Predicting microRNA targeting efficacy in Drosophila
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13059_2018_Article_1504.pdf
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Author(s)
Thiru, Prathapan
Ulitsky, Igor
Bartel, David P
Subtelny, Alexander O.
Agarwal, Vikram
Subtelny, Alexander Orest
Bartel, David
Date Issued
October 2018
Journal
Genome Biology
Publisher
BioMed Central
Citation
Agarwal, Vikram, et al. “Predicting MicroRNA Targeting Efficacy in Drosophila.” Genome Biology, vol. 19, no. 1, Dec. 2018. © 2018 The Authors
Version
Final published version
Abstract
Background: MicroRNAs (miRNAs) are short regulatory RNAs that derive from hairpin precursors. Important for understanding the functional roles of miRNAs is the ability to predict the messenger RNA (mRNA) targets most responsive to each miRNA. Progress towards developing quantitative models of miRNA targeting in Drosophila and other invertebrate species has lagged behind that of mammals due to the paucity of datasets measuring the effects of miRNAs on mRNA levels.
Results: We acquired datasets suitable for the quantitative study of miRNA targeting in Drosophila. Analyses of these data expanded the types of regulatory sites known to be effective in flies, expanded the mRNA regions with detectable targeting to include 5′ untranslated regions, and identified features of site context that correlate with targeting efficacy in fly cells. Updated evolutionary analyses evaluated the probability of conserved targeting for each predicted site and indicated that more than a third of the Drosophila genes are preferentially conserved targets of miRNAs. Based on these results, a quantitative model was developed to predict targeting efficacy in insects. This model performed better than existing models, and it drives the most recent version, v7, of TargetScanFly.
Conclusions: Our evolutionary and functional analyses expand the known scope of miRNA targeting in flies and other insects. The existence of a quantitative model that has been developed and trained using Drosophila data will provide a valuable resource for placing miRNAs into gene regulatory networks of this important experimental organism. Keywords: Non-coding RNAs, miRNA target prediction, Post-transcriptional gene regulation
MIT Department
Massachusetts Institute of Technology. Computational and Systems Biology Program
Massachusetts Institute of Technology. Department of Biology
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DOI of Published Version
https://doi.org/10.1186/s13059-018-1504-3
