RNAmmer: consistent and rapid annotation of ribosomal RNA genes

TLDR

Genome sequencing is usually accompanied by gene annotations, yet rRNA genes are often poorly annotated, hindering comparative studies. The authors developed RNAmmer, a program that predicts major rRNA species across all kingdoms of life. RNAmmer employs hidden Markov models trained on 5S rRNA and European rRNA databases, with a pre‑screening step that enables rapid analysis of a bacterial genome in under a minute. RNAmmer accurately predicts rRNA locations in many genomes, identifies novel unannotated rRNAs, and its software and results are available on the CBS web server.

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.