Print This PageCollective Analysis of Biological Interaction Networks
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Visit the CABIN Software page to download the CABIN Software and user manual.
Mudita Singhal, Principal Investigator
CABIN Contact Information: cabin@pnl.gov
Deciphering interaction networks forms the first step in annotating unknown proteins with functions, determining protein complexes, identifying target proteins, and inventing new drugs. Over the past decade, a large number of databases, websites, and prediction tools have emerged that assign confidence scores to individual interactions. Unfortunately, this growth in availability of interaction data is not coupled with an increase in computational tools facilitating conversion of this data into useful information.
Molecular interaction networks are complex entities generally containing thousands of interactions. Prediction tools as well as experimental techniques aim at assigning quantitative metrics to each interaction edge within such networks. In most cases, the evidence about these interactions is incomplete and associated with uncertainty. As a result, human judgment and expertise has to be exercised while deriving a set of high-confidence interactions after assessing each source of data. Methods are needed to examine this multi-dimensional, multi-source data in an automated and timely manner. The lack of computational tools facilitating such integration of evidence from multiple prediction and/or experimental sources, such as Gene Neighborhood-GN, Gene Cluster-GC, Phylogenetic Profiles-PP, Rosetta Stones-RS, BIND [Bioinformatics 16(5):465-477], and DIP [Nucleic Acids Research 28:289-91], is the motivation behind the Collective Analysis of Biological Interaction Networks (CABIN).
CABIN was developed as a plugin to Cytoscape [Genome Research 13(11):2498-2504], which is an open source network visualization and analysis tool. CABIN promotes analytical reasoning for integrating evidence of interaction data from multiple sources by the use of interactive visual interfaces. Multiple coordinated views within CABIN foster exploratory data analysis by users, accommodating expert domain knowledge. The functionalities available within CABIN maximize human perception and understanding of uncertain and complex data, facilitating high-quality human judgment with limited investment of the user's time.
Related publication
Singhal M, Domico K, "CABIN: Collective Analysis of Biological Interaction Networks", Computat. Biol. Chem. (2007), doi:10.1016/j.compbiolchem.2007.03.006
