Using expression profiling data to identify human microRNA targets.
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.
Huang
Jim C
JC
Department of Electrical and Computer Engineering, University of Toronto, 10 King's College Road, Toronto, Ontario M5S 3G4, Canada.
Babak
Tomas
T
Corson
Timothy W
TW
Chua
Gordon
G
Khan
Sofia
S
Gallie
Brenda L
BL
Hughes
Timothy R
TR
Blencowe
Benjamin J
BJ
Frey
Brendan J
BJ
Morris
Quaid D
QD
D001483:Base Sequence; D020869:Gene Expression Profiling / Q000379:methods*; D018390:Gene Targeting / Q000379:methods*; D006801:Humans; D035683:MicroRNAs / Q000235:genetics*; D008969:Molecular Sequence Data; D020411:Oligonucleotide Array Sequence Analysis / Q000379:methods*; D017423:Sequence Analysis, RNA / Q000379:methods*
D035683:MicroRNAs