Journal article
Algorithm of OMA for large-scale orthology inference.
- Roth AC ETH Zurich, and Swiss Institute of Bioinformatics, Zurich, Switzerland. alexande@inf.ethz.ch
- Gonnet GH
- Dessimoz C
- 2008-12-06
Published in:
- BMC bioinformatics. - 2008
English
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.
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.
- Language
-
- English
- Open access status
- gold
- Identifiers
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- DOI 10.1186/1471-2105-9-518
- PMID 19055798
- Persistent URL
- https://sonar.ch/global/documents/47269
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