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Volume 15
Issue 11
10.3390/biom15111532
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This is an early access version, the complete PDF, HTML, and XML versions will be available soon.
Article

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

by
Igor López-Cade
1,
Alicia Gómez-Sanz
2,
Adrián Sanvicente
1,
Cristina Díaz-Tejeiro
1,
Aránzazu Manzano
3,4,5,
Pedro Pérez-Segura
3,
Balázs Győrffy
6,7,8,
Alberto Ocaña
1,
Miguel de la Hoya
2,* and
Vanesa García-Barberán
2,*
1
Experimental Therapeutics Unit, Oncology Department, Hospital Clínico San Carlos (HCSC), Instituto de Investigación Sanitaria San Carlos (IdISSC), 28040 Madrid, Spain
2
“Clinical and Translational Research in Oncology” Group, Molecular Oncology Laboratory, Hospital Clinico San Carlos, IdISSC, 28040 Madrid, Spain
3
Department of Medical Oncology, Hospital Clínico San Carlos, IdISSC, 28040 Madrid, Spain
4
Department of Medical Oncology, University Hospital 12 de Octubre, 28041 Madrid, Spain
5
Research Institute [imas12], Complutense University of Madrid, 28040 Madrid, Spain
6
Department of Bioinformatics, Semmelweis University, H-1094 Budapest, Hungary
7
Cancer Biomarker Research Group, Institute of Molecular Life Sciences, HUN-REN Research Centre for Natural Sciences, H-1117 Budapest, Hungary
8
Institute of Transdisciplinary Discoveries, Medical School, University of Pecs, H-7624 Pecs, Hungary
*
Authors to whom correspondence should be addressed.
Biomolecules 2025, 15(11), 1532; https://doi.org/10.3390/biom15111532
Submission received: 23 July 2025 / Revised: 17 October 2025 / Accepted: 22 October 2025 / Published: 30 October 2025
(This article belongs to the Special Issue Advances in Computational Approaches for the Discovery of Therapeutics and Personalized Medicine)
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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 recall (63.1%), followed by Strelka2 (46.3%) and FreeBayes (45.2%). In real samples, FreeBayes detected the most variants, and only 5.1% of SNVs were shared across all three tools. We then integrated calls with SomaticSeq in consensus mode (Mutect2 + Strelka2) and kept variants with stronger allelic signals—showing higher VAFs and, typically, higher coverages relative to single-caller only. Caller-exclusive variants showed significant differences in allele frequency and sequencing depth. These results highlight substantial variability in SNV detection across tools. While all showed high specificity, differences in sensitivity and variant profiles underscore the need for context-specific caller selection or ensemble approaches in cancer genomics.
Keywords: variant caller; somatic; WES; variant allele frequency; read depth variant caller; somatic; WES; variant allele frequency; read depth
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MDPI and ACS Style

López-Cade, I.; Gómez-Sanz, A.; Sanvicente, A.; Díaz-Tejeiro, C.; Manzano, A.; Pérez-Segura, P.; Győrffy, B.; Ocaña, A.; de la Hoya, M.; García-Barberán, V. Comparative Evaluation of Mutect2, Strelka2, and FreeBayes for Somatic SNV Detection in Synthetic and Clinical Whole-Exome Sequencing Data. Biomolecules 2025, 15, 1532. https://doi.org/10.3390/biom15111532

AMA Style

López-Cade I, Gómez-Sanz A, Sanvicente A, Díaz-Tejeiro C, Manzano A, Pérez-Segura P, Győrffy B, Ocaña A, de la Hoya M, García-Barberán V. Comparative Evaluation of Mutect2, Strelka2, and FreeBayes for Somatic SNV Detection in Synthetic and Clinical Whole-Exome Sequencing Data. Biomolecules. 2025; 15(11):1532. https://doi.org/10.3390/biom15111532

Chicago/Turabian Style

López-Cade, Igor, Alicia Gómez-Sanz, Adrián Sanvicente, Cristina Díaz-Tejeiro, Aránzazu Manzano, Pedro Pérez-Segura, Balázs Győrffy, Alberto Ocaña, Miguel de la Hoya, and Vanesa García-Barberán. 2025. "Comparative Evaluation of Mutect2, Strelka2, and FreeBayes for Somatic SNV Detection in Synthetic and Clinical Whole-Exome Sequencing Data" Biomolecules 15, no. 11: 1532. https://doi.org/10.3390/biom15111532

APA Style

López-Cade, I., Gómez-Sanz, A., Sanvicente, A., Díaz-Tejeiro, C., Manzano, A., Pérez-Segura, P., Győrffy, B., Ocaña, A., de la Hoya, M., & García-Barberán, V. (2025). Comparative Evaluation of Mutect2, Strelka2, and FreeBayes for Somatic SNV Detection in Synthetic and Clinical Whole-Exome Sequencing Data. Biomolecules, 15(11), 1532. https://doi.org/10.3390/biom15111532

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