Benchmarking Variant Calling Algorithms for the Analysis of Genomic Data in Panel Sequencing
The result's identifiers
Result code in 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>
Result on the web
<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>
Alternative languages
Result language
angličtina
Original language name
Benchmarking Variant Calling Algorithms for the Analysis of Genomic Data in Panel Sequencing
Original language description
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.
Czech name
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Czech description
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Classification
Type
D - Article in proceedings
CEP classification
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OECD FORD branch
10200 - Computer and information sciences
Result continuities
Project
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Continuities
S - Specificky vyzkum na vysokych skolach
Others
Publication year
2026
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
Lecture Notes in Computer Science
ISBN
9783032084514
ISSN
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e-ISSN
1611-3349
Number of pages
12
Pages from-to
73-84
Publisher name
Springer Science and Business Media Deutschland GmbH
Place of publication
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Event location
Canaria, Spain
Event date
Jul 16, 2025
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
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