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      Predicting effective microRNA target sites in mammalian mRNAs.

      eLife

      evolutionary biology, genomics, human, miRNA target prediction, miRNA targets, miRNA-binding sites, zebrafish, mouse, post-transcriptional gene regulation, rat, systems biology, computational biology

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          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.

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          Most cited references 129

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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

                Journal
                26267216
                10.7554/eLife.05005

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