SUMMARY: Somatic internal tandem duplications (ITDs) are known to play important roles in cancer pathogenesis. Although recent advances in high-throughput sequencing technologies have enabled genome-wide detection of various types of genomic mutations, including single nucleotide variants, indels and structural variations, only a few studies have focused on ITDs. We have developed an analytical tool called 'Genomon ITDetector' for genome-wide detection of somatic ITDs. After evaluating the sensitivity and precision of the proposed approach using synthetic data, we have demonstrated that it can successfully detect not only common ITDs involving FLT3, but also a number of ITDs affecting other putative driver genes in acute myeloid leukemia exome sequencing data. Availability and implementaion: Genomon ITDetector is freely available at https://github.com/ken0-1n/Genomon-ITDetector.

MOTIVATION: Identifying somatic changes from tumor and matched normal sequences has become a standard approach in cancer research. More specifically, this requires accurate detection of somatic point mutations with low allele frequencies in impure and heterogeneous cancer samples. Although haplotype phasing information derived by using heterozygous germ line variants near candidate mutations would improve accuracy, no somatic mutation caller that uses such information is currently available. RESULTS: We propose a Bayesian hierarchical method, termed HapMuC, in which power is increased by using available information on heterozygous germ line variants located near candidate mutations. We first constructed two generative models (the mutation model and the error model). In the generative models, we prepared candidate haplotypes, considering a heterozygous germ line variant if available, and the observed reads were realigned to the haplotypes. We then inferred the haplotype frequencies and computed the marginal likelihoods using a variational Bayesian algorithm. Finally, we derived a Bayes factor for evaluating the possibility of the existence of somatic mutations. We also demonstrated that our algorithm has superior specificity and sensitivity compared with existing methods, as determined based on a simulation, the TCGA Mutation Calling Benchmark 4 datasets and data from the COLO-829 cell line. AVAILABILITY AND IMPLEMENTATION: The HapMuC source code is available from http://github.com/usuyama/hapmuc.