The capacity of highly parallel sequencing technologies to detect small RNAs at unprecedented depth suggests their value in systematically identifying microRNAs (miRNAs). However, the identification of miRNAs from the large pool of sequenced transcripts from a single deep sequencing run remains a major challenge.
Here, the authors present an algorithm, miRDeep, which uses a probabilistic model of miRNA biogenesis to score compatibility of the position and frequency of sequenced RNA with the secondary structure of the miRNA precursor.
The miRDeep package was developed to discover active known or novel miRNAs from deep sequencing data (Solexa/Illumina, 454, …). The package consists of everything you need to analyze your own deep sequencing data after removal of ligation adapters: a number of scripts to preprocess the mapped data, and the core miRDeep algorithm that will analyze and score these data.
They demonstrate its accuracy and robustness using published Caenorhabditis elegans data and data they generated by deep sequencing human and dog RNAs. miRDeep reports altogether approximately 230 previously unannotated miRNAs, of which four novel C. elegans miRNAs are validated by northern blot analysis.
miRDeep is freely available at: http://www.mdc-berlin.de/en/research/research_teams/systems_biology_of_gene_regulatory_elements/projects/miRDeep/index.html
Friedländer MR, Chen W, Adamidi C, Maaskola J, Einspanier R, Knespel S, Rajewsky N. (2008) Discovering microRNAs from deep sequencing data using miRDeep. Nat Biotechnol 26(4), 407-15. [abstract]