Author(s): Lagesen K, Hallin P, Rdland EA, Staerfeldt HH, Rognes T, , Lagesen K, Hallin P, Rdland EA, Staerfeldt HH, Rognes T, , Lagesen K, Hallin P, Rdland EA, Staerfeldt HH, Rognes T, , Lagesen K, Hallin P, Rdland EA, Staerfeldt HH, Rognes T,
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Abstract The publication of a complete genome sequence is usually accompanied by annotations of its genes. In contrast to protein coding genes, genes for ribosomal RNA (rRNA) are often poorly or inconsistently annotated. This makes comparative studies based on rRNA genes difficult. We have therefore created computational predictors for the major rRNA species from all kingdoms of life and compiled them into a program called RNAmmer. The program uses hidden Markov models trained on data from the 5S ribosomal RNA database and the European ribosomal RNA database project. A pre-screening step makes the method fast with little loss of sensitivity, enabling the analysis of a complete bacterial genome in less than a minute. Results from running RNAmmer on a large set of genomes indicate that the location of rRNAs can be predicted with a very high level of accuracy. Novel, unannotated rRNAs are also predicted in many genomes. The software as well as the genome analysis results are available at the CBS web server.
This article was published in Nucleic Acids Res
and referenced in Journal of Data Mining in Genomics & Proteomics