Identifying orthologs with OMA: A primer [version 1; peer review: 2 approved]

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 p...

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Main Authors: Monique Zahn-Zabal, Christophe Dessimoz, Natasha M. Glover
Format: Article
Language:English
Published: F1000 Research Ltd 2020-01-01
Series:F1000Research
Online Access:https://f1000research.com/articles/9-27/v1
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author Monique Zahn-Zabal
Christophe Dessimoz
Natasha M. Glover
author_facet Monique Zahn-Zabal
Christophe Dessimoz
Natasha M. Glover
author_sort Monique Zahn-Zabal
collection DOAJ
description 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.
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spelling doaj.art-cacaa56e042d42e68be3945c8e4914a32022-12-22T03:40:55ZengF1000 Research LtdF1000Research2046-14022020-01-01910.12688/f1000research.21508.123697Identifying orthologs with OMA: A primer [version 1; peer review: 2 approved]Monique Zahn-Zabal0Christophe Dessimoz1Natasha M. Glover2Swiss Institute of Bioinformatics, Lausanne, 1015, SwitzerlandSwiss Institute of Bioinformatics, Lausanne, 1015, SwitzerlandDepartment of Computational Biology, University of Lausanne, Lausanne, 1015, SwitzerlandThe 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.https://f1000research.com/articles/9-27/v1
spellingShingle Monique Zahn-Zabal
Christophe Dessimoz
Natasha M. Glover
Identifying orthologs with OMA: A primer [version 1; peer review: 2 approved]
F1000Research
title Identifying orthologs with OMA: A primer [version 1; peer review: 2 approved]
title_full Identifying orthologs with OMA: A primer [version 1; peer review: 2 approved]
title_fullStr Identifying orthologs with OMA: A primer [version 1; peer review: 2 approved]
title_full_unstemmed Identifying orthologs with OMA: A primer [version 1; peer review: 2 approved]
title_short Identifying orthologs with OMA: A primer [version 1; peer review: 2 approved]
title_sort identifying orthologs with oma a primer version 1 peer review 2 approved
url https://f1000research.com/articles/9-27/v1
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