Abstract
B. pertussis Tohama I, B. parapertussis 12822 and B. bronchiseptica RB50 are closely related Gram-negative β-proteobacteria. The B. pertussis and B. Parapertussis is infect human and is one of main cause for whooping cough. B. bronchiseptica is not much infective for humans but is responsibe for infections in other mammals including cats, dogs and rabbits. Bacterial genomes evolve continuously to acquire genomic features essential for their survival under different environmental conditions. Bordetella species are known to incorporate genomic islands in their genome through horizontal DNA transfer from other bacteria. Present study deals with the prediction and comparative analysis of genomic islands in genomic sequences of B. pertussis Tohama I, B. parapertussis 12822 and B. bronchiseptica RB50 using computational techniques. The IslandViewer 4 suite was used for prediction of genomic islands in selected species of Bordetella. The highest number of genomic islands were predicted in the genomic sequence of B. pertussis Tohama I (222) followed by B. bronchiseptica RB50 (140) and B. Parapertussis 12822 (71). Apart from unique genomic islands, selected species of Bordetella were also sharing significant number of common genomic islands in their genomes. The B. parapertussis and B. bronchiseptica were sharing higher number of genomic islands (61) than the B. pertussis with B. bronchiseptica (13) or B. pertussis with B. parapertussis (13). It was also found that total 38 genomic islands were shared among all three selected species of Bordetella. Significant number of hypothetical proteins was also predicted in genomes of all selected Bordetella species. Prediction of such a large number of common and unique genomic islands indicate occurrence of rapid horizontal gene transfer events during evolution of selected Bordetella species. Functional analysis of these predicted genomic islands can be carried out to further explore biological significance and role of predicted genomic islands in pathogenicity, virulence, drug resistance and evolution behaviour of the selected three classical Bordetella species. It will help in identification of novel drug targets which could further be exploited for discovering suitable ligands against Bordetella species.

Key words: Genomic island, Bordetella, Functional analysis, Bioinformatics, Pathogenicity, Virulence