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      Predicting effective microRNA target sites in mammalian mRNAs

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          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

          Abstract

          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.

          DOI: http://dx.doi.org/10.7554/eLife.05005.001

          eLife digest

          Proteins are built by using the information contained in molecules of messenger RNA (mRNA). Cells have several ways of controlling the amounts of different proteins they make. For example, a so-called ‘microRNA’ molecule can bind to an mRNA molecule to cause it to be more rapidly degraded and less efficiently used, thereby reducing the amount of protein built from that mRNA. Indeed, microRNAs are thought to help control the amount of protein made from most human genes, and biologists are working to predict the amount of control imparted by each microRNA on each of its mRNA targets.

          All RNA molecules are made up of a sequence of bases, each commonly known by a single letter—‘A’, ‘U’, ‘C’ or ‘G’. These bases can each pair up with one specific other base—‘A’ pairs with ‘U’, and ‘C’ pairs with ‘G’. To direct the repression of an mRNA molecule, a region of the microRNA known as a ‘seed’ binds to a complementary sequence in the target mRNA. ‘Canonical sites’ are regions in the mRNA that contain the exact sequence of partner bases for the bases in the microRNA seed. Some canonical sites are more effective at mRNA control than others. ‘Non-canonical sites’ also exist in which the pairing between the microRNA seed and mRNA does not completely match. Previous work has suggested that many non-canonical sites can also control mRNA degradation and usage.

          Agarwal et al. first used large experimental datasets from many sources to investigate microRNA activity in more detail. As expected, when mRNAs had canonical sites that matched the microRNA, mRNA levels and usage tended to drop. However, no effect was observed when the mRNAs only had recently identified non-canonical sites. This suggests that microRNAs primarily bind to canonical sites to control protein production.

          Based on these results, Agarwal et al. further developed a statistical model that predicts the effects of microRNAs binding to canonical sites. The updated model considers 14 different features of the microRNA, microRNA site, or mRNA—including the mRNA sequence around the site—to predict which sites within mRNAs are most effectively targeted by microRNAs. Tests showed that Agarwal et al.'s model was as good as experimental approaches at identifying the effective target sites, and was better than existing computational models.

          The model has been used to power the latest version of a freely available resource called TargetScan, and so could prove a valuable resource for researchers investigating the many important roles of microRNAs in controlling protein production.

          DOI: http://dx.doi.org/10.7554/eLife.05005.002

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          Most cited references 123

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          MicroRNAs: genomics, biogenesis, mechanism, and function.

           David Bartel (2004)
          MicroRNAs (miRNAs) are endogenous approximately 22 nt RNAs that can play important regulatory roles in animals and plants by targeting mRNAs for cleavage or translational repression. Although they escaped notice until relatively recently, miRNAs comprise one of the more abundant classes of gene regulatory molecules in multicellular organisms and likely influence the output of many protein-coding genes.
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            edgeR: a Bioconductor package for differential expression analysis of digital gene expression data

            Summary: It is expected that emerging digital gene expression (DGE) technologies will overtake microarray technologies in the near future for many functional genomics applications. One of the fundamental data analysis tasks, especially for gene expression studies, involves determining whether there is evidence that counts for a transcript or exon are significantly different across experimental conditions. edgeR is a Bioconductor software package for examining differential expression of replicated count data. An overdispersed Poisson model is used to account for both biological and technical variability. Empirical Bayes methods are used to moderate the degree of overdispersion across transcripts, improving the reliability of inference. The methodology can be used even with the most minimal levels of replication, provided at least one phenotype or experimental condition is replicated. The software may have other applications beyond sequencing data, such as proteome peptide count data. Availability: The package is freely available under the LGPL licence from the Bioconductor web site (http://bioconductor.org). Contact: mrobinson@wehi.edu.au
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              MicroRNAs: target recognition and regulatory functions.

               David Bartel (2009)
              MicroRNAs (miRNAs) are endogenous approximately 23 nt RNAs that play important gene-regulatory roles in animals and plants by pairing to the mRNAs of protein-coding genes to direct their posttranscriptional repression. This review outlines the current understanding of miRNA target recognition in animals and discusses the widespread impact of miRNAs on both the expression and evolution of protein-coding genes.
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                Author and article information

                Affiliations
                [1 ]Howard Hughes Medical Institute, Whitehead Institute for Biomedical Research , Cambridge, United States
                [2 ]deptDepartment of Biology , Massachusetts Institute of Technology , Cambridge, United States
                [3 ]deptComputational and Systems Biology Program , Massachusetts Institute of Technology , Cambridge, United States
                [4 ]deptBioinformatics and Research Computing , Whitehead Institute for Biomedical Research , Cambridge, United States
                [5 ]deptDepartment of Life Science, College of Natural Sciences , Hanyang University , Seoul, Republic of Korea
                Max Planck Institute Development Biology , Germany
                Max Planck Institute Development Biology , Germany
                Author notes
                [* ]For correspondence: dbartel@ 123456wi.mit.edu
                Contributors
                Role: Reviewing editor,
                Max Planck Institute Development Biology , Germany
                Journal
                eLife
                eLife
                eLife
                eLife
                eLife Sciences Publications, Ltd
                2050-084X
                2050-084X
                12 August 2015
                2015
                : 4
                26267216 4532895 05005 10.7554/eLife.05005
                © 2015, Agarwal et al

                This article is distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use and redistribution provided that the original author and source are credited.

                Product
                Funding
                Funded by: FundRef http://dx.doi.org/10.13039/100000002, National Institutes of Health (NIH);
                Award ID: GM067031
                Award Recipient :
                Funded by: FundRef http://dx.doi.org/10.13039/100000011, Howard Hughes Medical Institute (HHMI);
                Award ID: Investigator
                Award Recipient :
                Funded by: FundRef http://dx.doi.org/10.13039/100000001, National Science Foundation (NSF);
                Award ID: Graduate Research Fellowship
                Award Recipient :
                The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.
                Categories
                Research Article
                Computational and Systems Biology
                Genomics and Evolutionary Biology
                Custom metadata
                2.3
                Many experimentally identified microRNA-binding sites are ineffective at mediating repression, and an improved quantitative model for predicting the effective sites is as informative as high-throughput experimental approaches.

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