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 ∼230 previously unannotated miRNAs, of which four novel C. elegans miRNAs are validated by northern blot analysis.
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Acknowledgements
We thank H.-H. Ropers for making possible the deep sequencing of HeLa cell and the dog lymphocyte RNA at the Max Planck Institute for Molecular Genetics in Berlin. We are indebted to Alejandro Sánchez Alvarado and John Kim for the planarian data. Thomas Isenbarger helped at the very initial stage of the project. Eugene Berezikov kindly provided unpublished deep-sequencing data (not used in this study). Ralf Bundschuh helped with parameter estimations. M.R.F. acknowledges a fellowship from the Max Delbrück Center. J.M. acknowledges a fellowship from Deutsche Forschungsgemeinschaft (International Research Training Group 1360). Finally, many thanks to the members of the Rajewsky lab for countless hours of stimulating discussions, and in particular to Nadine Thierfelder and Svetlana Lebedeva for providing the HeLa cell and C. elegans samples.
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Friedländer, M., Chen, W., Adamidi, C. et al. Discovering microRNAs from deep sequencing data using miRDeep. Nat Biotechnol 26, 407–415 (2008). https://doi.org/10.1038/nbt1394
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DOI: https://doi.org/10.1038/nbt1394
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