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      Enhancers in the Peril lincRNA locus regulate distant but not local genes

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          Abstract

          Background

          Recently, it has become clear that some promoters function as long-range regulators of gene expression. However, direct and quantitative assessment of enhancer activity at long intergenic noncoding RNA (lincRNA) or mRNA gene bodies has not been performed. To unbiasedly assess the enhancer capacity across lincRNA and mRNA loci, we performed a massively parallel reporter assay (MPRA) on six lincRNA loci and their closest protein-coding neighbors.

          Results

          For both gene classes, we find significantly more MPRA activity in promoter regions than in gene bodies. However, three lincRNA loci, Lincp21, LincEnc1, and Peril, and one mRNA locus, Morc2a, display significant enhancer activity within their gene bodies. We hypothesize that such peaks may mark long-range enhancers, and test this in vivo using RNA sequencing from a knockout mouse model and high-throughput chromosome conformation capture (Hi-C). We find that ablation of a high-activity MPRA peak in the Peril gene body leads to consistent dysregulation of Mccc1 and Exosc9 in the neighboring topologically associated domain (TAD). This occurs irrespective of Peril lincRNA expression, demonstrating this regulation is DNA-dependent. Hi-C confirms long-range contacts with the neighboring TAD, and these interactions are altered upon Peril knockout. Surprisingly, we do not observe consistent regulation of genes within the local TAD. Together, these data suggest a long-range enhancer-like function for the Peril gene body.

          Conclusions

          A multi-faceted approach combining high-throughput enhancer discovery with genetic models can connect enhancers to their gene targets and provides evidence of inter-TAD gene regulation.

          Electronic supplementary material

          The online version of this article (10.1186/s13059-018-1589-8) contains supplementary material, which is available to authorized users.

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          Most cited references23

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          HiC-Pro: an optimized and flexible pipeline for Hi-C data processing

          HiC-Pro is an optimized and flexible pipeline for processing Hi-C data from raw reads to normalized contact maps. HiC-Pro maps reads, detects valid ligation products, performs quality controls and generates intra- and inter-chromosomal contact maps. It includes a fast implementation of the iterative correction method and is based on a memory-efficient data format for Hi-C contact maps. In addition, HiC-Pro can use phased genotype data to build allele-specific contact maps. We applied HiC-Pro to different Hi-C datasets, demonstrating its ability to easily process large data in a reasonable time. Source code and documentation are available at http://github.com/nservant/HiC-Pro. Electronic supplementary material The online version of this article (doi:10.1186/s13059-015-0831-x) contains supplementary material, which is available to authorized users.
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            ChIP-seq accurately predicts tissue-specific activity of enhancers.

            A major yet unresolved quest in decoding the human genome is the identification of the regulatory sequences that control the spatial and temporal expression of genes. Distant-acting transcriptional enhancers are particularly challenging to uncover because they are scattered among the vast non-coding portion of the genome. Evolutionary sequence constraint can facilitate the discovery of enhancers, but fails to predict when and where they are active in vivo. Here we present the results of chromatin immunoprecipitation with the enhancer-associated protein p300 followed by massively parallel sequencing, and map several thousand in vivo binding sites of p300 in mouse embryonic forebrain, midbrain and limb tissue. We tested 86 of these sequences in a transgenic mouse assay, which in nearly all cases demonstrated reproducible enhancer activity in the tissues that were predicted by p300 binding. Our results indicate that in vivo mapping of p300 binding is a highly accurate means for identifying enhancers and their associated activities, and suggest that such data sets will be useful to study the role of tissue-specific enhancers in human biology and disease on a genome-wide scale.
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              Comprehensive algorithm for quantitative real-time polymerase chain reaction.

              Quantitative real-time polymerase chain reactions (qRT-PCR) have become the method of choice for rapid, sensitive, quantitative comparison of RNA transcript abundance. Useful data from this method depend on fitting data to theoretical curves that allow computation of mRNA levels. Calculating accurate mRNA levels requires important parameters such as reaction efficiency and the fractional cycle number at threshold (CT) to be used; however, many algorithms currently in use estimate these important parameters. Here we describe an objective method for quantifying qRT-PCR results using calculations based on the kinetics of individual PCR reactions without the need of the standard curve, independent of any assumptions or subjective judgments which allow direct calculation of efficiency and CT. We use a four-parameter logistic model to fit the raw fluorescence data as a function of PCR cycles to identify the exponential phase of the reaction. Next, we use a three-parameter simple exponent model to fit the exponential phase using an iterative nonlinear regression algorithm. Within the exponential portion of the curve, our technique automatically identifies candidate regression values using the P-value of regression and then uses a weighted average to compute a final efficiency for quantification. For CT determination, we chose the first positive second derivative maximum from the logistic model. This algorithm provides an objective and noise-resistant method for quantification of qRT-PCR results that is independent of the specific equipment used to perform PCR reactions.
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                Author and article information

                Contributors
                john.rinn@colorado.edu
                Journal
                Genome Biol
                Genome Biol
                Genome Biology
                BioMed Central (London )
                1474-7596
                1474-760X
                11 December 2018
                11 December 2018
                2018
                : 19
                : 219
                Affiliations
                [1 ]ISNI 000000041936754X, GRID grid.38142.3c, Department of Stem Cell and Regenerative Biology, , Harvard University, ; Cambridge, MA 02138 USA
                [2 ]ISNI 000000041936754X, GRID grid.38142.3c, Department of Systems Biology, , Harvard Medical School, ; Boston, MA 02115 USA
                [3 ]ISNI 0000 0001 2157 2938, GRID grid.17063.33, Donnelly Centre, , University of Toronto, ; Toronto, ON M5S 3E1 Canada
                [4 ]ISNI 0000000096214564, GRID grid.266190.a, Department of Biochemistry, BioFrontiers, , University of Colorado Boulder, ; Boulder, CO 80301 USA
                Article
                1589
                10.1186/s13059-018-1589-8
                6290506
                30537984
                76ebabd3-971f-4906-af82-d6e53ca7e72e
                © The Author(s). 2018

                Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License ( http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

                History
                : 6 February 2018
                : 16 November 2018
                Funding
                Funded by: FundRef http://dx.doi.org/10.13039/100000057, National Institute of General Medical Sciences;
                Award ID: P01GM099117
                Funded by: FundRef http://dx.doi.org/10.13039/100000025, National Institute of Mental Health;
                Award ID: R01MH102416
                Funded by: FundRef http://dx.doi.org/10.13039/100000011, Howard Hughes Medical Institute;
                Award ID: Gilliam Fellowship
                Award Recipient :
                Categories
                Research
                Custom metadata
                © The Author(s) 2018

                Genetics
                lincrna,mouse models,mpra,cis-regulation,enhancer
                Genetics
                lincrna, mouse models, mpra, cis-regulation, enhancer

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