def get_cadd(mut, path='/u/sshuai/sshuai/func_score/cadd/v1.3'):
''' Get CADD scores with tabix
'''
# make chrom string
mut['chrom'] = mut.chrom.astype(str)
# create return table
# SNP only. TO DO: ADD MNP and INDEL Support
keep = mut['type'].isin(['SNP', 'DEL', 'INS'])
cadd = mut[keep].copy()
if cadd.shape[0] == 0:
logger.warning('No mutations left in CADD adjustment')
return None
logger.info('Retrieving CADD SNP Scores')
# name for version 1.3.
# A single file for SNP.
# Use pre-computed PCAWG indel scores
snp = 'whole_genome_SNVs.tsv.gz'
indel = 'PCAWG.INDELS.CADD.v1.3.tsv.gz'
snp_path = os.path.join(path, snp)
indel_path = os.path.join(path, indel)
assert os.path.isfile(
snp_path), 'Cannot find CADD SNP scores in {}'.format(snp_path)
assert os.path.isfile(
indel_path), 'Cannot find CADD PCAWG indel scores in {}'.format(
indel_path)
# open one CADD
tb_snp = tabix.open(snp_path)
tb_indel = tabix.open(indel_path)
# row apply
func = lambda x: query_cadd(x[3], tb_snp, tb_indel, x[0], x[1], x[2], x[4],
x[5])
cadd['fscore'] = cadd.apply(func, axis=1)
return cadd
def get_variants_by_tabix(sample_vcf,
contig=None,
start=None,
end=None,
query_str=None,
reference_vcf=None):
"""
:param sample_vcf: str or pytabix handler;
:param contig: str;
:param start: int;
:param end: int;
:param query_str: int;
:param reference_vcf: str or pytabix handler;
:return: list; list of dict
"""
if isinstance(sample_vcf, str): # Open sample VCF
sample_vcf = tabix.open(sample_vcf)
if query_str:
records = sample_vcf.querys(query_str)
else:
records = sample_vcf.query(contig, start, end)
if reference_vcf and len(
list(records)
) == 0: # If sample does not have the record, query reference if given
if isinstance(reference_vcf, str): # Open reference VCF
reference_vcf = tabix.open(reference_vcf)
records = reference_vcf.query(contig, start - 1, end)
return [parse_variant(r) for r in records]
def __init__(self, input_path, blacklist_regions=None, bases_order=None):
"""
Constructs a `Genome` object.
"""
self.genome = pyfaidx.Fasta(input_path)
self.chrs = sorted(self.genome.keys())
self.len_chrs = self._get_len_chrs()
self._blacklist_tabix = None
if blacklist_regions == "hg19":
self._blacklist_tabix = tabix.open(
pkg_resources.resource_filename(
"selene_sdk",
"sequences/data/hg19_blacklist_ENCFF001TDO.bed.gz"))
elif blacklist_regions == "hg38":
self._blacklist_tabix = tabix.open(
pkg_resources.resource_filename(
"selene_sdk", "sequences/data/hg38.blacklist.bed.gz"))
elif blacklist_regions is not None: # user-specified file
self._blacklist_tabix = tabix.open(blacklist_regions)
if bases_order is not None:
bases = [str.upper(b) for b in bases_order]
self.BASES_ARR = bases
lc_bases = [str.lower(b) for b in bases]
self.BASE_TO_INDEX = {
**{b: ix
for (ix, b) in enumerate(bases)},
**{b: ix
for (ix, b) in enumerate(lc_bases)}
}
self.INDEX_TO_BASE = {ix: b for (ix, b) in enumerate(bases)}
self.update_bases_order(bases)
def init_db(
self,
gene_file="/datd/huboqiang/test_hESC/database/refGene.up2000_down2000.promoter.Bsorted.longestTid.bed",
motifBed_file="/data/Analysis/huboqiang/software/encode-motifs-v1.3/matches.txt.gz"
):
""" reference file used. """
self.file_geneTSS_tb = tabix.open(gene_file)
self.file_motifBed_tb = tabix.open(motifBed_file)
def _unpicklable_init(self):
if not self.initialized:
self.genome = pyfaidx.Fasta(self.input_path)
self.chrs = sorted(self.genome.keys())
self.len_chrs = self._get_len_chrs()
self._blacklist_tabix = None
if self.blacklist_regions == "hg19":
self._blacklist_tabix = tabix.open(
pkg_resources.resource_filename(
"selene_sdk",
"sequences/data/hg19_blacklist_ENCFF001TDO.bed.gz"))
elif self.blacklist_regions == "hg38":
self._blacklist_tabix = tabix.open(
pkg_resources.resource_filename(
"selene_sdk", "sequences/data/hg38.blacklist.bed.gz"))
elif self.blacklist_regions is not None: # user-specified file
self._blacklist_tabix = tabix.open(self.blacklist_regions)
self.lens = np.array([self.len_chrs[c] for c in self.chrs])
self.inds = {
c: ind
for c, ind in zip(
self.chrs, np.concatenate([[0], np.cumsum(self.lens)]))
}
if self.memmapfile is not None and os.path.isfile(self.memmapfile):
# load memmap file
self.sequence_data = np.memmap(self.memmapfile,
dtype="float32",
mode="r")
self.sequence_data = np.reshape(
self.sequence_data,
(4, int(self.sequence_data.shape[0] / 4)))
else:
# convert all sequences into encoding
self.sequence_data = np.zeros((4, self.lens.sum()),
dtype=np.float32)
for c in self.chrs:
sequence = self.genome[c][:].seq
encoding = self.sequence_to_encoding(sequence)
self.sequence_data[:, self.inds[c]:self.inds[c] +
self.len_chrs[c]] = encoding.T
if self.memmapfile is not None:
# create memmap file
mmap = np.memmap(self.memmapfile,
dtype="float32",
mode="w+",
shape=self.sequence_data.shape)
mmap[:] = self.sequence_data
self.sequence_data = np.memmap(
self.memmapfile,
dtype="float32",
mode="r",
shape=self.sequence_data.shape)
self.initialized = True
def getJSONObject(params, positives, negatives, tbCladeSNPsFile,
tbSNPcladesFile, snpPanelConfigFile):
tbSNPclades = tabix.open(tbSNPcladesFile)
tbCladeSNPs = tabix.open(tbCladeSNPsFile)
uniqPositives = getUniqueSNPsetTabix(positives, tbSNPclades)
uniqNegatives = getUniqueSNPsetTabix(negatives, tbSNPclades)
conflicting = uniqPositives.intersection(uniqNegatives)
uniqPositives = uniqPositives.difference(conflicting)
uniqNegatives = uniqNegatives.difference(conflicting)
if len(uniqPositives) == 0:
return {"error": "unable to determine clade due to no positive SNPs"}
warning = None
if len(conflicting) > 0:
warning = "conflicting calls for same SNP with names " + ", ".join(
list(conflicting))
(ranked, hierarchy) = getRankedSolutionsScratch(uniqPositives,
uniqNegatives, tbCladeSNPs,
tbSNPclades)
if "all" in params:
result = []
for r in ranked:
clade = r[1]
score = r[4]
result.append(
decorateJSONObject(params, clade, score, uniqPositives,
uniqNegatives, tbCladeSNPs, tbSNPclades,
hierarchy, snpPanelConfigFile, conflicting,
warning))
return result
else:
if len(ranked) > 0:
clade = ranked[0][1]
score = ranked[0][4]
decorated = decorateJSONObject(params, clade, score, uniqPositives,
uniqNegatives, tbCladeSNPs,
tbSNPclades, hierarchy,
snpPanelConfigFile, conflicting,
warning)
if len(ranked) > 1 and "score" in params:
clade = ranked[1][1]
score = ranked[1][4]
decorated["nextPrediction"] = {"clade": clade, "score": score}
return decorated
else:
if len(positives) == 1:
return {
"error":
"unable to find " + list(positives)[0] +
" on the YFull tree"
}
else:
return {
"error":
"unable to find any of " + ", ".join(positives) +
" on the YFull tree"
}
def init_resource(self):
""" init features and other annotation resources """
for rname in ['dbsnp']:
if self.config.has_option(self.rv, 'dbsnp'):
import tabix
self.resources['dbsnp'] = tabix.open(self.config.get(self.rv, 'dbsnp'))
self.features = []
for rname in self.config.options(self.rv):
featdb = self.config.get(self.rv, rname)
if featdb.endswith('.featuredb'):
self.features.append((rname,tabix.open(featdb)))
def init_resource(self):
""" init features and other annotation resources """
for rname in ['dbsnp']:
if self.config.has_option(self.rv, 'dbsnp'):
import tabix
self.resources['dbsnp'] = tabix.open(self.config.get(self.rv, 'dbsnp'))
self.features = []
for rname in self.config.options(self.rv):
featdb = self.config.get(self.rv, rname)
if featdb.endswith('.featuredb'):
self.features.append((rname,tabix.open(featdb)))
def get_eigen(mut, path='/u/sshuai/sshuai/func_score/eigen/v1.1', coding=True):
''' Get eigen scores with tabix
'''
# make chrom string
mut['chrom'] = mut.chrom.astype(str)
# eigen only support chr 1-22
valid_chrom = [str(i) for i in range(1, 23)]
mut = mut[mut.chrom.isin(valid_chrom)]
# create return table
# SNP only. TO DO: ADD MNP and INDEL Support
keep = mut['type'] == 'SNP'
# chrom != X, Y
eigen = mut[keep].copy()
if eigen.shape[0] == 0:
logger.warning('No mutations left in Eigen adjustment')
return None
if coding:
logger.info('Retrieving Eigen Coding Scores')
# name for version 1.1. A single file for coding.
name = 'Eigen_hg19_coding_annot_04092016.tab.bgz'
file_path = os.path.join(path, name)
assert os.path.isfile(
file_path), 'Cannot find eigen coding in {}'.format(file_path)
# open one eigen
tb = tabix.open(file_path)
# row apply
func = lambda x: query_eigen_SNP(tb, x[0], x[1], x[2], x[4], x[5])
eigen['fscore'] = eigen.apply(func, axis=1)
else:
logger.info('Retrieving Eigen Non-Coding Scores')
# One file per chrom for non-coding. 22 in total.
file_dict = {
str(i):
os.path.join(path,
'Eigen_hg19_noncoding_annot_chr{}.tab.bgz'.format(i))
for i in range(1, 23)
}
# check files, must be 22 True
file_names = np.array(list(file_dict.values()))
check_file = np.array([os.path.isfile(f) for f in file_names])
assert np.sum(
check_file) == 22, 'Cannot find eigen noncoding in {}'.format(
", ".join(file_names[~check_file]))
# open 22 eigen files
file_dict = {k: tabix.open(v) for k, v in list(file_dict.items())}
# row apply
func = lambda x: query_eigen_SNP(file_dict[str(x[0])], x[0], x[1], x[
2], x[4], x[5])
eigen['fscore'] = eigen.apply(func, axis=1)
return eigen
def init_resource(self):
for rname in ['dbsnp']:
if self.config.has_option(self.rv, 'dbsnp'):
import tabix
self.resources['dbsnp'] = tabix.open(
self.config.get(self.rv, 'dbsnp'))
def get_job_results(job_id, job=None):
filters = request.args.to_dict()
epacts_filename = job.relative_path("output.epacts.gz")
with gzip.open(epacts_filename, "rt") as f:
header = f.readline().rstrip('\n').split('\t')
if header[1] == "BEG":
header[1] = "BEGIN"
if header[0] == "#CHROM":
header[0] = "CHROM"
assert len(header) > 0
headerpos = {x:i for i,x in enumerate(header)}
if filters.get("region", ""):
tb = tabix.open(epacts_filename)
indata = tb.query(chrom, start_pos, end_pos)
else:
indata = (x.split("\t") for x in gzip.open(epacts_filename))
pass_tests = []
if filters.get("non-monomorphic", False):
if "AC" not in headerpos:
raise Exception("Column AC not found")
ac_index = headerpos["AC"]
def mono_pass(row):
if float(row[ac_index])>0:
return True
else:
return False
pass_tests.append(mono_pass)
if "max-pvalue" in filters:
if "PVALUE" not in headerpos:
raise Exception("Column PVALUE not found")
pval_index = headerpos["PVALUE"]
thresh = float(filters.get("max-pvalue", 1))
def pval_pass(row):
if row[pval_index] == "NA":
return False
if float(row[pval_index])<thresh:
return True
else:
return False
pass_tests.append(pval_pass)
def pass_row(row):
if len(pass_tests)==0:
return True
for f in pass_tests:
if not f(row):
return False
return True
def generate():
yield "\t".join(header) + "\n"
next(indata) #skip header
for row in indata:
if pass_row(row):
yield "\t".join(row)
return Response(generate(), mimetype="text/plain")
def getLeadSNPs(chrom, snps, IndSigSNPs, params):
leadSNPs = []
checked = []
IndSigSNPs = IndSigSNPs[IndSigSNPs[:, 4].astype(float).argsort()]
for snp in IndSigSNPs:
if snp[1] in checked:
continue
ldfile = params.refgenome_dir + '/' + params.refpanel + '/' + params.pop + '/' + params.pop + '.chr' + str(
snp[2]) + '.ld.gz'
tb = tabix.open(ldfile)
ld_tmp = tb.querys(snp[2] + ":" + snp[3] + "-" + snp[3])
inSNPs = []
inSNPs.append(snp[1])
for l in ld_tmp:
if float(l[6]) < params.r2_2:
continue
if int(l[1]) != int(snp[3]):
continue
if int(l[4]) in IndSigSNPs[:, 3].astype(int):
rsID = IndSigSNPs[IndSigSNPs[:, 3].astype(int) == int(l[4]),
1][0]
checked.append(rsID)
inSNPs.append(rsID)
leadSNPs.append([
snp[0], snp[1], snp[2], snp[3], snp[4],
str(len(inSNPs)), ";".join(inSNPs)
])
leadSNPs = np.array(leadSNPs)
leadSNPs = leadSNPs[leadSNPs[:, 3].astype(int).argsort()]
return leadSNPs
def getChr15(filedir, snps, Chr15, Chr15cells, chr15dir):
if int(Chr15) == 1:
annot = pd.read_table(filedir + "annot.txt", sep="\t")
annothead = list(annot.columns.values)
annot = annot.as_matrix()
annot = annot[ArrayIn(annot[:, 0], snps[:, 0])]
if Chr15cells[0] == "all":
Chr15cells = list(annothead[3:len(annothead)])
for c in Chr15cells:
snps = np.c_[snps, annot[:, annothead.index(c)]]
Chr15data = []
chrom = int(snps[0, 1])
start = min(snps[:, 2])
end = max(snps[:, 2])
if end - start == 0:
end += 500
start -= 500
for i in Chr15cells:
tb = tabix.open(chr15dir + "/" + str(i) + "_core15.bed.gz")
tmp = tb.querys(str(chrom) + ":" + str(start) + "-" + str(end))
for l in tmp:
if int(l[1] < start):
l[1] = str(start)
if int(l[2] > str(end)):
l[2] = str(end)
Chr15data.append([i, int(l[1]), int(l[2]), int(l[3])])
# Chr15data = np.array(Chr15data)
return [snps, Chr15data]
else:
return [snps, []]
def compute_1000genomes_prs():
url = "ftp://ftp.1000genomes.ebi.ac.uk/vol1/ftp/release/20100804/"
url += "ALL.2of4intersection.20100804.genotypes.vcf.gz"
tb = tabix.open(url)
records = tb.query("1", 752720, 752721)
for record in records:
print record
def compute_prs(raw_genotype_file, variants):
counter = 0
prs_score = 0.0
url = "ftp://ftp.1000genomes.ebi.ac.uk/vol1/ftp/release/20100804/"
url += "ALL.2of4intersection.20100804.genotypes.vcf.gz"
tb = tabix.open(url)
bed_file = open(raw_genotype_file.split(".")[0] + ".bed", "wb")
with open(raw_genotype_file, "rb") as genotypes:
for line in genotypes:
if (line[0] == "#"):
continue
columns = line.split("\t")
bed_file.write(columns[1] + "\t" + str(int(columns[2]) - 1) +
"\t" + str(int(columns[2])) + "\n")
try:
genotype = columns[3][0] + ":" + columns[3][1]
variant = columns[1] + ":" + columns[2] + ":" + genotype
score = variants[variant]
prs_score += float(score)
except:
continue
counter += 1
print(counter)
bed_file.close()
return prs_score
def checkAndOpen(db):
if 'tabix' not in sys.modules:
return None
db = os.path.expanduser(db)
if not os.path.exists(db):
pass
else:
# 'source' is a variable used to title the column in the output
# it is defined by the user in the configuration script step when generating the JSON file
if os.path.splitext(db)[1] == ".gz" and os.path.exists(db + ".tbi"):
try:
database = gzip.open(db)
except IOError:
print("WARNING: could not open {}".format(db))
return None
elif os.path.splitext(db)[1] == ".vcf":
abortWithMessage("Error: database file {0} must compressed with bgzip".format(db))
elif os.path.splitext(db)[1] == ".gz" and not os.path.exists(db + ".tbi"):
abortWithMessage("Compressed database is not tabix indexed")
else: abortWithMessage("Error opening database files: {0}".format(db))
try:
row = database.readline()
except StopIteration:
print("Empty file {}".format(db))
return None
tb = tabix.open(db)
return tb
def get_job_results(job_id, job=None):
filters = request.args.to_dict()
epacts_filename = job.relative_path("output.epacts.gz")
with gzip.open(epacts_filename, "rt") as f:
header = f.readline().rstrip('\n').split('\t')
if header[1] == "BEG":
header[1] = "BEGIN"
if header[0] == "#CHROM":
header[0] = "CHROM"
assert len(header) > 0
headerpos = {x:i for i,x in enumerate(header)}
if filters.get("region", ""):
tb = tabix.open(epacts_filename)
indata = tb.query(chrom, start_pos, end_pos)
else:
indata = (x.split("\t") for x in gzip.open(epacts_filename))
pass_tests = []
if filters.get("non-monomorphic", False):
if "AC" not in headerpos:
raise Exception("Column AC not found")
ac_index = headerpos["AC"]
def mono_pass(row):
if float(row[ac_index])>0:
return True
else:
return False
pass_tests.append(mono_pass)
if "max-pvalue" in filters:
if "PVALUE" not in headerpos:
raise Exception("Column PVALUE not found")
pval_index = headerpos["PVALUE"]
thresh = float(filters.get("max-pvalue", 1))
def pval_pass(row):
if row[pval_index] == "NA":
return False
if float(row[pval_index])<thresh:
return True
else:
return False
pass_tests.append(pval_pass)
def pass_row(row):
if len(pass_tests)==0:
return True
for f in pass_tests:
if not f(row):
return False
return True
def generate():
yield "\t".join(header) + "\n"
next(indata) #skip header
for row in indata:
if pass_row(row):
yield "\t".join(row)
return Response(generate(), mimetype="text/plain")
def getNonCandidateSNPs(filedir, snps, min_pos, max_pos):
chrom = int(snps[0, 1])
chrcol = 0
poscol = 1
tb = tabix.open(filedir + "all.txt.gz")
tb_snps = tb.querys(
str(chrom) + ":" + str(min_pos - 500000) + "-" + str(max_pos + 500000))
tmp = []
for l in tb_snps:
tmp.append([int(l[0]), int(l[1]), float(l[2])])
tmp = np.array(tmp)
tmp = tmp[ArrayNotIn(tmp[:, poscol], snps[:, 3])]
### filter SNPs if there are too many #####
if len(tmp) > 10000:
tmp_keep = tmp[tmp[:, 2] < 0.05]
tmp = tmp[tmp[:, 2] >= 0.05]
step = int(len(tmp) / (10000 - len(tmp_keep))) + 1
tmp = tmp[np.arange(0, len(tmp), step)]
tmp = np.r_[tmp, tmp_keep]
out = []
for l in tmp:
out.append([int(l[0]), int(l[1]), l[2]])
return out
def read_vcf(genotype_files, chrom, start, end):
x = []
for record in tabix.open(genotype_files[chrom - 1]).query(
str(chrom), start, end):
dose = [_.split(':')[2] if _ != '.' else -1 for _ in record[9:]]
x.append([float(_) if _ != '.' else -1 for _ in dose])
return np.array(x)
def load_exclude(file_list):
exclude_list = []
if file_list is None:
return exclude_list
for filename in file_list:
exclude_list.append(tabix.open(filename))
return exclude_list
def LoadGenotypes(gtfile, gtind, regdata):
chrom = regdata["chrom"].values[0]
start = min(regdata["str.start"])
end = max(regdata["str.start"])
positions = list(regdata["str.start"])
loaded_positions = []
tb = tabix.open(gtfile)
records = tb.query(chrom, start - 1, end + 1)
data = []
for record in records:
pos = int(record[1])
if pos not in positions: continue
loaded_positions.append(pos)
data.append([GetFloat(record[i + 2])
for i in gtind]) # first two cols are chrom, start
# assert(len(positions)==len(loaded_positions))
# assert([positions[i]==loaded_positions[i] for i in range(len(positions))])
regdata = regdata[regdata["str.start"].apply(
lambda x: x in loaded_positions)]
assert ([
regdata["str.start"].values[i] == loaded_positions[i]
for i in range(len(loaded_positions))
])
return data, regdata
def get_repeats(region):
chrom = region.split(":")[0]
self = '/uufs/chpc.utah.edu/common/home/u1021864/analysis/exacresiduals/data/self-chains.gt90.bed.gz'
seg = '/uufs/chpc.utah.edu/common/home/u1021864/analysis/exacresiduals/data/hgsegmental.bed.gz'
tb = tabix.open(self)
rep = []
for r in tb.querys(region):
s = int(r[1])
e = int(r[2])
rep.append((s, e))
tb = tabix.open(seg)
for r in tb.querys(region):
s = int(r[1])
e = int(r[2])
rep.append((s, e))
return rep
def pairwise_indel_finder_query():
form = pairwise_indel_form()
if form.validate_on_submit():
data = form.data
results = []
strain_cmp = [data["strain_1"], data["strain_2"]]
tb = tabix.open(SV_BED_URL)
query = tb.query(data["chromosome"], data["start"], data["stop"])
results = []
for row in query:
row = dict(zip(SV_COLUMNS, row))
row["START"] = int(row["START"])
row["END"] = int(row["END"])
if row["STRAIN"] in strain_cmp and \
MIN_SV_SIZE <= int(row["SIZE"]) <= MAX_SV_SIZE:
row["site"] = f"{row['CHROM']}:{row['START']}-{row['END']} ({row['SVTYPE']})"
results.append(row)
# mark overlaps
if results:
results[0]['overlap'] = False
first = results[0]
for idx, row in enumerate(results[1:]):
row["overlap"] = overlaps(first["START"], first["END"],
row["START"], row["END"])
if row["overlap"]:
results[idx]['overlap'] = True
first = row
# Filter overlaps
results = [x for x in results if x['overlap'] is False]
sorted(results, key=lambda x: (x["START"], x["END"]))
return jsonify(results=results)
return jsonify(results=[])
return jsonify({"errors": form.errors})
def parseVCF(vcfFile, tbPositionSNPsFile):
tbPositionSNPs = tabix.open(tbPositionSNPsFile)
positives = []
negatives = []
if isMale(vcfFile):
vcf_reader = vcf.Reader(filename=vcfFile)
record = next(vcf_reader)
while record:
if record.CHROM == "chrY":
position = str(record.POS)
basesString = record.samples[0].gt_bases
if basesString:
allele = parseBases(basesString)
if allele:
(snp, call) = getPositionSNP(position, allele,
tbPositionSNPs)
if snp:
if call == "+":
positives.append(snp)
else:
negatives.append(snp)
try:
record = next(vcf_reader)
except:
record = None
return positives, negatives
def retrive_score(mut, conf):
''' Obtain functional scores based on mut and conf
'''
conf = conf.sort_values('order')
score = np.empty(shape=mut.shape[0])
score[:] = np.NAN
for ix_conf, conf_row in conf.iterrows():
# logger.info('Retriving {} - {} - chrom {}'.format(conf_row['name'], conf_row['type'], conf_row['chroms']))
tb = tabix.open(conf_row['path'])
for ix, var in mut.iterrows():
if (var['type'] == conf_row['type'] == 'SNP'
or conf_row['type'] == 'ALL'
or (var['type'] in ['INS', 'DEL']
and conf_row['type'] == 'INDEL')):
try:
query_res = tb.query(var.chrom, var.start, var.end)
except TabixError:
query_res = []
# Known error, eigen coding has no chrom X, Y data.
if not (conf_row['name']
in ['EIGEN_CODING', 'EIGEN_NONCODING']
and var.chrom in ['X', 'Y']):
logger.warning('Retriving {} - {} score error for {}:{}-{}'\
.format(conf_row['name'], conf_row['type'],
var.chrom, var.start, var.end))
if conf_row.ref_ix < 0:
# no ref alt info (e.g., LINSIGHT score)
score[ix] = np.mean(
[float(i[conf_row.score_ix]) for i in query_res])
else:
for res in query_res:
if var.ref == res[conf_row.ref_ix] and var.alt == res[
conf_row.alt_ix]:
score[ix] = float(res[conf_row.score_ix])
return score
def process_identical_query(query_obj):
"""
Loops through the output of a 'identical' query and processes all entries. The function used to update
the entries is defined before the loop to reduce the if-clauses evaluated in every iteration (slight
performance increase)
"""
# Execute the tabix query, skip query if empty result
try:
tb = tabix.open(query_obj.originalFile)
records = tb.query(query_obj.oldSeqID, 0, query_obj.oldSeqLength)
except tabix.TabixError:
return
# get frequently used attributes for faster lookup
newID = query_obj.newSeqID
# open files
# lock is acquired then released after writing is done
try:
lock.acquire()
with open(query_obj.dependentFile, 'a') as updated_file:
# Loop through tabix output
for entry in records:
# Modify the oldID with the newID
entry[0] = newID
# check if the updated coordinate is negative. if yes, the entry is discarded
if int(entry[1]) < 0:
continue
updated_file.write("\t".join(entry))
updated_file.write("\n")
finally:
lock.release()
def getVariants(chrom, start_pos, window_size):
directory = SNP_DIR # directory where .gz and .gz.tbi files are stored
fn = chrom + FILE_END
readFile = os.path.join(directory, fn)
# make sure inputs are integers
start_pos = int(start_pos)
window_size = int(window_size) - 1
# open the tabix
tb = tabix.open(readFile)
# query for the position
end_pos = start_pos + window_size
print("grabbing variants from {} {} {}".format(chrom, str(start_pos),
str(end_pos)))
# grab the variant records that fall between start_pos and end_pos
tb_records = tb.query(chrom, start_pos, end_pos)
# store tabix data into list
records = []
for record in tb_records:
records.append(record)
return records
def intersect_region(region_file, label, dbi_file, output_folder):
'''
intersect dbi with region file (bed format) and write final file in the output folder
'''
fin = open(region_file, 'r')
out_file = os.path.join(output_folder, label+'.bed')
fout = open(out_file, 'w')
try:
dbi = tabix.open(dbi_file)
except:
print >>sys.stderr, "Can't load tabix file %s"% (dbi_file)
exit(1)
for line in fin:
if line.strip().startswith('#') or line.strip() == '':
continue
row = line.strip().split()
chrom = row[0]
start = int(row[1])
stop = int(row[2])
result = dbi.query(chrom, start, stop)
for x in result:
print >>fout, '\t'.join(x)
fin.close()
fout.close()
create_tabix(out_file)
def gene_activity_matrix(fragments, features, barcodes):
'''
Computes the activity of each feature in scATAC-seq
fragments : fragment file that is bgzipped (provided by cellranger)
features: chr, start, end, gene
barcodes: list of barcodes
returns: gene activity matrix
'''
tb = tabix.open(fragments)
gene_activity = np.zeros((len(barcodes), len(features)))
barcode_lookup = dict(zip(barcodes, np.arange(
1, 1 +
len(barcodes)))) #hashmap to correspond barcodes with index in matrix
for i in range(features.shape[0]):
chrom, start, end = features.iloc[i, [0, 1, 2]]
fragment_df = utils.read_tabix(tb, (chrom, start, end))
if fragment_df.shape[0] > 0:
curr_barcodes = fragment_df[3].values
for b in curr_barcodes:
z = barcode_lookup.get(b)
if z:
gene_activity[z - 1, i] += 1
if i % 1000 == 0 or i == features.shape[0]:
percent_complete = str(
np.round(100 * ((i + 1) / features.shape[0]), decimals=2))
print('\r Progress: ' + percent_complete + '%', end="")
gene_activity = pd.DataFrame(gene_activity,
index=barcodes,
columns=features['gene'].values)
return gene_activity
def extract_CADD_score(arguments, q):
vcf_record, caddfile = arguments
tb = tabix.open(caddfile)
chromosome = (vcf_record.CHROM).replace("chr","")
vcf_record.INFO["RAWCADD"] = 0
vcf_record.INFO["PHREDCADD"] = 0
# Specific for CADD files
# FIXME: get info about chr or not from provided VCF file
records = tb.query(chromosome, vcf_record.POS-1, vcf_record.POS)
# Look for matching mutation
# Works for SNVs, InDels optimisation is ongoing
for rec in records:
if rec[3] == vcf_record.ALT[0]:
# FIXME: Make requested fields optional through arguments
vcf_record.INFO["RAWCADD"] = rec[4]
vcf_record.INFO["PHREDCADD"] = rec[5]
break
# workaround since multiprocess can't handle VCF record class objects
# FIXME: use VCF class records rather than this ugly string
annotated = VCF_WRITER._map(str, [vcf_record.CHROM, vcf_record.POS, vcf_record.ID, vcf_record.REF]) + [VCF_WRITER._format_alt(vcf_record.ALT), str(vcf_record.QUAL) or '.', VCF_WRITER._format_filter(vcf_record.FILTER), VCF_WRITER._format_info(vcf_record.INFO)]
# Return results to Queue
q.put(annotated)
return(annotated)
def get_cadd(config, chrom, start, ref, alt):
'''
add cadd of variant
'''
tabix_fp = config['data_paths']['cadd']['whole_genome_cadd']
try:
tb = tabix.open(tabix_fp)
except:
logging.warning('{0} not available'.format(tabix_fp))
return np.nan
'''if stop != start:
print('WARNING: the start {0} is different than stop {1}'.format(start, stop))
return np.nan'''
try:
records = tb.querys(str(chrom) + ':' + str(start) + '-' + str(start))
except:
logging.warning('Error when trying to query {0}-{1}-{2}-{3}'.format(chrom, start, ref, alt))
return np.nan
for record in records:
if record[2] != ref:
logging.warning('Reference {0} is not the one in CADD for entry {1}-{2}-{3}-{4}'.format(ref, chrom, start, ref, alt))
return np.nan
if record[3] == alt:
return float(record[5])
logging.warning('I do not find a cadd entry for {0}-{1}-{2}-{4}'.format(alt, chrom, start, ref, alt))
return np.nan
def calc_all(all_genes, bases_to_exclude, rscu_fh, gerp_fp, genome_fa,
syn_gerp_out, bed_out):
"""
Calculates mean gerp score for all Gene objects contained in list
all_genes and writes values to outfile
:param all_genes: dict of Gene objects
:param gerp_fp: path to tabix-indexed gerp file
:param genome_fa: path to reference genome fasta that has been indexed
via samtools faidx
:param outfile: path to output file
"""
with gzip.open(gerp_fp, 'rt') as gerp_f:
gerp_header = gerp_f.readline()
gerp_header = gerp_header.strip().split("\t")
gerp_tb = tabix.open(gerp_fp)
genome = pyfaidx.Fasta(genome_fa)
rscu = read_rscu_f(rscu_fh)
syn_gerp_out.write("#GENE\tSYN_GERP\n")
bed_out.write("#CHROM\tPOS\tSTRAND\tGENE\tCDS_POS\tCODON\tRSCU\tGERP\n")
for gene_obj in all_genes.values():
gene_obj.calc_syn_gerp(genome, gerp_header, gerp_tb, rscu,
bases_to_exclude)
syn_gerp_out.write("{}\t{}\n".format(gene_obj.gene, \
gene_obj.syn_gerp))
for line in gene_obj.bed:
bed_out.write(line)
def query_fragments(fragment_file, chrom, start, end):
"""
Counts number of fragments per barcode in fragment file.
Parameters
----------
fragment_file: path to fragment file
chrom: chromosome to query
start: start of query region
end: end of query region
Returns
-------
records: fragments in given region.
"""
tb = tabix.open(fragment_file)
results = tb.querys("%s:%d-%d" % (chrom, start, end))
records = []
for record in results:
records.append(record)
return records
def do_peak_feat_row(map_args, def_param=(scores1, scores2)):
"""
Loop definition for multithreading over table rows within add_peak_features.
"""
(i, train, BED, opt) = map_args
peaks = tb.open(BED)
row = train.iloc[i]
anchor1, anchor2 = prepare_anchors(row, opt.extension)
feats1 = get_features(anchor1.chrom, anchor1.start, anchor1.end, peaks, [
"chrom", "chromStart", "chromEnd", "name", "score", "strand",
"signalValue", "pValue", "qValue", "peak"
], [
"string", "int64", "int64", "string", "int64", "string", "float64",
"float64", "float64", "int64"
])
feats2 = get_features(anchor2.chrom, anchor2.start, anchor2.end, peaks, [
"chrom", "chromStart", "chromEnd", "name", "score", "strand",
"signalValue", "pValue", "qValue", "peak"
], [
"string", "int64", "int64", "string", "int64", "string", "float64",
"float64", "float64", "int64"
])
score1 = choose_feat(feats1, "signalValue", opt.collapse_peaks)
score2 = choose_feat(feats2, "signalValue", opt.collapse_peaks)
lock.acquire()
scores1[i] = (score1 + score2) / 2.0
scores2[i] = np.std([score1, score2])
lock.release()
def get_exons(chrom, start, stop, file): tb = tabix.open(file) records = tb.query(chrom, start, stop) exons = [] for record in records: exons.append(record) return exons
def Query(tbk_file=None,seqname=None,start=1,end=2,
idx=None,dtype=None,base_idx=8192):
"""
The main function for querying.
tbk_file: input a single .tbk file.
seqname: Chromosome (or the sequence name for tabix).
start: start position.
end: end position.
base_idx: Number of index that should be skipped.
"""
if dtype is None:
ver,dtype,num,idx1=Header(tbk_file)
dtype=dtype_map_rev[dtype]
if idx is None:
idx=idx1
fmt=dtype_fmt[dtype]
tb = tabix.open(idx)
records=tb.query(seqname,start,end)
lineNum=[record[3] for record in records]
if len(lineNum)==0:
return None
elif len(lineNum)==1:
return read_one_site(tbk_file,int(lineNum[0]),fmt,base_idx)
n1,n2=lineNum[0],lineNum[-1]
return read_multi_site(tbk_file,n1,n2,fmt,base_idx)
def __init__(self, _snp, _ref, _vcf, _restrict, \
_num_ctrls, _window, _match_context):
self.snp = _snp
self.ref = pyfasta.Fasta(_ref)
self.vcf = tabix.open(_vcf)
if _restrict is not None:
self.restrict = tabix.open(_restrict)
else: self.restrict = None
self.chromToKey = {}
for k in self.ref.keys():
chrom = k.split()[0]
self.chromToKey[chrom] = k
self.num_ctrls = _num_ctrls
self.window = _window
self.match_context = _match_context
if self.match_context >= 0:
self.snp_context = self.GetContext(self.snp)
def main(args):
chrom, coords = loadCoords(args.bedFile)
tb = ""
if chrom:
tabix.open(
"/home/evansj/me/data/ExAC/coverage/ftp.broadinstitute.org/pub/ExAC_release/current/coverage/Panel.chr%s.coverage.txt.gz"
% (chrom,)
)
with open(args.outFile, "w") as fout:
if chrom:
for st in coords:
# st is 1-idx in coords
# tabix needs 0-based
records = tb.query(chrom, st - 1, st)
for record in records:
thisChrom, pos, mean, median, c1, c5, c10, c15 = record[0:8]
print("\t".join((thisChrom, pos, c10)), file=fout)
def tabix_vcf(vcf_file, in_chr, in_start, in_stop): """A generator to get records in a VCF given a location.""" chrom = str(in_chr); start = int(in_start); stop = int(in_stop) try: vcf_tb = tabix.open(vcf_file) for rec in vcf_tb.query(chrom, start, stop): yield rec except: return
def get_tabixhandle(path):
"""Check if a file is zipped and that the index exists
If something looks wierd raise a TabixError
"""
if not path.endswith('.gz'):
raise TabixError("File {0} does not end with '.gz'".format(path))
index_file = path + '.tbi'
if not os.path.isfile(index_file):
raise TabixError("No index could be found for {0}".format(path))
return tabix.open(path)
def __init__(self, args):
self.args = args
# parse out TransciptInfos
print('Loading transcripts...', file=sys.stderr)
self.tx_infos = self._parse_tx_infos(args.gencode_gtf)
self.tx_info_by_id = dict([(info.transcript_id, info) for info in self.tx_infos])
# open tabix file
print('Opening tabix file...', file=sys.stderr)
self.tabix = tabix.open(args.gencode_gtf)
# open BAM file and iterate over it
print('Opening BAM file...', file=sys.stderr)
self.sam_file = pysam.AlignmentFile(args.alignment_bam, 'r')
def ld_expand(df, ld_beds):
"""
Expand a set of SNVs into all SNVs with LD >= 0.8 and return a BedTool of
the expanded SNPs.
Parameters
----------
df : pandas.DataFrame
Pandas dataframe with SNVs. The index is of the form chrom:pos where pos
is the one-based position of the SNV. The columns are chrom, start, end.
chrom, start, end make a zero-based bed file with the SNV coordinates.
ld_beds : dict
Dict whose keys are chromosomes and whose values are filenames of
tabixed LD bed files. The LD bed files should be formatted like this:
chr1 14463 14464 14464:51479:0.254183
where the the first three columns indicate the zero-based coordinates of
a SNV and the the fourth column has the one-based coordinate of that
SNV, the one-based coordinate of another SNV on the same chromosome, and
the LD between these SNVs (all separated by colons).
Returns
-------
bt : pybedtools.BedTool
BedTool with input SNVs and SNVs they are in LD with.
indepdent SNVs.
"""
import pybedtools as pbt
import tabix
out_snps = []
for chrom in ld_beds.keys():
t = tabix.open(ld_beds[chrom])
tdf = df[df['chrom'].astype(str) == chrom]
for ind in tdf.index:
p = tdf.ix[ind, 'end']
out_snps.append('{}\t{}\t{}\t{}\n'.format(chrom, p - 1, p, ind))
try:
r = t.query('{}'.format(chrom), p - 1, p)
while True:
try:
n = r.next()
p1, p2, r2 = n[-1].split(':')
if float(r2) >= 0.8:
out_snps.append('{}\t{}\t{}\t{}\n'.format(
n[0], int(p2) - 1, int(p2), ind))
except StopIteration:
break
except tabix.TabixError:
continue
bt = pbt.BedTool(''.join(out_snps), from_string=True)
bt = bt.sort()
return bt
def get_genotypes(CpG_location):
import tabix
import pandas as pd
tb_file = "/path/to/file/DF_meth_variants.gz"
df = pd.DataFrame(columns=xrange(0,782))
tb = tabix.open(tb_file)
# print CpG_location
records = tb.querys(CpG_location)
num = 0
for record in records:
df.loc[num] = record[3:]
num += 1
return(df)
def test_same_aa_different_positions(self):
''' check that same_aa() works correctly for different amino acids
'''
lines = make_vcf_header()
lines.append(make_vcf_line(pos=5, extra='Protein_position=2'))
lines.append(make_vcf_line(pos=7, extra='Protein_position=3'))
lines.append(make_vcf_line(pos=8, extra='Protein_position=4'))
self.write_vcf(lines)
vcf = tabix.open(self.path)
pairs = [[('1', 7), ('1', 8)]]
self.assertEqual(same_aa(vcf, pairs), [])
def test_same_aa(self):
''' check that same_aa() works correctly
'''
# get the VCF lines
lines = make_vcf_header()
lines.append(make_vcf_line(pos=2, extra='Protein_position=1'))
lines.append(make_vcf_line(pos=4, extra='Protein_position=1'))
self.write_vcf(lines)
vcf = tabix.open(self.path)
pairs = [[('1', 2), ('1', 4)]]
self.assertEqual(same_aa(vcf, pairs), [[('1', 2), ('1', 4)]])
def __search_pos_bdg(self):
self.pd_frame = {}
for i,bdg_file in enumerate(self.l_bdg_file):
tb = tabix.open( bdg_file )
record = tb.query( self.chrom, self.beg, self.end )
l_pos = []
l_cons = []
l_xticks = [ self.beg+1 ]
bin_size = int( (self.end-self.beg)/10 )
bin_size = 10**int(np.log10(bin_size))
pre_pos = 0
for rec in record:
for pos in xrange( int(rec[1]),int(rec[2]) ):
cons = float(rec[3])
if pre_pos == 0:
pre_pos = int(rec[1])
""" Only consider the given region. """
if self.__is_intersect(pos):
""" If bedGraph has gaps, """
if pos > pre_pos+1:
""" Using zero to fill bedGraph gaps """
for p in xrange( pre_pos+1,pos ):
l_pos.append( p )
l_cons.append( 0.0 )
l_pos.append( pos )
l_cons.append(cons )
if pos % bin_size == 0:
l_xticks.append( pos )
pre_pos = pos
l_xticks.append( self.end )
data = { 'pos':l_pos, 'con':l_cons }
self.pd_frame[ bdg_file ] = pd.DataFrame( data )
if i == 0:
self.l_xpos = l_xticks
self.l_xticks = [ str(tick) for tick in l_xticks ]
def __init__(self, task_queue, results_queue, families={}, phased=False,
vep=False, cadd_raw=False, cadd_file=None, cadd_1000g=None,
cadd_exac=None, cadd_ESP=None, cadd_InDels=None,
thousand_g=None, exac=None, dbNSFP=None, strict=False,
verbosity=False):
Process.__init__(self)
self.task_queue = task_queue
self.families = families
self.results_queue = results_queue
self.verbosity = verbosity
self.phased = phased
self.vep = vep
self.cadd_raw = cadd_raw
self.cadd_file = cadd_file
self.cadd_1000g = cadd_1000g
self.cadd_exac = cadd_exac
self.cadd_ESP = cadd_ESP
self.cadd_InDels = cadd_InDels
self.thousand_g = thousand_g
self.exac = exac
self.dbNSFP = dbNSFP
self.strict = strict
self.any_cadd_info = False
if self.cadd_file:
self.cadd_file = tabix.open(self.cadd_file)
self.any_cadd_info = True
if self.cadd_1000g:
self.cadd_1000g = tabix.open(self.cadd_1000g)
self.any_cadd_info = True
if self.cadd_exac:
self.cadd_exac = tabix.open(self.cadd_exac)
self.any_cadd_info = True
if self.cadd_ESP:
self.cadd_ESP = tabix.open(self.cadd_ESP)
self.any_cadd_info = True
if self.cadd_InDels:
self.cadd_InDels = tabix.open(self.cadd_InDels)
self.any_cadd_info = True
if self.thousand_g:
self.thousand_g = tabix.open(self.thousand_g)
if self.exac:
self.exac = tabix.open(self.exac)
if self.dbNSFP:
self.exac = tabix.open(self.exac)
def get_1mb_snps():
import tabix
tb = tabix.open('snps_all.gz')
fname = 'newMethPosFile.txt_2-3_col_1'
snps = {}
with open(fname) as f:
for line in f:
a = line.rstrip('\n').rsplit('\t')
start = str(int(a[1]) - 1000000)
stop = str(int(a[1]) + 1000000)
pos = a[0] + ":" + start + "-" + stop
records = tb.querys(pos)
for record in records:
snps[record[3]] = 0
return(snps)
def test_same_aa_missing_protein_positions(self):
''' check that same_aa() works correctly when the vars aren't in the CDS
'''
# if one of the variants in the pair does not have a protein position
# listed (i.e. residue number), that indicates the variant could be
# affecting the splice site, so we can't use the pair.
lines = make_vcf_header()
lines.append(make_vcf_line(pos=5))
lines.append(make_vcf_line(pos=7))
lines.append(make_vcf_line(pos=8, extra='Protein_position=4'))
self.write_vcf(lines)
vcf = tabix.open(self.path)
pairs = [[('1', 7), ('1', 8)]]
self.assertEqual(same_aa(vcf, pairs), [])
def test_screen_pairs_nonstandard_pair(self):
''' test that screen_pairs() works correctly
'''
# get the VCF lines
lines = make_vcf_header()
lines.append(make_vcf_line(pos=2))
lines.append(make_vcf_line(pos=4))
lines.append(make_vcf_line(pos=5))
lines.append(make_vcf_line(pos=7))
lines.append(make_vcf_line(pos=8))
self.write_vcf(lines)
vcf = tabix.open(self.path)
# set up a list of 'pairs', where one 'pair' has three variants in it.
# we exclude 'pairs' where n != 2.
pairs = [[('1', 2), ('1', 4), ('1', 5)], [('1', 7), ('1', 8)]]
self.assertEqual(screen_pairs(vcf, pairs, is_not_indel), [[('1', 7), ('1', 8)]])
def open_tabix_file(file_path):
"""docstring for open_tabix_file"""
file_handle = tabix.open(file_path)
try:
file_handle.query('1', 1, 100)
except tabix.TabixError as e:
logger.warning("Something wrong with tabix file: {0}".format(
file_path))
file_name, file_extension = os.path.splitext(file_path)
if file_extension != '.gz':
raise NotZippedError("File {0} does not seem to be bgzipped".format(
file_path))
else:
raise NotIndexedError("File {0} does not seem to be tabix"\
" indexed".format(file_path))
return file_handle
def gerp(vf, af, name="gerp"):
v = BedTool(vf)
t = tabix.open(af)
results = {}
for var in v:
try:
result = 0.0
num = 0
for res in t.query(var.chrom, var.start, var.end):
result += float(res[4])
num += 1
if num > 0:
results[var.name] = result/num
except:
pass
return Series(results, name=name)
def get_exac(config, chrom, start, stop, ref, alt):
'''
add exac annotation for the variant; in exac file:
# AN_Adj is the overall total of alleleles (I do not know the difference as compared to AN)
# AC_Adj is the overall number of mutatnt alleles observed across population
# AF = AC_Adj/AN_adj
'''
tabix_fp = config['data_paths']['exac']['exac']
tb = tabix.open(tabix_fp)
if stop != start:
logging.warning('The start {0} is different than stop {1}'.format(start, stop))
return np.nan
# A query returns an iterator over the results.
records = tb.querys(str(chrom) + ':' + str(start) + '-' + str(stop))
# if one single position is provided and this position exists, the iterator contains a single record (a single list)
# (if multiple alleles in that position, info is comma-separated)
for record in records:
# no entry with pass filter
if record[6] != 'PASS':
return np.nan
# the reference is not annotated well!
if record[3] != ref:
print('WARNING:the ref {0} is not matching the info in\n {1}'.format(ref, record[0:5]))
alt_s = record[4].split(',')
try:
# i split the alt entry in case there are multiple alleles in the same record
allele_pos = (alt_s.index(alt))
af_s = record[7].split('AF=')[1].split(';')[0].split(',')
return float(af_s[allele_pos])
except:
# the alt is not in the record and thus is not in the exac data
return np.nan
# the "for record in records" loop is not entered if the tabix query is empty (no entry for that position)
return np.nan
def test_get_matches(self):
''' check that get_matches works correctly
'''
# get the VCF lines
lines = make_vcf_header()
lines.append(make_vcf_line(pos=1))
lines.append(make_vcf_line(pos=2))
lines.append(make_vcf_line(pos=4))
lines.append(make_vcf_line(pos=5))
self.write_vcf(lines)
vcf = tabix.open(self.path)
pair = [('1', 2), ('1', 4)]
# define the expected lines
var1 = parse_vcf_line(make_vcf_line(pos=2).split('\t'), self.Variant)
var2 = parse_vcf_line(make_vcf_line(pos=4).split('\t'), self.Variant)
self.assertEqual(list(get_matches(vcf, pair)), [var1, var2])
def make_refGene_track(self,bed_tabix="/datd/huboqiang/ChIP_human/Week12/Database/refGene.sort.bed.gz"):
'''
cut -f 2- refGene.txt | awk '{OFS="\t";print $1"__"$12,$2,$3,$4,$5,$6,$7,$8,$9,$10,$11,$13,$14,$15}' | /data/Analysis/huboqiang/software/UCSC/genePredToBed /dev/stdin /dev/stdout | bedtools sort -i /dev/stdin >refGene.sort.bed
bgzip -fc refGene.sort.bed >refGene.sort.bed.gz
tabix -p bed -s 1 -b 2 -e 3 refGene.sort.bed.gz
'''
tb = tabix.open( bed_tabix )
record = tb.query( self.chrom, self.beg, self.end )
self.geneGraph = {}
for rec in record:
tran,gene = rec[3].split("__")
if gene not in self.geneGraph:
self.geneGraph[ gene ] = {}
if tran not in self.geneGraph[gene]:
self.geneGraph[ gene ][ tran ] = { 'beg':int(rec[1]),'end':int(rec[2]), 'exon_beg':[],'exon_ext':[],'cds_beg':[],'cds_ext':[], 'strand':rec[5] }
l_beg = [ int(beg)+int(rec[1]) for beg in rec[11].split(",")[:-1] ]
l_ext = [ int(ext) for ext in rec[10].split(",")[:-1] ]
self.geneGraph[ gene ][ tran ][ 'exon_beg' ] = l_beg
self.geneGraph[ gene ][ tran ][ 'exon_ext' ] = l_ext
cds_beg = int(rec[6])
cds_end = int(rec[7])
exon_cnt= int(rec[9])
if cds_beg == cds_end:
continue
for i in xrange( 0,exon_cnt ):
beg = l_beg[i]
end = l_beg[i] + l_ext[i]
if cds_end < beg or cds_beg > end:
continue
self.geneGraph[ gene ][ tran ][ 'cds_beg' ].append( max(cds_beg,beg) )
self.geneGraph[ gene ][ tran ][ 'cds_ext' ].append( min(cds_end,end)-max(cds_beg,beg) )
self.__Only_Region()
self.__Only_Longest_tran()
def test_screen_pairs(self):
''' test that screen_pairs() works correctly
'''
# get the VCF lines
lines = make_vcf_header()
lines.append(make_vcf_line(pos=1))
lines.append(make_vcf_line(pos=2))
lines.append(make_vcf_line(pos=4))
lines.append(make_vcf_line(pos=5))
lines.append(make_vcf_line(pos=7))
lines.append(make_vcf_line(pos=8))
self.write_vcf(lines)
vcf = tabix.open(self.path)
pairs = [[('1', 2), ('1', 4)], [('1', 7), ('1', 8)]]
self.assertEqual(screen_pairs(vcf, pairs, is_not_indel), pairs)
# check that the other filter function also works cleanly
self.assertEqual(screen_pairs(vcf, pairs, is_coding), pairs)
def get_coverage_stats(chrom, pos_start, pos_end):
tb = tabix.open(COVERAGE_FOLDER + COVERAGE_FILE % chrom)
records = tb.query(chrom, int(pos_start) - EXON_PADDING, int(pos_end) + EXON_PADDING)
pos_means = []
for record in records:
pos_means.append(float(record[COVERAGE_POS_MEAN_INDEX]))
coverage_stats = []
# Check that there is coverage data for the exon
if pos_means:
coverage_mean = "{0:.3f}".format(numpy.mean(pos_means))
coverage_standart_deviation = "{0:.3f}".format(numpy.std(pos_means))
coverage_max = max(pos_means)
coverage_min = min(pos_means)
coverage_stats = [coverage_mean, coverage_standart_deviation, coverage_max, coverage_min]
else:
coverage_stats = [0, 0, 0, 0]
return coverage_stats
def get_mnv_candidates(path):
''' identify MNV candidates, and their MNV consequences within a VCF.
Args:
path: path to VCF
Returns:
list of (variant, mnv_consequence) tuples, where variant is (chrom, pos)
'''
with open_vcf(path) as vcf:
exclude_header(vcf)
header = get_vcf_header(vcf)
pairs = find_nearby_variants(vcf)
# ensure variants are not indels, are coding, and pairs alter the same amino
# acid position
vcf = tabix.open(path)
pairs = screen_pairs(vcf, pairs, is_not_indel)
pairs = screen_pairs(vcf, pairs, is_coding)
pairs = same_aa(vcf, pairs)
pattern = re.compile('[ACGT]')
candidates = {}
for pair in pairs:
var1, var2 = list(get_matches(vcf, pair))
try:
cq = check_mnv_consequence(var1, var2, pattern)
candidates[pair[0]] = cq
candidates[pair[1]] = cq
except AssertionError:
print('{0}:{1} and {0}:{2} in {3} have multiple alternative ' \
'transcripts or odd codon sequences'.format(var1.chrom,
var1.pos, var2.pos, path))
return candidates
#! /usr/local/bin/python
import sys, tabix
from scipy import stats
inputFile = sys.argv[1]
tumorDepth = sys.argv[2]
normalDepth = sys.argv[3]
hIN = open(inputFile, 'r')
tumorDepth_tb = tabix.open(tumorDepth)
normalDepth_tb = tabix.open(normalDepth)
margin1 = 1000
margin2 = 500000
thres = 50
# for sorting
def cmp_chrPos(x1, x2):
key1 = x1.split('\t')
key2 = x2.split('\t')
if key1[0] < key2[0]:
return 1
elif key1[0] > key2[0]:
return -1
else:
if int(key1[1]) >= int(key2[1]):
return 1
else:
return -1
prog_name = sys.argv[0].split('/')[-1]
if len(sys.argv) == 4:
in_vcf = sys.argv[1]
in_db_dir = sys.argv[2]
maf_cut = float(sys.argv[3])
print >> sys.stderr, "[%s] %s run initiated." % (time.ctime(), prog_name)
else:
sys.exit("\nUsage: python %s <in.vcf> <in.EVS.vcf.gz.dir> <maf.cut>\n" % prog_name)
# fi
# Init tabix
dbs = {}
for chrom_id in [str(x) for x in xrange(1, 23)] + ['X', 'Y']: # no mito var in 1000G
file_to_glob = "%s/ESP6500SI-V2-SSA137.*.chr%s.*.vcf.gz" % (in_db_dir, chrom_id)
db_file = glob.glob(file_to_glob)[0]
dbs[chrom_id] = tabix.open(db_file)
#db = tabix.open(in_db)
# Proc VCF
for line in open(in_vcf, "r"):
flag_printed = False
if line.startswith('#'):
print line.strip()
continue
field = line.strip().split('\t')
chrom = field[0]
chrom_id = chrom.replace("chr", '')
chrom_id = 'M' if chrom_id == "MT" else chrom_id
one_pos = int(field[1])
chr_pos = "%s:%s" % (chrom_id, one_pos)
ref = field[3]