Publikationsdatenbank

Discovering microRNAs from deep sequencing data using miRDeep (2008)

Art

Zeitschriftenartikel / wissenschaftlicher Beitrag

Autoren

Friedländer, Marc R

Chen, Wei

Adamidi, Catherine

Maaskola, Jonas

Einspanier, Ralf

Knespel, Signe

Rajewsky, Nikolaus

Quelle

Nature biotechnology : the science and business of biotechnology

Bandzählung: 26

Heftzählung: 4

Seiten: 407 – 415

ISSN: 1087-0156

Sprache

Englisch

Verweise

DOI: 10.1038/nbt1394

Pubmed: 18392026

Kontakt

Institut für Veterinär-Biochemie

Oertzenweg 19 b
14163 Berlin
+49 30 838 62225
biochemie@vetmed.fu-berlin.de

Abstract / Zusammenfassung

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.