Statistical and Computational Methods for the Study of Gene Families

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Indiana University is awarded a grant to develop a statistical framework that would allow for inferences regarding gene family evolution among species. In order to take full advantage of the data being produced by various genome sequencing projects, this project aims to extend the statistical and computational tools necessary for biological researchers to study gene families. There are three main goals of this proposal: 1) Development of improved statistical tools. This work will enable more refined estimates of gene duplication and deletion parameters between species, and will provide new ways in which to study gene families within single genomes. The inclusion of methods for detecting and incorporating whole genome duplications will greatly extend statistical inferences. 2) Creation of easy-to-use software. A free software package will be implemented that can be used by researchers studying whole genomes or individual gene families. Statistical tools created in this project will be quickly disseminated to the community via this package. 3) Providing annotated gene families for Drosophila. The sequencing of 12 Drosophila species will be a boon to comparative genomics studies. By working with FlyBase to provide a well-annotated set of gene families from across these species, we will present new ways for biologists to connect this information with functional and comparative genomic data. The products of this research will provide a broad statistical and computational framework for all future studies of gene families. The research will also provide a diverse training environment for undergraduates, graduate students, and postdoctoral researchers in molecular evolution, statistics, and bioinformatics. The participation of under-represented groups and women via multiple scholarship programs will ensure that this specific research priority is achieved. The research will also be used in the development of classes and programs for understanding the relationship between biodiversity and genetic variation, and in graduate education for bioinformatics students.