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SNiPloid: A Utility to Exploit High-Throughput SNP Data Derived from RNA-Seq in Allopolyploid Species

High-throughput sequencing is a common approach to discover SNP variants, especially in plant species. However, methods to analyze predicted SNPs are often optimized for diploid plant species whereas many crop species are allopolyploids and combine related but divergent subgenomes (homoeologous chro...

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Autores principales: Peralta, Marine, Combes, Marie-Christine, Cenci, Alberto, Lashermes, Philippe, Dereeper, Alexis
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Hindawi Publishing Corporation 2013
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3791807/
https://www.ncbi.nlm.nih.gov/pubmed/24163691
http://dx.doi.org/10.1155/2013/890123
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author Peralta, Marine
Combes, Marie-Christine
Cenci, Alberto
Lashermes, Philippe
Dereeper, Alexis
author_facet Peralta, Marine
Combes, Marie-Christine
Cenci, Alberto
Lashermes, Philippe
Dereeper, Alexis
author_sort Peralta, Marine
collection PubMed
description High-throughput sequencing is a common approach to discover SNP variants, especially in plant species. However, methods to analyze predicted SNPs are often optimized for diploid plant species whereas many crop species are allopolyploids and combine related but divergent subgenomes (homoeologous chromosome sets). We created a software tool, SNiPloid, that exploits and interprets putative SNPs in the context of allopolyploidy by comparing SNPs from an allopolyploid with those obtained in its modern-day diploid progenitors. SNiPloid can compare SNPs obtained from a sample to estimate the subgenome contribution to the transcriptome or SNPs obtained from two polyploid accessions to search for SNP divergence.
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spelling pubmed-37918072013-10-27 SNiPloid: A Utility to Exploit High-Throughput SNP Data Derived from RNA-Seq in Allopolyploid Species Peralta, Marine Combes, Marie-Christine Cenci, Alberto Lashermes, Philippe Dereeper, Alexis Int J Plant Genomics Methodology Report High-throughput sequencing is a common approach to discover SNP variants, especially in plant species. However, methods to analyze predicted SNPs are often optimized for diploid plant species whereas many crop species are allopolyploids and combine related but divergent subgenomes (homoeologous chromosome sets). We created a software tool, SNiPloid, that exploits and interprets putative SNPs in the context of allopolyploidy by comparing SNPs from an allopolyploid with those obtained in its modern-day diploid progenitors. SNiPloid can compare SNPs obtained from a sample to estimate the subgenome contribution to the transcriptome or SNPs obtained from two polyploid accessions to search for SNP divergence. Hindawi Publishing Corporation 2013 2013-09-12 /pmc/articles/PMC3791807/ /pubmed/24163691 http://dx.doi.org/10.1155/2013/890123 Text en Copyright © 2013 Marine Peralta et al. https://creativecommons.org/licenses/by/3.0/ This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Methodology Report
Peralta, Marine
Combes, Marie-Christine
Cenci, Alberto
Lashermes, Philippe
Dereeper, Alexis
SNiPloid: A Utility to Exploit High-Throughput SNP Data Derived from RNA-Seq in Allopolyploid Species
title SNiPloid: A Utility to Exploit High-Throughput SNP Data Derived from RNA-Seq in Allopolyploid Species
title_full SNiPloid: A Utility to Exploit High-Throughput SNP Data Derived from RNA-Seq in Allopolyploid Species
title_fullStr SNiPloid: A Utility to Exploit High-Throughput SNP Data Derived from RNA-Seq in Allopolyploid Species
title_full_unstemmed SNiPloid: A Utility to Exploit High-Throughput SNP Data Derived from RNA-Seq in Allopolyploid Species
title_short SNiPloid: A Utility to Exploit High-Throughput SNP Data Derived from RNA-Seq in Allopolyploid Species
title_sort sniploid: a utility to exploit high-throughput snp data derived from rna-seq in allopolyploid species
topic Methodology Report
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3791807/
https://www.ncbi.nlm.nih.gov/pubmed/24163691
http://dx.doi.org/10.1155/2013/890123
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