The human genome contains several hundred clusters of related genes, in which duplication operations create new gene copies that serve as the raw ingredients for rapid change as humans evolve to adapt to their environment. For instance, many immunity-related genes lie in such clusters. Thus, understanding all of the genetic differences between humans and other primates, or among individual humans, will require a comparative analysis of gene clusters. One benefit will be a better idea of which primate species make the best biomedical models for the progression of infectious diseases like AIDS or influenza in humans. However, the presence of highly similar segments makes it difficult to determine complete and accurate sequence data for these clusters, which has inhibited development of computational methods for their analysis. We propose to collaborate in the generation of accurate primate sequence data for carefully chosen gene clusters, to develop new computational tools for comparative analysis of such data, and to deliver the results to the biomedical community.
Changes within clusters of closely related genes explain many of the differences among humans and primates, as well as among individual humans. We will produce data and computational methods that will greatly increase our understanding of these differences, which may assist with selection of biomedical models for progression of infectious diseases.
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