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Consensus Rules in Variant Detection from Next-Generation Sequencing Data

A critical step in detecting variants from next-generation sequencing data is post hoc filtering of putative variants called or predicted by computational tools. Here, we highlight four critical parameters that could enhance the accuracy of called single nucleotide variants and insertions/deletions:...

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Detalles Bibliográficos
Autores principales: Jia, Peilin, Li, Fei, Xia, Jufeng, Chen, Haiquan, Ji, Hongbin, Pao, William, Zhao, Zhongming
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2012
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3371040/
https://www.ncbi.nlm.nih.gov/pubmed/22715385
http://dx.doi.org/10.1371/journal.pone.0038470
Descripción
Sumario:A critical step in detecting variants from next-generation sequencing data is post hoc filtering of putative variants called or predicted by computational tools. Here, we highlight four critical parameters that could enhance the accuracy of called single nucleotide variants and insertions/deletions: quality and deepness, refinement and improvement of initial mapping, allele/strand balance, and examination of spurious genes. Use of these sequence features appropriately in variant filtering could greatly improve validation rates, thereby saving time and costs in next-generation sequencing projects.