseqranksum = info['BaseQRankSum']
except:
baseqranksum = None
try:
clippingranksum = info['ClippingRankSum']
except:
clippingranksum = None
try:
mqranksum = info['MQRankSum']
except:
mqranksum = None
try:
readposranksum = info['ReadPosRankSum']
except:
readposranksum = None
try:
qd = info['QD']
except:
qd = None
try:
inbreedingcoeff = info['InbreedingCoeff']
except:
inbreedingcoeff = None
# convert vcf object to string
item.ALT = [str(alt) for alt in item.ALT]
# if multiallelic, put all variants as a list in multi-allelic field
hgvs_list = None
if len(item.ALT) > 1:
hgvs_list = [get_hgvs_from_vcf(chrom, chromStart, ref, alt, mutant_type=False) for alt in item.ALT]
for i, alt in enumerate(item.ALT):
(HGVS, var_type) = get_hgvs_from_vcf(chrom, chromStart, ref, alt, mutant_type=True)
if HGVS is None:
return
assert len(item.ALT) == len(info['AC']), "Expecting length of item.ALT= length of info.AC, but not for %s" % (HGVS)
assert len(item.ALT) == len(info['AF']), "Expecting length of item.ALT= length of info.AF, but not for %s" % (HGVS)
assert len(item.ALT) == len(info['Hom_AFR']), "Expecting length of item.ALT= length of HOM_AFR, but not for %s" % (HGVS)
one_snp_json = {
"_id": HGVS,
doc_key : {
"chrom": chrom,
"pos": chromStart,
"multi-allelic": hgvs_list,
"ref": ref,
"alt": alt,
"alleles": item.ALT,
"type": var_type,
"ac": {
"ac": info['AC'][i],
"ac_afr": info['AC_AFR'][i],
"ac_amr": info['AC_AMR'][i],
"ac_adj": info['AC_Adj'][i],
"ac_eas": info['AC_EAS'][i],
"ac_fin": info['AC_FIN'][i],
"ac_het": info['AC_Het'][i],
"ac_hom": info['AC_Hom'][i],
"ac_nfe": info['AC_NFE'][i],
"ac_oth": info['AC_OTH'][i],
"ac_sas": info['AC_SAS'][i],
"ac_male": info['AC_MALE'][i],
"ac_female": info['AC_FEMALE'][i]
},
"af": info['AF'][i],
"an": {
"an": info['AN'],
"an_afr": info['AN_AFR'],
"an_amr": info['AN_AMR'],
"an_adj": info['AN_Adj'],
"an_eas": info['AN_EAS'],
"an_fin": info['AN_FIN'],
"an_nfe": info['AN_NFE'],
"an_oth": info['AN_OTH'],
"an_sas": info['AN_SAS'],
"an_female": info['AN_FEMALE'],
"an_male": info['AN_MALE']
},
"baseqranksum": baseqranksum,
"clippingranksum": clippingranksum,
"fs": info['FS'],
"het": {
"het_afr": info['Het_AFR'],
"het_amr": info['Het_AMR'],
"het_eas": info['Het_EAS'],
"het_fin": info['Het_FIN'],
"het_nfe": info['Het_NFE'],
"het_oth": info['Het_OTH'],
"het_sas": info['Het_SAS']
},
"hom": {
"hom_afr": info['Hom_AFR'],
"hom_amr": info['Hom_AMR'],
"hom_eas": info['Hom_EAS'],
"hom_fin": info['Hom_FIN'],
"hom_nfe": info['Hom_NFE'],
"hom_oth": info['Hom_OTH'],
"hom_sas": info['Hom_SAS']
},
"inbreedingcoeff": inbreedingcoeff,
"mq": {
"mq": info['MQ'],
"mq0": info['MQ0'],
"mqranksum": mqranksum
},
"ncc": info['NCC'],
"qd": qd,
"readposranksum": readposranksum,
"vqslod": info['VQSLOD'],
"culprit": info['culprit']
}
}
obj = (dict_sweep(unlist(value_convert_to_number(one_snp_json)), [None]))
yield obj
def _map_line_to_json(df):
chrom = df['chromosome']
if chrom == 'M':
chrom = 'MT'
ref = df["reference_allele"]
alt = df["tumor_seq_allele1"]
if alt == '-':
HGVS = get_hgvs_from_vcf(chrom,
int(df['start_position']) - 1,
'N' + ref,
'N',
mutant_type=False)
elif ref == '-':
HGVS = get_hgvs_from_vcf(chrom,
int(df['start_position']) - 1,
'N',
'N' + alt,
mutant_type=False)
else:
HGVS = get_hgvs_from_vcf(chrom,
int(df['start_position']),
ref,
alt,
mutant_type=False)
ccle_depmap = {
'gene': {
'id': df['entrez_gene_id'],
'symbol': df['hugo_symbol']
},
'chrom':
chrom,
'hg19': {
'start': df['start_position'],
'end': df['end_position']
},
'strand':
df['strand'],
'class':
df['variant_classification'],
'vartype':
df['variant_type'],
'ref':
df['reference_allele'],
'tumor_seq_allele1':
df['tumor_seq_allele1'],
'dbsnp': {
'rsid': df['dbsnp_rs'],
'val_status': df['dbsnp_val_status']
},
'genome_change':
df['genome_change'],
'annotation_transcript':
df['annotation_transcript'],
'tumor_sample_barcode':
df['tumor_sample_barcode'],
'cdna_change':
df['cdna_change'],
'codon_change':
df['codon_change'],
'protein_change':
df['protein_change'],
'isdeleterious':
to_boolean(df['isdeleterious'],
true_str=[
'TRUE',
],
false_str=[
'FALSE',
]),
'istcgahotspot':
to_boolean(df['istcgahotspot'],
true_str=[
'TRUE',
],
false_str=[
'FALSE',
]),
'tcgahscnt':
df['tcgahscnt'],
'iscosmichotspot':
to_boolean(df['iscosmichotspot'],
true_str=[
'TRUE',
],
false_str=[
'FALSE',
]),
'cosmichscnt':
df['cosmichscnt'],
'exac_af':
df['exac_af'],
'wes_ac':
df['wes_ac'],
'sanger': {
'wes_ac': df['sangerwes_ac'],
'recalibwes_ac': df['sangerrecalibwes_ac']
},
'rnaseq_ac':
df['rnaseq_ac'],
'hc_ac':
df['hc_ac'],
'rd_ac':
df['rd_ac'],
'wgs_ac':
df['wgs_ac'],
'broad_id':
df['broad_id']
}
ccle_depmap = dict_sweep(ccle_depmap)
# load as json data
one_snp_json = {"_id": HGVS, "ccle": ccle_depmap}
one_snp_json = value_convert_to_number(one_snp_json)
one_snp_json['ccle']['chrom'] = str(one_snp_json['ccle']['chrom'])
return one_snp_json
def annotate_by_snpeff(self, varobj_list):
'''load data'''
# title of vcf
vcf_stdin = '#CHROM\tPOS\tID\tREF\tALT\tQUAL\tFILTER\tINFO\n'
# extract each item from list, transform into vcf format
snpeff_valid_id = []
for item in varobj_list:
if '>' in item:
hgvs_info = self.snp_hgvs_id_parser(item)
try:
vcf_stdin += self.snp_vcf_constructer(hgvs_info)
except TypeError:
print(item)
continue
snpeff_valid_id.append(item)
elif item.endswith('del'):
hgvs_info = self.del_hgvs_id_parser(item)
try:
vcf_stdin += self.del_vcf_constructor(hgvs_info)
except TypeError:
print(item)
continue
snpeff_valid_id.append(item)
elif 'ins' in item and 'del' not in item:
hgvs_info = self.ins_hgvs_id_parser(item)
try:
vcf_stdin += self.ins_vcf_constructor(hgvs_info)
except TypeError:
print(item)
continue
snpeff_valid_id.append(item)
elif 'delins' in item:
hgvs_info = self.delins_hgvs_id_parser(item)
try:
vcf_stdin += self.delins_vcf_constructor(hgvs_info)
except TypeError:
print(item)
continue
else:
print(item)
print('beyond current capacity')
proc = subprocess.Popen(SNPEFF_CMD,
stdin=subprocess.PIPE,
stdout=subprocess.PIPE,
stderr=subprocess.PIPE)
(stdout, stderr) = proc.communicate(vcf_stdin)
assert stderr == '', stderr
vcf_stdout_raw = stdout.split('\n')
for vcf_line in vcf_stdout_raw:
if vcf_line.startswith('#'):
continue
elif vcf_line == '':
continue
else:
# assume the first item is 'ANN'
ann_info = vcf_line.split(';')[0]
ann = []
# Multiple annotations per VCF line
for item in ann_info.split(','):
if len(item.split('|')) > 1:
(effect, putative_impact, gene_name, gene_id,
feature_type, feature_id) = item.split('|')[1:7]
(transcript_biotype, exon, hgvs_coding, hgvs_protein,
cdna, cds, protein,
distance_to_feature) = item.split('|')[7:15]
print(effect)
if cdna:
(cdna_position, cdna_len) = cdna.split('/')
else:
cdna_position = None
cdna_len = None
if cds:
(cds_position, cds_len) = cds.split('/')
else:
cds_position = None
cds_len = None
if protein:
(protein_position,
protein_len) = protein.split('/')
else:
protein_position = None
protein_len = None
if exon:
(rank, total) = exon.split('/')
else:
rank = None
total = None
ann.append({
"effect": effect,
"putative_impact": putative_impact,
"genename": gene_name,
"gene_id": gene_id,
"feature_type": feature_type,
"feature_id": feature_id,
"transcript_biotype": transcript_biotype,
"rank": rank,
"total": total,
"hgvs.c": hgvs_coding,
"hgvs.p": hgvs_protein,
"cdna": {
"position": cdna_position,
"length": cdna_len
},
"cds": {
"position": cds_position,
"length": cds_len
},
"protein": {
"position": protein_position,
"length": protein_len
},
"distance_to_feature": distance_to_feature
})
print(ann)
# not all annotations include lof & nmd information. Set them to 'None' as default
lof = None
nmd = None
# the case that annotation include 'ann' & 'lof' & 'nmd'
if len(vcf_line.split(';')) == 3:
(lof_info, nmd_info) = vcf_line.split(';')[1:3]
# assume the second item is 'lof'
assert lof_info.startswith('LOF')
# the information to be parsed is like this: 'LOF=(PTEN|PTEN|1|1.00)'
lof_info = lof_info.split('(')[1].split(')')[0]
nmd_info = nmd_info.split('(')[1].split(')')[0]
(id_lof, name_lof, nt_lof, pt_lof) = lof_info.split('|')
(id_nmd, name_nmd, nt_nmd, pt_nmd) = nmd_info.split('|')
lof = {
"gene_id": id_lof,
"genename": name_lof,
"number_of_transcripts_in_gene": nt_lof,
"percent_of_transcripts_affected": pt_lof
}
nmd = {
"gene_id": id_nmd,
"genename": name_nmd,
"number_of_transcripts_in_gene": nt_nmd,
"percent_of_transcripts_affected": pt_nmd
}
# the case that annotation include 'ann' & 'lof or nmd'
elif len(vcf_line.split(';')) == 2:
(ann_info, idk_info) = vcf_line.split(';')
if idk_info.startswith('LOF'):
lof_info = idk_info.split('(')[1].split(')')[0]
(id_lof, name_lof, nt_lof,
pt_lof) = lof_info.split('|')
lof = {
"gene_id": id_lof,
"genename": name_lof,
"number_of_transcripts_in_gene": nt_lof,
"percent_of_transcripts_affected": pt_lof
}
else:
nmd_info = idk_info.split('(')[1].split(')')[0]
(id_nmd, name_nmd, nt_nmd,
pt_nmd) = nmd_info.split('|')
nmd = {
"gene_id": id_nmd,
"genename": name_nmd,
"number_of_transcripts_in_gene": nt_nmd,
"percent_of_transcripts_affected": pt_nmd
}
(chrom, pos, _id, ref, alt) = ann_info.split('\t')[0:5]
hgvs_id = get_hgvs_from_vcf(chrom, pos, ref, alt)
one_snp_json = {
"id": hgvs_id,
"snpeff": {
"ann": ann,
"lof": lof,
"nmd": nmd,
"vcf": {
"position": pos,
"ref": ref,
"alt": alt
}
}
}
snpeff_json = dict_sweep(unlist(one_snp_json), vals=['', None])
yield snpeff_json
def _map_line_to_json(fields):
assert len(fields) == VALID_COLUMN_NO
chrom = fields[0]
chromStart = fields[1]
ref = fields[2]
alt = fields[4]
HGVS = get_hgvs_from_vcf(chrom, chromStart, ref, alt)
# load as json data
if HGVS is None:
return
one_snp_json = {
"_id": HGVS,
"cadd": {
'chrom': fields[0],
'pos': fields[1],
'ref': fields[2],
'anc': fields[3],
'alt': fields[4],
'type': fields[5],
'length': fields[6],
'istv': fields[7],
'isderived': fields[8],
'annotype': fields[9],
'consequence': fields[10],
'consscore': fields[11],
'consdetail': fields[12],
'gc': fields[13],
'cpg': fields[14],
'mapability': {
'20bp': fields[15],
'35bp': fields[16]
},
'scoresegdup': fields[17],
'phast_cons': {
'primate': fields[18],
'mammalian': fields[19],
'vertebrate': fields[20]
},
'phylop': {
'primate': fields[21],
'mammalian': fields[22],
'vertebrate': fields[23]
},
'gerp': {
'n': fields[24],
's': fields[25],
'rs': fields[26],
'rs_pval': fields[27]
},
'bstatistic': fields[28],
'mutindex': fields[29],
'dna': {
'helt': fields[30],
'mgw': fields[31],
'prot': fields[32],
'roll': fields[33]
},
'mirsvr': {
'score': fields[34],
'e': fields[35],
'aln': fields[36]
},
'targetscans': fields[37],
'fitcons': fields[38],
'chmm': {
'tssa': fields[39],
'tssaflnk': fields[40],
'txflnk': fields[41],
'tx': fields[42],
'txwk': fields[43],
'enh': fields[44],
# 'enh': fields[45],
'znfrpts': fields[46],
'het': fields[47],
'tssbiv': fields[48],
'bivflnk': fields[49],
'enhbiv': fields[50],
'reprpc': fields[51],
'reprpcwk': fields[52],
'quies': fields[53],
},
'encode': {
'exp': fields[54],
'h3k27ac': fields[55],
'h3k4me1': fields[56],
'h3k4me3': fields[57],
'nucleo': fields[58],
'occ': fields[59],
'p_val': {
'comb': fields[60],
'dnas': fields[61],
'faire': fields[62],
'polii': fields[63],
'ctcf': fields[64],
'mycp': fields[65]
},
'sig': {
'dnase': fields[66],
'faire': fields[67],
'polii': fields[68],
'ctcf': fields[69],
'myc': fields[70]
},
},
'segway': fields[71],
'motif': {
'toverlap': fields[72],
'dist': fields[73],
'ecount': fields[74],
'ename': fields[75],
'ehipos': fields[76],
'escorechng': fields[77]
},
'tf': {
'bs': fields[78],
'bs_peaks': fields[79],
'bs_peaks_max': fields[80]
},
'isknownvariant': fields[81],
'esp': {
'af': fields[82],
'afr': fields[83],
'eur': fields[84]
},
'1000g': {
'af': fields[85],
'asn': fields[86],
'amr': fields[87],
'afr': fields[88],
'eur': fields[89]
},
'min_dist_tss': fields[90],
'min_dist_tse': fields[91],
'gene': {
'gene_id': fields[92],
'feature_id': fields[93],
'ccds_id': fields[94],
'genename': fields[95],
'cds': {
'cdna_pos': fields[96],
'rel_cdna_pos': fields[97],
'cds_pos': fields[98],
'rel_cds_pos': fields[99]
},
'prot': {
'protpos': fields[100],
'rel_prot_pos': fields[101],
'domain': fields[102]
}
},
'dst2splice': fields[103],
'dst2spltype': fields[104],
'exon': fields[105],
'intron': fields[106],
'oaa': fields[107], # ref aa
'naa': fields[108], # alt aa
'grantham': fields[109],
'polyphen': {
'cat': fields[110],
'val': fields[111]
},
'sift': {
'cat': fields[112],
'val': fields[113]
},
'rawscore': fields[114], # raw CADD score
'phred': fields[115] # log-percentile of raw CADD score
}
}
obj = dict_sweep(unlist(value_convert(one_snp_json)), ["NA"])
yield obj
def _map_line_to_json(fields, version):
chrInfo = fields[0].split(":") # grch37
chrom = chrInfo[0]
chromStart = int(chrInfo[1])
ma_fin_percent = fields[7].split("/")
if fields[3]:
mutation = fields[3].split(">")
ref = mutation[0]
alt = mutation[1]
hg19 = get_pos_start_end(chrom, chromStart, ref, alt)
hg38 = get_pos_start_end(chrom, int(fields[30].split(":")[1]), ref,
alt)
if version == 'hg19':
HGVS = get_hgvs_from_vcf(chrom, chromStart, ref, alt)
elif version == 'hg38':
HGVS = get_hgvs_from_vcf(chrom, hg38[0], ref, alt)
# load as json data
if HGVS is None:
return
one_snp_json = {
"_id": HGVS,
"evs": {
"chrom": chrom,
"hg19": {
"start": hg19[0],
"end": hg19[1]
},
"hg38": {
"start": hg38[0],
"end": hg38[1]
},
"rsid": fields[1],
"dbsnp_version": get_dbsnp(fields[2]),
"ref": ref,
"alt": alt,
"allele_count": {
"european_american": count_dict(fields[4]),
"african_american": count_dict(fields[5]),
"all": count_dict(fields[6])
},
"ma_fin_percent": {
"european_american": ma_fin_percent[0],
"african_american": ma_fin_percent[1],
"all": ma_fin_percent[2]
},
"genotype_count": {
"european_american": count_dict(fields[8]),
"african_american": count_dict(fields[9]),
"all_genotype": count_dict(fields[10])
},
"avg_sample_read": fields[11],
"gene": {
"symbol": fields[12],
"accession": fields[13]
},
"function_gvs": fields[14],
"hgvs": {
"coding": fields[16],
"protein": fields[15]
},
"coding_dna_size": fields[17],
"conservation": {
"phast_cons": fields[18],
"gerp": fields[19]
},
"grantham_score": fields[20],
"polyphen2": {
"class": polyphen(fields[21])[0],
"score": polyphen(fields[21])[1]
},
"ref_base_ncbi": fields[22],
"chimp_allele": fields[23],
"clinical_info": fields[24],
"filter_status": fields[25],
"on_illumina_human_exome_chip": fields[26],
"gwas_pubmed_info": fields[27],
"estimated_age_kyrs": {
"ea": fields[28],
"aa": fields[29]
}
}
}
return dict_sweep(value_convert(one_snp_json),
vals=["NA", "none", "unknown"])
def _map_line_to_json(doc_key, item):
chrom = item.CHROM
chromStart = item.POS
ref = item.REF
info = item.INFO
_filter = item.FILTER
try:
baseqranksum = info['BaseQRankSum']
except:
baseqranksum = None
try:
clippingranksum = info['ClippingRankSum']
except:
clippingranksum = None
try:
mqranksum = info['MQRankSum']
except:
mqranksum = None
try:
readposranksum = info['ReadPosRankSum']
except:
readposranksum = None
try:
qd = info['QD']
except:
qd = None
try:
inbreedingcoeff = info['InbreedingCoeff']
except:
inbreedingcoeff = None
# convert vcf object to string
item.ALT = [str(alt) for alt in item.ALT]
# if multiallelic, put all variants as a list in multi-allelic field
hgvs_list = None
if len(item.ALT) > 1:
hgvs_list = [
get_hgvs_from_vcf(chrom, chromStart, ref, alt, mutant_type=False)
for alt in item.ALT
]
for i, alt in enumerate(item.ALT):
(HGVS, var_type) = get_hgvs_from_vcf(chrom,
chromStart,
ref,
alt,
mutant_type=True)
if HGVS is None:
return
assert len(item.ALT) == len(
info['AC']
), "Expecting length of item.ALT= length of info.AC, but not for %s" % (
HGVS)
assert len(item.ALT) == len(
info['AF']
), "Expecting length of item.ALT= length of info.AF, but not for %s" % (
HGVS)
assert len(item.ALT) == len(
info['Hom_AFR']
), "Expecting length of item.ALT= length of HOM_AFR, but not for %s" % (
HGVS)
one_snp_json = {
"_id": HGVS,
doc_key: {
"chrom": chrom,
"pos": chromStart,
"filter": _filter,
"multi-allelic": hgvs_list,
"ref": ref,
"alt": alt,
"alleles": item.ALT,
"type": var_type,
"ac": {
"ac": info['AC'][i],
"ac_afr": info['AC_AFR'][i],
"ac_amr": info['AC_AMR'][i],
"ac_adj": info['AC_Adj'][i],
"ac_eas": info['AC_EAS'][i],
"ac_fin": info['AC_FIN'][i],
"ac_het": info['AC_Het'][i],
"ac_hom": info['AC_Hom'][i],
"ac_nfe": info['AC_NFE'][i],
"ac_oth": info['AC_OTH'][i],
"ac_sas": info['AC_SAS'][i],
"ac_male": info['AC_MALE'][i],
"ac_female": info['AC_FEMALE'][i]
},
"af": info['AF'][i],
"an": {
"an": info['AN'],
"an_afr": info['AN_AFR'],
"an_amr": info['AN_AMR'],
"an_adj": info['AN_Adj'],
"an_eas": info['AN_EAS'],
"an_fin": info['AN_FIN'],
"an_nfe": info['AN_NFE'],
"an_oth": info['AN_OTH'],
"an_sas": info['AN_SAS'],
"an_female": info['AN_FEMALE'],
"an_male": info['AN_MALE']
},
"baseqranksum": baseqranksum,
"clippingranksum": clippingranksum,
"fs": info['FS'],
"het": {
"het_afr": info['Het_AFR'],
"het_amr": info['Het_AMR'],
"het_eas": info['Het_EAS'],
"het_fin": info['Het_FIN'],
"het_nfe": info['Het_NFE'],
"het_oth": info['Het_OTH'],
"het_sas": info['Het_SAS']
},
"hom": {
"hom_afr": info['Hom_AFR'],
"hom_amr": info['Hom_AMR'],
"hom_eas": info['Hom_EAS'],
"hom_fin": info['Hom_FIN'],
"hom_nfe": info['Hom_NFE'],
"hom_oth": info['Hom_OTH'],
"hom_sas": info['Hom_SAS']
},
"inbreedingcoeff": inbreedingcoeff,
"mq": {
"mq": info['MQ'],
"mq0": info['MQ0'],
"mqranksum": mqranksum
},
"ncc": info['NCC'],
"qd": qd,
"readposranksum": readposranksum,
"vqslod": info['VQSLOD'],
"culprit": info['culprit']
}
}
obj = (dict_sweep(unlist(value_convert_to_number(one_snp_json)),
[None]))
yield obj
def _map_line_to_json(fields):
chrInfo = fields[0].split(":") # grch37
chrom = chrInfo[0]
chromStart = int(chrInfo[1])
ma_fin_percent = fields[7].split("/")
if fields[3]:
mutation = fields[3].split(">")
ref = mutation[0]
alt = mutation[1]
HGVS = get_hgvs_from_vcf(chrom, chromStart, ref, alt)
hg19 = get_pos_start_end(chrom, chromStart, ref, alt)
hg38 = get_pos_start_end(chrom, int(fields[30].split(":")[1]), ref, alt)
# load as json data
if HGVS is None:
return
one_snp_json = {
"_id": HGVS,
"evs":
{
"chrom": chrom,
"hg19":
{
"start": hg19[0],
"end": hg19[1]
},
"hg38":
{
"start": hg38[0],
"end": hg38[1]
},
"rsid": fields[1],
"dbsnp_version": get_dbsnp(fields[2]),
"ref": ref,
"alt": alt,
"allele_count":
{
"european_american": count_dict(fields[4]),
"african_american": count_dict(fields[5]),
"all": count_dict(fields[6])
},
"ma_fin_percent":
{
"european_american": ma_fin_percent[0],
"african_american": ma_fin_percent[1],
"all": ma_fin_percent[2]
},
"genotype_count":
{
"european_american": count_dict(fields[8]),
"african_american": count_dict(fields[9]),
"all_genotype": count_dict(fields[10])
},
"avg_sample_read": fields[11],
"gene":
{
"symbol": fields[12],
"accession": fields[13]
},
"function_gvs": fields[14],
"hgvs":
{
"coding": fields[16],
"protein": fields[15]
},
"coding_dna_size": fields[17],
"conservation":
{
"phast_cons": fields[18],
"gerp": fields[19]
},
"grantham_score": fields[20],
"polyphen2":
{
"class": polyphen(fields[21])[0],
"score": polyphen(fields[21])[1]
},
"ref_base_ncbi": fields[22],
"chimp_allele": fields[23],
"clinical_info": fields[24],
"filter_status": fields[25],
"on_illumina_human_exome_chip": fields[26],
"gwas_pubmed_info": fields[27],
"estimated_age_kyrs":
{
"ea": fields[28],
"aa": fields[29]
}
}
}
return dict_sweep(value_convert(one_snp_json), vals=["NA", "none", "unknown"])