Ortholog clustering on a multipartite graph

Akshay Vashist, Casimir A. Kulikowski, Llya Muchnik

Research output: Contribution to journalArticlepeer-review

8 Scopus citations


We present a method for automatically extracting groups of orthologous genes from a large set of genomes by a new clustering algorithm 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 the 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 runtime improves to O(|E| + |V\). The proposed method was applied to seven complete eukaryote genomes on which the manually curated database of eukaryotic ortholog clusters, KOG, is constructed. A comparison of our results with the manually curated ortholog clusters shows that our clusters are well correlated with the existing clusters.

Original languageEnglish (US)
Pages (from-to)17-27
Number of pages11
JournalIEEE/ACM Transactions on Computational Biology and Bioinformatics
Issue number1
StatePublished - Jan 2007

All Science Journal Classification (ASJC) codes

  • Biotechnology
  • Genetics
  • Applied Mathematics


  • Biology
  • Clustering algorithms
  • Genetics
  • Graph-theoretic methods

Fingerprint Dive into the research topics of 'Ortholog clustering on a multipartite graph'. Together they form a unique fingerprint.

Cite this