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Discovering microRNAs from deep sequencing data using miRDeep

Official URL:https://doi.org/10.1038/nbt1394
PubMed:View item in PubMed
Creators Name:Friedlaender, M.R. and Chen, W. and Adamidi, C. and Maaskola, J. and Einspanier, R. and Knespel, S. and Rajewsky, N.
Journal Title:Nature Biotechnology
Journal Abbreviation:Nat Biotechnol
Page Range:407-415
Date:April 2008
Keywords:Algorithms, Base Sequence, Database Management Systems, Genetic Databases, MicroRNAs, Molecular Sequence Data, Sequence Alignment, RNA Sequence Analysis, Software, Animals, Caenorhabditis Elegans, Dogs
Abstract: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.
Publisher:Nature Publishing Group (U.S.A.)
Item Type:Article

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