The human genome and other sequencing projects have now produced vast amounts of DNA sequence data from nuclear and mitochondrial genomes of diverse organisms. In order to generate a deeper understanding of genome evolution and gene function using this data, easy-to-use general-purpose computational tools for large-scale data analysis and new statistical/computational methods are needed. Therefore, the main objective of the proposed work is to develop the Molecular Evolutionary Genetics Analysis Professional software (called MEGA-PRO) for exploring, discovering and analyzing DNA and protein sequence variation in the context of protein structure. We plan to develop MEGA-PRO to support (i) functional genomics analysis of DNA and protein sequence variation among species and genes of multigene families and (ii) exploration of results from evolutionary analysis in a protein structural context. In MEGA-PRO, we will extend the repertoire of methods for molecular phylogenetics, evolutionary, and genomic analysis for evolutionary estimation and hypothesis testing. To address the specific needs of individual investigators, groups of investigators, and students, MEGA-PRO will be developed as a single-source solution to facilitate web-enabled analysis (in addition to the standalone desktop application) and will include facility for scripting to enable automated large-scale analyses. In addition to the proposed technological developments, many new algorithms and statistical methods need to be developed to estimate evolutionary parameters and test hypotheses when using a large number of genes and for large-scale data analysis. Therefore, we propose a project that integrates research and tool development in a multidisciplinary effort. Main goals of the research in statistical and computational methods involve (i) estimation of genomic distances and mutation rates (ii)tests of equality of mutation/substitution rate among genes and species and other fundamental assumptions used in comparative sequence analysis, and (iii) investigation of phylogentic inference methodologies for datasets containing a large number of genes with partial species overlaps. As always, MEGA-PRO programs will be made available free of charge for all uses, including research, education, and training.

National Institute of Health (NIH)
National Human Genome Research Institute (NHGRI)
Research Project (R01)
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Genome Study Section (GNM)
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Brooks, Lisa
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Arizona State University-Tempe Campus
Other Basic Sciences
Schools of Arts and Sciences
United States
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