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Michigan State University

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Superfund Research Program

Biomedical Informatics Core

Project Leader: Russell S. Thomas (The Hamner Institutes for Health Sciences)
Grant Number: P42ES004911
Funding Period: 2006-2013

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Project Summary (2006-2013)

An integrated approach for investigating the biomolecular responses to Ahr ligands necessitates the application of modern genomic and proteomic tools. By utilizing these tools, the individual projects are generating significant amounts of data that must be managed, stored, analyzed, and mined in order to derive knowledge from the mass of information. The Biomedical Informatics Core (BIC) was formed to perform these functions. The primary mission of the BIC is to enable the biomedical investigators to derive optimal use of genomic and proteomic data in the most efficient manner possible. To obtain this goal, the general philosophy of the BIC is that responsibility for data interpretation should reside with the individual investigators since they are the experts in the biological systems in which they are studying. The Core provides consistent preliminary data analysis, software tools, data management infrastructure, and training necessary for their success. This mission is being implemented through the following specific tasks: (1) develop the infrastructure necessary to manage and store microarray and tandem affinity purification (TAP) data; (2) perform initial microarray data analysis for biomedical investigators and provide commercial and custom software tools for the visualization and analysis of microarray and TAP data; and (3) provide training and support to all biomedical investigators on the application and utilization of the software tools in relation to their specific analysis needs. For the large, complex datasets present in genomic and proteomic studies, the visual and intuitive capabilities of individual investigators must be coupled with computational analysis in order to recognize important patterns within the data. Establishing the BIC insures a central repository will exist for all gene expression and TAP data and will enable efficient data mining to occur within and across projects.

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