Benchmarking Variant Calling Algorithms for the Analysis of Genomic Data in Panel Sequencing

Benchmarking Variant Calling Algorithms for the Analysis of Genomic Data in Panel Sequencing

Stať ve sborníku v databázi WoS či Scopus

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.

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.

Genetic Variants | Genomic Medicine | Next-generation Sequencing | Variant Calling

Genetic Variants | Genomic Medicine | Next-generation Sequencing | Variant Calling

NOVOTNY, J.; SCHWARZEROVÁ, J.; NEUWIRTHOVA, J.; INDRAKOVA, J.; VODICKOVA, T.; FALDYNOVA, L.; SKARDA, J.; WECKWERTH, W.; CIBULKOVA, P.; PROVAZNÍK, V.

01.01.2026

Springer Science and Business Media Deutschland GmbH

9783032084514

Lecture Notes in Computer Science

Lecture Notes in Computer Science

Švýcarská konfederace

73

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