Identifying orthologs with OMA: A primer

Monique Zahn-Zabal; Christophe Dessimoz; Natasha M Glover

doi:10.12688/f1000research.21508.1

Identifying orthologs with OMA: A primer

F1000Res. 2020 Jan 17:9:27. doi: 10.12688/f1000research.21508.1. eCollection 2020.

Authors

Monique Zahn-Zabal¹, Christophe Dessimoz^{1

2

3

4

5}, Natasha M Glover^{1

2

3}

Affiliations

¹ Swiss Institute of Bioinformatics, Lausanne, 1015, Switzerland.
² Department of Computational Biology, University of Lausanne, Lausanne, 1015, Switzerland.
³ Center for Integrative Genomics, University of Lausanne, Lausanne, 1015, Switzerland.
⁴ Department of Genetics, Evolution and Environment, University College London, London, WC1E 6BT, UK.
⁵ Department of Computer Science, University College London, London, WC1E 6BT, UK.

Abstract

The Orthologous Matrix (OMA) is a method and database that allows users to identify orthologs among many genomes. OMA provides three different types of orthologs: pairwise orthologs, OMA Groups and Hierarchical Orthologous Groups (HOGs). This Primer is organized in two parts. In the first part, we provide all the necessary background information to understand the concepts of orthology, how we infer them and the different subtypes of orthology in OMA, as well as what types of analyses they should be used for. In the second part, we describe protocols for using the OMA browser to find a specific gene and its various types of orthologs. By the end of the Primer, readers should be able to (i) understand homology and the different types of orthologs reported in OMA, (ii) understand the best type of orthologs to use for a particular analysis; (iii) find particular genes of interest in the OMA browser; and (iv) identify orthologs for a given gene. The data can be freely accessed from the OMA browser at https://omabrowser.org.

Keywords: Comparative genomics; Hierarchical orthologous groups; OMA; Orthologous Matrix; orthology; phylogenetics.

Publication types

Research Support, Non-U.S. Gov't

MeSH terms

Computational Biology
Genome*
Genomics / methods*
Software*

Grants and funding

This work was funded by a Service and Infrastructure grant from the Swiss Institute of Bioinformatics and a Swiss National Science Foundation Professorship grant (Grant 183723).