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Journal ArticleDOI

Enhancer transcripts mark active estrogen receptor binding sites

01 Aug 2013-Genome Research (Cold Spring Harbor Laboratory Press)-Vol. 23, Iss: 8, pp 1210-1223
TL;DR: New light is shed on the activity of ESR1 at its enhancer sites and an enhancer transcription "signature" based on GRO-seq data can be used for de novo enhancer prediction across cell types.
Abstract: We have integrated and analyzed a large number of data sets from a variety of genomic assays using a novel computational pipeline to provide a global view of estrogen receptor 1 (ESR1; a.k.a. ERα) enhancers in MCF-7 human breast cancer cells. Using this approach, we have defined a class of primary transcripts (eRNAs) that are transcribed uni- or bidirectionally from estrogen receptor binding sites (ERBSs) with an average transcription unit length of ∼3-5 kb. The majority are up-regulated by short treatments with estradiol (i.e., 10, 25, or 40 min) with kinetics that precede or match the induction of the target genes. The production of eRNAs at ERBSs is strongly correlated with the enrichment of a number of genomic features that are associated with enhancers (e.g., H3K4me1, H3K27ac, EP300/CREBBP, RNA polymerase II, open chromatin architecture), as well as enhancer looping to target gene promoters. In the absence of eRNA production, strong enrichment of these features is not observed, even though ESR1 binding is evident. We find that flavopiridol, a CDK9 inhibitor that blocks transcription elongation, inhibits eRNA production but does not affect other molecular indicators of enhancer activity, suggesting that eRNA production occurs after the assembly of active enhancers. Finally, we show that an enhancer transcription "signature" based on GRO-seq data can be used for de novo enhancer prediction across cell types. Together, our studies shed new light on the activity of ESR1 at its enhancer sites and provide new insights about enhancer function.

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Citations
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Journal ArticleDOI
TL;DR: The characteristics of lncRNAs, including their roles, functions, and working mechanisms are summarized, methods for identifying and annotating lnc RNAs are described, and future opportunities for lncRNA-based therapies using antisense oligonucleotides are discussed.

736 citations


Cites methods from "Enhancer transcripts mark active es..."

  • ...Using ChIA-PET, a correlation is revealed between expression level of elncRNAs and estrogen receptor a (ERa)-associated chromatin interactions [72]....

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Journal ArticleDOI
TL;DR: Although Mediator exists in all eukaryotes, a variety of Mediator functions seem to be specific to metazoans, which is indicative of more diverse regulatory requirements.
Abstract: The RNA polymerase II (Pol II) enzyme transcribes all protein-coding and most non-coding RNA genes and is globally regulated by Mediator - a large, conformationally flexible protein complex with a variable subunit composition (for example, a four-subunit cyclin-dependent kinase 8 module can reversibly associate with it) These biochemical characteristics are fundamentally important for Mediator's ability to control various processes that are important for transcription, including the organization of chromatin architecture and the regulation of Pol II pre-initiation, initiation, re-initiation, pausing and elongation Although Mediator exists in all eukaryotes, a variety of Mediator functions seem to be specific to metazoans, which is indicative of more diverse regulatory requirements

705 citations

Journal ArticleDOI
TL;DR: Research progress in the field of enhancer functions and mechanisms is reviewed and several important, unresolved questions regarding the roles and mechanisms of enhancers in gene regulation are discussed.
Abstract: The observation that many, if not all, functional enhancers generate non-coding enhancer RNAs (eRNAs) has raised critical questions regarding the potential biological roles of the enhancer transcription process and, indeed, of eRNAs. This article reviews fundamental insights into the inter-regulation of enhancers and promoters and discusses unresolved questions regarding the functional role of enhancers as transcription units in genome regulation.

581 citations

Journal ArticleDOI
TL;DR: Analysis of comprehensive mapping of transcription start sites in human lymphoblastoid B cell and chronic myelogenous leukemic ENCODE Tier 1 cell lines identifies a common architecture of initiation, including tightly spaced (110 bp apart) divergent initiation, similar frequencies of core promoter sequence elements, highly positioned flanking nucleosomes and two modes of transcription factor binding.
Abstract: Despite the conventional distinction between them, promoters and enhancers share many features in mammals, including divergent transcription and similar modes of transcription factor binding. Here we examine the architecture of transcription initiation through comprehensive mapping of transcription start sites (TSSs) in human lymphoblastoid B cell (GM12878) and chronic myelogenous leukemic (K562) ENCODE Tier 1 cell lines. Using a nuclear run-on protocol called GRO-cap, which captures TSSs for both stable and unstable transcripts, we conduct detailed comparisons of thousands of promoters and enhancers in human cells. These analyses identify a common architecture of initiation, including tightly spaced (110 bp apart) divergent initiation, similar frequencies of core promoter sequence elements, highly positioned flanking nucleosomes and two modes of transcription factor binding. Post-initiation transcript stability provides a more fundamental distinction between promoters and enhancers than patterns of histone modification and association of transcription factors or co-activators. These results support a unified model of transcription initiation at promoters and enhancers.

580 citations

Journal ArticleDOI
TL;DR: It is demonstrated that a long noncoding RNA, CCAT1-L, is transcribed specifically in human colorectal cancers from a locus 515 kb upstream of MYC, and this lncRNA plays a role in MYC transcriptional regulation and promotes long-range chromatin looping.
Abstract: The human 8q24 gene desert contains multiple enhancers that form tissue-specific long-range chromatin loops with the MYC oncogene, but how chromatin looping at the MYC locus is regulated remains poorly understood. Here we demonstrate that a long noncoding RNA (lncRNA), CCAT1-L, is transcribed specifically in human colorectal cancers from a locus 515 kb upstream of MYC. This lncRNA plays a role in MYC transcriptional regulation and promotes long-range chromatin looping. Importantly, the CCAT1-L locus is located within a strong super-enhancer and is spatially close to MYC. Knockdown of CCAT1-L reduced long-range interactions between the MYC promoter and its enhancers. In addition, CCAT1-L interacts with CTCF and modulates chromatin conformation at these loop regions. These results reveal an important role of a previously unannotated lncRNA in gene regulation at the MYC locus.

549 citations

References
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Journal ArticleDOI
TL;DR: Bowtie extends previous Burrows-Wheeler techniques with a novel quality-aware backtracking algorithm that permits mismatches and can be used simultaneously to achieve even greater alignment speeds.
Abstract: Bowtie is an ultrafast, memory-efficient alignment program for aligning short DNA sequence reads to large genomes. For the human genome, Burrows-Wheeler indexing allows Bowtie to align more than 25 million reads per CPU hour with a memory footprint of approximately 1.3 gigabytes. Bowtie extends previous Burrows-Wheeler techniques with a novel quality-aware backtracking algorithm that permits mismatches. Multiple processor cores can be used simultaneously to achieve even greater alignment speeds. Bowtie is open source http://bowtie.cbcb.umd.edu.

20,335 citations


"Enhancer transcripts mark active es..." refers methods in this paper

  • ...The raw files were aligned to hg18 using BOWTIE (Langmead et al. 2009)....

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Journal ArticleDOI
TL;DR: A new software suite for the comparison, manipulation and annotation of genomic features in Browser Extensible Data (BED) and General Feature Format (GFF) format, which allows the user to compare large datasets (e.g. next-generation sequencing data) with both public and custom genome annotation tracks.
Abstract: 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 webbased 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

18,858 citations

Journal ArticleDOI
TL;DR: The popular MEME motif discovery algorithm is now complemented by the GLAM2 algorithm which allows discovery of motifs containing gaps, and all of the motif-based tools are now implemented as web services via Opal.
Abstract: The MEME Suite web server provides a unified portal for online discovery and analysis of sequence motifs representing features such as DNA binding sites and protein interaction domains. The popular MEME motif discovery algorithm is now complemented by the GLAM2 algorithm which allows discovery of motifs containing gaps. Three sequence scanning algorithms—MAST, FIMO and GLAM2SCAN—allow scanning numerous DNA and protein sequence databases for motifs discovered by MEME and GLAM2. Transcription factor motifs (including those discovered using MEME) can be compared with motifs in many popular motif databases using the motif database scanning algorithm Tomtom. Transcription factor motifs can be further analyzed for putative function by association with Gene Ontology (GO) terms using the motif-GO term association tool GOMO. MEME output now contains sequence LOGOS for each discovered motif, as well as buttons to allow motifs to be conveniently submitted to the sequence and motif database scanning algorithms (MAST, FIMO and Tomtom), or to GOMO, for further analysis. GLAM2 output similarly contains buttons for further analysis using GLAM2SCAN and for rerunning GLAM2 with different parameters. All of the motif-based tools are now implemented as web services via Opal. Source code, binaries and a web server are freely available for noncommercial use at http://meme.nbcr.net.

7,733 citations


"Enhancer transcripts mark active es..." refers methods in this paper

  • ...…et al. (Kraus) April 16, 2013 14 To determine which transcription factors might underlie the predicted enhancers identified based on the called transcripts from GRO-seq, we performed de novo motif analyses on a 1 kb region around the center of the eRNA overlap using MEME (Bailey et al. 2009)....

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  • ...De novo motif searching was performed on a 1 kb region around the center of the plus and minus strand overlap (+/- 500 bp) using the command-line version of MEME (Bailey and Elkan 1994)....

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  • ...Cold Spring Harbor Laboratory Press on October 3, 2014 - Published by genome.cshlp.orgDownloaded from Hah et al. (Kraus) April 16, 2013 14 To determine which transcription factors might underlie the predicted enhancers identified based on the called transcripts from GRO-seq, we performed de novo motif analyses on a 1 kb region around the center of the eRNA overlap using MEME (Bailey et al. 2009)....

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  • ...De novo motif searching was performed on a 1 kb region around the center of the plus and minus strand overlap (+/- 500 bp) using MEME....

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  • ...The predicted motifs from MEME were matched to known motifs using STAMP (Parks and Beiko 2010)....

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Proceedings Article
01 Jan 1994
TL;DR: The algorithm described in this paper discovers one or more motifs in a collection of DNA or protein sequences by using the technique of expectation maximization to fit a two-component finite mixture model to the set of sequences.
Abstract: The algorithm described in this paper discovers one or more motifs in a collection of DNA or protein sequences by using the technique of expectation maximization to fit a two-component finite mixture model to the set of sequences Multiple motifs are found by fitting a mixture model to the data, probabilistically erasing the occurrences of the motif thus found, and repeating the process to find successive motifs The algorithm requires only a set of unaligned sequences and a number specifying the width of the motifs as input It returns a model of each motif and a threshold which together can be used as a Bayes-optimal classifier for searching for occurrences of the motif in other databases The algorithm estimates how many times each motif occurs in each sequence in the dataset and outputs an alignment of the occurrences of the motif The algorithm is capable of discovering several different motifs with differing numbers of occurrences in a single dataset

4,978 citations


"Enhancer transcripts mark active es..." refers methods in this paper

  • ...De novo motif searching was performed on a 1 kb region around the center of the plus and minus strand overlap (+/- 500 bp) using the command-line version of MEME (Bailey and Elkan 1994)....

    [...]

  • ...Cold Spring Harbor Laboratory Press on October 3, 2014 - Published by genome.cshlp.orgDownloaded from Hah et al. (Kraus) April 16, 2013 14 To determine which transcription factors might underlie the predicted enhancers identified based on the called transcripts from GRO-seq, we performed de novo motif analyses on a 1 kb region around the center of the eRNA overlap using MEME (Bailey et al. 2009)....

    [...]

  • ...De novo motif searching was performed on a 1 kb region around the center of the plus and minus strand overlap (+/- 500 bp) using MEME....

    [...]

  • ...The predicted motifs from MEME were matched to known motifs using STAMP (Parks and Beiko 2010)....

    [...]

  • ...The predicted motifs from MEME were then matched to known motifs using STAMP (Parks and Beiko 2010)....

    [...]

Journal ArticleDOI
Sarah Djebali, Carrie A. Davis1, Angelika Merkel, Alexander Dobin1, Timo Lassmann, Ali Mortazavi2, Ali Mortazavi3, Andrea Tanzer, Julien Lagarde, Wei Lin1, Felix Schlesinger1, Chenghai Xue1, Georgi K. Marinov3, Jainab Khatun4, Brian A. Williams3, Chris Zaleski1, Joel Rozowsky5, Marion S. Röder, Felix Kokocinski6, Rehab F. Abdelhamid, Tyler Alioto, Igor Antoshechkin3, Michael T. Baer1, Nadav Bar7, Philippe Batut1, Kimberly Bell1, Ian Bell8, Sudipto K. Chakrabortty1, Xian Chen9, Jacqueline Chrast10, Joao Curado, Thomas Derrien, Jorg Drenkow1, Erica Dumais8, Jacqueline Dumais8, Radha Duttagupta8, Emilie Falconnet11, Meagan Fastuca1, Kata Fejes-Toth1, Pedro G. Ferreira, Sylvain Foissac8, Melissa J. Fullwood12, Hui Gao8, David Gonzalez, Assaf Gordon1, Harsha P. Gunawardena9, Cédric Howald10, Sonali Jha1, Rory Johnson, Philipp Kapranov8, Brandon King3, Colin Kingswood, Oscar Junhong Luo12, Eddie Park2, Kimberly Persaud1, Jonathan B. Preall1, Paolo Ribeca, Brian A. Risk4, Daniel Robyr11, Michael Sammeth, Lorian Schaffer3, Lei-Hoon See1, Atif Shahab12, Jørgen Skancke7, Ana Maria Suzuki, Hazuki Takahashi, Hagen Tilgner13, Diane Trout3, Nathalie Walters10, Huaien Wang1, John A. Wrobel4, Yanbao Yu9, Xiaoan Ruan12, Yoshihide Hayashizaki, Jennifer Harrow6, Mark Gerstein5, Tim Hubbard6, Alexandre Reymond10, Stylianos E. Antonarakis11, Gregory J. Hannon1, Morgan C. Giddings4, Morgan C. Giddings9, Yijun Ruan12, Barbara J. Wold3, Piero Carninci, Roderic Guigó14, Thomas R. Gingeras1, Thomas R. Gingeras8 
06 Sep 2012-Nature
TL;DR: Evidence that three-quarters of the human genome is capable of being transcribed is reported, as well as observations about the range and levels of expression, localization, processing fates, regulatory regions and modifications of almost all currently annotated and thousands of previously unannotated RNAs that prompt a redefinition of the concept of a gene.
Abstract: Eukaryotic cells make many types of primary and processed RNAs that are found either in specific subcellular compartments or throughout the cells. A complete catalogue of these RNAs is not yet available and their characteristic subcellular localizations are also poorly understood. Because RNA represents the direct output of the genetic information encoded by genomes and a significant proportion of a cell's regulatory capabilities are focused on its synthesis, processing, transport, modification and translation, the generation of such a catalogue is crucial for understanding genome function. Here we report evidence that three-quarters of the human genome is capable of being transcribed, as well as observations about the range and levels of expression, localization, processing fates, regulatory regions and modifications of almost all currently annotated and thousands of previously unannotated RNAs. These observations, taken together, prompt a redefinition of the concept of a gene.

4,450 citations


"Enhancer transcripts mark active es..." refers methods in this paper

  • ...…II transcription, and the production of enhancer RNAs Cold Spring Harbor Laboratory Press on October 3, 2014 - Published by genome.cshlp.orgDownloaded from Hah et al. (Kraus) April 16, 2013 4 (“eRNAs”) (De Santa et al. 2010; Kim et al. 2010; Hah et al. 2011; Wang et al. 2011; Djebali et al. 2012)....

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