Cargando…
SHEsisPlus, a toolset for genetic studies on polyploid species
Currently, algorithms and softwares for genetic analysis of diploid organisms with bi-allelic markers are well-established, while those for polyploids are limited. Here, we present SHEsisPlus, the online algorithm toolset for both dichotomous and quantitative trait genetic analysis on polyploid spec...
Autores principales: | , , , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group
2016
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4822172/ https://www.ncbi.nlm.nih.gov/pubmed/27048905 http://dx.doi.org/10.1038/srep24095 |
_version_ | 1782425726019960832 |
---|---|
author | Shen, Jiawei Li, Zhiqiang Chen, Jianhua Song, Zhijian Zhou, Zhaowei Shi, Yongyong |
author_facet | Shen, Jiawei Li, Zhiqiang Chen, Jianhua Song, Zhijian Zhou, Zhaowei Shi, Yongyong |
author_sort | Shen, Jiawei |
collection | PubMed |
description | Currently, algorithms and softwares for genetic analysis of diploid organisms with bi-allelic markers are well-established, while those for polyploids are limited. Here, we present SHEsisPlus, the online algorithm toolset for both dichotomous and quantitative trait genetic analysis on polyploid species (compatible with haploids and diploids, too). SHEsisPlus is also optimized for handling multiple-allele datasets. It’s free, open source and also designed to perform a range of analyses, including haplotype inference, linkage disequilibrium analysis, epistasis detection, Hardy-Weinberg equilibrium and single locus association tests. Meanwhile, we developed an accurate and efficient haplotype inference algorithm for polyploids and proposed an entropy-based algorithm to detect epistasis in the context of quantitative traits. A study of both simulated and real datasets showed that our haplotype inference algorithm was much faster and more accurate than existing ones. Our epistasis detection algorithm was the first try to apply information theory to characterizing the gene interactions in quantitative trait datasets. Results showed that its statistical power was significantly higher than conventional approaches. SHEsisPlus is freely available on the web at http://shesisplus.bio-x.cn/. Source code is freely available for download at https://github.com/celaoforever/SHEsisPlus. |
format | Online Article Text |
id | pubmed-4822172 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2016 |
publisher | Nature Publishing Group |
record_format | MEDLINE/PubMed |
spelling | pubmed-48221722016-04-18 SHEsisPlus, a toolset for genetic studies on polyploid species Shen, Jiawei Li, Zhiqiang Chen, Jianhua Song, Zhijian Zhou, Zhaowei Shi, Yongyong Sci Rep Article Currently, algorithms and softwares for genetic analysis of diploid organisms with bi-allelic markers are well-established, while those for polyploids are limited. Here, we present SHEsisPlus, the online algorithm toolset for both dichotomous and quantitative trait genetic analysis on polyploid species (compatible with haploids and diploids, too). SHEsisPlus is also optimized for handling multiple-allele datasets. It’s free, open source and also designed to perform a range of analyses, including haplotype inference, linkage disequilibrium analysis, epistasis detection, Hardy-Weinberg equilibrium and single locus association tests. Meanwhile, we developed an accurate and efficient haplotype inference algorithm for polyploids and proposed an entropy-based algorithm to detect epistasis in the context of quantitative traits. A study of both simulated and real datasets showed that our haplotype inference algorithm was much faster and more accurate than existing ones. Our epistasis detection algorithm was the first try to apply information theory to characterizing the gene interactions in quantitative trait datasets. Results showed that its statistical power was significantly higher than conventional approaches. SHEsisPlus is freely available on the web at http://shesisplus.bio-x.cn/. Source code is freely available for download at https://github.com/celaoforever/SHEsisPlus. Nature Publishing Group 2016-04-06 /pmc/articles/PMC4822172/ /pubmed/27048905 http://dx.doi.org/10.1038/srep24095 Text en Copyright © 2016, Macmillan Publishers Limited http://creativecommons.org/licenses/by/4.0/ This work is licensed under a Creative Commons Attribution 4.0 International License. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in the credit line; if the material is not included under the Creative Commons license, users will need to obtain permission from the license holder to reproduce the material. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/ |
spellingShingle | Article Shen, Jiawei Li, Zhiqiang Chen, Jianhua Song, Zhijian Zhou, Zhaowei Shi, Yongyong SHEsisPlus, a toolset for genetic studies on polyploid species |
title | SHEsisPlus, a toolset for genetic studies on polyploid species |
title_full | SHEsisPlus, a toolset for genetic studies on polyploid species |
title_fullStr | SHEsisPlus, a toolset for genetic studies on polyploid species |
title_full_unstemmed | SHEsisPlus, a toolset for genetic studies on polyploid species |
title_short | SHEsisPlus, a toolset for genetic studies on polyploid species |
title_sort | shesisplus, a toolset for genetic studies on polyploid species |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4822172/ https://www.ncbi.nlm.nih.gov/pubmed/27048905 http://dx.doi.org/10.1038/srep24095 |
work_keys_str_mv | AT shenjiawei shesisplusatoolsetforgeneticstudiesonpolyploidspecies AT lizhiqiang shesisplusatoolsetforgeneticstudiesonpolyploidspecies AT chenjianhua shesisplusatoolsetforgeneticstudiesonpolyploidspecies AT songzhijian shesisplusatoolsetforgeneticstudiesonpolyploidspecies AT zhouzhaowei shesisplusatoolsetforgeneticstudiesonpolyploidspecies AT shiyongyong shesisplusatoolsetforgeneticstudiesonpolyploidspecies |