RareVar: A Framework for Detecting Low-Frequency Single-Nucleotide Variants.

ABSTRACT: Accurate identification of low-frequency somatic point mutations in tumor samples has important clinical utilities. Although high-throughput sequencing technology enables capturing such variants while sequencing primary tumor samples, our ability for accurate detection is compromised when the variant frequency is close to the sequencer error rate. Most current experimental and bioinformatic strategies target mutations with ≥5% allele frequency, which limits our ability to understand the cancer etiology and tumor evolution. We present an experimental and computational modeling framework, RareVar, to reliably identify low-frequency single-nucleotide variants from high-throughput sequencing data under standard experimental protocols. RareVar protocol includes a benchmark design by pooling DNA

SUBMITTER: Hao Y 

PROVIDER: S-EPMC5510701 | biostudies-literature | 2017 Jul

REPOSITORIES: biostudies-literature