Nature Methods
- 4, 1045 - 1049 (2007)
Published online: 18 November 2007; | doi:10.1038/nmeth1130
Using expression profiling data to identify human microRNA targetsJim C Huang1, 7, Tomas Babak2, 7, Timothy W Corson2, 3, 6, Gordon Chua4, Sofia Khan3, Brenda L Gallie2, 3, Timothy R Hughes2, 4, Benjamin J Blencowe2, 4, Brendan J Frey1, 4, 5 & Quaid D Morris2, 4, 51
Department of Electrical and Computer Engineering, University of Toronto, 10 King's College Road, Toronto, Ontario M5S 3G4, Canada. 2
Department of Molecular and Medical Genetics, University of Toronto, 1 King's College Rd., Toronto, Ontario M5S 1A8, Canada. 3
Division of Applied Molecular Oncology, Ontario Cancer Institute/Princess Margaret Hospital, University Health Network, Toronto, Ontario M5G 2M9, Canada. 4
Banting and Best Department of Medical Research, University of Toronto, 160 College Street, Toronto, Ontario M5G 1L6, Canada. 5
Department of Computer Science, University of Toronto, 10 King's College Road, Toronto, Ontario M5S 3G4, Canada. 6
Present address: Department of Molecular, Cellular and Developmental Biology, Yale University, P.O. Box 208103, New Haven, Connecticut 06520, USA. 7
These authors contributed equally to this work.
Correspondence should be addressed to Quaid D Morris quaid.morris@utoronto.ca or Brendan J Frey frey@psi.toronto.eu We demonstrate that paired expression profiles of microRNAs (miRNAs) and mRNAs can be used to identify functional miRNA-target relationships with high precision. We used a Bayesian data analysis algorithm, GenMiR++, to identify a network of 1,597 high-confidence target predictions for 104 human miRNAs, which was supported by RNA expression data across 88 tissues and cell types, sequence complementarity and comparative genomics data. We experimentally verified our predictions by investigating the result of let-7b downregulation in retinoblastoma using quantitative reverse transcriptase (RT)-PCR and microarray profiling: some of our verified let-7b targets include CDC25A and BCL7A. Compared to sequence-based predictions, our high-scoring GenMiR++ predictions had much more consistent Gene Ontology annotations and were more accurate predictors of which mRNA levels respond to changes in let-7b levels.
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