A comprehensive review of deep learning-based variant calling methods

Genome sequencing data have become increasingly important in the field of personalized medicine and diagnosis. However, accurately detecting genomic variations remains a challenging task. Traditional variation detection methods rely on manual inspection or predefined rules, which can be time-consumi...

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Published inBriefings in functional genomics Vol. 23; no. 4; p. 303
Main Authors Junjun, Ren, Zhengqian, Zhang, Ying, Wu, Jialiang, Wang, Yongzhuang, Liu
Format Journal Article
LanguageEnglish
Published England 19.07.2024
Subjects
Algorithms
Deep Learning
Genetic Variation
Genomics - methods
Humans
SNP and Indel
deep learning
genomic variation
structural variation
variant calling
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Summary:Genome sequencing data have become increasingly important in the field of personalized medicine and diagnosis. However, accurately detecting genomic variations remains a challenging task. Traditional variation detection methods rely on manual inspection or predefined rules, which can be time-consuming and prone to errors. Consequently, deep learning-based approaches for variation detection have gained attention due to their ability to automatically learn genomic features that distinguish between variants. In our review, we discuss the recent advancements in deep learning-based algorithms for detecting small variations and structural variations in genomic data, as well as their advantages and limitations.
ISSN:2041-2657
DOI:10.1093/bfgp/elae003