def is_nonsilent(mut_df, bed_dict, opts):
# convert dictionary to list for bed objects
gene_beds = [b
for chrom in bed_dict
for b in bed_dict[chrom]]
# initiate gene sequences
gene_fa = pysam.Fastafile(opts['input'])
gs = GeneSequence(gene_fa, nuc_context=opts['context'])
# non-silent SNV classes
non_silent_snv = ['Nonsense_Mutation', 'Nonstop_Mutation', 'Splice_Site',
'Translation_Start_Site', 'Missense_Mutation']
# record indels and get only snvs
mut_df['is_nonsilent'] = 0
indel_flag = indel.is_indel_annotation(mut_df)
mut_df.loc[indel_flag, 'is_nonsilent'] = 1
snv_df = mut_df[~indel_flag]
# iterate over each gene
for bed in gene_beds:
# initiate for this gene
tmp_df = snv_df[snv_df['Gene']==bed.gene_name]
gs.set_gene(bed)
# compute context counts and somatic bases for each context
gene_tuple = compute_mutation_context(bed, gs, tmp_df, opts)
context_cts, context_to_mutations, mutations_df, gs, sc = gene_tuple
if len(mutations_df):
# get snv information
tmp_mut_info = get_aa_mut_info(mutations_df['Coding Position'],
mutations_df['Tumor_Allele'].tolist(),
gs)
# get string describing variant
var_class = cutils.get_variant_classification(tmp_mut_info['Reference AA'],
tmp_mut_info['Somatic AA'],
tmp_mut_info['Codon Pos'])
# detect if non-silent snv
is_nonsilent_snv = [1 if (x in non_silent_snv) else 0
for x in var_class]
mut_df.loc[tmp_df.index, 'is_nonsilent'] = is_nonsilent_snv
# return a pandas series indicating nonsilent status
is_nonsilent_series = mut_df['is_nonsilent'].copy()
del mut_df['is_nonsilent']
return is_nonsilent_series
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