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Predicting microRNA targeting efficacy in Drosophila
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...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2018
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6172730/ https://www.ncbi.nlm.nih.gov/pubmed/30286781 http://dx.doi.org/10.1186/s13059-018-1504-3 |
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author | Agarwal, Vikram Subtelny, Alexander O. Thiru, Prathapan Ulitsky, Igor Bartel, David P. |
author_facet | Agarwal, Vikram Subtelny, Alexander O. Thiru, Prathapan Ulitsky, Igor Bartel, David P. |
author_sort | Agarwal, Vikram |
collection | PubMed |
description | 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. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1186/s13059-018-1504-3) contains supplementary material, which is available to authorized users. |
format | Online Article Text |
id | pubmed-6172730 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2018 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-61727302018-10-15 Predicting microRNA targeting efficacy in Drosophila Agarwal, Vikram Subtelny, Alexander O. Thiru, Prathapan Ulitsky, Igor Bartel, David P. Genome Biol Research 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. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1186/s13059-018-1504-3) contains supplementary material, which is available to authorized users. BioMed Central 2018-10-04 /pmc/articles/PMC6172730/ /pubmed/30286781 http://dx.doi.org/10.1186/s13059-018-1504-3 Text en © The Author(s). 2018 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated. |
spellingShingle | Research Agarwal, Vikram Subtelny, Alexander O. Thiru, Prathapan Ulitsky, Igor Bartel, David P. Predicting microRNA targeting efficacy in Drosophila |
title | Predicting microRNA targeting efficacy in Drosophila |
title_full | Predicting microRNA targeting efficacy in Drosophila |
title_fullStr | Predicting microRNA targeting efficacy in Drosophila |
title_full_unstemmed | Predicting microRNA targeting efficacy in Drosophila |
title_short | Predicting microRNA targeting efficacy in Drosophila |
title_sort | predicting microrna targeting efficacy in drosophila |
topic | Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6172730/ https://www.ncbi.nlm.nih.gov/pubmed/30286781 http://dx.doi.org/10.1186/s13059-018-1504-3 |
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