Performance of common analysis methods for detecting low-frequency single nucleotide variants in targeted next-generation sequence data.

ABSTRACT: Next-generation sequencing (NGS) is becoming a common approach for clinical testing of oncology specimens for mutations in cancer genes. Unlike inherited variants, cancer mutations may occur at low frequencies because of contamination from normal cells or tumor heterogeneity and can therefore be challenging to detect using common NGS analysis tools, which are often designed for constitutional genomic studies. We generated high-coverage (>1000×) NGS data from synthetic DNA mixtures with variant allele fractions (VAFs) of 25% to 2.5% to assess the performance of four variant callers, SAMtools, Genome Analysis Toolkit, VarScan2, and SPLINTER, in detecting low-frequency variants. SAMtools had the lowest sensitivity and detected only 49% of variants with VAFs of approximately 25%; whereas the G

SUBMITTER: Spencer DH 

PROVIDER: S-EPMC3873500 | biostudies-literature | 2014 Jan

REPOSITORIES: biostudies-literature