def main():
args = get_options()
ensembl = EnsemblRequest(args.cache, args.genome_build)
cadd = pysam.TabixFile(args.cadd)
constraint = load_regional_constraint(args.constraint)
# open de novo mutations
all_de_novos = open_mutations(args.de_novos)
mut_dict = load_mutation_rates()
output = open(args.output, 'w')
output.write('symbol\tseverity_p_value\n')
for symbol in all_de_novos:
if symbol in ['', '.']:
continue
print(symbol)
de_novos = all_de_novos[symbol]
p_value = analyse_gene(ensembl, mut_dict, cadd, symbol, de_novos,
constraint, WEIGHTS)
line = '{}\t{}\n'.format(symbol, p_value)
output.write(line)
def get_rates_by_constraint(constraint, cache_dir, threshold=1e-4, ratio=1.0):
''' get mutation rates in and out of constrained regions
'''
rates = {'constrained': [], 'unconstrained': []}
mut_dict = load_mutation_rates()
ensembl = EnsemblRequest(cache_dir, 'grch37')
for tx_id, group in constraint.groupby('transcript'):
tx = construct_gene_object(ensembl, tx_id.split('.')[0])
sites = SiteRates(tx, mut_dict)
constrained_sites = get_constrained_positions(tx, group, threshold,
ratio)
cqs = [
'nonsense', 'missense', 'synonymous', 'splice_lof', 'splice_region'
]
gene_rates = get_gene_rates(tx, sites, cqs, constrained_sites)
# now add the gene rates to the larger list of all genes
for category in ['constrained', 'unconstrained']:
gene_rates[category]['symbol'] = list(group['gene'])[0]
gene_rates[category]['chrom'] = list(group['chr'])[0]
gene_rates[category]['length'] = tx.chrom_pos_to_cds(
tx.get_cds_end())['pos']
rates[category].append(gene_rates[category])
return rates
def annotate_constraint(data, constraint_path, threshold=1e-3, ratio=0.4):
''' annotate per-site rates by whether the site is under regional constraint
'''
# default to unconstrained
data['constrained'] = False
constraint = load_regional_constraint(constraint_path)
mut_dict = load_mutation_rates()
ensembl = EnsemblRequest(cache_dir, 'grch37')
modified = []
for symbol, group in data.groupby('symbol'):
if symbol not in set(constraint['gene']):
sites = set([])
else:
regional = constraint[constraint['gene'] == symbol]
tx_id = list(regional['transcript'])[0]
tx = construct_gene_object(ensembl, tx_id.split('.')[0])
sites = get_constrained_positions(tx, regional, threshold, ratio)
gene_constraint = group['constrained'].copy()
gene_constraint.loc[group['pos'].isin(sites)] = True
group['constrained'] = gene_constraint
modified.append(group)
return pandas.concat(modified)
def main():
args = get_options()
ensembl = EnsemblRequest(args.cache_folder, args.genome_build.lower())
mut_dict = load_mutation_rates(args.rates)
output = open(args.out, "wt")
args.func(ensembl, mut_dict, output, args)
async def runner():
args = get_options()
FORMAT = '%(asctime)-15s %(message)s'
logging.basicConfig(filename=args.log, format=FORMAT, level=logging.INFO)
async with RateLimiter(per_second=15) as ensembl:
mut_dict = load_mutation_rates(args.rates)
with open(args.out, "wt") as output:
await args.func(ensembl, mut_dict, output, args)
def main():
args = get_options()
ensembl = EnsemblRequest(args.cache_folder, args.genome_build.lower())
mut_dict = load_mutation_rates(args.rates)
output = open(args.out, "wt")
args.func(ensembl, mut_dict, output, args)
def main():
args = get_options()
ensembl = EnsemblRequest('cache', 'grch37')
mut_dict = load_mutation_rates()
dominant = load_dominant(args.known)
data = pandas.DataFrame(
columns=['symbol', 'chrom', 'pos', 'ref', 'alt', 'cq', 'prob'])
data['pos'] = data['pos'].astype(int)
for symbol in dominant:
print(symbol)
rates = get_gene_rates(symbol, ensembl, mut_dict)
data = data.append(rates, ignore_index=True)
with gzip.open(args.output, 'wt') as handle:
data.to_csv(handle, sep='\t', index=False)
def main():
input_transcripts, input_genes, output_file, rates_file, cache_dir, \
genome_build = get_options()
# load all the data
ensembl = EnsemblRequest(cache_dir, genome_build)
mut_dict = load_mutation_rates(rates_file)
if input_transcripts is not None:
transcripts = load_transcripts(input_transcripts)
else:
transcripts = load_genes(input_genes)
output = open(output_file, "w")
output.write("transcript_id\tchrom\tlength\tmissense_rate\tnonsense_rate\t"
"splice_lof_rate\tsplice_region_rate\tsynonymous_rate\n")
for gene_id in sorted(transcripts):
print(gene_id)
try:
rates = get_mutation_rates(gene_id, transcripts, mut_dict, ensembl)
chrom = rates[0]
length = rates[1]
rates = rates[2:]
# log transform the rates, to keep them consistent with the rates from
# Daly et al.
line = "{0}\t{1}\t{2}\t{3}\t{4}\t{5}\t{6}\t{7}\n".format(gene_id, \
chrom, length, *log_transform(rates))
except ValueError as error:
line = "{0}\t{1}\n".format(gene_id, error)
except KeyError as error:
# ignore genes with odd genomic sequence eg ENST00000436041 in GRCh37
continue
output.write(line)
output.close()
include_indel_rates(output_file)
async def cluster_de_novos(symbol, de_novos, ensembl, iterations=1000000, mut_dict=None):
""" analysis proximity cluster of de novos in a single gene
Args:
symbol: HGNC symbol for a gene
de_novos: dictionary of de novo positions for the HGNC gene,
indexed by functional type
iterations: number of simulations to run
ensembl: EnsemblRequest object, for obtaing info from ensembl
mut_dict: dictionary of mutation rates, indexed by trinuclotide sequence
Returns:
a dictionary containing P values, and distances for missense, nonsense,
and synonymous de novos events. Missing data is represented by "NA".
"""
if mut_dict is None:
mut_dict = load_mutation_rates()
missense = de_novos["missense"]
nonsense = de_novos["nonsense"]
# load the set of transcripts that are the minimum set of transcripts
# required to contain all the de novos, unless we can't find any coding
# transcripts that contain the de novos.
try:
transcripts = await load_gene(ensembl, symbol, missense + nonsense)
except IndexError as e:
print(e)
return None
probs = {"miss_prob": [], "nons_prob": []}
dists = {"miss_dist": [], "nons_dist": []}
for transcript in transcripts:
missense_events = get_de_novos_in_transcript(transcript, missense)
nonsense_events = get_de_novos_in_transcript(transcript, nonsense)
rates = SiteRates(transcript, mut_dict)
(miss_dist, miss_prob) = get_p_value(transcript, rates, iterations, "missense", missense_events)
(nons_dist, nons_prob) = get_p_value(transcript, rates, iterations, "lof", nonsense_events)
dists["miss_dist"].append(miss_dist)
dists["nons_dist"].append(nons_dist)
probs["miss_prob"].append(miss_prob)
probs["nons_prob"].append(nons_prob)
# remove the de novos analysed in the current transcript, so that
# analysis of subsequent transcripts uses independent events. NOTE THAT
# THIS MIGHT MISS SOME CLUSTERING ACROSS MUTUALLY EXCLUSIVE TRANSCRIPTS
# IF THE DE NOVO EVENTS ARE NEAR THE TRANSCRIPT DIVERGENCE.
missense = [x for x in missense if x not in missense_events]
nonsense = [x for x in nonsense if x not in nonsense_events]
for key in dists:
dists[key] = ",".join([ str(x) for x in dists[key] ])
probs = {k: fishers_method(probs[k]) for k in probs}
probs.update(dists)
return probs
def get_rates(self, tx):
# load the sequence contect mutation rates, then assess each site in the
# CDS.
mut_dict = load_mutation_rates()
return SiteRates(tx, mut_dict)
def cluster_de_novos(symbol, de_novos, iterations=1000000, ensembl=None, mut_dict=None):
""" analysis proximity cluster of de novos in a single gene
Args:
symbol: HGNC symbol for a gene
de_novos: dictionary of de novo positions for the HGNC gene,
indexed by functional type
iterations: number of simulations to run
ensembl: EnsemblRequest object, for obtaing info from ensembl
mut_dict: dictionary of mutation rates, indexed by trinuclotide sequence
Returns:
a dictionary containing P values, and distances for missense, nonsense,
and synonymous de novos events. Missing data is represented by "NA".
"""
if ensembl is None:
ensembl = EnsemblRequest('cache', 'grch37')
if mut_dict is None:
mut_dict = load_mutation_rates()
missense = de_novos["missense"]
nonsense = de_novos["nonsense"]
# load the set of transcripts that are the minimum set of transcripts
# required to contain all the de novos, unless we can't find any coding
# transcripts that contain the de novos.
try:
transcripts = load_gene(ensembl, symbol, missense + nonsense)
except IndexError as e:
print(e)
return None
probs = {"miss_prob": [], "nons_prob": []}
dists = {"miss_dist": [], "nons_dist": []}
for transcript in transcripts:
missense_events = get_de_novos_in_transcript(transcript, missense)
nonsense_events = get_de_novos_in_transcript(transcript, nonsense)
rates = SiteRates(transcript, mut_dict)
(miss_dist, miss_prob) = get_p_value(transcript, rates, iterations, "missense", missense_events)
(nons_dist, nons_prob) = get_p_value(transcript, rates, iterations, "lof", nonsense_events)
dists["miss_dist"].append(miss_dist)
dists["nons_dist"].append(nons_dist)
probs["miss_prob"].append(miss_prob)
probs["nons_prob"].append(nons_prob)
# remove the de novos analysed in the current transcript, so that
# analysis of subsequent transcripts uses independent events. NOTE THAT
# THIS MIGHT MISS SOME CLUSTERING ACROSS MUTUALLY EXCLUSIVE TRANSCRIPTS
# IF THE DE NOVO EVENTS ARE NEAR THE TRANSCRIPT DIVERGENCE.
missense = [x for x in missense if x not in missense_events]
nonsense = [x for x in nonsense if x not in nonsense_events]
for key in dists:
dists[key] = ",".join([ str(x) for x in dists[key] ])
probs = combine_p_values(probs)
probs.update(dists)
return probs
def setUpClass(cls):
cls.temp_dir = tempfile.mkdtemp()
cls.ensembl = EnsemblRequest(cls.temp_dir, 'grch37')
cls.mut_dict = load_mutation_rates()
