Rakesh Nagarajan, Ph.D. - US grants

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.

university

Cardiovascular disease (CVD) and its associated risk factors such as hypertension and dyslipidemia constitute a major public-health burden due to increased mortality and morbidity and rising health care costs. Massive epidemiological data are needed to detect the small effects of many individual genes and the environment on these traits. However, sample sizes needed to make powerful inferences may only be reached by integrating multiple epidemiological studies. Meaningful integration of information from multiple studies requires the development of data ontologies which make it possible to integrate information across studies in an optimum manner so as to maximize the information content and hence the statistical power for detecting small effect sizes. A second compounding problem of data integration is that software applications that manage such study data are typically non-interoperable, i.e. "silos" of data, and are incapable of being shared in a syntactically and semantically meaningful manner. Consequently, an infrastructure that integrates across studies in an interoperable manner is needed to ensure that epidemiological cardiovascular research remains a viable and major player in the biomedical informatics revolution which is currently underway. The cancer Biomedical Informatics Grid (caBIGTM) is addressing these problems in the cancer domain by developing software systems that are able to exchange information or that are syntactically interoperable by accessing metadata that is semantically annotated using controlled vocabularies. Our overarching goal is to develop ontologies for integrating cardiovascular epidemiological data from multiple studies. Specifically, we propose three Aims: First, develop cardiovascular data ontologies and vocabularies for each of three disparate multi-center epidemiological studies that facilitate data integration across the studies and data mining for various phenotypes. Second, adopt a technology infrastructure that leverages the cardiovascular data ontologies and vocabularies using Model Driven Architecture (MDA) and caBIGTM tools to facilitate the integration and widespread sharing of cardiovascular data sets. Third, facilitate seamless data sharing and promote widespread data dissemination among research communities cutting across clinical, translational and epidemiological domains, primarily through collaboration with the established CardioVascular Research Grid (CVRG).

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