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dc.contributor.authorMcGeary, Sean E
dc.contributor.authorLin, Kathy S
dc.contributor.authorShi, Charlie Y
dc.contributor.authorPham, Thy M
dc.contributor.authorBisaria, Namita
dc.contributor.authorKelley, Gina M
dc.contributor.authorBartel, David P
dc.date.accessioned2021-10-27T20:35:40Z
dc.date.available2021-10-27T20:35:40Z
dc.date.issued2019
dc.identifier.urihttps://hdl.handle.net/1721.1/136496
dc.description.abstract© 2019 American Association for the Advancement of Science. All rights reserved. MicroRNAs (miRNAs) act within Argonaute proteins to guide repression of messenger RNA targets. Although various approaches have provided insight into target recognition, the sparsity of miRNA-target affinity measurements has limited understanding and prediction of targeting efficacy. Here, we adapted RNA bind-n-seq to enable measurement of relative binding affinities between Argonaute-miRNA complexes and all sequences ≤12 nucleotides in length. This approach revealed noncanonical target sites specific to each miRNA, miRNA-specific differences in canonical target-site affinities, and a 100-fold impact of dinucleotides flanking each site. These data enabled construction of a biochemical model of miRNA-mediated repression, which was extended to all miRNA sequences using a convolutional neural network. This model substantially improved prediction of cellular repression, thereby providing a biochemical basis for quantitatively integrating miRNAs into gene-regulatory networks.
dc.language.isoen
dc.publisherAmerican Association for the Advancement of Science (AAAS)
dc.relation.isversionof10.1126/SCIENCE.AAV1741
dc.rightsArticle is made available in accordance with the publisher's policy and may be subject to US copyright law. Please refer to the publisher's site for terms of use.
dc.sourcePMC
dc.titleThe biochemical basis of microRNA targeting efficacy
dc.typeArticle
dc.contributor.departmentHoward Hughes Medical Institute
dc.contributor.departmentWhitehead Institute for Biomedical Research
dc.contributor.departmentMassachusetts Institute of Technology. Department of Biology
dc.contributor.departmentMassachusetts Institute of Technology. Computational and Systems Biology Program
dc.relation.journalScience
dc.eprint.versionAuthor's final manuscript
dc.type.urihttp://purl.org/eprint/type/JournalArticle
eprint.statushttp://purl.org/eprint/status/PeerReviewed
dc.date.updated2021-07-14T13:46:57Z
dspace.orderedauthorsMcGeary, SE; Lin, KS; Shi, CY; Pham, TM; Bisaria, N; Kelley, GM; Bartel, DP
dspace.date.submission2021-07-14T13:46:59Z
mit.journal.volume366
mit.journal.issue6472
mit.licensePUBLISHER_POLICY
mit.metadata.statusAuthority Work and Publication Information Needed


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