kGC: finding groups of homologous genes across multiple genomes

Authors:
Guilherme P. Telles;Nalvo F. Almeida;Marcelo M. Brigido;Paulo Antonio Alvarez;Maria Emilia Walter
Affiliations:
Institute of Computing, University of Campinas, Campinas, Brazil;College of Computing, Federal University of Mato Grosso do Sul, Campo Grande, Brazil;Molecular Biology Laboratory, Institute of Biology, University of Brasilia, Brazil;Department of Computer Science, University of Brasilia, Brasilia, Brazil;Department of Computer Science, University of Brasilia, Brasilia, Brazil
Venue:
BSB'11 Proceedings of the 6th Brazilian conference on Advances in bioinformatics and computational biology
Year:
2011

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Abstract

We present a simple method to obtain groups of homologous genes across multiple (k) organisms, called kGC. It takes all-againstall BLASTP comparisons as input and produces groups of homologous sequences as output. The algorithm is based on the identification of maximal cliques in graphs of sequences and paralogous groups. We have used our method on six Actinobacterial complete genomes and investigated the Pfam classification of the homologous groups with respect to the results produced by OrthoMCL. Although kGC is simpler, it presented similar results with respect to Pfam classification in reasonable time.