Detecting MicroRNA Targets by Linking Sequence, MicroRNA and Gene Expression Data
MicroRNAs (miRNAs) have recently been discovered as an important class of non-coding RNA genes that play a major role in regulating gene expression, providing a means to control the relative amounts of mRNA transcripts and their protein products. Although much work has been done in the genome-wide computational prediction of miRNA genes and their target mRNAs, two open questions are how miRNAs regulate gene expression and how to efficiently detect bona fide miRNA targets from a large number of candidate miRNA targets predicted by existing computational algorithms. In this paper, we present evidence that miRNAs function by post-transcriptional degradation of mRNA target transcripts: based on this, we propose a novel probabilistic model that accounts for gene expression using miRNA expression data and a set of candidate miRNA targets. A set of underlying miRNA targets are learned from the data using our algorithm, GenMiR (Generative model for miRNA regulation). Our model scores and detects 601 out of 1,770 targets obtained from TargetScanS in mouse at a false detection rate of 5%. Our high-confidence miRNA targets include several which have been previously validated by experiment: the remainder potentially represent a dramatic increase in the number of known miRNA targets.
KeywordsmRNA Transcript miRNA Target Candidate Target miRNA Regulation MicroRNA Target
Unable to display preview. Download preview PDF.
- 7.Kent, W.J.: BLAT – The BLAST-Like Alignment Tool. Genome Research 4, 656–664 (2002)Google Scholar
- 10.Hartemink, A., Gifford, D., Jaakkola, T., Young, R.: Using graphical models and genomic expression data to statistically validate models of genetic regulatory networks. In: Proceedings of the Pacific Symposium on Biocomputing 2001, pp. 422–433. World Scientific, New Jersey (2001)Google Scholar
- 11.Huang, J.C., Morris, Q.D., Hughes, T.R., Frey, B.J.: GenXHC: A probabilistic generative model for cross-hybridization compensation in high-density, genome-wide microarray data. In: Proceedings of the Thirteenth Annual Conference on Intelligent Systems for Molecular Biology, June 25-29 (2005)Google Scholar
- 13.John, B., et al.: Human MicroRNA targets. PLoS Biol. 2(11), e363 (2004)Google Scholar
- 14.Jordan, M.I., Ghahramani, Z., Jaakkola, T.S., Saul, L.K.: An introduction to variational methods for graphical models. Learning in Graphical Models. MIT Press, Cambridge (1999)Google Scholar
- 15.Kislinger, T., et al.: Global survey of organ and organelle protein expression in mouse: combined proteomic and transcriptomic profiling. Submitted to Cell (2005)Google Scholar
- 22.Neal, R.M., Hinton, G.E.: A view of the EM algorithm that justifies incremental, sparse, and other variants. Learning in Graphical Models. Kluwer Academic Publishers, Dordrecht (1998)Google Scholar
- 24.Zilberstein, C.B.Z., Ziv-Ukelson, M., Pinter, R.Y., Yakhini, Z.: A high-throughput approach for associating microRNAs with their activity conditions. In: Proceedings of the Ninth Annual Conference on Research in Computational Molecular Biology, May 14-18 (2005)Google Scholar
- 26.Supplemental Data for Lewis et al. Cell 120, 15-20, http://web.wi.mit.edu/bartel/pub/Supplemental%20Material/Lewis%20et%20al%202005%20Supp/