A parsimony approach to genome-wide ortholog assignment

Authors:
Zheng Fu;Xin Chen;Vladimir Vacic;Peng Nan;Yang Zhong;Tao Jiang
Affiliations:
Computer Science Department, University of California, Riverside;School of Physical and Mathematical Sci., Nanyang Tech. Univ., Singapore;Computer Science Department, University of California, Riverside;Shanghai Center for Bioinformatics Technology, Shanghai, China;Computer Science Department, University of California, Riverside;Computer Science Department, University of California, Riverside
Venue:
RECOMB'06 Proceedings of the 10th annual international conference on Research in Computational Molecular Biology
Year:
2006

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Abstract

The assignment of orthologous genes between a pair of genomes is a fundamental and challenging problem in comparative genomics, since many computational methods for solving various biological problems critically rely on bona fide orthologs as input. While it is usually done using sequence similarity search, we recently proposed a new combinatorial approach that combines sequence similarity and genome rearrangement. This paper continues the development of the approach and unites genome rearrangement events and (post-speciation) duplication events in a single framework under the parsimony principle. In this framework, orthologous genes are assumed to correspond to each other in the most parsimonious evolutionary scenario involving both genome rearrangement and (post-speciation) gene duplication. Besides several original algorithmic contributions, the enhanced method allows for the detection of inparalogs. Following this approach, we have implemented a high-throughput system for ortholog assignment on a genome scale, called MSOAR, and applied it to the genomes of human and mouse. As the result will show, MSOAR is able to find 99 more true orthologs than the INPARANOID program did. We have also compared MSOAR with the iterated exemplar algorithm on simulated data and found that MSOAR performed very well in terms of assignment accuracy. These test results indiate that our approach is very promising for genome-wide ortholog assignment.