Cargando…

A highly robust and optimized sequence-based approach for genetic polymorphism discovery and genotyping in large plant populations

KEY MESSAGE: This optimized approach provides both a computational tool and a library construction protocol, which can maximize the number of genomic sequence reads that uniformly cover a plant genome and minimize the number of sequence reads representing chloroplast DNA and rRNA genes. One can impl...

Descripción completa

Detalles Bibliográficos
Autores principales: Jiang, Ning, Zhang, Fengjun, Wu, Jinhua, Chen, Yue, Hu, Xiaohua, Fang, Ou, Leach, Lindsey J., Wang, Di, Luo, Zewei
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer Berlin Heidelberg 2016
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4983294/
https://www.ncbi.nlm.nih.gov/pubmed/27316437
http://dx.doi.org/10.1007/s00122-016-2736-9
_version_ 1782447882303963136
author Jiang, Ning
Zhang, Fengjun
Wu, Jinhua
Chen, Yue
Hu, Xiaohua
Fang, Ou
Leach, Lindsey J.
Wang, Di
Luo, Zewei
author_facet Jiang, Ning
Zhang, Fengjun
Wu, Jinhua
Chen, Yue
Hu, Xiaohua
Fang, Ou
Leach, Lindsey J.
Wang, Di
Luo, Zewei
author_sort Jiang, Ning
collection PubMed
description KEY MESSAGE: This optimized approach provides both a computational tool and a library construction protocol, which can maximize the number of genomic sequence reads that uniformly cover a plant genome and minimize the number of sequence reads representing chloroplast DNA and rRNA genes. One can implement the developed computational tool to feasibly design their own RAD-seq experiment to achieve expected coverage of sequence variant markers for large plant populations using information of the genome sequence and ideally, though not necessarily, information of the sequence polymorphism distribution in the genome. ABSTRACT: Advent of the next generation sequencing techniques motivates recent interest in developing sequence-based identification and genotyping of genome-wide genetic variants in large populations, with RAD-seq being a typical example. Without taking proper account for the fact that chloroplast and rRNA genes may occupy up to 60 % of the resulting sequence reads, the current RAD-seq design could be very inefficient for plant and crop species. We presented here a generic computational tool to optimize RAD-seq design in any plant species and experimentally tested the optimized design by implementing it to screen for and genotype sequence variants in four plant populations of diploid and autotetraploid Arabidopsis and potato Solanum tuberosum. Sequence data from the optimized RAD-seq experiments shows that the undesirable chloroplast and rRNA contributed sequence reads can be controlled at 3–10 %. Additionally, the optimized RAD-seq method enables pre-design of the required uniformity and density in coverage of the high quality sequence polymorphic markers over the genome of interest and genotyping of large plant or crop populations at a competitive cost in comparison to other mainstream rivals in the literature. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1007/s00122-016-2736-9) contains supplementary material, which is available to authorized users.
format Online
Article
Text
id pubmed-4983294
institution National Center for Biotechnology Information
language English
publishDate 2016
publisher Springer Berlin Heidelberg
record_format MEDLINE/PubMed
spelling pubmed-49832942016-08-25 A highly robust and optimized sequence-based approach for genetic polymorphism discovery and genotyping in large plant populations Jiang, Ning Zhang, Fengjun Wu, Jinhua Chen, Yue Hu, Xiaohua Fang, Ou Leach, Lindsey J. Wang, Di Luo, Zewei Theor Appl Genet Original Article KEY MESSAGE: This optimized approach provides both a computational tool and a library construction protocol, which can maximize the number of genomic sequence reads that uniformly cover a plant genome and minimize the number of sequence reads representing chloroplast DNA and rRNA genes. One can implement the developed computational tool to feasibly design their own RAD-seq experiment to achieve expected coverage of sequence variant markers for large plant populations using information of the genome sequence and ideally, though not necessarily, information of the sequence polymorphism distribution in the genome. ABSTRACT: Advent of the next generation sequencing techniques motivates recent interest in developing sequence-based identification and genotyping of genome-wide genetic variants in large populations, with RAD-seq being a typical example. Without taking proper account for the fact that chloroplast and rRNA genes may occupy up to 60 % of the resulting sequence reads, the current RAD-seq design could be very inefficient for plant and crop species. We presented here a generic computational tool to optimize RAD-seq design in any plant species and experimentally tested the optimized design by implementing it to screen for and genotype sequence variants in four plant populations of diploid and autotetraploid Arabidopsis and potato Solanum tuberosum. Sequence data from the optimized RAD-seq experiments shows that the undesirable chloroplast and rRNA contributed sequence reads can be controlled at 3–10 %. Additionally, the optimized RAD-seq method enables pre-design of the required uniformity and density in coverage of the high quality sequence polymorphic markers over the genome of interest and genotyping of large plant or crop populations at a competitive cost in comparison to other mainstream rivals in the literature. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1007/s00122-016-2736-9) contains supplementary material, which is available to authorized users. Springer Berlin Heidelberg 2016-06-17 2016 /pmc/articles/PMC4983294/ /pubmed/27316437 http://dx.doi.org/10.1007/s00122-016-2736-9 Text en © The Author(s) 2016 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.
spellingShingle Original Article
Jiang, Ning
Zhang, Fengjun
Wu, Jinhua
Chen, Yue
Hu, Xiaohua
Fang, Ou
Leach, Lindsey J.
Wang, Di
Luo, Zewei
A highly robust and optimized sequence-based approach for genetic polymorphism discovery and genotyping in large plant populations
title A highly robust and optimized sequence-based approach for genetic polymorphism discovery and genotyping in large plant populations
title_full A highly robust and optimized sequence-based approach for genetic polymorphism discovery and genotyping in large plant populations
title_fullStr A highly robust and optimized sequence-based approach for genetic polymorphism discovery and genotyping in large plant populations
title_full_unstemmed A highly robust and optimized sequence-based approach for genetic polymorphism discovery and genotyping in large plant populations
title_short A highly robust and optimized sequence-based approach for genetic polymorphism discovery and genotyping in large plant populations
title_sort highly robust and optimized sequence-based approach for genetic polymorphism discovery and genotyping in large plant populations
topic Original Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4983294/
https://www.ncbi.nlm.nih.gov/pubmed/27316437
http://dx.doi.org/10.1007/s00122-016-2736-9
work_keys_str_mv AT jiangning ahighlyrobustandoptimizedsequencebasedapproachforgeneticpolymorphismdiscoveryandgenotypinginlargeplantpopulations
AT zhangfengjun ahighlyrobustandoptimizedsequencebasedapproachforgeneticpolymorphismdiscoveryandgenotypinginlargeplantpopulations
AT wujinhua ahighlyrobustandoptimizedsequencebasedapproachforgeneticpolymorphismdiscoveryandgenotypinginlargeplantpopulations
AT chenyue ahighlyrobustandoptimizedsequencebasedapproachforgeneticpolymorphismdiscoveryandgenotypinginlargeplantpopulations
AT huxiaohua ahighlyrobustandoptimizedsequencebasedapproachforgeneticpolymorphismdiscoveryandgenotypinginlargeplantpopulations
AT fangou ahighlyrobustandoptimizedsequencebasedapproachforgeneticpolymorphismdiscoveryandgenotypinginlargeplantpopulations
AT leachlindseyj ahighlyrobustandoptimizedsequencebasedapproachforgeneticpolymorphismdiscoveryandgenotypinginlargeplantpopulations
AT wangdi ahighlyrobustandoptimizedsequencebasedapproachforgeneticpolymorphismdiscoveryandgenotypinginlargeplantpopulations
AT luozewei ahighlyrobustandoptimizedsequencebasedapproachforgeneticpolymorphismdiscoveryandgenotypinginlargeplantpopulations
AT jiangning highlyrobustandoptimizedsequencebasedapproachforgeneticpolymorphismdiscoveryandgenotypinginlargeplantpopulations
AT zhangfengjun highlyrobustandoptimizedsequencebasedapproachforgeneticpolymorphismdiscoveryandgenotypinginlargeplantpopulations
AT wujinhua highlyrobustandoptimizedsequencebasedapproachforgeneticpolymorphismdiscoveryandgenotypinginlargeplantpopulations
AT chenyue highlyrobustandoptimizedsequencebasedapproachforgeneticpolymorphismdiscoveryandgenotypinginlargeplantpopulations
AT huxiaohua highlyrobustandoptimizedsequencebasedapproachforgeneticpolymorphismdiscoveryandgenotypinginlargeplantpopulations
AT fangou highlyrobustandoptimizedsequencebasedapproachforgeneticpolymorphismdiscoveryandgenotypinginlargeplantpopulations
AT leachlindseyj highlyrobustandoptimizedsequencebasedapproachforgeneticpolymorphismdiscoveryandgenotypinginlargeplantpopulations
AT wangdi highlyrobustandoptimizedsequencebasedapproachforgeneticpolymorphismdiscoveryandgenotypinginlargeplantpopulations
AT luozewei highlyrobustandoptimizedsequencebasedapproachforgeneticpolymorphismdiscoveryandgenotypinginlargeplantpopulations