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PRIN: a predicted rice interactome network
BACKGROUND: Protein-protein interactions play a fundamental role in elucidating the molecular mechanisms of biomolecular function, signal transductions and metabolic pathways of living organisms. Although high-throughput technologies such as yeast two-hybrid system and affinity purification followed...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2011
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3118165/ https://www.ncbi.nlm.nih.gov/pubmed/21575196 http://dx.doi.org/10.1186/1471-2105-12-161 |
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author | Gu, Haibin Zhu, Pengcheng Jiao, Yinming Meng, Yijun Chen, Ming |
author_facet | Gu, Haibin Zhu, Pengcheng Jiao, Yinming Meng, Yijun Chen, Ming |
author_sort | Gu, Haibin |
collection | PubMed |
description | BACKGROUND: Protein-protein interactions play a fundamental role in elucidating the molecular mechanisms of biomolecular function, signal transductions and metabolic pathways of living organisms. Although high-throughput technologies such as yeast two-hybrid system and affinity purification followed by mass spectrometry are widely used in model organisms, the progress of protein-protein interactions detection in plants is rather slow. With this motivation, our work presents a computational approach to predict protein-protein interactions in Oryza sativa. RESULTS: To better understand the interactions of proteins in Oryza sativa, we have developed PRIN, a Predicted Rice Interactome Network. Protein-protein interaction data of PRIN are based on the interologs of six model organisms where large-scale protein-protein interaction experiments have been applied: yeast (Saccharomyces cerevisiae), worm (Caenorhabditis elegans), fruit fly (Drosophila melanogaster), human (Homo sapiens), Escherichia coli K12 and Arabidopsis thaliana. With certain quality controls, altogether we obtained 76,585 non-redundant rice protein interaction pairs among 5,049 rice proteins. Further analysis showed that the topology properties of predicted rice protein interaction network are more similar to yeast than to the other 5 organisms. This may not be surprising as the interologs based on yeast contribute nearly 74% of total interactions. In addition, GO annotation, subcellular localization information and gene expression data are also mapped to our network for validation. Finally, a user-friendly web interface was developed to offer convenient database search and network visualization. CONCLUSIONS: PRIN is the first well annotated protein interaction database for the important model plant Oryza sativa. It has greatly extended the current available protein-protein interaction data of rice with a computational approach, which will certainly provide further insights into rice functional genomics and systems biology. PRIN is available online at http://bis.zju.edu.cn/prin/. |
format | Online Article Text |
id | pubmed-3118165 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2011 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-31181652011-06-19 PRIN: a predicted rice interactome network Gu, Haibin Zhu, Pengcheng Jiao, Yinming Meng, Yijun Chen, Ming BMC Bioinformatics Database BACKGROUND: Protein-protein interactions play a fundamental role in elucidating the molecular mechanisms of biomolecular function, signal transductions and metabolic pathways of living organisms. Although high-throughput technologies such as yeast two-hybrid system and affinity purification followed by mass spectrometry are widely used in model organisms, the progress of protein-protein interactions detection in plants is rather slow. With this motivation, our work presents a computational approach to predict protein-protein interactions in Oryza sativa. RESULTS: To better understand the interactions of proteins in Oryza sativa, we have developed PRIN, a Predicted Rice Interactome Network. Protein-protein interaction data of PRIN are based on the interologs of six model organisms where large-scale protein-protein interaction experiments have been applied: yeast (Saccharomyces cerevisiae), worm (Caenorhabditis elegans), fruit fly (Drosophila melanogaster), human (Homo sapiens), Escherichia coli K12 and Arabidopsis thaliana. With certain quality controls, altogether we obtained 76,585 non-redundant rice protein interaction pairs among 5,049 rice proteins. Further analysis showed that the topology properties of predicted rice protein interaction network are more similar to yeast than to the other 5 organisms. This may not be surprising as the interologs based on yeast contribute nearly 74% of total interactions. In addition, GO annotation, subcellular localization information and gene expression data are also mapped to our network for validation. Finally, a user-friendly web interface was developed to offer convenient database search and network visualization. CONCLUSIONS: PRIN is the first well annotated protein interaction database for the important model plant Oryza sativa. It has greatly extended the current available protein-protein interaction data of rice with a computational approach, which will certainly provide further insights into rice functional genomics and systems biology. PRIN is available online at http://bis.zju.edu.cn/prin/. BioMed Central 2011-05-16 /pmc/articles/PMC3118165/ /pubmed/21575196 http://dx.doi.org/10.1186/1471-2105-12-161 Text en Copyright ©2011 Gu et al; licensee BioMed Central Ltd. http://creativecommons.org/licenses/by/2.0 This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Database Gu, Haibin Zhu, Pengcheng Jiao, Yinming Meng, Yijun Chen, Ming PRIN: a predicted rice interactome network |
title | PRIN: a predicted rice interactome network |
title_full | PRIN: a predicted rice interactome network |
title_fullStr | PRIN: a predicted rice interactome network |
title_full_unstemmed | PRIN: a predicted rice interactome network |
title_short | PRIN: a predicted rice interactome network |
title_sort | prin: a predicted rice interactome network |
topic | Database |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3118165/ https://www.ncbi.nlm.nih.gov/pubmed/21575196 http://dx.doi.org/10.1186/1471-2105-12-161 |
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