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

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26220%2F26%3A0200046" target="_blank" >RIV/00216305:26220/26:0200046 - isvavai.cz</a>

Výsledek na webu

<a href="http://dx.doi.org/10.1007/978-3-032-08452-1_7" target="_blank" >http://dx.doi.org/10.1007/978-3-032-08452-1_7</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/978-3-032-08452-1_7" target="_blank" >10.1007/978-3-032-08452-1_7</a>

Jazyk výsledku

angličtina

Název v původním jazyce

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

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

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

Popis výsledku anglicky

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.

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

10200 - Computer and information sciences

Projekt

—

Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2026

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

Lecture Notes in Computer Science

ISBN

9783032084514

ISSN

—

e-ISSN

1611-3349

Počet stran výsledku

12

Strana od-do

73-84

Název nakladatele

Springer Science and Business Media Deutschland GmbH

Místo vydání

—

Místo konání akce

Canaria, Spain

Datum konání akce

16. 7. 2025

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

—