Junjun R, Zhengqian Z, Ying W, Jialiang W, Yongzhuang L. A comprehensive review of deep learning-based variant calling methods.
Brief Funct Genomics 2024:elae003. [PMID:
38366908 DOI:
10.1093/bfgp/elae003]
[Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2023] [Revised: 01/14/2024] [Accepted: 01/18/2023] [Indexed: 02/18/2024] Open
Abstract
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.
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