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      High-throughput functional analysis of lncRNA core promoters elucidates rules governing tissue specificity

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          Abstract

          Transcription initiates at both coding and noncoding genomic elements, including mRNA and long noncoding RNA (lncRNA) core promoters and enhancer RNAs (eRNAs). However, each class has a different expression profile with lncRNAs and eRNAs being the most tissue specific. How these complex differences in expression profiles and tissue specificities are encoded in a single DNA sequence remains unresolved. Here, we address this question using computational approaches and massively parallel reporter assays (MPRA) surveying hundreds of promoters and enhancers. We find that both divergent lncRNA and mRNA core promoters have higher capacities to drive transcription than nondivergent lncRNA and mRNA core promoters, respectively. Conversely, intergenic lncRNAs (lincRNAs) and eRNAs have lower capacities to drive transcription and are more tissue specific than divergent genes. This higher tissue specificity is strongly associated with having less complex transcription factor (TF) motif profiles at the core promoter. We experimentally validated these findings by testing both engineered single-nucleotide deletions and human single-nucleotide polymorphisms (SNPs) in MPRA. In both cases, we observe that single nucleotides associated with many motifs are important drivers of promoter activity. Thus, we suggest that high TF motif density serves as a robust mechanism to increase promoter activity at the expense of tissue specificity. Moreover, we find that 22% of common SNPs in core promoter regions have significant regulatory effects. Collectively, our findings show that high TF motif density provides redundancy and increases promoter activity at the expense of tissue specificity, suggesting that specificity of expression may be regulated by simplicity of motif usage.

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          Most cited references46

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          BEDTools: a flexible suite of utilities for comparing genomic features

          Motivation: Testing for correlations between different sets of genomic features is a fundamental task in genomics research. However, searching for overlaps between features with existing web-based methods is complicated by the massive datasets that are routinely produced with current sequencing technologies. Fast and flexible tools are therefore required to ask complex questions of these data in an efficient manner. Results: This article introduces a new software suite for the comparison, manipulation and annotation of genomic features in Browser Extensible Data (BED) and General Feature Format (GFF) format. BEDTools also supports the comparison of sequence alignments in BAM format to both BED and GFF features. The tools are extremely efficient and allow the user to compare large datasets (e.g. next-generation sequencing data) with both public and custom genome annotation tracks. BEDTools can be combined with one another as well as with standard UNIX commands, thus facilitating routine genomics tasks as well as pipelines that can quickly answer intricate questions of large genomic datasets. Availability and implementation: BEDTools was written in C++. Source code and a comprehensive user manual are freely available at http://code.google.com/p/bedtools Contact: aaronquinlan@gmail.com; imh4y@virginia.edu Supplementary information: Supplementary data are available at Bioinformatics online.
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            FIMO: scanning for occurrences of a given motif

            Summary: A motif is a short DNA or protein sequence that contributes to the biological function of the sequence in which it resides. Over the past several decades, many computational methods have been described for identifying, characterizing and searching with sequence motifs. Critical to nearly any motif-based sequence analysis pipeline is the ability to scan a sequence database for occurrences of a given motif described by a position-specific frequency matrix. Results: We describe Find Individual Motif Occurrences (FIMO), a software tool for scanning DNA or protein sequences with motifs described as position-specific scoring matrices. The program computes a log-likelihood ratio score for each position in a given sequence database, uses established dynamic programming methods to convert this score to a P-value and then applies false discovery rate analysis to estimate a q-value for each position in the given sequence. FIMO provides output in a variety of formats, including HTML, XML and several Santa Cruz Genome Browser formats. The program is efficient, allowing for the scanning of DNA sequences at a rate of 3.5 Mb/s on a single CPU. Availability and Implementation: FIMO is part of the MEME Suite software toolkit. A web server and source code are available at http://meme.sdsc.edu. Contact: t.bailey@imb.uq.edu.au; t.bailey@imb.uq.edu.au Supplementary information: Supplementary data are available at Bioinformatics online.
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              Human genomics. The Genotype-Tissue Expression (GTEx) pilot analysis: multitissue gene regulation in humans.

              (2015)
              Understanding the functional consequences of genetic variation, and how it affects complex human disease and quantitative traits, remains a critical challenge for biomedicine. We present an analysis of RNA sequencing data from 1641 samples across 43 tissues from 175 individuals, generated as part of the pilot phase of the Genotype-Tissue Expression (GTEx) project. We describe the landscape of gene expression across tissues, catalog thousands of tissue-specific and shared regulatory expression quantitative trait loci (eQTL) variants, describe complex network relationships, and identify signals from genome-wide association studies explained by eQTLs. These findings provide a systematic understanding of the cellular and biological consequences of human genetic variation and of the heterogeneity of such effects among a diverse set of human tissues. Copyright © 2015, American Association for the Advancement of Science.
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                Author and article information

                Journal
                Genome Res
                Genome Res
                genome
                genome
                GENOME
                Genome Research
                Cold Spring Harbor Laboratory Press
                1088-9051
                1549-5469
                March 2019
                March 2019
                : 29
                : 3
                : 344-355
                Affiliations
                [1 ]Department of Stem Cell and Regenerative Biology, Harvard University, Cambridge, Massachusetts 02138, USA;
                [2 ]Department of Biological and Biomedical Sciences, Harvard Medical School, Boston, Massachusetts 02115, USA;
                [3 ]Department of Biomolecular Medicine, Ghent University, 9000 Ghent, Belgium;
                [4 ]VIB-UGent Center for Medical Biotechnology, VIB, 9000 Ghent, Belgium;
                [5 ]Broad Institute of MIT and Harvard, Cambridge, Massachusetts 02142, USA;
                [6 ]Department of Medicine, University of Cambridge School of Clinical Medicine, Addenbrooke's Hospital, Cambridge CB2 0QQ, United Kingdom;
                [7 ]Genetics and Genome Biology Program, Sickkids Research Institute, Toronto, Ontario M5G 0A4, Canada;
                [8 ]Department of Molecular Genetics, University of Toronto, Toronto, Ontario M5S 1A1, Canada;
                [9 ]Life Sciences Department, Barcelona Supercomputing Center, Barcelona, Catalonia 08034, Spain;
                [10 ]Department of Pathology, Beth Israel Deaconess Medical Center, Boston, Massachusetts 02115, USA;
                [11 ]Department of Biochemistry, University of Colorado, BioFrontiers Institute, Boulder, Colorado 80301, USA
                Author notes
                [12]

                These authors contributed equally to this work.

                Article
                9509184
                10.1101/gr.242222.118
                6396428
                30683753
                ff082af3-d276-492b-843b-cc78ff7b39fc
                © 2019 Mattioli et al.; Published by Cold Spring Harbor Laboratory Press

                This article, published in Genome Research, is available under a Creative Commons License (Attribution-NonCommercial 4.0 International), as described at http://creativecommons.org/licenses/by-nc/4.0/.

                History
                : 29 July 2018
                : 17 January 2019
                Page count
                Pages: 12
                Funding
                Funded by: National Science Foundation , open-funder-registry 10.13039/100000001;
                Award ID: DGE1144152
                Funded by: Wellcome Trust , open-funder-registry 10.13039/100004440;
                Award ID: 105920/Z/14/Z
                Funded by: HHMI faculty scholar
                Funded by: U.S. National Institutes of Health , open-funder-registry 10.13039/100000002;
                Award ID: P01 GM099117
                Categories
                Research

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