OMA, a comprehensive, automated project for the identification of orthologs from complete genome data: introduction and first achievements

Authors:
Christophe Dessimoz;Gina Cannarozzi;Manuel Gil;Daniel Margadant;Alexander Roth;Adrian Schneider;Gaston H. Gonnet
Affiliations:
Institute of Computational Science, ETH Zurich, Zürich;Institute of Computational Science, ETH Zurich, Zürich;Institute of Computational Science, ETH Zurich, Zürich;Institute of Computational Science, ETH Zurich, Zürich;Institute of Computational Science, ETH Zurich, Zürich;Institute of Computational Science, ETH Zurich, Zürich;Institute of Computational Science, ETH Zurich, Zürich
Venue:
RCG'05 Proceedings of the 2005 international conference on Comparative Genomics
Year:
2005

Citing 2
Cited 3

An improved fixed-parameter algorithm for vertex cover

Information Processing Letters
Automated annotation of microbial proteomes in SWISS-PROT

Computational Biology and Chemistry

SynPAM—A Distance Measure Based on Synonymous Codon Substitutions

IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
DLIGHT: lateral gene transfer detection using pairwise evolutionary distances in a statistical framework

RECOMB'08 Proceedings of the 12th annual international conference on Research in computational molecular biology
Synonymous codon substitution matrices

ICCS'06 Proceedings of the 6th international conference on Computational Science - Volume Part II

Quantified Score

Hi-index	0.00

Visualization

Abstract

The OMA project is a large-scale effort to identify groups of orthologs from complete genome data, currently 150 species. The algorithm relies solely on protein sequence information and does not require any human supervision. It has several original features, in particular a verification step that detects paralogs and prevents them from being clustered together. Consistency checks and verification are performed throughout the process. The resulting groups, whenever a comparison could be made, are highly consistent both with EC assignments, and with assignments from the manually curated database HAMAP. A highly accurate set of orthologous sequences constitutes the basis for several other investigations, including phylogenetic analysis and protein classification.