Research article

In silico miRNA prediction in metazoan genomes: balancing between sensitivity and specificity

Ate van der Burgt^1,2 , Mark WJE Fiers^1,4 , Jan-Peter Nap^1,3 and Roeland CHJ van Ham^1,2

¹Applied Bioinformatics, Plant Research International, Wageningen University & Research Centre, PO Box 16, 6700 AA Wageningen, The Netherlands

²Laboratory of Bioinformatics, Wageningen University, Dreijenlaan 3, 6703 HA Wageningen, The Netherlands

³Centre for BioSystems Genomics 2012 (CBSG2012), PO Box 98, 6700 AB Wageningen, The Netherlands

⁴Current address: New Zealand Institute for Plant & Food Research Ltd, Private Bag 4704, Christchurch, New Zealand

author email corresponding author email

BMC Genomics 2009, 10:204doi:10.1186/1471-2164-10-204

Published:	30 April 2009

Abstract

Background

MicroRNAs (miRNAs), short ~21-nucleotide RNA molecules, play an important role in post-transcriptional regulation of gene expression. The number of known miRNA hairpins registered in the miRBase database is rapidly increasing, but recent reports suggest that many miRNAs with restricted temporal or tissue-specific expression remain undiscovered. Various strategies for in silico miRNA identification have been proposed to facilitate miRNA discovery. Notably support vector machine (SVM) methods have recently gained popularity. However, a drawback of these methods is that they do not provide insight into the biological properties of miRNA sequences.

Results

We here propose a new strategy for miRNA hairpin prediction in which the likelihood that a genomic hairpin is a true miRNA hairpin is evaluated based on statistical distributions of observed biological variation of properties (descriptors) of known miRNA hairpins. These distributions are transformed into a single and continuous outcome classifier called the L score. Using a dataset of known miRNA hairpins from the miRBase database and an exhaustive set of genomic hairpins identified in the genome of Caenorhabditis elegans, a subset of 18 most informative descriptors was selected after detailed analysis of correlation among and discriminative power of individual descriptors. We show that the majority of previously identified miRNA hairpins have high L scores, that the method outperforms miRNA prediction by threshold filtering and that it is more transparent than SVM classifiers.

Conclusion

The L score is applicable as a prediction classifier with high sensitivity for novel miRNA hairpins. The L-score approach can be used to rank and select interesting miRNA hairpin candidates for downstream experimental analysis when coupled to a genome-wide set of in silico-identified hairpins or to facilitate the analysis of large sets of putative miRNA hairpin loci obtained in deep-sequencing efforts of small RNAs. Moreover, the in-depth analyses of miRNA hairpins descriptors preceding and determining the L score outcome could be used as an extension to miRBase entries to help increase the reliability and biological relevance of the miRNA registry.