Predicting effective microRNA target sites in mammalian mRNAs
MicroRNA targets are often recognized through pairing between the miRNA seed region and complementary sites within target mRNAs, but not all of these canonical sites are equally effective, and both computational and in vivo UV-crosslinking approaches suggest that many mRNAs are targeted through non-...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | en_US |
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eLife Sciences Publications, Ltd.
2015
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| Online Access: | http://hdl.handle.net/1721.1/99103 https://orcid.org/0000-0001-8148-952X https://orcid.org/0000-0002-3872-2856 |
| _version_ | 1826193015744495616 |
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| author | Agarwal, Vikram Bell, George W Nam, Jin-Wu Bartel, David |
| author2 | Massachusetts Institute of Technology. Computational and Systems Biology Program |
| author_facet | Massachusetts Institute of Technology. Computational and Systems Biology Program Agarwal, Vikram Bell, George W Nam, Jin-Wu Bartel, David |
| author_sort | Agarwal, Vikram |
| collection | MIT |
| description | MicroRNA targets are often recognized through pairing between the miRNA seed region and complementary sites within target mRNAs, but not all of these canonical sites are equally effective, and both computational and in vivo UV-crosslinking approaches suggest that many mRNAs are targeted through non-canonical interactions. Here, we show that recently reported non-canonical sites do not mediate repression despite binding the miRNA, which indicates that the vast majority of functional sites are canonical. Accordingly, we developed an improved quantitative model of canonical targeting, using a compendium of experimental datasets that we pre-processed to minimize confounding biases. This model, which considers site type and another 14 features to predict the most effectively targeted mRNAs, performed significantly better than existing models and was as informative as the best high-throughput in vivo crosslinking approaches. It drives the latest version of TargetScan (v7.0; targetscan.org), thereby providing a valuable resource for placing miRNAs into gene-regulatory networks. |
| first_indexed | 2024-09-23T09:32:20Z |
| format | Article |
| id | mit-1721.1/99103 |
| institution | Massachusetts Institute of Technology |
| language | en_US |
| last_indexed | 2024-09-23T09:32:20Z |
| publishDate | 2015 |
| publisher | eLife Sciences Publications, Ltd. |
| record_format | dspace |
| spelling | mit-1721.1/991032022-09-30T15:06:54Z Predicting effective microRNA target sites in mammalian mRNAs Agarwal, Vikram Bell, George W Nam, Jin-Wu Bartel, David Massachusetts Institute of Technology. Computational and Systems Biology Program Massachusetts Institute of Technology. Department of Biology Whitehead Institute for Biomedical Research Agarwal, Vikram Agarwal, Vikram Nam, Jin-Wu Bartel, David MicroRNA targets are often recognized through pairing between the miRNA seed region and complementary sites within target mRNAs, but not all of these canonical sites are equally effective, and both computational and in vivo UV-crosslinking approaches suggest that many mRNAs are targeted through non-canonical interactions. Here, we show that recently reported non-canonical sites do not mediate repression despite binding the miRNA, which indicates that the vast majority of functional sites are canonical. Accordingly, we developed an improved quantitative model of canonical targeting, using a compendium of experimental datasets that we pre-processed to minimize confounding biases. This model, which considers site type and another 14 features to predict the most effectively targeted mRNAs, performed significantly better than existing models and was as informative as the best high-throughput in vivo crosslinking approaches. It drives the latest version of TargetScan (v7.0; targetscan.org), thereby providing a valuable resource for placing miRNAs into gene-regulatory networks. National Science Foundation (U.S.). Graduate Research Fellowship National Institutes of Health (U.S.) (Grant GM067031) 2015-09-29T19:31:39Z 2015-09-29T19:31:39Z 2015-08 2014-10 Article http://purl.org/eprint/type/JournalArticle 2050-084X http://hdl.handle.net/1721.1/99103 Agarwal, Vikram, George W Bell, Jin-Wu Nam, and David P Bartel. “Predicting Effective microRNA Target Sites in Mammalian mRNAs.” eLife 4 (August 12, 2015). https://orcid.org/0000-0001-8148-952X https://orcid.org/0000-0002-3872-2856 en_US http://dx.doi.org/10.7554/elife.05005 eLife Creative Commons Attribution http://creativecommons.org/licenses/by/4.0/ application/pdf eLife Sciences Publications, Ltd. Agarwal |
| spellingShingle | Agarwal, Vikram Bell, George W Nam, Jin-Wu Bartel, David Predicting effective microRNA target sites in mammalian mRNAs |
| title | Predicting effective microRNA target sites in mammalian mRNAs |
| title_full | Predicting effective microRNA target sites in mammalian mRNAs |
| title_fullStr | Predicting effective microRNA target sites in mammalian mRNAs |
| title_full_unstemmed | Predicting effective microRNA target sites in mammalian mRNAs |
| title_short | Predicting effective microRNA target sites in mammalian mRNAs |
| title_sort | predicting effective microrna target sites in mammalian mrnas |
| url | http://hdl.handle.net/1721.1/99103 https://orcid.org/0000-0001-8148-952X https://orcid.org/0000-0002-3872-2856 |
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