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Nucleic Acids Research Advance Access published online on May 24, 2008
Nucleic Acids Research, doi:10.1093/nar/gkn257
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© 2008 The Author(s)
This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/2.0/uk/) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

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Hubba: hub objects analyzer—a framework of interactome hubs identification for network biology

Chung-Yen Lin1,2,3, Chia-Hao Chin1,4, Hsin-Hung Wu1, Shu-Hwa Chen1, Chin-Wen Ho2,* and Ming-Tat Ko1

1Institute of Information Science, Academia Sinica, No. 128 Yan-Chiu-Yuan Rd., Sec. 2, Taipei 115, 2Division of Biostatistics and Bioinformatics, National Health Research Institutes. No. 35 Keyan Rd. Zhunan, Miaoli County 350, 3Institute of Fishery Science, College of Life Science, National Taiwan University, No. 1, Roosevelt Rd. Sec 4, Taipei and 4Department of Computer Science and Information Engineering, National Central University, No. 300, Jung-da Rd, Chung-li, Tao-yuan 320, Taiwan

*To whom correspondence should be addressed. Tel: +886 3 4227151 4461; Fax: +886 3 4222681; Email: hocw{at}csie.ncu.edu.tw Correspondence may also be addressed to Ming-Tat Ko. Tel: +886 2 27883799 1821; Fax: +886 2 27824814; Email: mtko{at}iis.sinica.edu.tw

Received February 11, 2008. Revised April 17, 2008. Accepted April 20, 2008.

One major task in the post-genome era is to reconstruct proteomic and genomic interacting networks using high-throughput experiment data. To identify essential nodes/hubs in these interactomes is a way to decipher the critical keys inside biochemical pathways or complex networks. These essential nodes/hubs may serve as potential drug-targets for developing novel therapy of human diseases, such as cancer or infectious disease caused by emerging pathogens. Hub Objects Analyzer (Hubba) is a web-based service for exploring important nodes in an interactome network generated from specific small- or large-scale experimental methods based on graph theory. Two characteristic analysis algorithms, Maximum Neighborhood Component (MNC) and Density of Maximum Neighborhood Component (DMNC) are developed for exploring and identifying hubs/essential nodes from interactome networks. Users can submit their own interaction data in PSI format (Proteomics Standards Initiative, version 2.5 and 1.0), tab format and tab with weight values. User will get an email notification of the calculation complete in minutes or hours, depending on the size of submitted dataset. Hubba result includes a rank given by a composite index, a manifest graph of network to show the relationship amid these hubs, and links for retrieving output files. This proposed method (DMNC || MNC) can be applied to discover some unrecognized hubs from previous dataset. For example, most of the Hubba high-ranked hubs (80% in top 10 hub list, and >70% in top 40 hub list) from the yeast protein interactome data (Y2H experiment) are reported as essential proteins. Since the analysis methods of Hubba are based on topology, it can also be used on other kinds of networks to explore the essential nodes, like networks in yeast, rat, mouse and human. The website of Hubba is freely available at http://hub.iis.sinica.edu.tw/Hubba.


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