title: Ab initio human miRNA and pre-miRNA prediction.
abstract: MicroRNAs (miRNAs) are small single-stranded noncoding RNAs that play an important role in post-transcriptional regulation of gene expression. In this paper, we present a web server for ab initio prediction of the human miRNAs and their precursors. The prediction methods are based on the hidden Markov Models and the context-structural characteristics. By taking into account the identified patterns of primary and secondary structures of the pre-miRNAs, a new HMM model is proposed and the existing context-structural Markov model is modified. The evaluation of the method performance has shown that it can accurately predict novel human miRNAs. Comparing with the existing methods we demonstrate that our method has a higher prediction quality both for human pre-miRNAs and miRNAs. The models have also showed good results in the prediction of the mouse miRNAs. The web server is available at http://wwwmgs.bionet.nsc.ru/mgs/programs/rnaanalys (mirror http://miRNA.at.nsu.ru ).
authors.affiliation: Institute of Cytology and Genetics, SB RAS, 10 Lavrentyev Avenue, Novosibirsk 630090, Russian Federation , Novosibirsk State University, 2 Pirogov Street, Novosibirsk 630090, Russian Federation.
authors.forename: Igor I
authors.initials: II
authors.lastname: Titov
authors.forename: Pavel S
authors.initials: PS
authors.lastname: Vorozheykin
mesh_terms: D019295:Computational Biology / Q000379:methods*; D030541:Databases, Genetic; D006801:Humans; D020407:Internet; D035683:MicroRNAs / Q000737:chemistry; D009690:Nucleic Acid Conformation; D012322:RNA Precursors
chemical_list: D035683:MicroRNAs; D012322:RNA Precursors