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Bioinformatics Advance Access originally published online on June 24, 2004
Bioinformatics 2004 20(17):3273-3276; doi:10.1093/bioinformatics/bth366
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Bioinformatics vol. 20 issue 17 © Oxford University Press 2004; all rights reserved.

Applications Note

POINT: a database for the prediction of protein–protein interactions based on the orthologous interactome

Tao-Wei Huang 1,2,{dagger}, An-Chi Tien 1,{dagger}, Wen-Shien Huang 2, Yuan-Chii G. Lee 1,3, Chin-Lin Peng 2, Huei-Hun Tseng 1,2, Cheng-Yan Kao 2 and Chi-Ying F. Huang 1,2,4,*

1 Division of Molecular and Genomic Medicine, National Health Research Institutes, Taipei 115, Taiwan, Republic of China, 2 Department of Computer Science and Information Engineering, National Taiwan University, Taipei 106, Taiwan, Republic of China, 3 Graduate Institute of Medical Informatics, Taipei Medical University, Taipei 110, Taiwan, Republic of China and 4 Institute of Biotechnology in Medicine, National Yang-Ming University, Taipei 112, Taiwan, Republic of China

Received on November 10, 2003; revised on May 17, 2004; accepted on June 4, 2004
Advance Access Publication June 24, 2004

Summary: One possible path towards understanding the biological function of a target protein is through the discovery of how it interfaces within protein–protein interaction networks. The goal of this study was to create a virtual protein–protein interaction model using the concepts of orthologous conservation (or interologs) to elucidate the interacting networks of a particular target protein. POINT (the prediction of interactome database) is a functional database for the prediction of the human protein–protein interactome based on available orthologous interactome datasets. POINT integrates several publicly accessible databases, with emphasis placed on the extraction of a large quantity of mouse, fruit fly, worm and yeast protein–protein interactions datasets from the Database of Interacting Proteins (DIP), followed by conversion of them into a predicted human interactome. In addition, protein–protein interactions require both temporal synchronicity and precise spatial proximity. POINT therefore also incorporates correlated mRNA expression clusters obtained from cell cycle microarray databases and subcellular localization from Gene Ontology to further pinpoint the likelihood of biological relevance of each predicted interacting sets of protein partners.

Availability: POINT can be freely accessed at http://insilico.csie.ntu.edu.tw:9999/point/.

Contact: chiying{at}nhri.org.tw

* To whom correspondence should be addressed.

{dagger} The authors wish it to be known that, in their opinion, the first two authors should be regarded as joint First Authors.


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