Comparative Evaluation of Mutect2, Strelka2, and FreeBayes for Somatic SNV Detection in Synthetic and Clinical Whole-Exome Sequencing Data.

ABSTRACT: Somatic variant calling is a critical step in cancer genome analysis, but the performance of available tools can vary depending on their underlying algorithms and filtering strategies. We compared three widely used variant callers-Mutect2, Strelka2, and FreeBayes-for their performance in somatic single-nucleotide variant (SNV) detection using both synthetic and real whole-exome sequencing (WES) data. Synthetic data were generated by introducing 4709 SNVs into a variant-free BAM file, while real data consisted of tumor and matched normal WES samples from five ovarian cancer (OC) patients. All callers were run using the nf-core/sarek pipeline with default settings and appropriate filtering. In the synthetic dataset, all tools showed high precision (~99.9%), with Mutect2 achieving the highest

SUBMITTER: Lopez-Cade I 

PROVIDER: S-EPMC12650410 | biostudies-literature | 2025 Oct

REPOSITORIES: biostudies-literature