• Register
Forgot Password

If you have forgotten your password you can enter your email here and get a temporary password sent to your email.


Leaving Community

Are you sure you want to leave this community? Leaving the community will revoke any permissions you have been granted in this community.


Discovering microRNAs from deep sequencing data using miRDeep.

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.

Pubmed ID: 18392026


  • Friedl√§nder MR
  • Chen W
  • Adamidi C
  • Maaskola J
  • Einspanier R
  • Knespel S
  • Rajewsky N


Nature biotechnology

Publication Data

April 8, 2008

Associated Grants


Mesh Terms

  • Algorithms
  • Animals
  • Base Sequence
  • Caenorhabditis elegans
  • Database Management Systems
  • Databases, Genetic
  • Dogs
  • Humans
  • MicroRNAs
  • Molecular Sequence Data
  • Sequence Alignment
  • Sequence Analysis, RNA
  • Software