The study of Pathogenicity Islands (PAIs) plays an important role since they harbours diverse virulence genes that are important for bacteria pathogeny . PAIs are characterised by being large genome regions presenting several horizontal gene transfer properties such as codon usage, G+C content or dinucleotide frequency deviation, the presence of insertion sequences or tRNA flanking regions jointly with transposases coding genes, which are important in incorporation of DNA from transformation, conjugation and bacteriophage infection . Bacterial genome plasticity, that involves genomic rearrangements and DNA gain and loss, is influenced by the presence of PAIS making their study fundamental for the understanding of bacterial heterogeneity and the biological mechanisms involved in genome plasticity.
In order to perform the identification and classification of PAIs in pathogenic bacteria genomes, we developed a computational approach integrating the prediction of several algorithms named: Colombo-SIGIHMM ; Artemis ; tRNAscan-SE ; and ACT (Artemis Comparison Tool) . The structural and functional annotation of PAIs is performed using an in house developed pipeline with similarity searches executed against several public domain databases including a virulence database (mVIRdb) that contains virulence factors, toxin sequences and antibiotic resistence genes  and the data integration using Artemis  and ACT .
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