Evaluation of genomic island predictors using a comparative genomics approach

We developed a comparative genomics approach (IslandPick) that identifies both very probable islands and non-island regions. The approach involves 1) flexible, automated selection of comparative genomes for each query genome, using a distance function that picks appropriate genomes for identification of GIs, 2) identification of regions unique to the query genome, compared with the chosen genomes (positive dataset) and 3) identification of regions conserved across all genomes (negative dataset). Using our constructed datasets, we investigated the accuracy of several sequence composition-based GI prediction tools.Our results indicate that AlienHunter has the highest recall, but the lowest measured precision, while SIGI-HMM is the most precise method. SIGI-HMM and IslandPath/DIMOB have comparable overall highest accuracy. Our comparative genomics approach, IslandPick, was the most accurate, compared with a curated list of GIs, indicating that we have constructed suitable datasets. This represents the first evaluation, using diverse and, independent datasets that were not artificially constructed, of the accuracy of several sequence composition-based GI predictors. The caveats associated with this analysis and proposals for optimal island prediction are discussed.Bacteria are the most abundant Domain of life that exists on earth (based on biomass) [1]. The species we see today are highly diverse, reflecting adaptations to a wide range of environments over billions of years. One of the major sources of adaptability for bacteria is the ability to obtain genes horizontally from other sources, including other prokaryotes, viruses, and even eukaryotes [2]. Analysis of bacterial genomic sequences has indicated that many of the horizontal gene transfer (HGT) events observed in bacteria involve clusters of genes. Collectively, these genomic regions are referred to as genomic islands (GIs) [3]. GIs, which range in size from ~5–500 kb, have become of significant interest, since