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YeastNet v3: a public database of data-specific and integrated functional gene networks for Saccharomyces cerevisiae
Saccharomyces cerevisiae, i.e. baker’s yeast, is a widely studied model organism in eukaryote genetics because of its simple protocols for genetic manipulation and phenotype profiling. The high abundance of publicly available data that has been generated through diverse ‘omics’ approaches has led to...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Oxford University Press
2014
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3965021/ https://www.ncbi.nlm.nih.gov/pubmed/24165882 http://dx.doi.org/10.1093/nar/gkt981 |
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author | Kim, Hanhae Shin, Junha Kim, Eiru Kim, Hyojin Hwang, Sohyun Shim, Jung Eun Lee, Insuk |
author_facet | Kim, Hanhae Shin, Junha Kim, Eiru Kim, Hyojin Hwang, Sohyun Shim, Jung Eun Lee, Insuk |
author_sort | Kim, Hanhae |
collection | PubMed |
description | Saccharomyces cerevisiae, i.e. baker’s yeast, is a widely studied model organism in eukaryote genetics because of its simple protocols for genetic manipulation and phenotype profiling. The high abundance of publicly available data that has been generated through diverse ‘omics’ approaches has led to the use of yeast for many systems biology studies, including large-scale gene network modeling to better understand the molecular basis of the cellular phenotype. We have previously developed a genome-scale gene network for yeast, YeastNet v2, which has been used for various genetics and systems biology studies. Here, we present an updated version, YeastNet v3 (available at http://www.inetbio.org/yeastnet/), that significantly improves the prediction of gene–phenotype associations. The extended genome in YeastNet v3 covers up to 5818 genes (∼99% of the coding genome) wired by 362 512 functional links. YeastNet v3 provides a new web interface to run the tools for network-guided hypothesis generations. YeastNet v3 also provides edge information for all data-specific networks (∼2 million functional links) as well as the integrated networks. Therefore, users can construct alternative versions of the integrated network by applying their own data integration algorithm to the same data-specific links. |
format | Online Article Text |
id | pubmed-3965021 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2014 |
publisher | Oxford University Press |
record_format | MEDLINE/PubMed |
spelling | pubmed-39650212014-03-25 YeastNet v3: a public database of data-specific and integrated functional gene networks for Saccharomyces cerevisiae Kim, Hanhae Shin, Junha Kim, Eiru Kim, Hyojin Hwang, Sohyun Shim, Jung Eun Lee, Insuk Nucleic Acids Res IV. Viruses, bacteria, protozoa and fungi Saccharomyces cerevisiae, i.e. baker’s yeast, is a widely studied model organism in eukaryote genetics because of its simple protocols for genetic manipulation and phenotype profiling. The high abundance of publicly available data that has been generated through diverse ‘omics’ approaches has led to the use of yeast for many systems biology studies, including large-scale gene network modeling to better understand the molecular basis of the cellular phenotype. We have previously developed a genome-scale gene network for yeast, YeastNet v2, which has been used for various genetics and systems biology studies. Here, we present an updated version, YeastNet v3 (available at http://www.inetbio.org/yeastnet/), that significantly improves the prediction of gene–phenotype associations. The extended genome in YeastNet v3 covers up to 5818 genes (∼99% of the coding genome) wired by 362 512 functional links. YeastNet v3 provides a new web interface to run the tools for network-guided hypothesis generations. YeastNet v3 also provides edge information for all data-specific networks (∼2 million functional links) as well as the integrated networks. Therefore, users can construct alternative versions of the integrated network by applying their own data integration algorithm to the same data-specific links. Oxford University Press 2014-01-01 2013-10-26 /pmc/articles/PMC3965021/ /pubmed/24165882 http://dx.doi.org/10.1093/nar/gkt981 Text en © The Author(s) 2013. Published by Oxford University Press. http://creativecommons.org/licenses/by/3.0/ This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by/3.0/), which permits non-commercial reuse, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact journals.permissions@oup.com |
spellingShingle | IV. Viruses, bacteria, protozoa and fungi Kim, Hanhae Shin, Junha Kim, Eiru Kim, Hyojin Hwang, Sohyun Shim, Jung Eun Lee, Insuk YeastNet v3: a public database of data-specific and integrated functional gene networks for Saccharomyces cerevisiae |
title | YeastNet v3: a public database of data-specific and integrated functional gene networks for Saccharomyces cerevisiae |
title_full | YeastNet v3: a public database of data-specific and integrated functional gene networks for Saccharomyces cerevisiae |
title_fullStr | YeastNet v3: a public database of data-specific and integrated functional gene networks for Saccharomyces cerevisiae |
title_full_unstemmed | YeastNet v3: a public database of data-specific and integrated functional gene networks for Saccharomyces cerevisiae |
title_short | YeastNet v3: a public database of data-specific and integrated functional gene networks for Saccharomyces cerevisiae |
title_sort | yeastnet v3: a public database of data-specific and integrated functional gene networks for saccharomyces cerevisiae |
topic | IV. Viruses, bacteria, protozoa and fungi |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3965021/ https://www.ncbi.nlm.nih.gov/pubmed/24165882 http://dx.doi.org/10.1093/nar/gkt981 |
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