Integrated Bioinformatic Analysis of Genomic Datasets

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DESCRIPTION: The two objectives of this application are to support my training as a clinical and biomedical informatician and to develop and utilize software applications that will perform integrated analyses of functional genomic datasets with clinical data and gene annotation. This award will facilitate my transition into an independent investigator by providing bioinformatics training under the mentorship of Dr. Gary Stormo, Professor of Genetics at Washington University. Dr. Stormo is ideally suited to be my mentor because his laboratory has extensive experience in developing and using computational biology and bioinformatics tools and because he has all the resources already in place to facilitate the successful completion of this application. He has also demonstrated the ability to be a good mentor by serving as one to past research career award recipients. A didactic program in computational biology will complement the intellectual environment provided by Dr. Stormo's laboratory, the meetings with other collaborators on this project, and national conferences in the field of bioinformatics. The goal of biomedical research is to uncover perturbations in important pathways that lead to disease states so that therapeutic treatments may be designed. It is clear that most diseases are caused by alterations at one or more levels of the genetic program. Only through simultaneous monitoring of DMA, RNA, and protein can the comprehensive understanding of underlying processes occurring in multi-factorial diseases be made. While there is a great need to analyze data derived from such genome-wide profiling experiments with that ascertained from clinical data simultaneously, there are no software applications which can effectively perform this task. The specific aims of this proposal are to develop a novel application to concurrently analyze and visualize functional genomic datasets with clinicopathology patient data and gene annotation using advanced statistical algorithms and to utilize this software to analyze data from two ongoing studies in myeloid leukemia and prostate cancer. Such combinatorial analyses will facilitate the identification of previously underappreciated pathways and molecular markers which may be used to design diagnostic assays and customized therapies.