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1 rRNA operons and promoter analysis 3.8 Paper VI: RNAmmer: Fast two-level HMM prediction of rRNA in prokaryotic genome sequences 121
3100–3108 Nucleic Acids Research, 2007, Vol. 35, No. 9 Published online 22 April 2007 doi:10.1093/nar/gkm160 RNAmmer: consistent and rapid annotation of ribosomal RNA genes Karin Lagesen 1,2, *, Peter Hallin 3 , Einar Andreas Rødland 1,2,4,5 , Hans-Henrik Stærfeldt 3 , Torbjørn Rognes 1,2,4 and David W. Ussery 1,2,3 1 Centre for Molecular Biology and Neuroscience and Institute of Medical Microbiology, University of Oslo, NO-0027 Oslo, Norway, 2 Centre for Molecular Biology and Neuroscience and Institute of Medical Microbiology, Rikshospitalet-Radiumhospitalet Medical Centre, NO-0027 Oslo, Norway, 3 Center for Biological Sequence Analysis, Biocentrum-DTU, Technical University of Denmark, DK-2800 Lyngby, Denmark, 4 Department of Informatics, University of Oslo, PO Box 1080 Blindern, NO-0316 Oslo, Norway and 5 Norwegian Computing Center, PO Box 114 Blindern, NO-0314 Oslo, Norway Received December 1, 2006; Revised and Accepted March 2, 2007 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. INTRODUCTION Ribosomes are the molecular machines which form the connection between nucleic acids and proteins in all living organisms. The ribosome’s dependence on ribosomal RNAs (rRNAs) for its function has caused them to be conserved at both the sequence and the structure level. Because of this, rRNAs are often used in comparative studies such as phylogenetic inference. Comparative studies have become more popular as more genomes have been completely sequenced, but can potentially *To whom correspondence should be addressed. Tel: þ4722844786; Email: karin.lagesen@medisin.uio.no become complicated when some of the genes they are based on are poorly annotated or not annotated at all. Unfortunately, this is often a problem with rRNAs as genome annotation pipelines usually do not include tools specific for rRNA detection. Instead, rRNAs are often located by sequence similarity searches such as BLAST. Although such searches may give reasonable answers due to the high level of sequence conservation in the core regions of the genes, using such results for annotation purposes can be problematic. The validity of the search results depends on the program and database used. Changing one or both of these can drastically change the results. Genomic databases have grown exponentially over the past two decades and search programs have as a consequence had to undergo constant revisions in order to meet the requirements of the research community. Thus, the results of a search done today are probably very different from those produced several years ago. An added complication is that the most commonly used database search methods have poor performance for noncoding RNAs. A recent study comparing several different methods for predicting noncoding RNAs, including rRNAs, found that the most commonly used methods gave the most inaccurate results (1). Through our work on the GenomeAtlas database (2), we have seen the results of poor annotation of rRNAs. Some genomes do not have any rRNAs annotated at all, whereas other genomes seem to have rRNAs annotated on the wrong strand. We initially tried to do systematic BLAST (3) searches, but it proved difficult to maintain consistency throughout this process. The high level of sequence conservation among the rRNAs enabled us to create hidden Markov models (HMMs) from structural alignments. Such models are more capable of capturing the sequence variation that is inherently present in the rRNA gene families than simple BLAST searches. ß 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.
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Chapter 1 Introduction Introduction
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Chapter 2 Comparative Genomics 2.1
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Comparative Genomics the publicly a
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Comparative Genomics source CDS tot
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Comparative Genomics 1 mysql -N -B
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1 Comparative Genomics 2.7 Paper II
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Appendix: Software 25 [ ] A l t e r
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BIBLIOGRAPHY J. Rogers, P. F. Stadl
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BIBLIOGRAPHY Q. Jin, Z. Yuan, J. Xu
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BIBLIOGRAPHY Velicer, F.-J. Vorholt
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