Somatic mutation detection workflow validity distinctly influences clinical decision.
Evaluating robustness of somatic mutation detections is essential when utilizing whole exome sequencing (WES) for treatment decision-making. A comprehensive evaluation was conducted using tumor WES from the FDA-led Sequencing Quality Control Phase 2 (SEQC2) project, in which multiple library kits sequenced identical DNA materials across three labs to benchmark analytical validity. These workflows included various read aligner (BWA, Bowtie2, DRAGEN-Aligner, DRAGMAP, and HISAT2) and mutation caller (Mutect2, TNscope, DRAGEN-Caller, and DeepVariant) combinations. The results revealed that DRAGEN exhibited superior performance, achieving mean F1-scores of 0.966 and 0.791 for SNV and INDEL detection, respectively. Among open-source software, BWA Mutect2 and HISAT2 Mutect2 combinations showed the highest mean F1-scores for SNV (0.949) and IN-DEL (0.722), respectively. The analyses indicated that high-quality data can be analyzed as having worse results, and vice versa. Evaluations of COSMIC reported mutations unveiled discrepancies across enrichment kits. IDT enrichment kits showed a higher false negative rate, while Agilent WES kits tended to miss mutations in CBL and IDH1, and Roche library kits tended to miss the mutations in PIK3CB. For drug-related biomarkers, Sentieon TNscope tended to underestimate tumor mutation burden and overlook crucial drug-resistance mutations such as FLT3 (c.G1879A: p.A627T) for cytarabine resistance in leukemia and MAP2K1 (c.G199A:p.D67N) for BRAF inhibitors in melanoma. The findings highlight the importance of robust bioinformatic analysis in identifying tumor mutations and guiding clinical decision-making. HighlightsO_LIMutation callers had a significantly higher effect on overall sensitivity than aligners. C_LIO_LIBenchmarking analyses demonstrated that high-quality sequencing reads can be analyzed as having worse results, and vice versa. C_LIO_LIDRAGEN exhibited the best performance among other aligner-caller combinations. C_LIO_LIThe combination of BWA with Mutect2 and HISAT2 with Mutect2 yielded the highest mean F1 scores for detecting SNVs and INDELs by open-source software, respectively. C_LIO_LISentieon TNscope tended to underestimate the tumor mutation burden and missed several drug-resistant mutations. C_LI