Identification of clustered microRNAs using an ab initio prediction method

Abstract

BackgroundMicroRNAs (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.ResultsIn 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.ConclusionOur 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.

A novel monoclonal antibody against human Argonaute proteins reveals unexpected characteristics of miRNAs in human blood cells

“…Berezikov et al (2005) showed that miR-451 (which they had designated ''Cand919'') was not expressed in mouse ES cells but was expressed in adult mouse brain and also in increasing amounts through embryonic stages (8.5 dpc < 12.5 dpc < 16.5 dpc). miR-451 has also been described by others (Altuvia et al 2005;Sewer et al 2005), but never in the context of human blood cells. We find that there is considerable heterogeneity of the 39-end of miR-451 with many miR-451 molecules being longer than most miRNAs (Fig.…”

MicroRNAs in Amoebozoa: Deep sequencing of the small RNA population in the social amoeba Dictyostelium discoideum reveals developmentally regulated microRNAs

“…To test this, the 19 pre-miRNA sequences were analyzed using miR-abela, a program optimized to predict human pre-miRNAs (Sewer et al 2005). The program predicted that 17 of the pre-miRNA structures were human pre-miRNA-like (Supplemental Figs.…”

“…To identify pre-miRNA structures, sequences from 200 nt upstream of to 200 nt downstream from a mapped location were extracted and analyzed using MiRfold (Billoud et al 2005;Boccara et al 2007) with standard settings (default temperature 22°C, the growth temperature for D. discoideum). Pre-miRNA structures identified by MiRfold with a penalty <5 and a DG-value per nt <À0.4 kcal/mol were considered as premiRNA candidates and were subsequently reanalyzed with miR-abela (Sewer et al 2005) (http://www.mirz.unibas.ch/cgi/pred_miRNA_ genes.cgi). Further analyses are described in Results.…”

miRRim: A novel system to find conserved miRNAs with high sensitivity and specificity

“…Xie et al (2005) reported a target-sequence-driven approach in which hairpin structures are considered to be miRNA candidates if they have conserved motifs that are reverse-complements of sequence segments overrepresented in conserved 39-UTRs. Methods that do not rely on sequence conservation have been reported (Sewer et al 2005;Xue et al 2005;Yousef et al 2006) in which detailed structural and nucleotide sequence features such as nucleotide frequency, length of predicted stem, and size of symmetric bulges are used as the features of machine learning algorithms.…”