Visual Programming Tool for Integration of Gene and Protein Cancer Profiling Data

DESCRIPTION (provided by applicant): The Internet has put the combined efforts, experience and knowledge of entire research communities literally at the fingertips of scientists. These resources offer potentially enormous value toward obtaining a more comprehensive understanding of cancer and, subsequently, developing more effective diagnostic, prognostic, and treatment strategies against it. However, these resources remain vastly underutilized because of a lack of tools and technologies that allow researchers easy access to those assets. Furthermore, information from these diverse resources is rarely - if ever - complete, curated, or semantically homogeneous with the information produced in-house by the researcher. Therefore, most life science projects present researchers with the daunting task of collecting, integrating, and analyzing heterogeneous sets of data in various states of integrity and completeness, while often using inefficient, ad hoc methods that do not sufficiently address these critical differences and discrepancies or the semantic heterogeneity. The proposed project represents a contribution toward current bioinformatics efforts to provide researchers with tools that enable them to conduct effective research in the absence of a full solution to these vast and complex problems. Specifically, we propose to develop robust and cost-effective visual programming software that will allow researchers to integrate knowledge gained from mass spectrometry (MS) and microarray (MA) experiments in cancer research. The proposed software will be built upon two existing INCOGEN bioinformatics tools, and will leverage the growing resources provided by open community efforts, such as the NCI Cancer Biomedical Information Grid (caBIG). The project team deems that it is highly sensible and efficient to make the proposed work complementary in nature to such efforts and to incorporate the resources proposed by caBIG (even if not all planned resources become available) rather than developing technologies and resources from first principles. Knowledge integration must be successful in three aspects: information technology, statistics, and semantics. Toward that goal, during our Phase I work we will: 1) evaluate current standards for the representation of MS and MA data, 2) develop a proof-of-concept knowledge-integration application based on the evaluated technologies, and 3) evaluate the impact of the proof-of-concept tool on the productivity of researchers by soliciting feedback from two beta sites and other scientists. Ultimately, after completion of the Phase II work, the proposed project will result in a commercial platform for integrative research that will provide a visual programming interface to access distributed analysis and visualization tools and to build complete analysis workflows. The software package will 1) increase the ability of researchers to correctly integrate and analyze in-house data from MS and MA experiments, 2) increase the efficiency of integration and federation of external resources, and 3) enable groups of researchers to establish multidisciplinary and multi-laboratory partnerships to pursue a systems-level understanding of complex diseases such as cancer.