Score-based prediction of genomic islands in prokaryotic genomes using hidden Markov models

By Stephan Waack, Oliver Keller, Roman Asper, Thomas Brodag, Carsten Damm, W. F. Fricke, Katharina Surovcik, Peter Meinicke, Rainer Merkl. BMC Bioinformatics, 2006

388 citations recorded by the source corpus.

BackgroundHorizontal 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.ResultsWe 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.ConclusionSIGI-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.

Subfields: Genetics, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/2eeb52d22743fa8c4bed7882b832dc9d9603d5e1

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In Genetics

1. Geoffrey L. Winsor
2. Robert E. W. Hancock
3. Alla L. Lapidus

In Molecular Biology

1. Robert E. W. Hancock
2. Raymond Lo
3. David A. Mills
4. Paul Richardson
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Citations in the corpus

• IslandViewer 4: expanded prediction of genomic islands for larger-scale datasets (2017, positive): “Islander predictions provide a second method, in addition to IslandPick (18), to define more accurately GI boundaries and complement well the two other predictive methods, namely SIGI-HMM (29) and IslandPath-DIMOB (7), already integrated in Island-Viewer that predict GI regions with less boundary ac”
• Pseudomonas Genome Database: improved comparative analysis and population genomics capability for Pseudomonas genomes (2010, methodology): “To identify genomic islands (GIs), our lab developed IslandViewer (27), a computational tool integrating two sequence composition GI prediction methods, SIGI-HMM (28) and IslandPathDIMOB (29), with a comparative GI prediction method, IslandPick (30).”
• The genome sequence of Bifidobacterium longum subsp. infantis reveals adaptations for milk utilization within the infant microbiome (2008, background): “These results are consistent with the utilization pathway reconstructed from the genome sequence, as the 9-carbon carbohydrate backbone of N-acetyl-[2-13C]neuraminic acid was metabolized into 3-carbon [2-13]pyruvate, plus unlabeled N-acetylmannosamine (ManNAc).”
• IslandViewer: an integrated interface for computational identification and visualization of genomic islands (2009, methodology): “All methods are run in parallel for each genome so that updates are quickly performed on a computer cluster, while all dynamic web pages are implemented using PHP.”