A High Throughtput Approach To The Assignment Of Orthologous Genes Base On Genome Rearrangement
Start Date: 09/01/2007
End Date: 11/01/2008
Orthologous genes, or orthologs, are genes in different species that have evolved directly from a common ancestral gene. Genome-scale assignment of orthologs is a fundamental and challenging problem in computational biology, and has a wide range of applications in comparative genomics and functional genomics.
This project continues the development of the parsimony approach for assigning orthologs between closely related genomes which essentially attempts to transform one genome into another by the smallest number of genome rearrangement events including reversal, translocation, fusion, and fission, as well as gene duplication events. The project addresses three key algorithmic problems including (i) signed reversal distance with duplicates, (ii) signed transposition distance with duplicates, and (iii) minimum common string partition.
Efficient solutions to each of these problems are combined and incorporated into a software system for ortholog assignment, called MSOAR. The project encompasses genome-wide analysis of orthologous (and paralogous) relationships on the human and mouse genomes to valdiate the approach, and more importantly, to address several important evolutionary biological questions including the characterization of gains and losses of duplicated genes in the two genomes, the elucidation of gene movements in one genome with respect to the other genome, and the quantification of different mechanisms of gene duplication.
The parsimony approach presents a novel method for performing genome-wide ortholog assignment that takes into account both gene sequences and locations. The above algorithmic problems are new in the literature and their solutions likely require the introduction of novel algorithm design and analysis techniques. The questions regarding gene duplication and quantification of the duplication mechanisms in model species are of fundamental importance in evolutionary biology.
Grant Institution: National Science Foundation
Amount: $60,560
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