Waack, Stephan and Keller, Oliver and Asper, Roman and Brodag, Thomas and Damm, Carsten and Fricke, Wolfgang Florian and Surovcik, Katharina and Meinicke, Peter and Merkl, Rainer (2006) Score-based prediction of genomic islands in prokaryotic genomes using hidden Markov models. BMC BIOINFORMATICS, 7: 142. ISSN 1471-2105,
Full text not available from this repository. (Request a copy)Abstract
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.
| Item Type: | Article |
|---|---|
| Uncontrolled Keywords: | LATERAL GENE-TRANSFER; BACILLUS-SUBTILIS; MICROBIAL GENOMES; BACTERIAL GENOMES; CODON USAGE; COMPARATIVE CATEGORIZATION; PATHOGENICITY ISLANDS; NUCLEOTIDE-SEQUENCE; DNA-SEQUENCE; EVOLUTION; |
| Subjects: | 500 Science > 570 Life sciences |
| Divisions: | Biology, Preclinical Medicine > Institut für Biophysik und physikalische Biochemie > Prof. Dr. Rainer Merkl |
| Depositing User: | Dr. Gernot Deinzer |
| Date Deposited: | 17 Feb 2021 12:37 |
| Last Modified: | 17 Feb 2021 12:37 |
| URI: | https://pred.uni-regensburg.de/id/eprint/34783 |
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