A comprehensive review of deep learning-based variant calling methods.

Junjun, Ren; Zhengqian, Zhang; Ying, Wu; Jialiang, Wang; Yongzhuang, Liu

Junjun, Ren; Zhengqian, Zhang; Ying, Wu; Jialiang, Wang; Yongzhuang, Liu.

Afiliación

Junjun R; Harbin Institute of Technology, School of Computer Science and Technology, Harbin 150001, China.
Zhengqian Z; Harbin Institute of Technology, School of Computer Science and Technology, Harbin 150001, China.
Ying W; Harbin Institute of Technology, School of Computer Science and Technology, Harbin 150001, China.
Jialiang W; Harbin Institute of Technology, School of Computer Science and Technology, Harbin 150001, China.
Yongzhuang L; Harbin Institute of Technology, School of Computer Science and Technology, Harbin 150001, China.

Brief Funct Genomics ; 23(4): 303-313, 2024 Jul 19.

Article en En | MEDLINE | ID: mdl-38366908

ABSTRACT

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.

Asunto(s)

Aprendizaje Profundo; Humanos; Variación Genética; Genómica/métodos; Algoritmos

Palabras clave

SNP and Indel; deep learning; genomic variation; structural variation; variant calling

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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Aprendizaje Profundo Límite: Humans Idioma: En Revista: Brief Funct Genomics Año: 2024 Tipo del documento: Article País de afiliación: China Pais de publicación: Reino Unido

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