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AraNet v2: an improved database of co-functional gene networks for the study of Arabidopsis thaliana and 27 other nonmodel plant species

Arabidopsis thaliana is a reference plant that has been studied intensively for several decades. Recent advances in high-throughput experimental technology have enabled the generation of an unprecedented amount of data from A. thaliana, which has facilitated data-driven approaches to unravel the gen...

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Autores principales: Lee, Tak, Yang, Sunmo, Kim, Eiru, Ko, Younhee, Hwang, Sohyun, Shin, Junha, Shim, Jung Eun, Shim, Hongseok, Kim, Hyojin, Kim, Chanyoung, Lee, Insuk
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Oxford University Press 2014
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4383895/
https://www.ncbi.nlm.nih.gov/pubmed/25355510
http://dx.doi.org/10.1093/nar/gku1053
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author Lee, Tak
Yang, Sunmo
Kim, Eiru
Ko, Younhee
Hwang, Sohyun
Shin, Junha
Shim, Jung Eun
Shim, Hongseok
Kim, Hyojin
Kim, Chanyoung
Lee, Insuk
author_facet Lee, Tak
Yang, Sunmo
Kim, Eiru
Ko, Younhee
Hwang, Sohyun
Shin, Junha
Shim, Jung Eun
Shim, Hongseok
Kim, Hyojin
Kim, Chanyoung
Lee, Insuk
author_sort Lee, Tak
collection PubMed
description Arabidopsis thaliana is a reference plant that has been studied intensively for several decades. Recent advances in high-throughput experimental technology have enabled the generation of an unprecedented amount of data from A. thaliana, which has facilitated data-driven approaches to unravel the genetic organization of plant phenotypes. We previously published a description of a genome-scale functional gene network for A. thaliana, AraNet, which was constructed by integrating multiple co-functional gene networks inferred from diverse data types, and we demonstrated the predictive power of this network for complex phenotypes. More recently, we have observed significant growth in the availability of omics data for A. thaliana as well as improvements in data analysis methods that we anticipate will further enhance the integrated database of co-functional networks. Here, we present an updated co-functional gene network for A. thaliana, AraNet v2 (available at http://www.inetbio.org/aranet), which covers approximately 84% of the coding genome. We demonstrate significant improvements in both genome coverage and accuracy. To enhance the usability of the network, we implemented an AraNet v2 web server, which generates functional predictions for A. thaliana and 27 nonmodel plant species using an orthology-based projection of nonmodel plant genes on the A. thaliana gene network.
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spelling pubmed-43838952015-04-08 AraNet v2: an improved database of co-functional gene networks for the study of Arabidopsis thaliana and 27 other nonmodel plant species Lee, Tak Yang, Sunmo Kim, Eiru Ko, Younhee Hwang, Sohyun Shin, Junha Shim, Jung Eun Shim, Hongseok Kim, Hyojin Kim, Chanyoung Lee, Insuk Nucleic Acids Res Database Issue Arabidopsis thaliana is a reference plant that has been studied intensively for several decades. Recent advances in high-throughput experimental technology have enabled the generation of an unprecedented amount of data from A. thaliana, which has facilitated data-driven approaches to unravel the genetic organization of plant phenotypes. We previously published a description of a genome-scale functional gene network for A. thaliana, AraNet, which was constructed by integrating multiple co-functional gene networks inferred from diverse data types, and we demonstrated the predictive power of this network for complex phenotypes. More recently, we have observed significant growth in the availability of omics data for A. thaliana as well as improvements in data analysis methods that we anticipate will further enhance the integrated database of co-functional networks. Here, we present an updated co-functional gene network for A. thaliana, AraNet v2 (available at http://www.inetbio.org/aranet), which covers approximately 84% of the coding genome. We demonstrate significant improvements in both genome coverage and accuracy. To enhance the usability of the network, we implemented an AraNet v2 web server, which generates functional predictions for A. thaliana and 27 nonmodel plant species using an orthology-based projection of nonmodel plant genes on the A. thaliana gene network. Oxford University Press 2014-10-29 2015-01-28 /pmc/articles/PMC4383895/ /pubmed/25355510 http://dx.doi.org/10.1093/nar/gku1053 Text en © The Author(s) 2014. Published by Oxford University Press on behalf of Nucleic Acids Research. https://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/ (https://creativecommons.org/licenses/by/4.0/) ), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Database Issue
Lee, Tak
Yang, Sunmo
Kim, Eiru
Ko, Younhee
Hwang, Sohyun
Shin, Junha
Shim, Jung Eun
Shim, Hongseok
Kim, Hyojin
Kim, Chanyoung
Lee, Insuk
AraNet v2: an improved database of co-functional gene networks for the study of Arabidopsis thaliana and 27 other nonmodel plant species
title AraNet v2: an improved database of co-functional gene networks for the study of Arabidopsis thaliana and 27 other nonmodel plant species
title_full AraNet v2: an improved database of co-functional gene networks for the study of Arabidopsis thaliana and 27 other nonmodel plant species
title_fullStr AraNet v2: an improved database of co-functional gene networks for the study of Arabidopsis thaliana and 27 other nonmodel plant species
title_full_unstemmed AraNet v2: an improved database of co-functional gene networks for the study of Arabidopsis thaliana and 27 other nonmodel plant species
title_short AraNet v2: an improved database of co-functional gene networks for the study of Arabidopsis thaliana and 27 other nonmodel plant species
title_sort aranet v2: an improved database of co-functional gene networks for the study of arabidopsis thaliana and 27 other nonmodel plant species
topic Database Issue
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4383895/
https://www.ncbi.nlm.nih.gov/pubmed/25355510
http://dx.doi.org/10.1093/nar/gku1053
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