A correction for this article has been published in BMC Bioinformatics 2009, 10:220

Methodology article

Algorithm of OMA for large-scale orthology inference

Alexander CJ Roth , Gaston H Gonnet and Christophe Dessimoz

ETH Zurich, and Swiss Institute of Bioinformatics, 8092 Zurich, Switzerland

author email corresponding author email

BMC Bioinformatics 2008, 9:518doi:10.1186/1471-2105-9-518

Published:	4 December 2008

Abstract

Background

OMA is a project that aims to identify orthologs within publicly available, complete genomes. With 657 genomes analyzed to date, OMA is one of the largest projects of its kind.

Results

The algorithm of OMA improves upon standard bidirectional best-hit approach in several respects: it uses evolutionary distances instead of scores, considers distance inference uncertainty, includes many-to-many orthologous relations, and accounts for differential gene losses. Herein, we describe in detail the algorithm for inference of orthology and provide the rationale for parameter selection through multiple tests.

Conclusion

OMA contains several novel improvement ideas for orthology inference and provides a unique dataset of large-scale orthology assignments.