Benchmarking Variant Calling Algorithms for the Analysis of Genomic Data in Panel Sequencing
- Author(s)
- Jiri Novotny, Jana Schwarzerova, Jana Neuwirthova, Jana Indrakova, Tereza Vodickova, Lucie Faldynova, Jozef Skarda, Wolfram Weckwerth, Petra Cibulkova, Valentyna Provaznik
- Abstract
Recent advancements in next-generation sequencing (NGS) technologies have significantly improved our ability to investigate the genetic foundations of various diseases, ranging from rare genetic disorders to complex polygenic conditions and hereditary cancers. Accurate identification of genetic variants, such as single nucleotide variants (SNVs), insertions, deletions, and structural variations, is essential for enhancing diagnosis, prognosis, and personalized treatment strategies. However, the performance of variant calling algorithms can vary depending on factors such as sequencing quality, read depth, and the complexity of the analyzed genomic regions. This study aims to evaluate the performance of three widely used variant calling tools—DeepVariant, Strelka2, and Haplotyper—on genomic data from fifteen patients who underwent NGS sequencing at the University Hospital Ostrava. The patients represent a diverse array of genetic profiles, including rare genetic diseases, inherited kidney disorders, and hereditary cancers, such as breast and ovarian cancer associated with BRCA1/2 mutations. The primary objective is to assess the accuracy, sensitivity, and efficiency of these tools in detecting a broad range of genetic variants. The results of this study offer a valuable perspective on the strengths and limitations of individual variant calling tools and may assist in selecting appropriate approaches for genetic variant detection in both clinical and research settings. Improved variant detection could contribute to a deeper understanding of genetic diseases and support more accurate diagnoses and personalized treatment, thereby fostering further advancement in genomic medicine.
- Organisation(s)
- Functional and Evolutionary Ecology
- External organisation(s)
- University Hospital Ostrava, Brno University of Technology, Palacky University and University Hospital Olomouc, Charles University Prague, Czech Institute of Informatics, Robotics and Cybernetics, University of Ostrava
- Pages
- 73-84
- No. of pages
- 12
- DOI
- https://doi.org/10.1007/978-3-032-08452-1_7
- Publication date
- 2026
- Peer reviewed
- Yes
- Austrian Fields of Science 2012
- 102020 Medical informatics
- Keywords
- ASJC Scopus subject areas
- Theoretical Computer Science, General Computer Science
- Portal url
- https://ucrisportal.univie.ac.at/en/publications/445c2dc6-d12b-4e44-9bed-93ba0c94c8e5