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RNAmmer: consistent and rapid annotation of ribosomal RNA genes.

Nucleic acids research | May 15, 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.

Pubmed ID: 17452365 RIS Download

Mesh terms: Computational Biology | Genes, rRNA | Genome, Bacterial | Genomics | Markov Chains | Software

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European Molecular Biology Laboratory

Intergovernmental organisation funded by public research money from its member states in Europe. Groups and laboratories perform basic research in molecular biology and molecular medicine, training for scientists, students and visitors. Provides development of services, new instruments and methods, data and technology in its member states.

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Hmmer

Tool for searching sequence databases for homologs of protein sequences, and for making protein sequence alignments. It implements methods using probabilistic models called profile hidden Markov models (profile HMMs). Compared to BLAST, FASTA, and other sequence alignment and database search tools based on older scoring methodology, HMMER aims to be significantly more accurate and more able to detect remote homologs because of the strength of its underlying mathematical models. In the past, this strength came at significant computational expense, but in the new HMMER3 project, HMMER is now essentially as fast as BLAST.

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