Display Settings:

Format

Send to:

Choose Destination
See comment in PubMed Commons below
BMC Bioinformatics. 2008 Dec 4;9:518. doi: 10.1186/1471-2105-9-518.

Algorithm of OMA for large-scale orthology inference.

Author information

  • 1ETH Zurich, and Swiss Institute of Bioinformatics, Zurich, Switzerland. alexande@inf.ethz.ch

Erratum in

  • BMC Bioinformatics.2009;10. doi:10.1186/1471-2105-10-220.

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.

PMID:
19055798
[PubMed - indexed for MEDLINE]
PMCID:
PMC2639434
Free PMC Article

Images from this publication.See all images (11)Free text

Figure 1
Figure 2
Figure 3
Figure 4
Figure 5
Figure 6
Figure 7
Figure 8
Figure 9
Figure 10
Figure 11
PubMed Commons home

PubMed Commons

0 comments
How to join PubMed Commons

    Supplemental Content

    Icon for BioMed Central Icon for PubMed Central
    Loading ...
    Write to the Help Desk