OrthoMCL

OrthoMCL: Ortholog and Paralog Clustering Using Markov Clustering

OrthoMCL clusters putative orthologs and paralogs across multiple eukaryotic taxa using sequence similarity and the Markov Cluster (MCL) algorithm to generate ortholog groups beyond pairwise approaches such as INPARANOID.

Key Features:

• Markov Cluster-Based Clustering: Applies the Markov Cluster (MCL) algorithm to group orthologs and paralogs across numerous species, enabling scalable multi-species clustering.
• Recent Paralog Recognition: Improves detection of recent paralogs and merges overlapping groups identified by methods such as EGO (Eukaryotic Gene Orthology).
• Genome Annotation Validation: Demonstrates reliability through comparison with EC annotations and analysis of proteomes from human, fly, worm, yeast, Arabidopsis, Plasmodium falciparum, and Escherichia coli.
• Precomputed Ortholog Database: Provides OrthoMCL-DB containing ortholog groups derived from 1,270,853 proteins across 88 eukaryotic, 16 archaeal, and 34 bacterial genomes.

Scientific Applications:

• Genome Annotation: Supports automated annotation of eukaryotic genomes using ortholog group assignments.
• Comparative Genomics: Enables cross-species protein clustering for evolutionary and gene/protein conservation studies.
• Taxonomically Restricted Sequence Identification: Detects lineage-specific sequences and incompletely annotated enzymes.

Methodology:

OrthoMCL computes all-against-all sequence similarity, constructs similarity graphs, and applies the Markov Cluster (MCL) algorithm to delineate ortholog groups. It supports mapping of user-provided protein sequences to existing clusters and stand-alone clustering of proteomes from selected genomes.