title: POINeT: protein interactome with sub-network analysis and hub prioritization.
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.
  
  
  CONCLUSIONS
  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/).
authors.affiliation: Institute of Clinical Medicine, National Yang-Ming University, Taipei, Taiwan, ROC. d93922005@ntu.edu.tw
authors.forename: Sheng-An
authors.initials: SA
authors.lastname: Lee
authors.forename: Chen-Hsiung
authors.initials: CH
authors.lastname: Chan
authors.forename: Tzu-Chi
authors.initials: TC
authors.lastname: Chen
authors.forename: Chia-Ying
authors.initials: CY
authors.lastname: Yang
authors.forename: Kuo-Chuan
authors.initials: KC
authors.lastname: Huang
authors.forename: Chi-Hung
authors.initials: CH
authors.lastname: Tsai
authors.forename: Jin-Mei
authors.initials: JM
authors.lastname: Lai
authors.forename: Feng-Sheng
authors.initials: FS
authors.lastname: Wang
authors.forename: Cheng-Yan
authors.initials: CY
authors.lastname: Kao
authors.forename: Chi-Ying F
authors.initials: CY
authors.lastname: Huang
mesh_terms: D019295:Computational Biology / Q000379:methods*; D030562:Databases, Protein; D025941:Protein Interaction Mapping / Q000379:methods*; D011506:Proteins / Q000737:chemistry*; D020543:Proteome / Q000032:analysis; D012984:Software
chemical_list: D011506:Proteins; D020543:Proteome