ArticleFrontiers in genetics2022
Towards an accurate and robust analysis pipeline for somatic mutation calling.
Article in Frontiers in genetics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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10 citing papers in PubMed, 9 citations in OpenAlex.
- Review
- Benchmarking major somatic structural variant callers on the HG008 genome.Frontiers in genetics · 2026Article
- Comparative Evaluation of Mutect2, Strelka2, and FreeBayes for Somatic SNV Detection in Synthetic and Clinical Whole-Exome Sequencing Data.Biomolecules · 2025Article
- Genomic Insights into Idiopathic Granulomatous Mastitis through Whole-Exome Sequencing: A Case Report of Eight Patients.International journal of molecular sciences · 2024Article
- DeepSomatic: Accurate somatic small variant discovery for multiple sequencing technologies.bioRxiv : the preprint server for biology · 2024Article
- Integrated approach to generate artificial samples with low tumor fraction for somatic variant calling benchmarking.BMC bioinformatics · 2024Article
- Neoantigen cancer vaccines: a new star on the horizon.Cancer biology & medicine · 2023Review
- Personalized Cancer Vaccines Go Viral: Viral Vectors in the Era of Personalized Immunotherapy of Cancer.International journal of molecular sciences · 2023Review
- Technological and computational approaches to detect somatic mosaicism in epilepsy.Neurobiology of disease · 2023Review
- An effective prognostic model for assessing prognosis of non-small cell lung cancer with brain metastases.Frontiers in genetics · 2023Article
Corrections and comments
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Authors and funding
5 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
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∼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.
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