A Novel Method to Predict Genomic Islands Based on Mean Shift Clustering Algorithm

• DOI: 10.1371/journal.pone.0146352
• Publication Date: 2016-01-05T14:13:57Z
• Authors: Daniel M. de Brito; Vinicius Maracaja...; Savio T. de Farias; Leonardo V. Batista; Thaís G. do Rêgo
• Keywords: 0301 basic medicine; Genomic Islands; Science; Q; R; Genomics; 03 medical and health sciences; Medicine; Cluster Analysis; Algorithms; Genome, Bacterial; Research Article

Genomic Islands (GIs) are regions of bacterial genomes that acquired from other organisms by the phenomenon horizontal transfer. These often responsible for many important adaptations bacteria, with great impact on their evolution and behavior. Nevertheless, these usually associated pathogenicity, antibiotic resistance, degradation metabolism. Identification such is medical industrial interest. For this reason, different approaches genomic islands prediction have been proposed. However, none them capable predicting precisely complete repertory GIs in a genome. The difficulties arise due to changes performance algorithms face variety nucleotide distribution species. In paper, we present novel method predict built upon mean shift clustering algorithm. It does not require any information regarding number clusters, bandwidth parameter automatically calculated based heuristic approach. was implemented new user-friendly tool named MSGIP—Mean Shift Island Predictor. Genomes bacteria discussed papers were used evaluate proposed method. application revealed same predicted methods also unpredicted islands. A detailed investigation features related typical GI elements inserted confirmed its effectiveness. Stand-alone versions methodology available at http://msgip.integrativebioinformatics.me.