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A widely used web-based resource for the prediction and analysis of genomic islands (GIs) in bacterial and archaeal genomes. GIs are clusters of genes of probable horizontal origin, and are of high interest since they disproportionately encode genes involved in medically and environmentally important adaptations, including antimicrobial resistance and virulence. IslandViewer integrates three different genomic island prediction methods: IslandPick, IslandPath-DIMOB, and SIGI-HMM.
An application for the prediction of putative horizontal gene transfer (HGT) events with the implementation of interpolated variable order motifs (IVOMs). An IVOM approach exploits compositional biases using variable order motif distributions and captures more reliably the local composition of a sequence compared to fixed-order methods. Optionally the predictions can be parsed into a 2-state 2nd order Hidden Markov Model (HMM), in a change-point detection framework, to optimize the localization of the boundaries of the predicted regions. The predictions (EMBL format) can be automatically loaded into the freely available Artemis genome viewer.
Identifies genomic regions as candidates for Horizontal gene transfer (HGT) assessment in eukaryotes. SigHunt is a surrogate method which detects non-ameliorated HGT in large genomic sequences. It is computationally efficient and its implementation provides step-wise user access to results that enables data exploration and analytical optimization. It has been demonstrated good success in using SigHunt to find introduced Genomic islands (GIs) across kingdoms and GIs in real genomic sequences (particularly in some fungi). Furthermore, with the recent finding of bacterial horizontally transferred genes in human tumour cells, SigHunt shows promise to be used in rapid screening of such events in genomic assemblies of specific cell lines.
Assists users in collecting summary statistics. Goldilocks is a package that was developed to identify shifts in variation. It can also discover outlier regions, or locate and extract interesting regions from one or more arbitrary genomes for further analysis, for a user-provided definition of interesting. It must also be supplied with a desired census strategy defining the criteria of interest, such as occurrences of individual nucleotide bases, motifs, deviations from a reference or GC-content.
Zisland Explorer
Predicts genomic islands based on the segmental cumulative GC profile. Zisland Explorer was designed with a novel strategy, as well as a combination of the homogeneity and heterogeneity of genomic sequences. While the sequence homogeneity reflects the composition consistence within each island, the heterogeneity measures the composition bias between an island and the core genome. The performance of Zisland Explorer was evaluated on the data sets of 11 different organisms. Our results suggested that the true-positive rate (TPR) of Zisland Explorer was at least 10.3% higher than that of four other widely used tools. On the other hand, the new tool did not lose overall accuracy with the improvement in the TPR and showed better equilibrium among various evaluation indexes. Also, Zisland Explorer showed better accuracy in the prediction of experimental island data. Overall, the tool provides an alternative solution over other tools, which expands the field of island prediction and offers a supplement to increase the performance of the distinct predicting strategy.
Measures the local GC-content bias in genomes and a survey of published fungal species. OcculterCut identifies species containing distinct AT-rich regions. It can be used to identify genes undergoing RIP-assisted diversifying selection, such as small, secreted effector proteins that mediate host-microbe disease interactions. The tool returns information about the proximity of genes to AT-rich regions, making it a useful tool for identifying genes likely to be under the influence of repeat-induced point (RIP)-mediated “hypermutation”, such as candidate avirulence/effector genes or other genes involved in rapid evolution and specialization.
MSGIP / Mean Shift Genomic Island Preditor
An automated tool for the prediction of genomic islands (GIs). MSGIP is built upon mean shift clustering algorithm. It does not require any information regarding the number of clusters, and the bandwidth parameter is automatically calculated based on a heuristic approach. Genomes of bacteria with GIs discussed in other papers were used to evaluate the proposed method. The application of MSGIP revealed the same GIs predicted by other methods and also different novel unpredicted islands. A detailed investigation of the different features related to typical GI elements inserted in these new regions confirmed its effectiveness.
Provides a user-friendly interface that allows researcher to easily analyze the location of potential genomic and pathogenicity islands in a prokaryotic genome. PredictBias identifies genomic and pathogenicity islands in prokaryotes based on composition bias, presence of insertion elements, proximity with virulence-associated genes and absence in related non-pathogenic species. It performs a RPSBLAST search for regions with significant composition bias against a profile database of virulence factors (VFPD). This database has been developed using 213 protein families associated to virulence retrieved from Pfam and PRINTS database.
IGIPT / Integrated Genomic Island Prediction Tool
A web-based integrated platform for the identification of genomic islands (GIs). IGIPT incorporates thirteen parametric measures based on anomalous nucleotide composition on a single platform, thus improving the predictive power of a horizontally acquired region, since it is known that no single measure can absolutely predict a horizontally transferred region. The tool filters putative GIs based on standard deviation from genomic average and also provides raw output in MS excel format for further analysis.
GIV / Genomic Island Visualization
A visualization tool to displays the locations of genomic islands in a genome, as well as the corresponding supportive feature information for GIs, including 1) sequence composition based feature, interpolated variable order motifs (IVOM) by third-party software Alien_hunter, 2) mobile gene information Integrase, 3) mobile gene information transposases, 4) tRNA gene, 5) phage information, 6) gene density, 7) intergenic distance and 8) highly expressed genes (HEGs).
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