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IntNetDB v1.0: an integrated protein-protein interaction network database generated by a probabilistic model
BACKGROUND: Although protein-protein interaction (PPI) networks have been explored by various experimental methods, the maps so built are still limited in coverage and accuracy. To further expand the PPI network and to extract more accurate information from existing maps, studies have been carried o...
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Formato: | Texto |
Lenguaje: | English |
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BioMed Central
2006
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Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC1661597/ https://www.ncbi.nlm.nih.gov/pubmed/17112386 http://dx.doi.org/10.1186/1471-2105-7-508 |
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author | Xia, Kai Dong, Dong Han, Jing-Dong J |
author_facet | Xia, Kai Dong, Dong Han, Jing-Dong J |
author_sort | Xia, Kai |
collection | PubMed |
description | BACKGROUND: Although protein-protein interaction (PPI) networks have been explored by various experimental methods, the maps so built are still limited in coverage and accuracy. To further expand the PPI network and to extract more accurate information from existing maps, studies have been carried out to integrate various types of functional relationship data. A frequently updated database of computationally analyzed potential PPIs to provide biological researchers with rapid and easy access to analyze original data as a biological network is still lacking. RESULTS: By applying a probabilistic model, we integrated 27 heterogeneous genomic, proteomic and functional annotation datasets to predict PPI networks in human. In addition to previously studied data types, we show that phenotypic distances and genetic interactions can also be integrated to predict PPIs. We further built an easy-to-use, updatable integrated PPI database, the Integrated Network Database (IntNetDB) online, to provide automatic prediction and visualization of PPI network among genes of interest. The networks can be visualized in SVG (Scalable Vector Graphics) format for zooming in or out. IntNetDB also provides a tool to extract topologically highly connected network neighborhoods from a specific network for further exploration and research. Using the MCODE (Molecular Complex Detections) algorithm, 190 such neighborhoods were detected among all the predicted interactions. The predicted PPIs can also be mapped to worm, fly and mouse interologs. CONCLUSION: IntNetDB includes 180,010 predicted protein-protein interactions among 9,901 human proteins and represents a useful resource for the research community. Our study has increased prediction coverage by five-fold. IntNetDB also provides easy-to-use network visualization and analysis tools that allow biological researchers unfamiliar with computational biology to access and analyze data over the internet. The web interface of IntNetDB is freely accessible at . Visualization requires Mozilla version 1.8 (or higher) or Internet Explorer with installation of SVGviewer. |
format | Text |
id | pubmed-1661597 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2006 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-16615972006-11-29 IntNetDB v1.0: an integrated protein-protein interaction network database generated by a probabilistic model Xia, Kai Dong, Dong Han, Jing-Dong J BMC Bioinformatics Database BACKGROUND: Although protein-protein interaction (PPI) networks have been explored by various experimental methods, the maps so built are still limited in coverage and accuracy. To further expand the PPI network and to extract more accurate information from existing maps, studies have been carried out to integrate various types of functional relationship data. A frequently updated database of computationally analyzed potential PPIs to provide biological researchers with rapid and easy access to analyze original data as a biological network is still lacking. RESULTS: By applying a probabilistic model, we integrated 27 heterogeneous genomic, proteomic and functional annotation datasets to predict PPI networks in human. In addition to previously studied data types, we show that phenotypic distances and genetic interactions can also be integrated to predict PPIs. We further built an easy-to-use, updatable integrated PPI database, the Integrated Network Database (IntNetDB) online, to provide automatic prediction and visualization of PPI network among genes of interest. The networks can be visualized in SVG (Scalable Vector Graphics) format for zooming in or out. IntNetDB also provides a tool to extract topologically highly connected network neighborhoods from a specific network for further exploration and research. Using the MCODE (Molecular Complex Detections) algorithm, 190 such neighborhoods were detected among all the predicted interactions. The predicted PPIs can also be mapped to worm, fly and mouse interologs. CONCLUSION: IntNetDB includes 180,010 predicted protein-protein interactions among 9,901 human proteins and represents a useful resource for the research community. Our study has increased prediction coverage by five-fold. IntNetDB also provides easy-to-use network visualization and analysis tools that allow biological researchers unfamiliar with computational biology to access and analyze data over the internet. The web interface of IntNetDB is freely accessible at . Visualization requires Mozilla version 1.8 (or higher) or Internet Explorer with installation of SVGviewer. BioMed Central 2006-11-18 /pmc/articles/PMC1661597/ /pubmed/17112386 http://dx.doi.org/10.1186/1471-2105-7-508 Text en Copyright © 2006 Xia 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 Xia, Kai Dong, Dong Han, Jing-Dong J IntNetDB v1.0: an integrated protein-protein interaction network database generated by a probabilistic model |
title | IntNetDB v1.0: an integrated protein-protein interaction network database generated by a probabilistic model |
title_full | IntNetDB v1.0: an integrated protein-protein interaction network database generated by a probabilistic model |
title_fullStr | IntNetDB v1.0: an integrated protein-protein interaction network database generated by a probabilistic model |
title_full_unstemmed | IntNetDB v1.0: an integrated protein-protein interaction network database generated by a probabilistic model |
title_short | IntNetDB v1.0: an integrated protein-protein interaction network database generated by a probabilistic model |
title_sort | intnetdb v1.0: an integrated protein-protein interaction network database generated by a probabilistic model |
topic | Database |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC1661597/ https://www.ncbi.nlm.nih.gov/pubmed/17112386 http://dx.doi.org/10.1186/1471-2105-7-508 |
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