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    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; 26(4) — S. 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.