Name
Abstract | ||
|---|---|---|
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.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.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/). |
Year | DOI | Venue |
|---|---|---|
2009 | 10.1186/1471-2105-10-114 | BMC Bioinformatics |
Keywords | Field | DocType |
algorithms,biological process,network analysis,bioinformatics,modular design,high throughput,protein protein interaction,web service,microarrays | Data science,Data mining,Interactome,Biology,Biological network,Centrality,Prioritization,Software,Network construction,Bioinformatics,Network analysis,Web server | Journal |
Volume | Issue | ISSN |
10 | 1 | 1471-2105 |
Citations | PageRank | References |
19 | 0.46 | 19 |
Authors | ||
10 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Sheng-An Lee | 1 | 111 | 4.77 |
| Chen-Hsiung Chan | 2 | 115 | 5.42 |
| Tzu-Chi Chen | 3 | 19 | 0.80 |
| Chia-Ying Yang | 4 | 28 | 1.32 |
| Kuo-chuan Huang | 5 | 63 | 4.60 |
| Chi-Hung Tsai | 6 | 116 | 8.70 |
| Jinmei Lai | 7 | 145 | 20.38 |
| Feng-Sheng Wang | 8 | 261 | 13.90 |
| Cheng-yan Kao | 9 | 586 | 61.50 |
| Chi-Ying F Huang | 10 | 204 | 12.95 |