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...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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BioMed Central
2018
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| Online Access: | http://hdl.handle.net/1721.1/118834 https://orcid.org/0000-0001-8148-952X https://orcid.org/0000-0001-5029-5909 https://orcid.org/0000-0002-3872-2856 |
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| author | Thiru, Prathapan Ulitsky, Igor Bartel, David P Subtelny, Alexander O. Agarwal, Vikram Subtelny, Alexander Orest Bartel, David |
| author2 | Massachusetts Institute of Technology. Computational and Systems Biology Program |
| author_facet | Massachusetts Institute of Technology. Computational and Systems Biology Program Thiru, Prathapan Ulitsky, Igor Bartel, David P Subtelny, Alexander O. Agarwal, Vikram Subtelny, Alexander Orest Bartel, David |
| author_sort | Thiru, Prathapan |
| collection | MIT |
| 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. Keywords: Non-coding RNAs, miRNA target prediction, Post-transcriptional gene regulation |
| first_indexed | 2024-09-23T09:34:25Z |
| format | Article |
| id | mit-1721.1/118834 |
| institution | Massachusetts Institute of Technology |
| language | English |
| last_indexed | 2024-09-23T09:34:25Z |
| publishDate | 2018 |
| publisher | BioMed Central |
| record_format | dspace |
| spelling | mit-1721.1/1188342022-09-30T15:23:34Z Predicting microRNA targeting efficacy in Drosophila Thiru, Prathapan Ulitsky, Igor Bartel, David P Subtelny, Alexander O. Agarwal, Vikram Subtelny, Alexander Orest Bartel, David Massachusetts Institute of Technology. Computational and Systems Biology Program Massachusetts Institute of Technology. Department of Biology Agarwal, Vikram Subtelny, Alexander Orest Bartel, David 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 National Science Foundation (U.S.). Graduate Research Fellowship Program National Institutes of Health (U.S.) (T32GM007753) National Institutes of Health (U.S.) (GM067031) National Institutes of Health (U.S.) (GM118135) 2018-11-02T13:35:38Z 2018-11-02T13:35:38Z 2018-10 2018-10-07T03:19:44Z Article http://purl.org/eprint/type/JournalArticle 1474-760X http://hdl.handle.net/1721.1/118834 Agarwal, Vikram, et al. “Predicting MicroRNA Targeting Efficacy in Drosophila.” Genome Biology, vol. 19, no. 1, Dec. 2018. © 2018 The Authors https://orcid.org/0000-0001-8148-952X https://orcid.org/0000-0001-5029-5909 https://orcid.org/0000-0002-3872-2856 en https://doi.org/10.1186/s13059-018-1504-3 Genome Biology Creative Commons Attribution http://creativecommons.org/licenses/by/4.0/ The Author(s). application/pdf BioMed Central BioMed Central |
| spellingShingle | Thiru, Prathapan Ulitsky, Igor Bartel, David P Subtelny, Alexander O. Agarwal, Vikram Subtelny, Alexander Orest Bartel, David 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 |
| url | http://hdl.handle.net/1721.1/118834 https://orcid.org/0000-0001-8148-952X https://orcid.org/0000-0001-5029-5909 https://orcid.org/0000-0002-3872-2856 |
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