Discovering microRNAs from deep sequencing data using miRDeep

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Item Type:	Article
Title:	Discovering microRNAs from deep sequencing data using miRDeep
Creators Name:	Friedlaender, M.R., Chen, W., Adamidi, C., Maaskola, J., Einspanier, R., Knespel, S. and Rajewsky, N.
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.
Keywords:	Algorithms, Base Sequence, Database Management Systems, Genetic Databases, MicroRNAs, Molecular Sequence Data, Sequence Alignment, RNA Sequence Analysis, Software, Animals, Caenorhabditis Elegans, Dogs
Source:	Nature Biotechnology
ISSN:	1087-0156
Publisher:	Nature Publishing Group
Volume:	26
Number:	4
Page Range:	407-415
Date:	April 2008
Official Publication:	https://doi.org/10.1038/nbt1394
PubMed:	View item in PubMed

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