Analizing the contexts in which mutations occur

Not all mutations are the same and here we analyze how are the common mutation consequences distributed,



In [20]:

    
%matplotlib inline

import matplotlib.pyplot as plt
import seaborn as sns

sns.set()

We first map genes to the number of mutations they harbor (read from a random sample of 100,000 mutations)



In [9]:

    
from collections import Counter
from ICGC_data_parser import SSM_Reader

counter = Counter()

# Open the mutations file
mutations = SSM_Reader(filename='data/ssm_sample.vcf')
consequences = mutations.subfield_parser('CONSEQUENCE')

for record in mutations:
    consequence_types = [c.consequence_type for c in consequences(record)]
    counter.update(consequence_types)
    

total = sum(counter.values())
for consequence_type,n in counter.most_common():
    print(f'{n/total :<10.3%} : {consequence_type}')









    Out[9]:





[('intron_variant', 179634),
 ('intergenic_region', 51253),
 ('downstream_gene_variant', 25290),
 ('upstream_gene_variant', 24513),
 ('missense_variant', 4898),
 ('exon_variant', 3922),
 ('synonymous_variant', 2204),
 ('3_prime_UTR_variant', 1933),
 ('splice_region_variant', 494),
 ('5_prime_UTR_variant', 425),
 ('stop_gained', 303),
 ('frameshift_variant', 292),
 ('splice_acceptor_variant', 117),
 ('splice_donor_variant', 110),
 ('5_prime_UTR_premature_start_codon_gain_variant', 69),
 ('intragenic_variant', 16),
 ('inframe_deletion', 15),
 ('disruptive_inframe_deletion', 11),
 ('stop_retained_variant', 5),
 ('disruptive_inframe_insertion', 4),
 ('start_lost', 3),
 ('stop_lost', 3),
 ('inframe_insertion', 2)]