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Comprehensive benchmarking and guidelines of mosaic variant calling strategies.
Ha, Yoo-Jin; Kang, Seungseok; Kim, Jisoo; Kim, Junhan; Jo, Se-Young; Kim, Sangwoo.
Afiliación
  • Ha YJ; Translational Genome Informatics Laboratory, Department of Biomedical Systems Informatics, Yonsei University College of Medicine, Seoul, Republic of Korea.
  • Kang S; Brain Korea 21 PLUS Project for Medical Science, Yonsei University College of Medicine, Seoul, Republic of Korea.
  • Kim J; Translational Genome Informatics Laboratory, Department of Biomedical Systems Informatics, Yonsei University College of Medicine, Seoul, Republic of Korea.
  • Kim J; Brain Korea 21 PLUS Project for Medical Science, Yonsei University College of Medicine, Seoul, Republic of Korea.
  • Jo SY; Translational Genome Informatics Laboratory, Department of Biomedical Systems Informatics, Yonsei University College of Medicine, Seoul, Republic of Korea.
  • Kim S; Translational Genome Informatics Laboratory, Department of Biomedical Systems Informatics, Yonsei University College of Medicine, Seoul, Republic of Korea.
Nat Methods ; 20(12): 2058-2067, 2023 Dec.
Article en En | MEDLINE | ID: mdl-37828153
ABSTRACT
Rapid advances in sequencing and analysis technologies have enabled the accurate detection of diverse forms of genomic variants represented as heterozygous, homozygous and mosaic mutations. However, the best practices for mosaic variant calling remain disorganized owing to the technical and conceptual difficulties faced in evaluation. Here we present our benchmark of 11 feasible mosaic variant detection approaches based on a systematically designed whole-exome-level reference standard that mimics mosaic samples, supported by 354,258 control positive mosaic single-nucleotide variants and insertion-deletion mutations and 33,111,725 control negatives. We identified not only the best practice for mosaic variant detection but also the condition-dependent strengths and weaknesses of the current methods. Furthermore, feature-level evaluation and their combinatorial usage across multiple algorithms direct the way for immediate to prolonged improvements in mosaic variant detection. Our results will guide researchers in selecting suitable calling algorithms and suggest future strategies for developers.
Asunto(s)

Texto completo: 1 Banco de datos: MEDLINE Asunto principal: Benchmarking / Secuenciación de Nucleótidos de Alto Rendimiento Límite: Humans Idioma: En Año: 2023 Tipo del documento: Article

Texto completo: 1 Banco de datos: MEDLINE Asunto principal: Benchmarking / Secuenciación de Nucleótidos de Alto Rendimiento Límite: Humans Idioma: En Año: 2023 Tipo del documento: Article