A population model for genotyping indels from next-generation sequence data

Insertion and deletion polymorphisms (indels) are an important source of genomic variation in plant and animal genomes, but accurate genotyping from low-coverage and exome next-generation sequence data remains challenging. We introduce an efficient population clustering algorithm for diploids and po...

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Detalles Bibliográficos
Autores principales: Shao, Haojing, Bellos, Evangelos, Yin, Hanjiudai, Liu, Xiao, Zou, Jing, Li, Yingrui, Wang, Jun, Coin, Lachlan J. M.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Oxford University Press 2013
Materias:
Methods Online
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3562001/
https://www.ncbi.nlm.nih.gov/pubmed/23221639
http://dx.doi.org/10.1093/nar/gks1143
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author Shao, Haojing
Bellos, Evangelos
Yin, Hanjiudai
Liu, Xiao
Zou, Jing
Li, Yingrui
Wang, Jun
Coin, Lachlan J. M.
author_facet Shao, Haojing
Bellos, Evangelos
Yin, Hanjiudai
Liu, Xiao
Zou, Jing
Li, Yingrui
Wang, Jun
Coin, Lachlan J. M.
author_sort Shao, Haojing
collection PubMed
description Insertion and deletion polymorphisms (indels) are an important source of genomic variation in plant and animal genomes, but accurate genotyping from low-coverage and exome next-generation sequence data remains challenging. We introduce an efficient population clustering algorithm for diploids and polyploids which was tested on a dataset of 2000 exomes. Compared with existing methods, we report a 4-fold reduction in overall indel genotype error rates with a 9-fold reduction in low coverage regions.
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spelling pubmed-35620012013-02-01 A population model for genotyping indels from next-generation sequence data Shao, Haojing Bellos, Evangelos Yin, Hanjiudai Liu, Xiao Zou, Jing Li, Yingrui Wang, Jun Coin, Lachlan J. M. Nucleic Acids Res Methods Online Insertion and deletion polymorphisms (indels) are an important source of genomic variation in plant and animal genomes, but accurate genotyping from low-coverage and exome next-generation sequence data remains challenging. We introduce an efficient population clustering algorithm for diploids and polyploids which was tested on a dataset of 2000 exomes. Compared with existing methods, we report a 4-fold reduction in overall indel genotype error rates with a 9-fold reduction in low coverage regions. Oxford University Press 2013-02 2012-12-05 /pmc/articles/PMC3562001/ /pubmed/23221639 http://dx.doi.org/10.1093/nar/gks1143 Text en © The Author(s) 2012. Published by Oxford University Press. http://creativecommons.org/licenses/by-nc/3.0 This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by-nc/3.0/), which permits non-commercial reuse, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact journals.permissions@oup.com.
spellingShingle Methods Online
Shao, Haojing
Bellos, Evangelos
Yin, Hanjiudai
Liu, Xiao
Zou, Jing
Li, Yingrui
Wang, Jun
Coin, Lachlan J. M.
A population model for genotyping indels from next-generation sequence data
title A population model for genotyping indels from next-generation sequence data
title_full A population model for genotyping indels from next-generation sequence data
title_fullStr A population model for genotyping indels from next-generation sequence data
title_full_unstemmed A population model for genotyping indels from next-generation sequence data
title_short A population model for genotyping indels from next-generation sequence data
title_sort population model for genotyping indels from next-generation sequence data
topic Methods Online
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3562001/
https://www.ncbi.nlm.nih.gov/pubmed/23221639
http://dx.doi.org/10.1093/nar/gks1143
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