title: Identification of clustered microRNAs using an ab initio prediction method.
abstract: BACKGROUND
  MicroRNAs (miRNAs) are endogenous 21 to 23-nucleotide RNA molecules that regulate protein-coding gene expression in plants and animals via the RNA interference pathway. Hundreds of them have been identified in the last five years and very recent works indicate that their total number is still larger. Therefore miRNAs gene discovery remains an important aspect of understanding this new and still widely unknown regulation mechanism. Bioinformatics approaches have proved to be very useful toward this goal by guiding the experimental investigations.
  
  
  RESULTS
  In this work we describe our computational method for miRNA prediction and the results of its application to the discovery of novel mammalian miRNAs. We focus on genomic regions around already known miRNAs, in order to exploit the property that miRNAs are occasionally found in clusters. Starting with the known human, mouse and rat miRNAs we analyze 20 kb of flanking genomic regions for the presence of putative precursor miRNAs (pre-miRNAs). Each genome is analyzed separately, allowing us to study the species-specific identity and genome organization of miRNA loci. We only use cross-species comparisons to make conservative estimates of the number of novel miRNAs. Our ab initio method predicts between fifty and hundred novel pre-miRNAs for each of the considered species. Around 30% of these already have experimental support in a large set of cloned mammalian small RNAs. The validation rate among predicted cases that are conserved in at least one other species is higher, about 60%, and many of them have not been detected by prediction methods that used cross-species comparisons. A large fraction of the experimentally confirmed predictions correspond to an imprinted locus residing on chromosome 14 in human, 12 in mouse and 6 in rat. Our computational tool can be accessed on the world-wide-web.
  
  
  CONCLUSIONS
  Our results show that the assumption that many miRNAs occur in clusters is fruitful for the discovery of novel miRNAs. Additionally we show that although the overall miRNA content in the observed clusters is very similar across the three considered species, the internal organization of the clusters changes in evolution.
authors.affiliation: Biozentrum, Universität Basel, Basel, Switzerland. alain.sewer@unibas.ch
authors.forename: Alain
authors.initials: A
authors.lastname: Sewer
authors.forename: Nicodème
authors.initials: N
authors.lastname: Paul
authors.forename: Pablo
authors.initials: P
authors.lastname: Landgraf
authors.forename: Alexei
authors.initials: A
authors.lastname: Aravin
authors.forename: Sébastien
authors.initials: S
authors.lastname: Pfeffer
authors.forename: Michael J
authors.initials: MJ
authors.lastname: Brownstein
authors.forename: Thomas
authors.initials: T
authors.lastname: Tuschl
authors.forename: Erik
authors.initials: E
authors.lastname: van Nimwegen
authors.forename: Mihaela
authors.initials: M
authors.lastname: Zavolan
mesh_terms: D000465:Algorithms; D000818:Animals; D002874:Chromosome Mapping; D016000:Cluster Analysis; D019295:Computational Biology / Q000379:methods*; D016678:Genome; D006801:Humans; D020407:Internet; D051379:Mice; D035683:MicroRNAs / Q000737:chemistry*; D008957:Models, Genetic; D005810:Multigene Family; D009690:Nucleic Acid Conformation; D012313:RNA / Q000737:chemistry; D051381:Rats; D012680:Sensitivity and Specificity; D016415:Sequence Alignment; D012984:Software
chemical_list: D035683:MicroRNAs; D012313:RNA