def analyse_gene(ensembl, mut_dict, cadd, symbol, de_novos, constraint,
weights):
''' analyse the severity of de novos found in a gene
Args:
ensembl: EnsemblRequest object, for transcript coordinates and sequence
mut_dict: list of sequence-context mutation probabilities.
cadd: pysam.TabixFile object for CADD scores (SNVs only)
symbol: HGNC symbol for current gene
de_novos: list of de novo mutations observed in current gene. Each entry
is a dict with 'position', 'ref', 'alt', and 'consequence' keys.
weights: dictionary of objects to weight CADD severity scores. We have
different weights for protein-truncating and protein-altering
variants, and within the protein-altering variants, different
weights for variants in constrained and unconstrained regions.
Returns:
p-value for the observed total severity with respect to a null
distribution of severities for the gene.
'''
sites = [x['pos'] for x in de_novos]
try:
# create gene/transcript for de novo mutations
transcripts = load_gene(ensembl, symbol, sites)
except IndexError:
return 'NA'
# get per site/allele mutation rates
rates_by_cq = get_site_sampler(transcripts, mut_dict)
chrom = transcripts[0].get_chrom()
# get per site/allele severity scores, weighted by enrichment of missense
# in known dominant at different severity thresholds
constrained = get_constrained_positions(ensembl, constraint, symbol)
severity = get_severity(cadd, chrom, rates_by_cq, weights, constrained)
# convert the rates per site per consequence to rates per site
rates = WeightedChoice()
for cq in sorted(rates_by_cq):
rates.append(rates_by_cq[cq])
# get summed score for observed de novos
observed = sum((get_severity(cadd, chrom, de_novos, weights, constrained)))
# simulate distribution of summed scores within transcript
return analyse(rates, severity, observed, len(de_novos), 1000000)
def test_append(self):
""" test that append() works correctly
"""
# construct two objects
a = WeightedChoice()
a.add_choice(1, 0.5)
b = WeightedChoice()
b.add_choice(2, 1)
# add one object to the other
a.append(b)
# check that the first object has changed correctly, but the other
# remains unchanged
self.assertEqual(a.get_summed_rate(), 1.5)
self.assertEqual(b.get_summed_rate(), 1.0)
def test_append(self):
""" test that append() works correctly
"""
# construct two objects
a = WeightedChoice()
a.add_choice(1, 0.5)
b = WeightedChoice()
b.add_choice(2, 1)
# add one object to the other
a.append(b)
# check that the first object has changed correctly, but the other
# remains unchanged
self.assertEqual(a.get_summed_rate(), 1.5)
self.assertEqual(b.get_summed_rate(), 1.0)
