def group_inversions(cls, adjs):
"""Group 2 inversion adjacencies into a single event"""
inversions = sorted(adjs,
key=lambda adj: (adj.chroms[0], adj.breaks[0]))
max_homology = 25
variants = []
i = 0
while i < len(inversions) - 1:
if inversions[i].chroms[0] == inversions[i + 1].chroms[0] and\
inversions[i + 1].breaks[0] - inversions[i].breaks[0] <= max_homology and\
((inversions[i].orients == ('L', 'L') and inversions[i + 1].orients == ('R', 'R')) or
(inversions[i].orients == ('R', 'R') and inversions[i + 1].orients == ('L', 'L'))):
(adj1,
adj2) = (inversions[i],
inversions[i + 1]) if inversions[i].orients == (
'L', 'L') else (inversions[i + 1], inversions[i])
variants.append(Variant('INV', [adj1, adj2]))
i += 2
else:
if not inversions[i].dubious:
variants.append(Variant('INV', [inversions[i]]))
i += 1
if i == len(inversions) - 1 and not inversions[i].dubious:
variants.append(Variant('INV', [inversions[i]]))
return variants
def test_pc_iter_2():
seq = 'AAAAAAAAAA'
# 0123456789
# CG
variants = [
Variant('t', 't', 4, 'A', 'C', 0.25),
Variant('t', 't', 5, 'A', 'G', 0.25)
]
it = PseudocontigIterator(seq, variants, 4)
pc = it.next()
pcs = []
while pc:
pcs.append(pc)
pc = it.next()
assert 2 == len(pcs)
assert 'AAACAAA' in pcs
assert 'AACGAA' in pcs
it = PseudocontigIterator(seq, variants, 5)
pc = it.next()
pcs = []
while pc:
pcs.append(pc)
pc = it.next()
assert 2 == len(pcs)
assert 'AAAACAAAA' in pcs
assert 'AAACGAAA' in pcs
def process_vcf(self, cols):
"""Build object from vcf
"""
vcf = open(self.name, 'r')
info_dict, format_dict = {}, {}
# Read the meta-information lines from the vcf
for i, line in enumerate(vcf):
# Handle exceptions: the AF will be calcualted regardless;
if line.startswith('##FORMAT=<ID=AF'):
pass
# Select the INFO/FORMAT lines
elif line.startswith('##FORMAT'):
vcf_header = VcfHeader(line)
format_dict.update({vcf_header.meta_id: vcf_header})
elif line.startswith('##INFO'):
vcf_header = VcfHeader(line)
info_dict.update({vcf_header.meta_id: vcf_header})
# Keep other meta-info lines
elif line.startswith('##'):
if line.startswith('##source='):
self.caller = line.replace('##source=', '').strip()
self.meta_info.append(line)
else:
break
# Only extract the (filtered) DP in the format
if "DP" in info_dict.keys() and format_dict.keys():
info_dict.pop("DP", None)
if not self.caller:
sys.exit("Cannot identify caller from file {}\nPlease add caller \
identify line '##source=(caller name)' to vcf header"
.format(self.name))
# When user specify the AF and vcf does not have, try to calculate that
# for the user
if ('AF' in cols) and ('AF' not in info_dict.keys()):
vcf_header = VcfHeader(AF_LINE)
info_dict.update({vcf_header.meta_id: vcf_header})
# Select the columns from INFO/FORMAT
info_cols, format_cols = extract_cols(info_dict, format_dict, cols)
# Add the INFO line (with caller) / FORMAT (unchanged) to header_list
self.meta_info += [VcfHeader.write(VcfHeader.add_caller(v,
self.caller)) for k, v in info_cols.items()]
self.meta_info += [VcfHeader.write(v) for k, v in format_cols.items()]
self.header = line
# Continue to read the file, this time the variants
for j, line in enumerate(vcf):
variant = Variant().process_variant(line, caller=self.caller)
if variant.alt == '*':
print("Warning: Vcf {} line {} has variant with alt=*".format(self.caller, str(i+j+1)))
cleaned_variant = Variant.select_info(variant, info_cols, format_cols)
# The dictionary is query by chr\tpos\tref\talt
self.variants.update({cleaned_variant.variant_key: cleaned_variant})
return self
def getVariants():
# Returns initialized variants for testing
ret = {}
pid = "DCIS_1"
rows = ["", ""]
ret["1"] = [Variant(pid, "1", "100.0", "200.0", rows)]
ret["1"].append(Variant(pid, "1", "1025", "1119", rows))
ret["2"] = [Variant(pid, "2", "25006", "25124", rows)]
ret["X"] = [Variant(pid, "X", "90045", "90157.5", rows)]
return ret
def main():
global options, args
separator = '|'
# Open and parse each line of the vcf file
input_vcf = vcf.Reader(open(options.input_vcf, 'r'))
if options.non_model:
variant = Variant(samples=input_vcf.samples,
organism_type='non_model',
ploidy=options.ploidy)
else:
variant = Variant(samples=input_vcf.samples, ploidy=options.ploidy)
# Open output file
with open(options.output_vcf, 'w') as output_psv:
# Generate output file header
#variant = ConsequenceType(input_vcf.samples)
output_psv.write(variant.create_psv_header(separator=separator))
# Now parse lines in .vcf and output with new format:
for record in input_vcf:
# Only output sites that hasn't been filtered out
if len(record.FILTER) == 0:
#for consequence in range(0, len(record.INFO['CSQ'])):
variant.get_from_record(record=record)
output_psv.write(variant.put_to_psv(separator=separator))
def group_trls(cls, adjs):
"""Group 2 translocation adjacencies into single reciprocal event"""
trls = sorted([adj for adj in adjs if not adj.dubious],
key=lambda adj: (adj.chroms[0], adj.breaks[0]))
grouped_trl_ids = Set()
neighborhood = 10000
variants = []
i = 0
if len(trls) > 1:
while i < len(trls) - 1:
if trls[i].chroms[0] == trls[i + 1].chroms[0] and\
trls[i].chroms[1] == trls[i + 1].chroms[1] and\
abs(trls[i + 1].breaks[0] - trls[i].breaks[0]) <= neighborhood and\
abs(trls[i + 1].breaks[1] - trls[i].breaks[1]) <= neighborhood and\
((trls[i].orients == ('L', 'R') and trls[i + 1].orients == ('R', 'L')) or\
(trls[i].orients == ('R', 'L') and trls[i + 1].orients == ('L', 'R')) or\
(trls[i].orients == ('L', 'L') and trls[i + 1].orients == ('R', 'R')) or\
(trls[i].orients == ('R', 'R') and trls[i + 1].orients == ('L', 'L'))
):
variants.append(Variant('TRL', [trls[i], trls[i + 1]]))
grouped_trl_ids.add(trls[i].id)
grouped_trl_ids.add(trls[i + 1].id)
i += 2
else:
i += 1
grouped_trl_ids = Set()
trls_remained = [trl for trl in trls if trl.id not in grouped_trl_ids]
return variants, trls_remained
def main():
if len(sys.argv) < 3:
print(
'usage:\nconvert <pipeline>.xml <output>.sh (arg_name=arg_value)*')
return
pl_file = sys.argv[1]
script = sys.argv[2]
args = {
arg: Variant.from_string(value, 'string')
for (arg, value) in [item.split('=') for item in sys.argv[3:]]
}
pm = PackageManager()
include_sh = open('Test/diff_expr/include.sh', 'w')
include_sh.write(pm.get_header())
include_sh.close()
pipeline = Pipeline(pl_file, pm)
out_file = open(script, 'w')
output = pipeline.generate(args)
out_file.write('DIR="${BASH_SOURCE%/*}"\n. "$DIR/include.sh"\n\n')
out_file.write(output)
out_file.close()
system('chmod +x ' + script)
print(output)
def _get_explicit_value(self, node):
'''Return Variant with step's arg value'''
if 'val' in node.attrib:
return Variant.from_string(node.attrib['val'], node.attrib.get('type'))
elif 'ref' in node.attrib:
parts = node.attrib['ref'].split('.')
if len(parts) == 1: #local variable
option_name = parts[0]
if option_name in self._inputs:
return self._inputs[option_name].get()
if option_name in self._outputs:
return self._outputs[option_name].get()
else:
raise RuntimeError('Reference to undefined option: ' + option_name)
elif len(parts) == 2: #some pipeline output
step_name, output_name = parts
if step_name not in self._step_pipelines:
raise RuntimeError('Reference to undefined step: ' + step_name)
output = self._step_pipelines[step_name]._get_output(output_name)
if output is None:
raise RuntimeError('Undefined step output %s.%s has:%s' % (parts[0], parts[1], self._step_pipelines[parts[0]]._outputs))
return output.get()
else:
raise RuntimeError('Wrong reference format')
return None
def parse_MAF(self):
''' maf filetype parser function. Input: InputParser object. Output: Variant object '''
row = self.row
fieldId = self.fieldId
header = self.header
fn = self.fn
position = int(
str(row[fieldId['Start_position']]).split('.')[0]
) # case sensitive. what if, 'Start_Position' instead? case-insensitive hash lookup, or make everything lowercase befor making comparisons?
dp = int(str(row[fieldId['TTotCov']]).split('.')[0])
vf = float(float(row[fieldId['TVarCov']]) / float(dp))
chrom = str(row[fieldId['Chromosome']])
ref = str(row[fieldId['Reference_Allele']])
alt = str(row[fieldId['Tumor_Seq_Allele2']])
effect = self.eff
fc = self.fc
if ref == "-":
ref = ""
if alt == "-":
alt = ""
var = Variant(source=fn.split('/')[-1],
pos=HTSeq.GenomicPosition(chrom, int(position)),
ref=ref,
alt=alt,
frac=vf,
dp=dp,
eff=effect.strip(';'),
fc=fc.strip(';'))
return var
def parse_MuTectOUT(self):
''' MuTect '.out' parser function. Input: InputParser object. Output: Variant object '''
row = self.row
fieldId = self.fieldId
header = self.header
fn = self.fn
chrom = row[0]
ref = row[3]
alt = row[4]
effect = self.eff
fc = self.fc
vf = float(row[fieldId['tumor_f']])
dp = int(
int(str(row[fieldId['t_ref_count']]).strip()) +
int(str(row[fieldId['t_alt_count']]).strip()))
position = int(row[fieldId['position']])
var = Variant(source=fn.split('/')[-1],
pos=HTSeq.GenomicPosition(chrom, int(position)),
ref=ref,
alt=alt,
frac=vf,
dp=dp,
eff=effect.strip(';'),
fc=fc.strip(';'))
return var
def get_product_skus(self, product):
# scrape product variants and stock status from its info
# returns a list of variant objects
logt(self.tid, 'fetching product variants')
variants = []
try:
params = {
"expand": "variations,informationBlocks,customisations",
"channel": "iphone-app"
}
url = "https://commerce.mesh.mx/stores/{}/products/{}".format(self.sitename, product)
r = requests.request(
'GET',
url,
headers=self.headers,
params=params
).json()
for size in r['options']:
logt(self.tid,"[size] {} \t sku {} \t {}".format(
size,
r['options'][size]['SKU'],
r['options'][size]['stockStatus']
))
v = Variant(
size,
r['options'][size]['SKU'],
r['options'][size]['stockStatus']
)
variants.append(v)
return variants
except KeyError:
logt(self.tid,"[error] exception while getting product info json")
exit(-1)
def parseVCF(self, file):
#load the file to be parsed
fileReader = open(file, "r")
#loop over the file
for line in fileReader:
#see if line starts with # and skip
if line.startswith("#"):
continue
#tokenize the line
lineTokens = line.split("\t")
#set up the variables just so it clear what we are using
chromosome = lineTokens[0]
position = int(lineTokens[1])
id = lineTokens[2]
referenceAllele = lineTokens[3]
alternateAllele = lineTokens[4]
qualityScore = float(lineTokens[5])
filterFlag = lineTokens[6]
infoGroup = lineTokens[7]
formatGroup = lineTokens[8]
noneGroup = lineTokens[9]
#create the variant and add it
variant = Variant(chromosome, position, id, referenceAllele,
alternateAllele, qualityScore, filterFlag,
infoGroup, formatGroup, noneGroup)
self.__variants.append(variant)
def parse_SamTools(self):
''' samtools vcf parser function. Input: InputParser object. Output: Variant object '''
row = self.row
fieldId = self.fieldId
header = self.header
fn = self.fn
chrom = row[0]
ref = row[3]
alt = row[4]
effect = self.eff
fc = self.fc
position = int(row[fieldId['POS']])
for i in row[fieldId['INFO']].split(';'):
if i.startswith("DP4="):
j = i.split('=')[1].split(',')
ro = int(int(j[0]) + int(j[1]))
ao = int(int(j[2]) + int(j[3]))
dp = int(int(ro) + int(ao))
vf = float(float(ao) / float(dp))
var = Variant(source=fn.split('/')[-1],
pos=HTSeq.GenomicPosition(chrom, int(position)),
ref=ref,
alt=alt,
frac=vf,
dp=dp,
eff=effect.strip(';'),
fc=fc.strip(';'))
return var
def parse_SomaticIndelDetector(self):
''' GATK SomaticIndelDetector vcf parser function. Input: InputParser object. Output: Variant object '''
row = self.row
fieldId = self.fieldId
header = self.header
fn = self.fn
chrom = row[0]
ref = row[3]
alt = row[4]
effect = self.eff
fc = self.fc
j = 0
# Below attempts to grab sample ID.
# assumes that sample ID is the final column in the self.header. always true?
# if not always true, adopt the parse_mutect solution here as well
tmpsampID = header[-1]
for i in row[fieldId['FORMAT']].split(':'):
if i == "AD":
ALT_count = row[fieldId[tmpsampID]].split(':')[j].split(',')[1]
elif i == "DP":
dp = row[fieldId[tmpsampID]].split(':')[j]
vf = float(float(ALT_count) / float(dp))
j += 1
position = int(row[fieldId['POS']])
var = Variant(source=fn.split('/')[-1],
pos=HTSeq.GenomicPosition(chrom, int(position)),
ref=ref,
alt=alt,
frac=vf,
dp=dp,
eff=effect.strip(';'),
fc=fc.strip(';'))
return var
def parse_VarScan(self):
''' varscan vcf parser function. Input: InputParser object. Output: Variant object '''
row = self.row
fieldId = self.fieldId
header = self.header
fn = self.fn
chrom = row[0]
ref = row[3]
alt = row[4]
effect = self.eff
fc = self.fc
j = 0
position = int(row[fieldId['POS']])
for i in row[fieldId['FORMAT']].split(':'):
if str(i) == "DP":
dp = int(row[fieldId[header[-1]]].split(':')[j])
if str(i) == "FREQ":
vf = float(
float(
str(row[fieldId[header[-1]]].split(':')[j]).strip('%'))
/ float(100))
j += 1
var = Variant(source=fn.split('/')[-1],
pos=HTSeq.GenomicPosition(chrom, int(position)),
ref=ref,
alt=alt,
frac=vf,
dp=dp,
eff=effect.strip(';'),
fc=fc.strip(';'))
return var
def parse_HapCaller(self):
''' GATK haplotype caller vcf parser function. Input: InputParser object. Output: Variant object '''
row = self.row
fieldId = self.fieldId
header = self.header
fn = self.fn
chrom = row[0]
ref = row[3]
alt = row[4]
effect = self.eff
fc = self.fc
j = 0
position = int(row[fieldId['POS']])
'''
for i in row[fieldId['INFO']].split(';'):
if i.startswith("DP="):
dp = i.split('=')[1]
if i.startswith("AF="):
vf1 = float(i.split('=')[1])
'''
for i in row[fieldId['FORMAT']].split(':'):
if str(i) == "DP":
dp = int(row[fieldId[header[-1]]].split(':')[j])
if str(i) == "AD":
ad = str(row[fieldId[header[-1]]].split(':')[j])
if str(',') in ad:
ref_count = int(ad.split(',')[0])
alt_count = int(ad.split(',')[1])
try:
vf = float(
float(alt_count) /
(float(ref_count) + float(alt_count)))
except:
vf = 0.0
else:
abortWithMessage(
"Sample {0} may not have Haplotype Caller mutations with no ALT or vf"
.format(header[-1]))
j += 1
try:
vf
except:
print(row, file=sys.stderr)
vf = 0.0
try:
dp
except:
print(row, file=sys.stderr)
dp = 0.0
var = Variant(source=fn.split('/')[-1],
pos=HTSeq.GenomicPosition(chrom, int(position)),
ref=ref,
alt=alt,
frac=vf,
dp=dp,
eff=effect.strip(';'),
fc=fc.strip(';'))
return var
def parse_MiSeq(self):
''' MiSeq vcf parser function. Input: InputParser object. Output: Variant object '''
row = self.row
fieldId = self.fieldId
header = self.header
fn = self.fn
chrom = row[0]
ref = row[3]
alt = row[4]
fc = self.fc
effect = self.eff
for i in row[fieldId['INFO']].split(';'):
if i.startswith("DP="):
dp = i.split('=')[1]
# if the MiSeq software reported functional consequence and effect and the file is not snpEff anotated, the MiSeq annotations will be used instead
if i.startswith("FC=") and not fc:
for j in i.split('=')[1].split(','):
if str(j.split('_')[0]) not in str(fc):
fc += str(j.split('_')[0]) + ";"
try:
if str(j.split('_')[1]) not in str(effect):
effect += str(j.split('_')[1]) + ";"
except:
pass
elif str(i) == "EXON":
fc += 'EXON'
if not fc:
fc = str("?")
if not effect:
effect = str("?")
k = 0
for i in row[fieldId['FORMAT']].split(':'):
if str(i) == "VF":
vf = float(row[fieldId[header[-1]]].split(':')[k])
'''
#for when vf is not in the format column, but AD is
if str(i) == "AD" and not dp or not vf:
dp = 0
rd = int(row[fieldId[header[-1]]].split(':')[k].split(',')[0])
ad = int(row[fieldId[header[-1]]].split(':')[k].split(',')[1])
dp = int(rd) + int(ad)
'''
k += 1
position = int(row[fieldId['POS']])
var = Variant(source=fn.split('/')[-1],
pos=HTSeq.GenomicPosition(chrom, int(position)),
ref=ref,
alt=alt,
frac=vf,
dp=dp,
eff=effect.strip(';'),
fc=fc.strip(';'))
return var
def main():
global options, args
# **********************
# store in DBNLVar
# **********************
# Define connection parameters andd perform connection:
connection = httplib2.Http(".cache")
connection.add_credentials('*****@*****.**',
'prueba')
# Open annotation file and parse each line in it
annotation_vcf = vcf.Reader(open(options.input_vcf, 'r'))
# Load metadata in variant object
variant = Variant(samples=annotation_vcf.samples)
for record in annotation_vcf:
# Load variant information in DBNLVar, from consequences
variant.get_from_record(record=record)
for consequence in variant.consequences:
resp = load_consequence(consequence=consequence)
quit()
# Store consequence non-relating data in DBNLVar
if not check_record(table='chromosome',
value=chrom_to_number(record.CHROM)):
#payload = {'id': chrom_to_number(record.CHROM), 'name': number_to_chrom(chrom_to_number(record.CHROM))}
load_record(payload=record)
# Store consequence relating data in DBNLVar
for consequence in record.INFO['CSQ']:
for index, annotation in enumerate(consequence.split(separator)):
payload = {}
resp = requests.get(uri + 'chromosome/id/3/24.json', auth=auth)
print resp.json()
if resp.status_code == 200:
content = resp.json()['content']
else:
print "ERROR: Problem in query"
raise
print content
def test_pc_iter_3():
seq = 'AAAAAAAAAAA'
# 01234567890
# CGT
variants = [
Variant('t', 't', 4, 'A', 'C', 0.25),
Variant('t', 't', 5, 'A', 'G', 0.25),
Variant('t', 't', 6, 'A', 'T', 0.25)
]
it = PseudocontigIterator(seq, variants, 4)
pc = it.next()
pcs = []
while pc:
pcs.append(pc)
pc = it.next()
assert 'AAACAAA' in pcs
assert 'AACGAA' in pcs
assert 'ACATA' in pcs
assert 'ACGTA' in pcs
def main():
global options, args
# **********************
# store in DBNLVar
# **********************
# Define connection parameters andd perform connection:
connection = httplib2.Http(".cache")
connection.add_credentials("*****@*****.**", "prueba")
# Open annotation file and parse each line in it
annotation_vcf = vcf.Reader(open(options.input_vcf, "r"))
# Load metadata in variant object
variant = Variant(samples=annotation_vcf.samples)
for record in annotation_vcf:
# Load variant information in DBNLVar, from consequences
variant.get_from_record(record=record)
for consequence in variant.consequences:
resp = load_consequence(consequence=consequence)
quit()
# Store consequence non-relating data in DBNLVar
if not check_record(table="chromosome", value=chrom_to_number(record.CHROM)):
# payload = {'id': chrom_to_number(record.CHROM), 'name': number_to_chrom(chrom_to_number(record.CHROM))}
load_record(payload=record)
# Store consequence relating data in DBNLVar
for consequence in record.INFO["CSQ"]:
for index, annotation in enumerate(consequence.split(separator)):
payload = {}
resp = requests.get(uri + "chromosome/id/3/24.json", auth=auth)
print resp.json()
if resp.status_code == 200:
content = resp.json()["content"]
else:
print "ERROR: Problem in query"
raise
print content
def variant_from_index_list(idx_list, line):
# Inputs: list of indexes of the line
# One line of the input file as a list
chrom = line[idx_list[0]]
start = line[idx_list[1]]
end = line[idx_list[2]]
ref = line[idx_list[3]]
alt = line[idx_list[4]]
gene = line[idx_list[5]]
var_type = line[idx_list[6]].replace(" ", "_")
var_type = var_type.strip()
return Variant(chrom, start, end, ref, alt, gene, var_type, None)
def __setVariant__(self, row):
# Reads row of variant file into dict by id and chromosome
pid = row[self.vhead["Patient"]]
c = self.__setChromosome__(row[self.vhead["Chr"]])
start = row[self.vhead["Start"]]
end = row[self.vhead["End"]]
name = row[self.vhead["Name"]]
if pid not in self.variants.keys():
self.variants[pid] = {}
if c not in self.variants[pid].keys():
self.variants[pid][c] = []
self.variants[pid][c].append(Variant(pid, c, start, end, row, name))
def main():
global options, args
separator = '|'
# Parse the HGVS name into genomic coordinates and alleles.
#chrom, offset, ref, alt = hgvs.parse_hgvs_name('ENST00000515609.1:c.30G>T', genome, get_transcript=get_transcript)
#print chrom, offset, ref, alt
# Format an HGVS name.
chrom, offset, ref, alt = ('chr2', 179616770, 'GAA', 'G')
transcript = get_transcript('ENST00000359218.5')
hgvs_name = hgvs.format_hgvs_name(chrom, offset, ref, alt, genome, transcript)
print hgvs_name
chrom, offset, ref, alt = ('chr2', 179616770, 'GAA', 'GA')
transcript = get_transcript('ENST00000359218.5')
hgvs_name = hgvs.format_hgvs_name(chrom, offset, ref, alt, genome, transcript)
hgvs_var = hgvs.HGVSName(hgvs_name)
hgvs_str = 'ENST00000359218.5:c.10597+1079_10597+1080delTTinsT'
hgvs_var2 = hgvs.HGVSName(hgvs_str)
print hgvs_name
quit()
# Open and parse each line of the vcf file
input_vcf = vcf.Reader(open(options.input_vcf, 'r'))
variant = Variant(samples=input_vcf.samples)
# Open output file
with open(options.output_vcf, 'w') as output_psv:
# Generate output file header
#variant = ConsequenceType(input_vcf.samples)
output_psv.write(variant.create_psv_header(separator=separator))
# Now parse lines in .vcf and output with new format:
for record in input_vcf:
# Only output sites that hasn't been filtered out
if len(record.FILTER) == 0:
#for consequence in range(0, len(record.INFO['CSQ'])):
variant.get_from_record(record=record)
output_psv.write(variant.put_to_psv(separator=separator))
def create_variant (self, value, scope, datatype=None):
"""Creates a `Variant` of this topic name with the specified
string `value` and `scope`.
If `datatype` is None, the newly created `Variant` will have
the datatype xsd:string.
The newly created `Variant` will contain all themes from the
parent name and the themes specified in `scope`.
:param value: the string value or locator which represents an IRI
:type value: string or `Locator`
:param scope: list of themes
:type scope: list of `Topic`s
:rtype: `Variant`
"""
if value is None:
raise ModelConstraintException(self, 'The value may not be None')
if not scope:
raise ModelConstraintException(self, 'The scope may not be None')
if type(scope) not in (type([]), type(())):
scope = [scope]
if scope == list(self.get_scope()):
raise ModelConstraintException(
self, 'The variant would be in the same scope as the parent')
if datatype is None:
if isinstance(value, Locator):
datatype = Locator(XSD_ANY_URI)
elif isinstance(value, str):
datatype = Locator(XSD_STRING)
if isinstance(value, Locator):
value = value.to_external_form()
variant = Variant(name=self, datatype=datatype.to_external_form(),
value=value, topic_map=self.topic_map)
variant.save()
for theme in scope:
variant.scope.add(theme)
return variant
def test_pc_iter_4():
seq = 'AAANAAAAA'
# 012345678
# T
variants = [Variant('t', 't', 4, 'A', 'T', 0.25)]
it = PseudocontigIterator(seq, variants, 4)
pc = it.next()
pcs = []
while pc:
pcs.append(pc)
pc = it.next()
assert 0 == len(pcs)
def test_pc_iter_deletion_2():
seq = 'AAAAAAAAA'
# 012345678
# xxx
variants = [Variant('t', 't', 3, 'AAA', [''], 0.25)]
it = PseudocontigIterator(seq, variants, 4)
pc = it.next()
pcs = []
while pc:
pcs.append(pc)
pc = it.next()
assert 1 == len(pcs)
assert 'AAAAAA' == pcs[0]
def find_nocov_variants(covlist,chrom='',caller='',min_cov=5):
variants = []
assert min(covlist[1:]) >= 0
nocov = [i for i,v in enumerate(covlist) if v < min_cov]
nocov.remove(0) # take off the -1 at index 0
if len(covlist)-1 == len(nocov): return None # entire sequence has no coverage
nocov_intervals = list(intervals(nocov))
for iv in nocov_intervals:
data = {'chrom':chrom,'caller':caller,'pos':iv[0], 'type': 'no_cov'}
data['length'] = iv[1] - iv[0] + 1
data['mean_cov'] = scipy.mean(covlist[iv[0]:(iv[1]+1)])
variants.append(Variant.from_dict(data))
return variants
def parse_variants(line):
# Parses variant information from annovar-annotated vcf-file.
variants = []
chromosome = line[0]
start = line[1]
end = line[2]
ref = line[3]
alt = line[4]
info = line[7]
genes, type, af = parse_info(info.split(';'))
for g in genes:
new_var = Variant(chromosome, start, end, ref, alt, g, type, af, None)
variants.append(new_var)
return variants
def test_pc_iter_insertion_2():
seq = 'AAAAAAAAA'
# 012345678
# ^
# TT
variants = [Variant('t', 't', 4, '', ['TT'], 0.25)]
it = PseudocontigIterator(seq, variants, 4)
pc = it.next()
pcs = []
while pc:
pcs.append(pc)
pc = it.next()
assert 1 == len(pcs)
assert 'AATTAA' == pcs[0]
def uniqueVariants(self):
'''Return the set of unique variants from the set of all variants (for this feature)'''
# exploit the hashtable and uniqueness of sets to quickly find
# unique tuples (contig, pos, ref, alt) of variant info
# sorted by chrom, pos
uniqueVariantsTemp = set()
for var in self.variants:
candidate = (var.pos.chrom, var.pos.pos, var.ref, var.alt)
uniqueVariantsTemp.add(candidate)
# sort by chr, then position
# TO DO: python sorted() will sort as: chr1, chr10, chr2, chr20, chrX. Fix.
uniqueVariantsTemp = sorted(uniqueVariantsTemp,
key=lambda varx: (varx[0] + str(varx[1])))
# Now construct a returnable set of Variant objects,
# specifying multiple "sources" in the source field
# this loop's inner-product is #unique variants * #total variants, times #features
# and is a major inefficiency
uniqueVariants = set()
for uniqueVarTup in uniqueVariantsTemp:
source = ""
frac = ""
dp = ""
eff = ""
fc = ""
#annot = ""
for varClass in self.variants:
if (varClass.pos.chrom, varClass.pos.pos, varClass.ref,
varClass.alt) == uniqueVarTup:
source += varClass.source + ", "
frac += str(varClass.frac) + ", "
dp += str(varClass.dp) + ", "
eff += str(varClass.eff) + ", "
fc += str(varClass.fc) + ", "
#annot += str(varClass.annot) + ", "
pos = HTSeq.GenomicPosition(uniqueVarTup[0], uniqueVarTup[1])
uniqueVar = Variant(
source.strip(", "),
pos,
ref=uniqueVarTup[2],
alt=uniqueVarTup[3],
frac=str(frac).strip(", "),
dp=str(dp).strip(", "),
eff=str(eff).strip(", "),
fc=str(fc).strip(", ")) ######## Karl Modified ##############
uniqueVariants.add(uniqueVar)
return uniqueVariants
def parse_IonTorrent(self):
''' Ion Torrent vcf parser function. Input: InputParser object. Output: Variant object '''
row = self.row
fieldId = self.fieldId
header = self.header
fn = self.fn
chrom = row[0]
ref = row[3]
alt = row[4]
effect = self.eff
fc = self.fc
for i in row[fieldId['INFO']].split(';'):
if i.startswith("AO="):
tempval = i.split('=')[1]
if i.startswith("RO="):
ro = i.split('=')[1]
if i.startswith("DP="):
dp = i.split("=")[1]
if str(',') in str(tempval):
tempval2 = [
int(numeric_string) for numeric_string in tempval.split(',')
]
try:
ao = sum(tempval2)
except:
abortWithMessage(
"AO should be an int, or a list of ints: AO = {0}/".format(
tempval2))
else:
ao = tempval
vf = float(float(ao) / float(float(ro) + float(ao)))
position = int(row[fieldId['POS']])
for i in str(row[fieldId['ALT']]).split(','):
if len(str(row[fieldId['REF']])) > len(i):
# this is a deletion in Ion Torrent data
position = int(row[fieldId['POS']])
break
var = Variant(source=fn.split('/')[-1],
pos=HTSeq.GenomicPosition(chrom, int(position)),
ref=ref,
alt=alt,
frac=vf,
dp=dp,
eff=effect.strip(';'),
fc=fc.strip(';'))
return var
def parse_GenericGATK(self):
'''
Generic GATK parser function. This was written for the Illumina BaseSpace BWA Enrichment Workflow vcf files, but may apply to more filetypes
Input: InputParser object. Output: Variant object
'''
row = self.row
fieldId = self.fieldId
header = self.header
fn = self.fn
chrom = row[0]
ref = row[3]
alt = row[4]
effect = self.eff
fc = self.fc
j = 0
position = int(row[fieldId['POS']])
for i in row[fieldId['FORMAT']].split(':'):
if str(i) == "AD":
ro = int(row[fieldId[header[-1]]].split(':')[j].split(',')[0])
#ao = int(row[fieldId[header[-1]]].split(':')[j].split(',')[-1]) # fails when the mutation has two alternate alleles in the same VCF line
ao = sum([
int(x) for x in row[fieldId[header[-1]]].split(':')
[j].split(',')[1:]
])
dp = ro + ao
try:
vf = float(
float(ao) / float(dp)
) # one VF for all possible alternate alleles. Nothing unusual, unless the mutation has multiple alt alleles in 1 vcf line
except:
print("\nwarning: no vaf?\n" + str(row) + "\n")
vf = 0
break
j += 1
var = Variant(source=fn.split('/')[-1],
pos=HTSeq.GenomicPosition(chrom, int(position)),
ref=ref,
alt=alt,
frac=vf,
dp=dp,
eff=effect.strip(';'),
fc=fc.strip(';'))
return var
def __setBlastResults__(self, name, infile):
# Reads in infile as a dictionary stored by chromosome (each file is one sample)
first = True
with open(infile, "r") as f:
for line in f:
if first == True:
delim = getDelim(line)
first = False
row = line.strip().split(delim)
c = row[self.bhead["subjectid"]]
pas = self.__evaluateRows__(row)
if pas == True and c in self.variants[name].keys():
# Only proceed if there is sufficient match quality and chromosome is present in variants
qid = row[self.bhead["queryid"]]
start = row[self.bhead["sstart"]]
end = row[self.bhead["send"]]
if c not in self.results.keys():
self.results[c] = []
self.results[c].append(Variant(qid, c, start, end, row))
def get_product_variants(self, product):
if not(isinstance(product, Product)):
raise Exception('Expected product object')
log('[{}.json] Getting product variants'.format(product.url), color='blue')
endpoint = '{}.json'.format(product.url)
r = self.S.get(
endpoint,
headers=self.headers,
verify=False
)
try:
r.raise_for_status()
except requests.exceptions.HTTPError:
log('[error][{}][{}.json] Failed to get variants'.format(r.status_code, product.url), color='red')
return None
with r.json() as json:
variant_objects = []
for var in json['variants']:
variant_objects.append(Variant(var['id'], var['title']))
return variant_objects
def main():
if len(sys.argv) < 3:
print('usage:\nconvert <pipeline>.xml <output>.sh (arg_name=arg_value)*')
return
pl_file = sys.argv[1]
script = sys.argv[2]
args = {arg : Variant.from_string(value, 'string') for (arg, value) in [item.split('=') for item in sys.argv[3:]]}
pm = PackageManager()
include_sh = open('Test/diff_expr/include.sh', 'w')
include_sh.write(pm.get_header())
include_sh.close()
pipeline = Pipeline(pl_file, pm)
out_file = open(script, 'w')
output = pipeline.generate(args)
out_file.write('DIR="${BASH_SOURCE%/*}"\n. "$DIR/include.sh"\n\n')
out_file.write(output)
out_file.close()
system('chmod +x ' + script)
print(output)
def _process_option(self, node, args):
'''Return Option, that contain values produced using args comed from step declaration'''
opt = Option(node.attrib.get('repr'))
name = node.attrib['name']
if 'default' in node.attrib:
opt.set_default_val(Variant.from_string(node.attrib['default'], node.attrib['type']))
elif 'default_ref' in node.attrib:
ref = node.attrib['default_ref'].strip()
if ref in self._inputs:
opt.set_default_val(self._inputs[ref].get())
else:
raise RuntimeError('Reference to currently undefined symbol: ' + ref)
else:
for child in node: #check by RELAXNG
if child.tag == 'default':
opt.set_default_val(self._eval_expression(child))
if name in args:
opt.set_val(args[name])
return opt
def main():
global options, args
separator = '|'
# Open and parse each line of the vcf file
input_vcf = vcf.Reader(open(options.input_vcf, 'r'))
if options.non_model:
variant = Variant(samples=input_vcf.samples, organism_type='non_model', ploidy=options.ploidy)
else:
variant = Variant(samples=input_vcf.samples, ploidy=options.ploidy)
# Open output file
with open(options.output_vcf, 'w') as output_psv:
# Generate output file header
#variant = ConsequenceType(input_vcf.samples)
output_psv.write(variant.create_psv_header(separator=separator))
# Now parse lines in .vcf and output with new format:
for record in input_vcf:
# Only output sites that hasn't been filtered out
if len(record.FILTER) == 0:
#for consequence in range(0, len(record.INFO['CSQ'])):
variant.get_from_record(record=record)
output_psv.write(variant.put_to_psv(separator=separator))
def join(mod_params, args):
str_value = (mod_params or '').join([arg.to_string() for arg in args])
return Variant.from_string(str_value, 'string')
def base_name(mod_params, args):
assert len(args) == 1
return Variant.from_string('.'.join(args[0].to_string().split('.')[:-1]))
def to_list(mod_params, args): return Variant.from_variant_list(args)
def find_variants(covlist, seq, chrom, min_cov=5, min_score=30, exclude_edges=False, exclude_overlaps=False):
''' identify coverage variants in covlist
Returns dict with keys 'mean_cov','pct_cov', and 'variants', where dict['variants']
is a list of Variant objects
'''
assert min(covlist[1:]) >= 0
assert len(covlist) - 1 == len(seq), "Number of coverage values (%d) is not equal to sequence length (%d)" % (len(covlist)-1,len(seq))
retval = {}
nocov = [i for i,v in enumerate(covlist) if v < min_cov]
nocov.remove(0)
retval['mean_cov'] = scipy.mean(covlist[1:])
retval['pct_cov'] = 1 - (float(len(nocov)) / (len(covlist) - 1))
if len(nocov) == len(seq):
return retval
nocov_intervals = list(intervals(nocov))
#covscores,localmeans = local_coverage_score(covlist)
covscores,localmeans = adjusted_coverage_score(covlist)
covdip = [i for i,v in enumerate(covscores) if v >= min_score]
covdip_intervals = list(intervals(covdip))
# refine intervals
if exclude_edges:
# ignore intervals that overlap the beginning and end of reference
covdip_intervals = [iv for iv in covdip_intervals if not iv[0]==1 and not iv[1]==(len(covlist)-1)]
if exclude_overlaps:
# ignore covdip intervals that overlap with nocov intervals
covdip_intervals = remove_overlap(covdip_intervals,nocov_intervals)
# covdip = list(itertools.chain(*[range(v1,v2+1) for v1,v2 in covdip_intervals])
# positions with no coverage are not considered to be coverage dips
covdip = [p for p in covdip if p not in nocov]
variants = []
for iv in nocov_intervals:
data = {'chrom':chrom, 'pos':iv[0], 'type': 'no_cov'}
data['length'] = iv[1] - iv[0] + 1
data['mean_cov'] = scipy.mean(covlist[iv[0]:(iv[1]+1)])
variants.append(Variant.from_dict(data))
for iv in covdip_intervals:
data = {'chrom':chrom, 'pos':iv[0], 'type': 'cov_dip'}
data['length'] = iv[1] - iv[0] + 1
data['mean_cov'] = scipy.mean(covlist[iv[0]:(iv[1]+1)])
intscores = covscores[iv[0]:(iv[1]+1)]
intmeans = localmeans[iv[0]:(iv[1]+1)]
data['quality'] = max(intscores)
data['info'] = {'CovScores':'%s' % ','.join(['%d' % int(round(v)) for v in intscores]),
'LocalMeans':'%s' % ','.join(['%d' % int(round(v)) for v in intmeans]),
}
data['ref'] = str(seq[iv[0]:(iv[1]+1)].seq).upper()
# data['alt'] = data['ref'].lower()
variants.append(Variant.from_dict(data))
if variants:
retval['variants'] = variants
return retval
def script(res1, res2, out, **kwargs):
variants = Variant.load_res_file(res1)
rand_variants = Variant.load_res_file(res2)
#import matplotlib.pyplot as plt
from variant import Variant
"""Plot AF vs. silva score, given a list of variants"""
def plot_freq(variants):
afs = [x.af for x in variants]
scores = [x.score for x in variants]
#plt.scatter(scores, afs)
#plt.show()
f = open('/dupa-filer/talf/silva-pipeline/test.out', 'w')
for a,s in zip(afs, scores):
f.write('\t'.join([str(a),str(s)]) + '\n')
f.close()
if __name__ == '__main__':
variants = Variant.load_res_file('/dupa-filer/talf/silva-pipeline/1000gp_rare_results.txt')
plot_freq(variants)
if not os.path.isdir(args.jobdir): sys.exit('Error: directory "%s" does not exist' % args.pooldir)
if not os.path.exists(args.reffile): sys.exit('Error: reference file "%s" does not exist' % args.reffile)
job_path = os.path.abspath(args.jobdir)
reference_file = os.path.abspath(args.reffile)
# load sequences
seqs = dict( [(s.id,s) for s in SeqIO.parse(reference_file,'fasta')] )
summaries = dict(( (name,{'variants':[]}) for name in seqs.keys()))
''' GATK variants '''
print >>sys.stderr, "[ Reading GATK variants ]"
vlines = [l.strip('\n') for l in open('%s/GATK/snps.gatk.vcf' % job_path,'rU') if not l.startswith('#')]
for l in vlines:
v = Variant.from_vcf(l)
v.caller = 'gatk'
summaries[v.chrom]['variants'].append(v)
''' PacBio variants '''
print >>sys.stderr, "[ Reading GenCons variants ]"
glines = [l.strip('\n') for l in gzip.open('%s/data/variants.gff.gz' % job_path,'rb') if not l.startswith('#')]
for l in glines:
v = Variant.from_gff(l)
v.caller = 'gencons'
summaries[v.chrom]['variants'].append(v)
''' coverage variants '''
print >>sys.stderr, "[ Reading coverage variants ]"
covdata = parse_covdepth('%s/GATK/covdepth' % job_path)
covvars = {}
