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GWASpro: a high-performance genome-wide association analysis server

SUMMARY: We present GWASpro, a high-performance web server for the analyses of large-scale genome-wide association studies (GWAS). GWASpro was developed to provide data analyses for large-scale molecular genetic data, coupled with complex replicated experimental designs such as found in plant scienc...

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Autores principales: Kim, Bongsong, Dai, Xinbin, Zhang, Wenchao, Zhuang, Zhaohong, Sanchez, Darlene L, Lübberstedt, Thomas, Kang, Yun, Udvardi, Michael K, Beavis, William D, Xu, Shizhong, Zhao, Patrick X
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Oxford University Press 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6612817/
https://www.ncbi.nlm.nih.gov/pubmed/30508039
http://dx.doi.org/10.1093/bioinformatics/bty989
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author Kim, Bongsong
Dai, Xinbin
Zhang, Wenchao
Zhuang, Zhaohong
Sanchez, Darlene L
Lübberstedt, Thomas
Kang, Yun
Udvardi, Michael K
Beavis, William D
Xu, Shizhong
Zhao, Patrick X
author_facet Kim, Bongsong
Dai, Xinbin
Zhang, Wenchao
Zhuang, Zhaohong
Sanchez, Darlene L
Lübberstedt, Thomas
Kang, Yun
Udvardi, Michael K
Beavis, William D
Xu, Shizhong
Zhao, Patrick X
author_sort Kim, Bongsong
collection PubMed
description SUMMARY: We present GWASpro, a high-performance web server for the analyses of large-scale genome-wide association studies (GWAS). GWASpro was developed to provide data analyses for large-scale molecular genetic data, coupled with complex replicated experimental designs such as found in plant science investigations and to overcome the steep learning curves of existing GWAS software tools. GWASpro supports building complex design matrices, by which complex experimental designs that may include replications, treatments, locations and times, can be accounted for in the linear mixed model. GWASpro is optimized to handle GWAS data that may consist of up to 10 million markers and 10 000 samples from replicable lines or hybrids. GWASpro provides an interface that significantly reduces the learning curve for new GWAS investigators. AVAILABILITY AND IMPLEMENTATION: GWASpro is freely available at https://bioinfo.noble.org/GWASPRO. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
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spelling pubmed-66128172019-07-12 GWASpro: a high-performance genome-wide association analysis server Kim, Bongsong Dai, Xinbin Zhang, Wenchao Zhuang, Zhaohong Sanchez, Darlene L Lübberstedt, Thomas Kang, Yun Udvardi, Michael K Beavis, William D Xu, Shizhong Zhao, Patrick X Bioinformatics Applications Notes SUMMARY: We present GWASpro, a high-performance web server for the analyses of large-scale genome-wide association studies (GWAS). GWASpro was developed to provide data analyses for large-scale molecular genetic data, coupled with complex replicated experimental designs such as found in plant science investigations and to overcome the steep learning curves of existing GWAS software tools. GWASpro supports building complex design matrices, by which complex experimental designs that may include replications, treatments, locations and times, can be accounted for in the linear mixed model. GWASpro is optimized to handle GWAS data that may consist of up to 10 million markers and 10 000 samples from replicable lines or hybrids. GWASpro provides an interface that significantly reduces the learning curve for new GWAS investigators. AVAILABILITY AND IMPLEMENTATION: GWASpro is freely available at https://bioinfo.noble.org/GWASPRO. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online. Oxford University Press 2019-07 2018-12-03 /pmc/articles/PMC6612817/ /pubmed/30508039 http://dx.doi.org/10.1093/bioinformatics/bty989 Text en © The Author(s) 2018. Published by Oxford University Press. http://creativecommons.org/licenses/by/4.0/ This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Applications Notes
Kim, Bongsong
Dai, Xinbin
Zhang, Wenchao
Zhuang, Zhaohong
Sanchez, Darlene L
Lübberstedt, Thomas
Kang, Yun
Udvardi, Michael K
Beavis, William D
Xu, Shizhong
Zhao, Patrick X
GWASpro: a high-performance genome-wide association analysis server
title GWASpro: a high-performance genome-wide association analysis server
title_full GWASpro: a high-performance genome-wide association analysis server
title_fullStr GWASpro: a high-performance genome-wide association analysis server
title_full_unstemmed GWASpro: a high-performance genome-wide association analysis server
title_short GWASpro: a high-performance genome-wide association analysis server
title_sort gwaspro: a high-performance genome-wide association analysis server
topic Applications Notes
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6612817/
https://www.ncbi.nlm.nih.gov/pubmed/30508039
http://dx.doi.org/10.1093/bioinformatics/bty989
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