Clustering orthologs based on sequence and domain similarities

In this paper, we present a fully automatic computational method to cluster orthologs and in-paralogs from multiple species. We use the program Blastp to generate a pairwise distance matrix, which is then normalized for each homologous group between and within the species included. We also used protein domains and their organization in protein sequences as an additional criterion for filtering false relationships. Ortholog clusters are first seeded with multiple reciprocal best pairwise matches, after which the Markov graph-flow algorithm is applied to include in-paralogs. Classification parameters such as the inflation index are optimized according to the functional consistency in each of the clusters. This was inferred by the comparison of ontological annotations available for each of the sequences belonging to the same cluster. We apply our program on six completely sequenced eukaryotic genomes, assigns confidence values for both orthologs and in-paralogs. We note significant improvement for the clustering of orthologs with recent paralogs, comparing our results with similar efforts at NCBI and TIGR. This provides an automatic and robust method to cluster orthologous genes of multiple genomes