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Towards an accurate and robust analysis pipeline for somatic mutation calling.
Jingjie Jin1,2, Zixi Chen3, Jinchao Liu4
1Key Laboratory of Functional Protein Research, Guangdong Higher Education Institutes, Jinan University, Guangzhou, China.
Frontiers in Genetics
|December 2, 2022
Summary
FANSe demonstrated superior accuracy and speed for somatic mutation detection compared to VarScan, VarDictJava, Mutect2, and Strelka2. This evaluation is crucial for selecting optimal pipelines in cancer research and diagnostics.
Area of Science:
- Genomics
- Bioinformatics
- Cancer Research
Background:
- Accurate somatic mutation detection is critical for cancer treatment, diagnostics, and research.
- Existing somatic mutation calling pipelines yield variable results, necessitating systematic evaluation.
- Whole-exome sequencing data is widely used for identifying genetic alterations in cancer.
Purpose of the Study:
- To benchmark five common somatic mutation calling pipelines: VarScan, VarDictJava, Mutect2, Strelka2, and FANSe.
- To evaluate their performance in terms of precision, recall, and speed using real-world whole-exome sequencing data.
- To identify the most accurate and efficient pipeline for somatic mutation detection in cancer.
Main Methods:
- Benchmarking of five somatic mutation calling pipelines (VarScan, VarDictJava, Mutect2, Strelka2, FANSe).
- Utilized standard benchmarking datasets derived from real-world whole-exome sequencing datasets.
- Assessed precision, recall, and computational speed of each pipeline.
Main Results:
- All pipelines exhibited high precision and recall for mutations >10%.
- FANSe showed the highest accuracy and sensitivity, especially for low-frequency mutations.
- FANSe was significantly faster (8.8–19x) than other pipelines; filter flaws impacted other pipelines' sensitivity.
Conclusions:
- FANSe offers superior performance in accuracy and speed for somatic mutation detection.
- Pipeline choice significantly impacts results, particularly for low-frequency mutations.
- This study provides a reference for selecting appropriate somatic mutation calling pipelines in cancer applications.
