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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
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author Jia, Peilin
Li, Fei
Xia, Jufeng
Chen, Haiquan
Ji, Hongbin
Pao, William
Zhao, Zhongming
author_facet Jia, Peilin
Li, Fei
Xia, Jufeng
Chen, Haiquan
Ji, Hongbin
Pao, William
Zhao, Zhongming
author_sort Jia, Peilin
collection PubMed
description 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.
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spelling pubmed-33710402012-06-19 Consensus Rules in Variant Detection from Next-Generation Sequencing Data Jia, Peilin Li, Fei Xia, Jufeng Chen, Haiquan Ji, Hongbin Pao, William Zhao, Zhongming PLoS One Research Article 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. Public Library of Science 2012-06-08 /pmc/articles/PMC3371040/ /pubmed/22715385 http://dx.doi.org/10.1371/journal.pone.0038470 Text en Jia et al. http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited.
spellingShingle Research Article
Jia, Peilin
Li, Fei
Xia, Jufeng
Chen, Haiquan
Ji, Hongbin
Pao, William
Zhao, Zhongming
Consensus Rules in Variant Detection from Next-Generation Sequencing Data
title Consensus Rules in Variant Detection from Next-Generation Sequencing Data
title_full Consensus Rules in Variant Detection from Next-Generation Sequencing Data
title_fullStr Consensus Rules in Variant Detection from Next-Generation Sequencing Data
title_full_unstemmed Consensus Rules in Variant Detection from Next-Generation Sequencing Data
title_short Consensus Rules in Variant Detection from Next-Generation Sequencing Data
title_sort consensus rules in variant detection from next-generation sequencing data
topic Research Article
url 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
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