MOTIVATION: Whole genome and exome sequencing of matched tumor-normal sample 
pairs is becoming routine in cancer research. The consequent increased demand 
for somatic variant analysis of paired samples requires methods specialized to 
model this problem so as to sensitively call variants at any practical level of 
tumor impurity.
RESULTS: We describe Strelka, a method for somatic SNV and small indel detection 
from sequencing data of matched tumor-normal samples. The method uses a novel 
Bayesian approach which represents continuous allele frequencies for both tumor 
and normal samples, while leveraging the expected genotype structure of the 
normal. This is achieved by representing the normal sample as a mixture of 
germline variation with noise, and representing the tumor sample as a mixture of 
the normal sample with somatic variation. A natural consequence of the model 
structure is that sensitivity can be maintained at high tumor impurity without 
requiring purity estimates. We demonstrate that the method has superior accuracy 
and sensitivity on impure samples compared with approaches based on either 
diploid genotype likelihoods or general allele-frequency tests.
AVAILABILITY: The Strelka workflow source code is available at 
ftp://strelka@ftp.illumina.com/.
CONTACT: csaunders@illumina.com