Towards an accurate and robust analysis pipeline for somatic mutation calling

Accurate and robust somatic mutation detection is essential for cancer treatment, diagnostics and research. Various analysis pipelines give different results and thus should be systematically evaluated. In this study, we benchmarked 5 commonly-used somatic mutation calling pipelines (VarScan, VarDictJava, Mutect2, Strelka2 and FANSe) for their precision, recall and speed, using standard benchmarking datasets based on a series of real-world whole-exome sequencing datasets. All the 5 pipelines showed very high precision in all cases, and high recall rate in mutation rates higher than 10%. However, for the low frequency mutations, these pipelines showed large difference. FANSe showed the highest accuracy (especially the sensitivity) in all cases, and VarScan and VarDictJava outperformed Mutect2 and Strelka2 in low frequency mutations at all sequencing depths. The flaws in filter was the major cause of the low sensitivity of the four pipelines other than FANSe. Concerning the speed, FANSe pipeline was 8.8 & SIM;19x faster than the other pipelines. Our benchmarking results demonstrated performance of the somatic calling pipelines and provided a reference for a proper choice of such pipelines in cancer applications.

Automated summary

Accurate and robust somatic mutation detection is crucial for cancer treatment and research. This study compared five commonly-used somatic mutation calling pipelines and evaluated their precision, recall, and speed. The results showed high accuracy and recall rate for all pipelines in cases with high mutation rates. However, there were significant differences among the pipelines for low frequency mutations, with FANSe performing the best. The flaws in filter were identified as the major cause of low sensitivity in the other pipelines. In terms of speed, FANSe pipeline was much faster than the others, with a speed advantage of 8.8 to 19 times. These benchmarking results provide valuable insights for choosing appropriate somatic mutation calling pipelines in cancer applications.