Published online 24 June 2005
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Human microRNA prediction through a probabilistic co-learning model of sequence and structure
1Graduate Program in Bioinformatics, Seoul National University Seoul 151-744, Korea 2Center for Bioinformation Technology (CBIT), Seoul National University Seoul 151-744, Korea 3Biointelligence Laboratory, School of Computer Science and Engineering, Seoul National University Seoul 151-744, Korea 4Department of Biological Sciences, Seoul National University Seoul 151-744, Korea
*To whom correspondence should be addressed. Tel: +82 2 880 1847; Fax: +82 2 875 2240; Email: btzhang{at}bi.snu.ac.kr
Received January 10, 2005. Revised May 27, 2005. Accepted June 6, 2005.
MicroRNAs (miRNAs) are small regulatory RNAs of 
22 nt. Although hundreds of miRNAs have been identified through experimental complementary DNA cloning methods and computational efforts, previous approaches could detect only abundantly expressed miRNAs or close homologs of previously identified miRNAs. Here, we introduce a probabilistic co-learning model for miRNA gene finding, ProMiR, which simultaneously considers the structure and sequence of miRNA precursors (pre-miRNAs). On 5-fold cross-validation with 136 referenced human datasets, the efficiency of the classification shows 73% sensitivity and 96% specificity. When applied to genome screening for novel miRNAs on human chromosomes 16, 17, 18 and 19, ProMiR effectively searches distantly homologous patterns over diverse pre-miRNAs, detecting at least 23 novel miRNA gene candidates. Importantly, the miRNA gene candidates do not demonstrate clear sequence similarity to the known miRNA genes. By quantitative PCR followed by RNA interference against Drosha, we experimentally confirmed that 9 of the 23 representative candidate genes express transcripts that are processed by the miRNA biogenesis enzyme Drosha in HeLa cells, indicating that ProMiR may successfully predict miRNA genes with at least 40% accuracy. Our study suggests that the miRNA gene family may be more abundant than previously anticipated, and confer highly extensive regulatory networks on eukaryotic cells.
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