0000000000800488
AUTHOR
Srinivas Aluru
SNVSniffer: an integrated caller for germline and somatic single-nucleotide and indel mutations
Various approaches to calling single-nucleotide variants (SNVs) or insertion-or-deletion (indel) mutations have been developed based on next-generation sequencing (NGS). However, most of them are dedicated to a particular type of mutation, e.g. germline SNVs in normal cells, somatic SNVs in cancer/tumor cells, or indels only. In the literature, efficient and integrated callers for both germline and somatic SNVs/indels have not yet been extensively investigated. We present SNVSniffer, an efficient and integrated caller identifying both germline and somatic SNVs/indels from NGS data. In this algorithm, we propose the use of Bayesian probabilistic models to identify SNVs and investigate a mult…
SNVSniffer: An integrated caller for germline and somatic SNVs based on Bayesian models
The discovery of single nucleotide variants (SNVs) from next-generation sequencing (NGS) data typically works by aligning reads to a given genome and then creating an alignment map to interpret the presence of SNVs. Various approaches have been developed to call whether germline SNVs (or SNPs) in normal cells or somatic SNVs in cancer/tumor cells. Nonetheless, efficient callers for both germline and somatic SNVs have not yet been extensively investigated. In this paper, we present SNVSniffer, an integrated caller for germline and somatic SNVs from NGS data based on Bayesian probabilistic models. In SNVSniffer, our germline SNV calling models allele counts per site as a multinomial condition…