Software

POINeT: protein interactome with sub-network analysis and hub prioritization

Sheng-An Lee⁵ , Chen-Hsiung Chan⁵ , Tzu-Chi Chen² , Chia-Ying Yang⁵ , Kuo-Chuan Huang^5,6 , Chi-Hung Tsai⁷ , Jin-Mei Lai⁸ , Feng-Sheng Wang⁹ , Cheng-Yan Kao⁵ and Chi-Ying F Huang^1,2,3,4,5

¹  Institute of Clinical Medicine, National Yang-Ming University, Taipei, Taiwan, ROC

²  Institute of Biotechnology in Medicine, National Yang-Ming University, Taipei, Taiwan, ROC

³  Institute of Bio-Pharmaceutical Sciences, National Yang-Ming University, Taipei, Taiwan, ROC

⁴  Institute of BioMedical Informatics, National Yang-Ming University, Taipei, Taiwan, ROC

⁵  Department of Computer Science and Information Engineering, National Taiwan University, Taipei, Taiwan, ROC

⁶  Armed Forces Peitou Hospital, Taipei, Taiwan, ROC

⁷  Institute for Information Industry, Taipei, Taiwan, ROC

⁸  Department of Life Science, Fu-Jen Catholic University, Taipei County, Taiwan, ROC

⁹  Department of Chemical Engineering, National Chung Cheng University, Chia-Yi County, Taiwan, ROC

author email corresponding author email

BMC Bioinformatics 2009, 10:114doi:10.1186/1471-2105-10-114

Published:	21 April 2009

Abstract

Background

Protein-protein interactions (PPIs) are critical to every aspect of biological processes. Expansion of all PPIs from a set of given queries often results in a complex PPI network lacking spatiotemporal consideration. Moreover, the reliability of available PPI resources, which consist of low- and high-throughput data, for network construction remains a significant challenge. Even though a number of software tools are available to facilitate PPI network analysis, an integrated tool is crucial to alleviate the burden on querying across multiple web servers and software tools.

Results

We have constructed an integrated web service, POINeT, to simplify the process of PPI searching, analysis, and visualization. POINeT merges PPI and tissue-specific expression data from multiple resources. The tissue-specific PPIs and the numbers of research papers supporting the PPIs can be filtered with user-adjustable threshold values and are dynamically updated in the viewer. The network constructed in POINeT can be readily analyzed with, for example, the built-in centrality calculation module and an integrated network viewer. Nodes in global networks can also be ranked and filtered using various network analysis formulas, i.e., centralities. To prioritize the sub-network, we developed a ranking filtered method (S3) to uncover potential novel mediators in the midbody network. Several examples are provided to illustrate the functionality of POINeT. The network constructed from four schizophrenia risk markers suggests that EXOC4 might be a novel marker for this disease. Finally, a liver-specific PPI network has been filtered with adult and fetal liver expression profiles.

Conclusion

The functionalities provided by POINeT are highly improved compared to previous version of POINT. POINeT enables the identification and ranking of potential novel genes involved in a sub-network. Combining with tissue-specific gene expression profiles, PPIs specific to selected tissues can be revealed. The straightforward interface of POINeT makes PPI search and analysis just a few clicks away. The modular design permits further functional enhancement without hampering the simplicity. POINeT is available at http://poinet.bioinformatics.tw/ webcite.