MSOAR: a high-throughput ortholog assignment system based on genome rearrangement

Area of Science:

• Comparative genomics
• Bioinformatics
• Evolutionary biology

Background:

• Accurate ortholog assignment is crucial for comparative genomics and downstream biological analyses.
• Existing methods often rely solely on sequence similarity, neglecting evolutionary events like genome rearrangement and gene duplication.

Purpose of the Study:

• To develop a unified computational framework for ortholog assignment that incorporates both genome rearrangement and gene duplication events.
• To implement a high-throughput system (MSOAR) for genome-scale ortholog assignment.
• To detect inparalogs alongside orthologs.

Main Methods:

• A parsimony-based combinatorial approach integrating sequence similarity, genome rearrangement, and post-speciation gene duplication events.
• Development and application of the MSOAR (Mapping and Ortholog Assignment by Rearrangement) system.
• Validation using human and mouse genomes, comparing against INPARANOID and iterated exemplar algorithms, and utilizing public datasets, synteny, and gene function information.

Main Results:

• MSOAR identified 99 more true orthologs between human and mouse than INPARANOID.
• MSOAR demonstrated favorable assignment accuracy compared to the iterated exemplar algorithm on simulated data.
• Validation tests confirmed the promising performance of the MSOAR approach for genome-wide ortholog assignment.

Conclusions:

• The developed framework provides a more comprehensive approach to ortholog assignment by considering multiple evolutionary events.
• MSOAR is an effective high-throughput tool for accurate genome-wide ortholog and inparalog identification.
• The method shows significant promise for advancing comparative genomics research.