Vasant Honavar part of USDA-funded collaboration to develop bioinformatics tools for comparative animal genomics

January 21, 2008
News

The United States Department of Agriculture has awarded a 3-year, $1,000,000 grant to a team led by Professor Vasant Honavar of the Department of Computer Science and Professor James Reecy of the Department of Animal Science at Iowa State University and Professor Anne Kwitek of the Department of Internal Medicine at the University of Iowa to develop bioinformatics tools for integrative and comparative annotation, analysis, and visualization of quantitative trait loci (QTL) data across multiple animal species. Recent advances in high-throughput sequencing have led to the mapping and sequencing of whole genomes of multiple vertebrate and non-vertebrate species. This offers unprecedented opportunities for comparative studies that leverage data from large numbers of sequenced genomes for developing improved methods for gene identification and genome annotation, and for advancing our understanding of evolution of species, as well as the principles that govern gene function, and genetic interactions that orchestrate processes such as cellular development, differentiation, aging and disease. However, the paucity of computational tools that help integrate animal trait information across species is a major hurdle to realizing such opportunities. This research aims to overcome this hurdle by developing an animal trait ontology for integrative cross-species analysis of trait data, along with software tools for annotation and visualization of QTL and phenotype data across multiple animal species, including humans, livestock species, and model organisms. This work will leverage as well as drive advances in the theoretical foundations of, and software tools for, collaborative ontology development and ontology-based federated approaches to information integration in the ISU Artificial Intelligence Laboratory. The project will offer research opportunities in Computer Science and Bioinformatics and Computational Biology for Ph.D., Masters, and undergraduate students. 

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