HHMMiR: efficient de novo prediction of microRNAs using hierarchical hidden Markov models.

ABSTRACT:

Background

MicroRNAs (miRNAs) are small non-coding single-stranded RNAs (20-23 nts) that are known to act as post-transcriptional and translational regulators of gene expression. Although, they were initially overlooked, their role in many important biological processes, such as development, cell differentiation, and cancer has been established in recent times. In spite of their biological significance, the identification of miRNA genes in newly sequenced organisms is still based, to a large degree, on extensive use of evolutionary conservation, which is not always available.

Results

We have developed HHMMiR, a novel approach for de novo miRNA hairpin prediction in the absence of evolutionary conservation. Our method implements a Hierarchical Hidden Markov Model (HHMM) that

SUBMITTER: Kadri S 

PROVIDER: S-EPMC2648761 | biostudies-literature | 2009 Jan

REPOSITORIES: biostudies-literature