Cargando…
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:...
Autores principales: | , , , , , , |
---|---|
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 |
_version_ | 1782235170977349632 |
---|---|
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. |
format | Online Article Text |
id | pubmed-3371040 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2012 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
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 |
work_keys_str_mv | AT jiapeilin consensusrulesinvariantdetectionfromnextgenerationsequencingdata AT lifei consensusrulesinvariantdetectionfromnextgenerationsequencingdata AT xiajufeng consensusrulesinvariantdetectionfromnextgenerationsequencingdata AT chenhaiquan consensusrulesinvariantdetectionfromnextgenerationsequencingdata AT jihongbin consensusrulesinvariantdetectionfromnextgenerationsequencingdata AT paowilliam consensusrulesinvariantdetectionfromnextgenerationsequencingdata AT zhaozhongming consensusrulesinvariantdetectionfromnextgenerationsequencingdata |