Collective Analysis of Biological Interaction Networks
CABIN enables analysis of interaction data obtained from multiple sources. View full image.
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.
- Singhal, M., Domico, K., "CABIN: Collective Analysis of Biological Interaction Networks", Computat. Biol. Chem. (2007), doi:10.1016/j.compbiolchem.2007.03.006.
- Taylor RC, M Singhal, DS Daly, JM Gilmore, KO Domico, AM White, DL Auberry, KJ Auberry, BS Hooker, GB Hurst, JE McDermott, WH McDonald, DA Pelletier, DA Schmoyer, and WR Cannon. "An analysis pipeline for the inference of protein-protein interaction networks." The International Journal of Data Mining and Bioinformatics, 2008 (accepted for publication).
- Taylor RC, G Acquaah-Mensah, M Singhal, D Malhotra, and Biswal. "Network Inference Algorithms Elucidate Nrf2 Regulation of Mouse Lung Oxidative Stress." PLoS Computational Biology, 2008 (accepted for publication).
- Petyuk VA, W Qian, C Hinault, M Singhal, ME Monroe, DG Camp, II, RN Kulkarni, and RD Smith. "Characterization of the Mouse Pancreatic Islets Proteome by Mass Spectrometry and Comparative Analysis against other Mouse Tissues." Journal of Proteome Research, 2008 (accepted for publication).
- Cannon WR, BJM Webb-Robertson, M Singhal, LA McCue, JE McDermott, RC Taylor, KM Waters, and CS Oehmen. "An Integrative Computational Framework for Hypotheses-Driven Systems Biology Research in Proteomics and Genomics." In Computational and Systems Biology: Methods and Applications, 2008 (accepted for publication).
- Taylor RC, and M Singhal. "Biological Network Inference and Analysis using SEBINI and CABIN." In Computational Systems Biology, 2007. Part of the Methods in Molecular Biology series from Humana Press. Humana Press, Totowa, NJ. (accepted for publication).
- Ronald C Taylor, Mudita Singhal, Don S Daly, Kelly Domico, Amanda M White, Deanna L Auberry, Kenneth J Auberry, Brian Hooker, Greg Hurst, Jason McDermott, W. Hayes McDonald, Dale Pelletier, Denise Schmoyer, William R Cannon, "SEBINI-CABIN: an analysis pipeline for biological network inference, with a case study in protein-protein interaction network reconstruction", The Sixth International Conference on Machine Learning and Applications (ICMLA ’07) December 13-15, 2007, Cincinnati, Ohio, pp. 587-593. IEEE Press, San Francisco, CA. doi:10.1109/ICMLA.2007.63.