Optimization and Practical Use of Composition Based Approaches Towards Identification and Collection of Genomic Islands and Their Ontology in Prokaryotes

Motivation: Horizontally transferred genomic islands (islands, GIs) have been referred to as important factors which contribute towards bacterial evolution in general and particularly towards the emergences of pathogens and outbreak instances. The development of tools for identification of such elements and retracing their distribution will help to understand how such cases arise. Sequence composition has been used to identify GIs, infer their phylogeny; and determine their relative time of insertion. Collection of metadata on known GIs will enhance insight into horizontal gene transfer ontology and flow. Results: This paper introduces the merger of SeqWord Genomic Islands Sniffer (SWGIS), which utilizes composition based approaches for identification of GIs in bacterial genomic sequences, and the Predicted Genomic Islands (Pre_GI) database, which houses 26,744 islands found in 2,407 bacterial plasmids and chromosomes. SWGIS is a standalone program that detects GIs using a set of optimized parametric measures with estimates of acceptable false positive and false negative rates. Pre_GI is a novel repository that includes island ontology and flux. This study furthermore illustrates the need for parametric optimization towards the prediction of GIs to minimize false negative and false positive predictions. In addition Pre_GI emphasizes the practicality of the compounded knowledge that the database affords in detection and visualization of ontological links between GIs. Availability: SWGIS is freely available on the web at http://www.bi.up.ac.za/SeqWord/sniffer/index.html, and Pre_GI is freely accessible at http://pregi.bi.up.ac.za/index.php.