A new approach for detecting low-level mutations in next-generation sequence data

We propose a new method that incorporates population re-sequencing data, distribution of reads, and strand bias in detecting low-level mutations. The method can accurately identify low-level mutations down to a level of 2.3%, with an average coverage of 500×, and with a false discovery rate of less...

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Detalles Bibliográficos
Autores principales: Li, Mingkun, Stoneking, Mark
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2012
Materias:
Method
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3446287/
https://www.ncbi.nlm.nih.gov/pubmed/22621726
http://dx.doi.org/10.1186/gb-2012-13-5-r34
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author Li, Mingkun
Stoneking, Mark
author_facet Li, Mingkun
Stoneking, Mark
author_sort Li, Mingkun
collection PubMed
description We propose a new method that incorporates population re-sequencing data, distribution of reads, and strand bias in detecting low-level mutations. The method can accurately identify low-level mutations down to a level of 2.3%, with an average coverage of 500×, and with a false discovery rate of less than 1%. In addition, we also discuss other problems in detecting low-level mutations, including chimeric reads and sample cross-contamination, and provide possible solutions to them.
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spelling pubmed-34462872012-09-20 A new approach for detecting low-level mutations in next-generation sequence data Li, Mingkun Stoneking, Mark Genome Biol Method We propose a new method that incorporates population re-sequencing data, distribution of reads, and strand bias in detecting low-level mutations. The method can accurately identify low-level mutations down to a level of 2.3%, with an average coverage of 500×, and with a false discovery rate of less than 1%. In addition, we also discuss other problems in detecting low-level mutations, including chimeric reads and sample cross-contamination, and provide possible solutions to them. BioMed Central 2012 2012-05-23 /pmc/articles/PMC3446287/ /pubmed/22621726 http://dx.doi.org/10.1186/gb-2012-13-5-r34 Text en Copyright ©2012 Li and Stoneking; licensee BioMed Central Ltd. http://creativecommons.org/licenses/by/2.0 This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Method
Li, Mingkun
Stoneking, Mark
A new approach for detecting low-level mutations in next-generation sequence data
title A new approach for detecting low-level mutations in next-generation sequence data
title_full A new approach for detecting low-level mutations in next-generation sequence data
title_fullStr A new approach for detecting low-level mutations in next-generation sequence data
title_full_unstemmed A new approach for detecting low-level mutations in next-generation sequence data
title_short A new approach for detecting low-level mutations in next-generation sequence data
title_sort new approach for detecting low-level mutations in next-generation sequence data
topic Method
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3446287/
https://www.ncbi.nlm.nih.gov/pubmed/22621726
http://dx.doi.org/10.1186/gb-2012-13-5-r34
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