Algorithm of OMA for large-scale orthology inference.
Détails
ID Serval
serval:BIB_2FB29FA83B67
Type
Article: article d'un périodique ou d'un magazine.
Collection
Publications
Institution
Titre
Algorithm of OMA for large-scale orthology inference.
Périodique
BMC bioinformatics
ISSN
1471-2105 (Electronic)
ISSN-L
1471-2105
Statut éditorial
Publié
Date de publication
04/12/2008
Peer-reviewed
Oui
Volume
9
Pages
518
Langue
anglais
Notes
Publication types: Journal Article
Publication Status: epublish
Publication Status: epublish
Résumé
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.
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.
OMA contains several novel improvement ideas for orthology inference and provides a unique dataset of large-scale orthology assignments.
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.
OMA contains several novel improvement ideas for orthology inference and provides a unique dataset of large-scale orthology assignments.
Mots-clé
Algorithms, Computational Biology/methods, Evolution, Molecular, Genomics, Sequence Alignment
Pubmed
Web of science
Open Access
Oui
Création de la notice
02/09/2015 8:16
Dernière modification de la notice
06/03/2024 9:24