Nucleic Acids Research Advance Access originally published online on April 22, 2007
Nucleic Acids Research 2007 35(9):3100-3108; doi:10.1093/nar/gkm160
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Nucleic Acids Research, 2007, Vol. 35, No. 9 3100-3108
© 2007 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.
Computational Biology |
RNAmmer: consistent and rapid annotation of ribosomal RNA genes
1Centre for Molecular Biology and Neuroscience and Institute of Medical Microbiology, University of Oslo, NO-0027 Oslo, Norway, 2Centre for Molecular Biology and Neuroscience and Institute of Medical Microbiology, Rikshospitalet-Radiumhospitalet Medical Centre, NO-0027 Oslo, Norway, 3Center for Biological Sequence Analysis, Biocentrum-DTU, Technical University of Denmark, DK-2800 Lyngby, Denmark, 4Department of Informatics, University of Oslo, PO Box 1080 Blindern, NO-0316 Oslo, Norway and 5Norwegian Computing Center, PO Box 114 Blindern, NO-0314 Oslo, Norway
*To whom correspondence should be addressed. Tel: +4722844786; Email: karin.lagesen{at}medisin.uio.no
Received December 1, 2006. Revised March 2, 2007. Accepted March 2, 2007.
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.
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