The biochemical basis of microRNA targeting efficacy

Sean E. McGeary; Kathy S. Lin; Charlie Y. Shi; Thy Pham; Namita Bisaria; Gina M. Kelley; David P. Bartel

doi:10.1126/science.aav1741

Abstract

Biochemical prediction of miRNA targeting MicroRNAs (miRNAs) regulate most human messenger RNAs and play essential roles in diverse developmental and physiological processes. Correctly predicting the function of each miRNA requires a better understanding of miRNA targeting efficacy. McGeary et al. measured binding affinities between six miRNAs and synthetic targets, built a biochemical model of miRNA-mediated repression, and expanded it to all miRNAs using a convolutional neural network. This approach offers insights into miRNA targeting and enables more accurate prediction of intracellular miRNA repression efficacy than previous algorithms. Science , this issue p. eaav1741

Keywords

ArgonautemicroRNAPsychological repressionComputational biologyBiologyRNABinding affinitiesRNA interferenceGeneGeneticsGene expression

MeSH Terms

Argonaute ProteinsBase SequenceGene Expression RegulationHEK293 CellsHumansMicroRNAsProtein BindingSequence AnalysisRNA

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Publication Info

Year: 2019
Type: article
Volume: 366
Issue: 6472
Citations: 1449
Access: Closed

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APA Style

                            
                                
                                    Sean E. McGeary, 
                                
                                    Kathy S. Lin, 
                                
                                    Charlie Y. Shi
                                
                                et al.
                            
                            (2019). 
                            The biochemical basis of microRNA targeting efficacy. 
                            Science
                            , 366
                            (6472)
                            .
                            https://doi.org/10.1126/science.aav1741
                        

Identifiers

DOI: 10.1126/science.aav1741
PMID: 31806698
PMCID: PMC7051167

Data Quality

Data completeness: 90%