Discovering microRNAs from deep sequencing data using miRDeep.

Friedländer MR, Chen W, Adamidi C, Maaskola J, Einspanier R, Knespel S, Rajewsky N

Nat. Biotechnol. 26 (4) 407-415 [2008-04-00; online 2008-04-09]

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, we 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. We demonstrate its accuracy and robustness using published Caenorhabditis elegans data and data we 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.

Marc Friedländer

PubMed 18392026

DOI 10.1038/nbt1394

Crossref 10.1038/nbt1394

pii: nbt1394
GEO: GSE10825
GEO: GSE10829


Publications 7.1.2