The paper introduces RNAmmer, a computational tool designed to accurately and rapidly annotate ribosomal RNA (rRNA) genes in complete genome sequences. rRNAs are crucial for ribosome function and are often poorly annotated, making comparative studies difficult. RNAmmer uses hidden Markov models (HMMs) trained on data from the 5S ribosomal RNA database and the European ribosomal RNA database project. A pre-screening step with a 'spotter' HMM increases the speed of the analysis while maintaining high sensitivity. The program is tested on a large set of genomes and shows high accuracy in predicting rRNA locations, including novel, unannotated rRNAs. The software is available through the CBS web server and has been validated against experimentally verified rRNAs and other databases. The authors discuss the limitations and performance of the method, particularly for eukaryotic and archaeal rRNAs, and compare it to other non-coding RNA prediction methods.The paper introduces RNAmmer, a computational tool designed to accurately and rapidly annotate ribosomal RNA (rRNA) genes in complete genome sequences. rRNAs are crucial for ribosome function and are often poorly annotated, making comparative studies difficult. RNAmmer uses hidden Markov models (HMMs) trained on data from the 5S ribosomal RNA database and the European ribosomal RNA database project. A pre-screening step with a 'spotter' HMM increases the speed of the analysis while maintaining high sensitivity. The program is tested on a large set of genomes and shows high accuracy in predicting rRNA locations, including novel, unannotated rRNAs. The software is available through the CBS web server and has been validated against experimentally verified rRNAs and other databases. The authors discuss the limitations and performance of the method, particularly for eukaryotic and archaeal rRNAs, and compare it to other non-coding RNA prediction methods.