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      IslandViewer: an integrated interface for computational identification and visualization of genomic islands

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      Bioinformatics
      Oxford University Press

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          Abstract

          Summary: Genomic islands (clusters of genes of probable horizontal origin; GIs) play a critical role in medically important adaptations of bacteria. Recently, several computational methods have been developed to predict GIs that utilize either sequence composition bias or comparative genomics approaches. IslandViewer is a web accessible application that provides the first user-friendly interface for obtaining precomputed GI predictions, or predictions from user-inputted sequence, using the most accurate methods for genomic island prediction: IslandPick, IslandPath-DIMOB and SIGI-HMM. The graphical interface allows easy viewing and downloading of island data in multiple formats, at both the chromosome and gene level, for method-specific, or overlapping, GI predictions.

          Availability: The IslandViewer web service is available at http://www.pathogenomics.sfu.ca/islandviewer and the source code is freely available under the GNU GPL license.

          Contact: brinkman@ 123456sfu.ca

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          Most cited references14

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          Genomic islands in pathogenic and environmental microorganisms.

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            Interpolated variable order motifs for identification of horizontally acquired DNA: revisiting the Salmonella pathogenicity islands.

            There is a growing literature on the detection of Horizontal Gene Transfer (HGT) events by means of parametric, non-comparative methods. Such approaches rely only on sequence information and utilize different low and high order indices to capture compositional deviation from the genome backbone; the superiority of the latter over the former has been shown elsewhere. However even high order k-mers may be poor estimators of HGT, when insufficient information is available, e.g. in short sliding windows. Most of the current HGT prediction methods require pre-existing annotation, which may restrict their application on newly sequenced genomes. We introduce a novel computational method, Interpolated Variable Order Motifs (IVOMs), which exploits compositional biases using variable order motif distributions and captures more reliably the local composition of a sequence compared with fixed-order methods. For optimal localization of the boundaries of each predicted region, a second order, two-state hidden Markov model (HMM) is implemented in a change-point detection framework. We applied the IVOM approach to the genome of Salmonella enterica serovar Typhi CT18, a well-studied prokaryote in terms of HGT events, and we show that the IVOMs outperform state-of-the-art low and high order motif methods predicting not only the already characterized Salmonella Pathogenicity Islands (SPI-1 to SPI-10) but also three novel SPIs (SPI-15, SPI-16, SPI-17) and other HGT events. The software is available under a GPL license as a standalone application at http://www.sanger.ac.uk/Software/analysis/alien_hunter gsv@sanger.ac.uk Supplementary data are available at Bioinformatics online.
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              Score-based prediction of genomic islands in prokaryotic genomes using hidden Markov models

              Background Horizontal gene transfer (HGT) is considered a strong evolutionary force shaping the content of microbial genomes in a substantial manner. It is the difference in speed enabling the rapid adaptation to changing environmental demands that distinguishes HGT from gene genesis, duplications or mutations. For a precise characterization, algorithms are needed that identify transfer events with high reliability. Frequently, the transferred pieces of DNA have a considerable length, comprise several genes and are called genomic islands (GIs) or more specifically pathogenicity or symbiotic islands. Results We have implemented the program SIGI-HMM that predicts GIs and the putative donor of each individual alien gene. It is based on the analysis of codon usage (CU) of each individual gene of a genome under study. CU of each gene is compared against a carefully selected set of CU tables representing microbial donors or highly expressed genes. Multiple tests are used to identify putatively alien genes, to predict putative donors and to mask putatively highly expressed genes. Thus, we determine the states and emission probabilities of an inhomogeneous hidden Markov model working on gene level. For the transition probabilities, we draw upon classical test theory with the intention of integrating a sensitivity controller in a consistent manner. SIGI-HMM was written in JAVA and is publicly available. It accepts as input any file created according to the EMBL-format. It generates output in the common GFF format readable for genome browsers. Benchmark tests showed that the output of SIGI-HMM is in agreement with known findings. Its predictions were both consistent with annotated GIs and with predictions generated by different methods. Conclusion SIGI-HMM is a sensitive tool for the identification of GIs in microbial genomes. It allows to interactively analyze genomes in detail and to generate or to test hypotheses about the origin of acquired genes.
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                Author and article information

                Journal
                Bioinformatics
                bioinformatics
                bioinfo
                Bioinformatics
                Oxford University Press
                1367-4803
                1460-2059
                1 March 2009
                16 January 2009
                16 January 2009
                : 25
                : 5
                : 664-665
                Affiliations
                Department of Molecular Biology and Biochemistry, Simon Fraser University, Burnaby, BC, Canada
                Author notes
                *To whom correspondence should be addressed.

                Associate Editor: Dmitrij Frishman

                Article
                btp030
                10.1093/bioinformatics/btp030
                2647836
                19151094
                c288cb21-6ece-4994-a253-c0875d8ff83e
                © 2009 The Author(s)

                This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License ( http://creativecommons.org/licenses/by-nc/2.0/uk/) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

                History
                : 18 October 2008
                : 12 December 2008
                : 12 January 2009
                Categories
                Applications Note
                Genome Analysis

                Bioinformatics & Computational biology
                Bioinformatics & Computational biology

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