OrthoMCL: Identification of Ortholog Groups for Eukaryotic Genomes

TLDR

Ortholog group identification aids genome annotation, evolutionary studies, and comparative genomics, but existing prokaryotic methods struggle with eukaryotic genomes due to paralogs and incomplete data. OrthoMCL scales across eukaryotic taxa by applying a Markov Cluster algorithm to proteomes from seven species and offers a web interface for gene and phylogenetic pattern queries. OrthoMCL matches INPARANOID on two genomes, extends to multiple species, aligns with EGO groups while better handling recent paralogs, shows high annotation reliability, and uncovers previously missing enzymes in the *Plasmodium falciparum* genome.

Abstract

The identification of orthologous groups is useful for genome annotation, studies on gene/protein evolution, comparative genomics, and the identification of taxonomically restricted sequences. Methods successfully exploited for prokaryotic genome analysis have proved difficult to apply to eukaryotes, however, as larger genomes may contain multiple paralogous genes, and sequence information is often incomplete. OrthoMCL provides a scalable method for constructing orthologous groups across multiple eukaryotic taxa, using a Markov Cluster algorithm to group (putative) orthologs and paralogs. This method performs similarly to the INPARANOID algorithm when applied to two genomes, but can be extended to cluster orthologs from multiple species. OrthoMCL clusters are coherent with groups identified by EGO, but improved recognition of “recent” paralogs permits overlapping EGO groups representing the same gene to be merged. Comparison with previously assigned EC annotations suggests a high degree of reliability, implying utility for automated eukaryotic genome annotation. OrthoMCL has been applied to the proteome data set from seven publicly available genomes (human, fly, worm, yeast, Arabidopsis, the malaria parasite Plasmodium falciparum , and Escherichia coli ). A Web interface allows queries based on individual genes or user-defined phylogenetic patterns ( http://www.cbil.upenn.edu/gene-family ). Analysis of clusters incorporating P. falciparum genes identifies numerous enzymes that were incompletely annotated in first-pass annotation of the parasite genome.