Ortholog clustering on a multipartite graph

• Authors: Akshay Vashist;Casimir Kulikowski;Ilya Muchnik
• Venue: WABI'05 Proceedings of the 5th International conference on Algorithms in Bioinformatics
• Year: 2005

We present a method for automatically extracting groups of orthologous genes from a large set of genomes through the development of a new clustering method on a weighted multipartite graph. The method assigns a score to an arbitrary subset of genes from multiple genomes to assess the orthologous relationships between genes in the subset. This score is computed using sequence similarities between the member genes and the phylogenetic relationship between the corresponding genomes. An ortholog cluster is found as the subset with highest score, so ortholog clustering is formulated as a combinatorial optimization problem. The algorithm for finding an ortholog cluster runs in time O(|E| + |V| log |V|), where V and E are the sets of vertices and edges, respectively in the graph. However, if we discretize the similarity scores into a constant number of bins, the run time improves to O(|E| + |V|). The proposed method was applied to seven complete eukaryote genomes on which manually curated ortholog clusters, KOG (eukaryotic ortholog clusters, http://www.ncbi.nlm.nih.gov/COG/new/) are constructed. A comparison of our results with the manually curated ortholog clusters shows that our clusters are well correlated with the existing clusters. Finally, we demonstrate how gene order information can be incorporated in the proposed method for improving ortholog detection.