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AnnotatorCore.py
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AnnotatorCore.py
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#!/usr/bin/python
import json
import sys
import csv
from enum import Enum
import requests
import os.path
import logging
import re
import matplotlib
matplotlib.use('Agg')
import matplotlib.pyplot as plt
from datetime import date
import ctypes as ct
logging.basicConfig(level=logging.INFO)
logging.getLogger("requests").setLevel(logging.WARNING)
logging.getLogger("urllib3").setLevel(logging.WARNING)
log = logging.getLogger('AnnotatorCore')
# API timeout is set to two minutes
REQUEST_TIMEOUT = 120
csv.field_size_limit(int(ct.c_ulong(-1).value // 2)) # Deal with overflow problem on Windows, https://stackoverflow.co/120m/questions/15063936/csv-error-field-larger-than-field-limit-131072
sizeLimit = csv.field_size_limit()
csv.field_size_limit(sizeLimit) # for reading large files
oncokbapiurl = "https://www.oncokb.org/api/v1"
oncokbapibearertoken = ""
def setoncokbbaseurl(u):
global oncokbapiurl
oncokbapiurl = u.rstrip('/') + '/api/v1'
def setoncokbapitoken(t):
global oncokbapibearertoken
oncokbapibearertoken = t.strip()
cancerhotspotsbaseurl = "http://www.cancerhotspots.org"
def setcancerhotspotsbaseurl(u):
global cancerhotspotsbaseurl
cancerhotspotsbaseurl = u
_3dhotspotsbaseurl = "http://www.3dhotspots.org"
def set3dhotspotsbaseurl(u):
global _3dhotspotsbaseurl
_3dhotspotsbaseurl = u
sampleidsfilter = None
def setsampleidsfileterfile(f):
global sampleidsfilter
content = [line.rstrip() for line in open(f)]
sampleidsfilter = set(content)
log.info(len(sampleidsfilter))
GENE_IN_ONCOKB_HEADER = 'GENE_IN_ONCOKB'
VARIANT_IN_ONCOKB_HEADER = 'VARIANT_IN_ONCOKB'
GENE_IN_ONCOKB_DEFAULT = 'False'
VARIANT_IN_ONCOKB_DEFAULT = 'False'
levels = [
'LEVEL_1',
'LEVEL_2',
'LEVEL_3A',
'LEVEL_3B',
'LEVEL_4',
'LEVEL_R1',
'LEVEL_R2',
'LEVEL_R3'
]
dxLevels = [
'LEVEL_Dx1',
'LEVEL_Dx2',
'LEVEL_Dx3'
]
pxLevels = [
'LEVEL_Px1',
'LEVEL_Px2',
'LEVEL_Px3'
]
mutationtypeconsequencemap = {
'3\'Flank': ['any'],
'5\'Flank ': ['any'],
'Targeted_Region': ['inframe_deletion', 'inframe_insertion'],
'COMPLEX_INDEL': ['inframe_deletion', 'inframe_insertion'],
'ESSENTIAL_SPLICE_SITE': ['feature_truncation'],
'Exon skipping': ['inframe_deletion'],
'Frameshift deletion': ['frameshift_variant'],
'Frameshift insertion': ['frameshift_variant'],
'FRAMESHIFT_CODING': ['frameshift_variant'],
'Frame_Shift_Del': ['frameshift_variant'],
'Frame_Shift_Ins': ['frameshift_variant'],
'Fusion': ['fusion'],
'Indel': ['frameshift_variant', 'inframe_deletion', 'inframe_insertion'],
'In_Frame_Del': ['inframe_deletion'],
'In_Frame_Ins': ['inframe_insertion'],
'Missense': ['missense_variant'],
'Missense_Mutation': ['missense_variant'],
'Nonsense_Mutation': ['stop_gained'],
'Nonstop_Mutation': ['stop_lost'],
'Splice_Site': ['splice_region_variant'],
'Splice_Site_Del': ['splice_region_variant'],
'Splice_Site_SNP': ['splice_region_variant'],
'splicing': ['splice_region_variant'],
'Translation_Start_Site': ['start_lost'],
'vIII deletion': ['any']
}
# column headers
HUGO_HEADERS = ['HUGO_SYMBOL', 'HUGO_GENE_SYMBOL', 'GENE']
CONSEQUENCE_HEADERS = ['VARIANT_CLASSIFICATION', 'MUTATION_TYPE']
ALTERATION_HEADER = 'ALTERATION'
HGVSP_SHORT_HEADER = 'HGVSP_SHORT'
HGVSP_HEADER = 'HGVSP'
HGVSG_HEADER = 'HGVSG'
HGVS_HEADERS = [ALTERATION_HEADER, HGVSP_SHORT_HEADER, HGVSP_HEADER, HGVSG_HEADER, 'AMINO_ACID_CHANGE', 'FUSION']
SAMPLE_HEADERS = ['SAMPLE_ID', 'TUMOR_SAMPLE_BARCODE']
PROTEIN_START_HEADERS = ['PROTEIN_START']
PROTEIN_END_HEADERS = ['PROTEIN_END']
PROTEIN_POSITION_HEADERS = ['PROTEIN_POSITION']
CANCER_TYPE_HEADERS = ['ONCOTREE_CODE', 'CANCER_TYPE']
FUSION_HEADERS = ['FUSION']
REFERENCE_GENOME_HEADERS = ['NCBI_BUILD', 'REFERENCE_GENOME']
# columns for genomic change annotation
GC_CHROMOSOME_HEADER = 'CHROMOSOME'
GC_START_POSITION_HEADER = 'START_POSITION'
GC_END_POSITION_HEADER = 'END_POSITION'
GC_REF_ALLELE_HEADER = 'REFERENCE_ALLELE'
GC_VAR_ALLELE_1_HEADER = 'TUMOR_SEQ_ALLELE1'
GC_VAR_ALLELE_2_HEADER = 'TUMOR_SEQ_ALLELE2'
GENOMIC_CHANGE_HEADERS = [GC_CHROMOSOME_HEADER, GC_START_POSITION_HEADER, GC_END_POSITION_HEADER, GC_REF_ALLELE_HEADER, GC_VAR_ALLELE_1_HEADER, GC_VAR_ALLELE_2_HEADER]
class QueryType(Enum):
HGVSP_SHORT = 'HGVSP_SHORT'
HGVSP = 'HGVSP'
HGVSG = 'HGVSG'
GENOMIC_CHANGE = 'GENOMIC_CHANGE'
class ReferenceGenome(Enum):
GRCH37 = 'GRCh37'
GRCH38 = 'GRCh38'
REQUIRED_QUERY_TYPE_COLUMNS = {
QueryType.HGVSP_SHORT: [HGVSP_SHORT_HEADER],
QueryType.HGVSP: [HGVSP_HEADER],
QueryType.HGVSG: [HGVSG_HEADER],
QueryType.GENOMIC_CHANGE: GENOMIC_CHANGE_HEADERS
}
POST_QUERIES_THRESHOLD = 1000
POST_QUERIES_THRESHOLD_GC_HGVSG = 100
def getOncokbInfo():
ret = ['Files annotated on ' + date.today().strftime('%m/%d/%Y') + "\nOncoKB API URL: "+oncokbapiurl]
try:
info = requests.get(oncokbapiurl + "/info", timeout=REQUEST_TIMEOUT).json()
ret.append('\nOncoKB data version: ' + info['dataVersion']['version']+', released on ' + info['dataVersion']['date'])
except:
log.error("error when fetch OncoKB info")
return ''.join(ret)
def generateReadme(outfile):
outf = open(outfile, 'w+', 1000)
outf.write(getOncokbInfo())
outf.close()
def gethotspots(url, type):
hotspots = {}
response = requests.get(url, timeout=REQUEST_TIMEOUT)
if response.status_code == 200:
hotspotsjson = response.json()
for hs in hotspotsjson:
gene = hs['hugoSymbol']
start = hs['aminoAcidPosition']['start']
end = hs['aminoAcidPosition']['end']
if type is None or hs['type'] == type:
if gene not in hotspots:
hotspots[gene] = set()
for i in range(start, end + 1):
hotspots[gene].add(i)
else:
log.error("error when processing %s \n" % url +
"reason: %s" % response.reason)
return hotspots
def makeoncokbpostrequest(url, body):
headers = {
'Content-Type': 'application/json',
'Authorization': 'Bearer %s' % oncokbapibearertoken
}
return requests.post(url, headers=headers, data=json.dumps(body, default=lambda o: o.__dict__),
timeout=REQUEST_TIMEOUT)
def makeoncokbgetrequest(url):
headers = {
'Content-Type': 'application/json',
'Authorization': 'Bearer %s' % oncokbapibearertoken
}
return requests.get(url, headers=headers, timeout=REQUEST_TIMEOUT)
_3dhotspots = None
def init_3d_hotspots():
global _3dhotspots
_3dhotspots = gethotspots(_3dhotspotsbaseurl+"/api/hotspots/3d", None)
conversiondict = {'Ala': 'A',
'Asx': 'B',
'Cys': 'C',
'Asp': 'D',
'Glu': 'E',
'Phe': 'F',
'Gly': 'G',
'His': 'H',
'Ile': 'I',
'Lys': 'K',
'Leu': 'L',
'Met': 'M',
'Asn': 'N',
'Pro': 'P',
'Gln': 'Q',
'Arg': 'R',
'Ser': 'S',
'Thr': 'T',
'Val': 'V',
'Trp': 'W',
'Tyr': 'Y',
'Glx': 'Z'
}
conversionlist = conversiondict.keys()
def conversion(hgvs):
threecharactersearch = re.findall('[a-zA-Z]{3}\d+', hgvs, flags=re.IGNORECASE)
if threecharactersearch:
if any(letters.lower() in hgvs.lower() for letters in conversionlist):
return replace_all(hgvs)
return hgvs
def replace_all(hgvs):
# Author: Thomas Glaessle
pattern = re.compile('|'.join(conversionlist), re.IGNORECASE)
return pattern.sub(lambda m: conversiondict[m.group().capitalize()], hgvs)
def append_annotation_to_file(outf, ncols, rows, annotations):
if len(rows) != len(annotations):
log.error('The length of the rows and annotations do not match')
for index, annotation in enumerate(annotations):
row = rows[index]
if annotation is not None:
row = row + annotation
row = padrow(row, ncols)
rowstr = '\t'.join(row)
rowstr = rowstr.encode('ascii', 'ignore').decode('ascii')
outf.write(rowstr + "\n")
def get_tumor_type_from_row(row, row_index, defaultCancerType, icancertype, cancerTypeMap, sample):
cancertype = defaultCancerType
if icancertype >= 0:
row_cancer_type = get_cell_content(row, icancertype)
if row_cancer_type is not None:
cancertype = row_cancer_type
if sample in cancerTypeMap:
cancertype = cancerTypeMap[sample]
if cancertype == "":
log.info("Cancer type for the sample should be defined for a more accurate result\nline %s: %s\n" % (row_index, row))
# continue
return cancertype
def has_desired_headers(desired_headers, file_headers):
has_required_headers = True
for header in desired_headers:
if header not in file_headers:
has_required_headers = False
break
return has_required_headers
def resolve_query_type(user_input_query_type, headers):
selected_query_type = None
if isinstance(user_input_query_type, QueryType):
selected_query_type = user_input_query_type
if selected_query_type is None and HGVSP_SHORT_HEADER in headers:
selected_query_type = QueryType.HGVSP_SHORT
if selected_query_type is None and HGVSP_HEADER in headers:
selected_query_type = QueryType.HGVSP
if selected_query_type is None and HGVSG_HEADER in headers:
selected_query_type = QueryType.HGVSG
if selected_query_type is None and has_desired_headers(REQUIRED_QUERY_TYPE_COLUMNS[QueryType.GENOMIC_CHANGE], headers):
selected_query_type = QueryType.GENOMIC_CHANGE
# default to HGVSp_Short
if selected_query_type is None:
selected_query_type = QueryType.HGVSP_SHORT
# check the file has required columns
if has_desired_headers(REQUIRED_QUERY_TYPE_COLUMNS[selected_query_type], headers) == False:
# when it is False, it will never be GENOMIC_CHANGE. For other types, we need to check whether ALTERATION column is available
if ALTERATION_HEADER not in headers:
raise Exception("The file does not have required columns "
+ ', '.join(REQUIRED_QUERY_TYPE_COLUMNS[user_input_query_type])
+ " for the query type: " + user_input_query_type.value)
return selected_query_type
def get_reference_genome_from_row(row_reference_genome, default_reference_genome):
reference_genome = default_reference_genome
if row_reference_genome is not None and row_reference_genome != '':
try:
reference_genome = ReferenceGenome[row_reference_genome.upper()]
except KeyError:
log.warning('Unexpected reference genome, only GRCh37 and GRCh38 are supported.' + (
' Use default.' if default_reference_genome is not None else ' Skipping.'))
return reference_genome
def processalterationevents(eventfile, outfile, previousoutfile, defaultCancerType, cancerTypeMap,
annotatehotspots, user_input_query_type, default_reference_genome):
if annotatehotspots:
init_3d_hotspots()
if os.path.isfile(previousoutfile):
cacheannotated(previousoutfile, defaultCancerType, cancerTypeMap)
outf = open(outfile, 'w+', 1000)
with open(eventfile, 'rU') as infile:
reader = csv.reader(infile, delimiter='\t')
headers = readheaders(reader)
ncols = headers["length"]
if ncols == 0:
return
newncols = 0
outf.write(headers['^-$'])
if annotatehotspots:
outf.write("\tIS-A-HOTSPOT")
outf.write("\tIS-A-3D-HOTSPOT")
newncols += 2
outf.write("\t" + GENE_IN_ONCOKB_HEADER)
outf.write("\t" + VARIANT_IN_ONCOKB_HEADER)
outf.write("\tMUTATION_EFFECT")
outf.write("\tMUTATION_EFFECT_CITATIONS")
outf.write("\tONCOGENIC")
newncols += 5
for l in levels:
outf.write('\t' + l)
newncols += len(levels)
outf.write("\tHIGHEST_LEVEL")
outf.write("\tTX_CITATIONS")
newncols += 2
for l in dxLevels:
outf.write('\t' + l)
newncols += len(dxLevels)
outf.write("\tHIGHEST_DX_LEVEL")
outf.write("\tDX_CITATIONS")
newncols += 2
for l in pxLevels:
outf.write('\t' + l)
newncols += len(pxLevels)
outf.write("\tHIGHEST_PX_LEVEL")
outf.write("\tPX_CITATIONS")
newncols += 2
outf.write("\n")
query_type = resolve_query_type(user_input_query_type, headers)
if (query_type == QueryType.HGVSP_SHORT):
process_alteration(reader, outf, headers, [HGVSP_SHORT_HEADER, ALTERATION_HEADER], ncols, newncols,
defaultCancerType,
cancerTypeMap, annotatehotspots, default_reference_genome)
if (query_type == QueryType.HGVSP):
process_alteration(reader, outf, headers, [HGVSP_HEADER, ALTERATION_HEADER], ncols, newncols, defaultCancerType,
cancerTypeMap, annotatehotspots, default_reference_genome)
if (query_type == QueryType.HGVSG):
process_hvsg(reader, outf, headers, [HGVSG_HEADER, ALTERATION_HEADER], ncols, newncols, defaultCancerType,
cancerTypeMap, annotatehotspots, default_reference_genome)
if (query_type == QueryType.GENOMIC_CHANGE):
process_genomic_change(reader, outf, headers, ncols, newncols, defaultCancerType, cancerTypeMap, annotatehotspots, default_reference_genome)
outf.close()
def get_cell_content(row, index, return_empty_string=False):
if index >= 0 and row[index] != 'NULL' and row[index] != '':
return row[index]
elif return_empty_string:
return ''
else:
return None
def process_alteration(maffilereader, outf, maf_headers, alteration_column_names, ncols, nannotationcols, defaultCancerType, cancerTypeMap,
annotatehotspots, default_reference_genome):
ihugo = geIndexOfHeader(maf_headers, HUGO_HEADERS)
iconsequence = geIndexOfHeader(maf_headers, CONSEQUENCE_HEADERS)
ihgvs = geIndexOfHeader(maf_headers, alteration_column_names)
isample = geIndexOfHeader(maf_headers, SAMPLE_HEADERS)
istart = geIndexOfHeader(maf_headers, PROTEIN_START_HEADERS)
iend = geIndexOfHeader(maf_headers, PROTEIN_END_HEADERS)
iproteinpos = geIndexOfHeader(maf_headers, PROTEIN_POSITION_HEADERS)
icancertype = geIndexOfHeader(maf_headers, CANCER_TYPE_HEADERS)
ireferencegenome= geIndexOfHeader(maf_headers, REFERENCE_GENOME_HEADERS)
posp = re.compile('[0-9]+')
i = 0
queries = []
rows = []
for row in maffilereader:
i = i + 1
if i % POST_QUERIES_THRESHOLD == 0:
log.info(i)
row = padrow(row, ncols)
sample = row[isample]
if sampleidsfilter and sample not in sampleidsfilter:
continue
hugo = row[ihugo]
consequence = get_cell_content(row, iconsequence)
if consequence in mutationtypeconsequencemap:
consequence = '%2B'.join(mutationtypeconsequencemap[consequence])
hgvs = row[ihgvs]
if hgvs.startswith('p.'):
hgvs = hgvs[2:]
cancertype = get_tumor_type_from_row(row, i, defaultCancerType, icancertype, cancerTypeMap, sample)
reference_genome = get_reference_genome_from_row(get_cell_content(row, ireferencegenome), default_reference_genome)
hgvs = conversion(hgvs)
start = get_cell_content(row, istart)
end = get_cell_content(row, iend)
if start is None and iproteinpos >= 0 and row[iproteinpos] != "" and row[iproteinpos] != "." and row[iproteinpos] != "-":
poss = row[iproteinpos].split('/')[0].split('-')
try:
if len(poss) > 0:
start = int(poss[0])
if len(poss) == 2:
end = int(poss[1])
except ValueError:
log.info("position wrong at line %s: %s" % (str(i), row[iproteinpos]))
if start is None and consequence == "missense_variant":
m = posp.search(hgvs)
if m:
start = m.group()
if start is not None and end is None:
end = start
query = ProteinChangeQuery(hugo, hgvs, cancertype, reference_genome, consequence, start, end)
queries.append(query)
rows.append(row)
if len(queries) == POST_QUERIES_THRESHOLD:
annotations = pull_protein_change_info(queries,annotatehotspots)
append_annotation_to_file(outf, ncols + nannotationcols, rows, annotations)
queries = []
rows = []
if len(queries) > 0:
annotations = pull_protein_change_info(queries,annotatehotspots)
append_annotation_to_file(outf, ncols + nannotationcols, rows, annotations)
# this method is from genome-nexus annotation-tools
# https://github.com/genome-nexus/annotation-tools/blob/53ff7f7fe673e961282f871ebc78d2ecc0831919/standardize_mutation_data.py
def get_var_allele(ref_allele, tumor_seq_allele1, tumor_seq_allele2):
# set the general tumor_seq_allele as the first non-ref allele encountered
# this will be used to resolve the variant classification and variant type
# if there are no tumor alleles that do not match the ref allele then use empty string
# in the event that this happens then there might be something wrong with the data itself
# if both alleles are different, use allele2. Stick with the logic of GenomeNexus
try:
tumor_seq_allele = ""
if ref_allele != tumor_seq_allele2:
tumor_seq_allele = tumor_seq_allele2
elif ref_allele != tumor_seq_allele1:
tumor_seq_allele = tumor_seq_allele1
except:
tumor_seq_allele = ""
return tumor_seq_allele
def process_genomic_change(maffilereader, outf, maf_headers, ncols, nannotationcols, defaultCancerType, cancerTypeMap, annotatehotspots, default_reference_genome):
ichromosome = geIndexOfHeader(maf_headers, [GC_CHROMOSOME_HEADER])
istart = geIndexOfHeader(maf_headers, [GC_START_POSITION_HEADER])
iend = geIndexOfHeader(maf_headers, [GC_END_POSITION_HEADER])
irefallele = geIndexOfHeader(maf_headers, [GC_REF_ALLELE_HEADER])
ivarallele1 = geIndexOfHeader(maf_headers, [GC_VAR_ALLELE_1_HEADER])
ivarallele2 = geIndexOfHeader(maf_headers, [GC_VAR_ALLELE_2_HEADER])
isample = geIndexOfHeader(maf_headers, SAMPLE_HEADERS)
icancertype = geIndexOfHeader(maf_headers, CANCER_TYPE_HEADERS)
ireferencegenome= geIndexOfHeader(maf_headers, REFERENCE_GENOME_HEADERS)
posp = re.compile('[0-9]+')
i = 0
queries = []
rows = []
for row in maffilereader:
i = i + 1
if i % POST_QUERIES_THRESHOLD_GC_HGVSG == 0:
log.info(i)
row = padrow(row, ncols)
sample = row[isample]
if sampleidsfilter and sample not in sampleidsfilter:
continue
cancertype = get_tumor_type_from_row(row, i, defaultCancerType, icancertype, cancerTypeMap, sample)
reference_genome = get_reference_genome_from_row(get_cell_content(row, ireferencegenome), default_reference_genome)
chromosome = get_cell_content(row, ichromosome, True)
start = get_cell_content(row, istart, True)
end = get_cell_content(row, iend, True)
ref_allele = get_cell_content(row, irefallele, True)
var_allele_1 = get_cell_content(row, ivarallele1, True)
var_allele_2 = get_cell_content(row, ivarallele2, True)
var_allele = get_var_allele(ref_allele, var_allele_1, var_allele_2)
query = GenomicChangeQuery(chromosome, start, end, ref_allele, var_allele, cancertype, reference_genome)
queries.append(query)
rows.append(row)
if len(queries) == POST_QUERIES_THRESHOLD_GC_HGVSG:
annotations = pull_genomic_change_info(queries,annotatehotspots)
append_annotation_to_file(outf, ncols+nannotationcols, rows, annotations)
queries = []
rows = []
if len(queries) > 0:
annotations = pull_genomic_change_info(queries,annotatehotspots)
append_annotation_to_file(outf, ncols+nannotationcols, rows, annotations)
def process_hvsg(maffilereader, outf, maf_headers, alteration_column_names, ncols, nannotationcols, defaultCancerType, cancerTypeMap, annotatehotspots, default_reference_genome):
ihgvsg = geIndexOfHeader(maf_headers, alteration_column_names)
isample = geIndexOfHeader(maf_headers, SAMPLE_HEADERS)
icancertype = geIndexOfHeader(maf_headers, CANCER_TYPE_HEADERS)
ireferencegenome= geIndexOfHeader(maf_headers, REFERENCE_GENOME_HEADERS)
i = 0
queries = []
rows = []
for row in maffilereader:
i = i + 1
if i % POST_QUERIES_THRESHOLD_GC_HGVSG == 0:
log.info(i)
row = padrow(row, ncols)
sample = row[isample]
if sampleidsfilter and sample not in sampleidsfilter:
continue
hgvsg = get_cell_content(row, ihgvsg)
cancertype = get_tumor_type_from_row(row, i, defaultCancerType, icancertype, cancerTypeMap, sample)
reference_genome = get_reference_genome_from_row(get_cell_content(row, ireferencegenome), default_reference_genome)
if hgvsg is None:
if annotatehotspots:
default_cols = [['', '', GENE_IN_ONCOKB_DEFAULT, VARIANT_IN_ONCOKB_DEFAULT]]
else:
default_cols = [[GENE_IN_ONCOKB_DEFAULT, VARIANT_IN_ONCOKB_DEFAULT]]
append_annotation_to_file(outf, ncols + nannotationcols, [row],
default_cols)
else:
query = HGVSgQuery(hgvsg, cancertype, reference_genome)
queries.append(query)
rows.append(row)
if len(queries) == POST_QUERIES_THRESHOLD_GC_HGVSG:
annotations = pull_hgvsg_info(queries, annotatehotspots)
append_annotation_to_file(outf, ncols+nannotationcols, rows, annotations)
queries = []
rows = []
if len(queries) > 0:
annotations = pull_hgvsg_info(queries,annotatehotspots)
append_annotation_to_file(outf, ncols+nannotationcols, rows, annotations)
def getgenesfromfusion(fusion, nameregex=None):
GENES_REGEX = "([A-Za-z\d]+-[A-Za-z\d]+)" if nameregex is None else nameregex
searchresult = re.search(GENES_REGEX, fusion, flags=re.IGNORECASE)
gene1=None
gene2=None
if searchresult:
parts = searchresult.group(1).split("-")
gene1 = parts[0]
gene2 = gene1
if len(parts) > 1 and parts[1] != "intragenic":
gene2 = parts[1]
else:
gene1=gene2=fusion
return gene1, gene2
def processsv(svdata, outfile, previousoutfile, defaultCancerType, cancerTypeMap, nameregex):
if os.path.isfile(previousoutfile):
cacheannotated(previousoutfile, defaultCancerType, cancerTypeMap)
outf = open(outfile, 'w+')
with open(svdata, 'rU') as infile:
reader = csv.reader(infile, delimiter='\t')
headers = readheaders(reader)
ncols = headers["length"]
if ncols == 0:
return
outf.write(headers['^-$'])
outf.write("\t" + GENE_IN_ONCOKB_HEADER)
outf.write("\t" + VARIANT_IN_ONCOKB_HEADER)
outf.write("\tMUTATION_EFFECT")
outf.write("\tONCOGENIC")
for l in levels:
outf.write('\t' + l)
outf.write("\tHIGHEST_LEVEL")
outf.write("\tCITATIONS")
for l in dxLevels:
outf.write('\t' + l)
outf.write("\tHIGHEST_DX_LEVEL")
for l in pxLevels:
outf.write('\t' + l)
outf.write("\tHIGHEST_PX_LEVEL\n")
newcols = ncols + 8 + len(levels) + len(dxLevels) + len(pxLevels)
igene1 = geIndexOfHeader(headers, ['GENE1'])
igene2 = geIndexOfHeader(headers, ['GENE2'])
ifusion = geIndexOfHeader(headers, FUSION_HEADERS)
isample = geIndexOfHeader(headers, SAMPLE_HEADERS)
icancertype = geIndexOfHeader(headers, CANCER_TYPE_HEADERS)
i = 0
queries = []
rows = []
for row in reader:
i = i + 1
if i % POST_QUERIES_THRESHOLD == 0:
log.info(i)
row = padrow(row, ncols)
sample = row[isample]
if sampleidsfilter and sample not in sampleidsfilter:
continue
gene1 = None
gene2 = None
if igene1 >= 0:
gene1 = row[igene1]
if igene2 >= 0:
gene2 = row[igene2]
if igene1 < 0 and igene2 < 0 and ifusion >= 0:
fusion = row[ifusion]
gene1, gene2 = getgenesfromfusion(fusion, nameregex)
cancertype = get_tumor_type_from_row(row, i, defaultCancerType, icancertype, cancerTypeMap, sample)
queries.append(StructuralVariantQuery(gene1, gene2, 'FUSION', cancertype))
rows.append(row)
if len(queries) == POST_QUERIES_THRESHOLD:
annotations = pull_structural_variant_info(queries)
append_annotation_to_file(outf, newcols, rows, annotations)
queries = []
rows = []
if len(queries) > 0:
annotations = pull_structural_variant_info(queries)
append_annotation_to_file(outf, newcols, rows, annotations)
outf.close()
def processcnagisticdata(cnafile, outfile, previousoutfile, defaultCancerType, cancerTypeMap, annotate_gain_loss=False):
CNA_AMPLIFICATION_TXT = 'Amplification'
CNA_DELETION_TXT = 'Deletion'
CNA_LOSS_TXT = 'Loss'
CNA_GAIN_TXT = 'Gain'
cnaEventMap = {
"-2": CNA_DELETION_TXT,
"-1.5": CNA_DELETION_TXT,
"2": CNA_AMPLIFICATION_TXT
}
if annotate_gain_loss:
cnaEventMap.update({
"-1": CNA_LOSS_TXT,
"1": CNA_GAIN_TXT
})
if os.path.isfile(previousoutfile):
cacheannotated(previousoutfile, defaultCancerType, cancerTypeMap)
outf = open(outfile, 'w+', 1000)
with open(cnafile, 'rU') as infile:
reader = csv.reader(infile, delimiter='\t')
headers = readheaders(reader)
samples = []
rawsamples = []
if headers["length"] != 0:
startofsamples = getfirstcolumnofsampleingisticdata(headers['^-$'].split('\t'))
rawsamples = headers['^-$'].split('\t')[startofsamples:]
for rs in rawsamples:
samples.append(rs)
if defaultCancerType == '' and not set(cancerTypeMap.keys()).issuperset(set(samples)):
log.info(
"Cancer type for all samples should be defined for a more accurate result\nsamples in cna file: %s\n" % (
samples))
outf.write('SAMPLE_ID\tCANCER_TYPE\tHUGO_SYMBOL\tALTERATION')
outf.write("\t"+GENE_IN_ONCOKB_HEADER)
outf.write("\t"+VARIANT_IN_ONCOKB_HEADER)
outf.write("\tMUTATION_EFFECT")
outf.write("\tONCOGENIC")
for l in levels:
outf.write('\t' + l)
outf.write("\tHIGHEST_LEVEL")
outf.write("\tCITATIONS")
for l in dxLevels:
outf.write('\t' + l)
outf.write("\tHIGHEST_DX_LEVEL")
for l in pxLevels:
outf.write('\t' + l)
outf.write("\tHIGHEST_PX_LEVEL\n")
ncols = 12 + len(levels) + len(dxLevels) + len(pxLevels)
i = 0
rows = []
queries = []
for row in reader:
i = i + 1
if i % POST_QUERIES_THRESHOLD == 0:
log.info(i)
hugo = row[0]
if len(row) == 1:
log.warning("No CNA specified for gene " + hugo)
continue
for rawsample in rawsamples:
if rawsample in headers:
if len(row) <= headers[rawsample]:
log.warning('No CNA specified for ' + row[0] + ' ' + rawsample)
continue
cna = row[headers[rawsample]]
if cna in cnaEventMap:
cna_type = cnaEventMap[cna]
if cna_type is not None:
cancertype = defaultCancerType
sample = rawsample
if sampleidsfilter and sample not in sampleidsfilter:
continue
if sample in cancerTypeMap:
cancertype = cancerTypeMap[sample]
rows.append([sample, cancertype, hugo, cna_type])
queries.append(CNAQuery(hugo, cna_type, cancertype))
if len(queries) == POST_QUERIES_THRESHOLD:
annotations = pull_cna_info(queries)
append_annotation_to_file(outf, ncols, rows, annotations)
rows = []
queries = []
if len(queries) > 0:
annotations = pull_cna_info(queries)
append_annotation_to_file(outf, ncols, rows, annotations)
outf.close()
def getfirstcolumnofsampleingisticdata(headers):
header0 = headers[0].lower()
if header0 != "hugo_symbol" and header0 != "gene symbol":
log.info("Gistic data should start with Hugo_Symbol")
quit()
header1 = headers[1].lower()
if header1 != "entrez_gene_id" and header1 != "locus id":
return 1
header2 = headers[2].lower()
if header2 != "cytoband":
return 2
return 3
def file_len(fname):
with open(fname) as f:
for i, l in enumerate(f):
pass
return i + 1
def processclinicaldata(annotatedmutfiles, clinicalfile, outfile):
samplelevels = {}
sampledxlevels = {}
samplepxlevels = {}
sampleleveltreatments = {}
sampledrivers = {}
samplemutationswithdiagnosis = {}
samplemutationswithprognosis = {}
sampleactionablecount = {}
samplealterationcount = {}
for annotatedmutfile in annotatedmutfiles:
with open(annotatedmutfile, 'rU') as mutfile:
reader = csv.reader(mutfile, delimiter='\t')
headers = readheaders(reader)
ncols = headers["length"]
if ncols == 0:
return
igene1 = geIndexOfHeader(headers, ['GENE1'] + HUGO_HEADERS) # fusion
igene2 = geIndexOfHeader(headers, ['GENE2'] + HUGO_HEADERS) # fusion
ifusion = geIndexOfHeader(headers, ['FUSION'])
ihugo = geIndexOfHeader(headers, HUGO_HEADERS)
iconsequence = geIndexOfHeader(headers, CONSEQUENCE_HEADERS)
ihgvs = geIndexOfHeader(headers, HGVS_HEADERS)
isample = geIndexOfHeader(headers, SAMPLE_HEADERS)
istart = geIndexOfHeader(headers, PROTEIN_START_HEADERS)
iend = geIndexOfHeader(headers, PROTEIN_END_HEADERS)
icancertype = geIndexOfHeader(headers, CANCER_TYPE_HEADERS)
# imutationeffect = headers['MUTATION_EFFECT']
ioncogenic = headers['ONCOGENIC']
isfusion = (igene1 != -1 & igene2 != -1) or ifusion != -1
ismutorcna = ihugo != -1 & ihgvs != -1
if not isfusion and not ismutorcna:
log.error("missing proper header")
exit()
for row in reader:
row = padrow(row, ncols)
sample = row[isample]
oncogenic = ""
if ioncogenic < len(row):
oncogenic = row[ioncogenic].lower()
if sample not in samplelevels:
samplelevels[sample] = {}
sampledxlevels[sample] = []
samplepxlevels[sample] = []
sampleleveltreatments[sample] = {}
sampledrivers[sample] = []
sampleactionablecount[sample] = {}
if sample not in samplemutationswithdiagnosis:
samplemutationswithdiagnosis[sample] = []
if sample not in samplemutationswithprognosis:
samplemutationswithprognosis[sample] = []
if sample not in samplealterationcount:
samplealterationcount[sample] = 1
else:
samplealterationcount[sample] += 1
hugo = row[ihugo]
alteration = row[ihgvs]
gene1 = row[igene1]
gene2 = row[igene2]
variant = "NA"
if ismutorcna:
variant = hugo + " " + alteration
elif isfusion:
if ifusion != -1:
variant = row[ifusion]
else:
if gene1 == gene2:
variant = gene1 + " intragenic deletion"
else:
variant = gene1 + "-" + gene2 + " fusion"
if oncogenic == "oncogenic" or oncogenic == "likely oncogenic" or oncogenic == "predicted oncogenic":
sampledrivers[sample].append(variant)
for l in levels:
il = headers[l]
if il < len(row) and row[il] != '':
if l not in samplelevels[sample]:
samplelevels[sample][l] = []
sampleleveltreatments[sample][l] = []
samplelevels[sample][l].append(row[il] + "(" + variant + ")")
sampleleveltreatments[sample][l].extend(row[il].split(","))
if not l.startswith('LEVEL_R'):
sampleactionablecount[sample][variant] = True
for l in dxLevels:
il = headers[l]
if il < len(row) and row[il] != '':
if l not in samplelevels[sample]:
samplelevels[sample][l] = []
samplelevels[sample][l].append(row[il] + "(" + variant + ")")
for l in pxLevels:
il = headers[l]
if il < len(row) and row[il] != '':
if l not in samplelevels[sample]:
samplelevels[sample][l] = []
samplelevels[sample][l].append(row[il] + "(" + variant + ")")
ihighestdxlevel = geIndexOfHeader(headers, ['HIGHEST_DX_LEVEL'])
if ihighestdxlevel != -1:
if row[ihighestdxlevel] != '':
samplemutationswithdiagnosis[sample].append(variant)
sampledxlevels[sample].append(row[ihighestdxlevel])
ihighestpxlevel = geIndexOfHeader(headers, ['HIGHEST_PX_LEVEL'])
if ihighestpxlevel != -1:
if row[ihighestpxlevel] != '':
samplemutationswithprognosis[sample].append(variant)
samplepxlevels[sample].append(row[ihighestpxlevel])
outf = open(outfile, 'w+')
# export to anntoated file
with open(clinicalfile, 'rU') as clinfile:
reader = csv.reader(clinfile, delimiter='\t')
headers = readheaders(reader)
outf.write(headers['^-$'])
for l in levels:
outf.write('\t' + l)
outf.write('\tHIGHEST_LEVEL')
for l in dxLevels:
outf.write('\t' + l)
outf.write('\tHIGHEST_DX_LEVEL')
for l in pxLevels:
outf.write('\t' + l)
outf.write('\tHIGHEST_PX_LEVEL')
outf.write('\tONCOGENIC_MUTATIONS\t#ONCOGENIC_MUTATIONS\t#MUTATIONS_WITH_THERAPEUTIC_IMPLICATIONS\t#MUTATIONS_WITH_DIAGNOSTIC_IMPLICATIONS\t#MUTATIONS_WITH_PROGNOSTIC_IMPLICATIONS\t#MUTATIONS\n')
isample = headers['SAMPLE_ID']