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Computational Approaches for Mining GRO-Seq Data to Identify and Characterize Active Enhancers

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Enhancer RNAs

Part of the book series: Methods in Molecular Biology ((MIMB,volume 1468))

Abstract

Transcriptional enhancers are DNA regulatory elements that are bound by transcription factors and act to positively regulate the expression of nearby or distally located target genes. Enhancers have many features that have been discovered using genomic analyses. Recent studies have shown that active enhancers recruit RNA polymerase II (Pol II) and are transcribed, producing enhancer RNAs (eRNAs). GRO-seq, a method for identifying the location and orientation of all actively transcribing RNA polymerases across the genome, is a powerful approach for monitoring nascent enhancer transcription. Furthermore, the unique pattern of enhancer transcription can be used to identify enhancers in the absence of any information about the underlying transcription factors. Here, we describe the computational approaches required to identify and analyze active enhancers using GRO-seq data, including data pre-processing, alignment, and transcript calling. In addition, we describe protocols and computational pipelines for mining GRO-seq data to identify active enhancers, as well as known transcription factor binding sites that are transcribed. Furthermore, we discuss approaches for integrating GRO-seq-based enhancer data with other genomic data, including target gene expression and function. Finally, we describe molecular biology assays that can be used to confirm and explore further the function of enhancers that have been identified using genomic assays. Together, these approaches should allow the user to identify and explore the features and biological functions of new cell type-specific enhancers.

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Acknowledgments

The authors thank Minho Chae and Hector L. Franco for helpful comments and suggestions about enhancer identification using GRO-seq, as well as this manuscript. The enhancer-related work in the Kraus lab is supported by grants from the NIH/NIDDK and the Cancer Prevention and Research Institute of Texas (CPRIT).

Author information

Authors and Affiliations

  1. The Laboratory of Signaling and Gene Expression, Cecil H. and Ida Green Center for Reproductive Biology Sciences, University of Texas Southwestern Medical Center, 5323 Harry Hines Boulevard, Dallas, TX, 75390-8511, USA

    Anusha Nagari, Shino Murakami, Venkat S. Malladi & W. Lee Kraus Ph.D.

  2. The Division of Basic Research, Department of Obstetrics and Gynecology, University of Texas Southwestern Medical Center, Dallas, TX, 75390-8511, USA

    Anusha Nagari, Shino Murakami, Venkat S. Malladi & W. Lee Kraus Ph.D.

  3. Program in Genetics, Development and Disease, Graduate School of Biomedical Sciences, University of Texas Southwestern Medical Center, Dallas, TX, 75390, USA

    Shino Murakami & W. Lee Kraus Ph.D.

Authors
  1. Anusha Nagari
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  2. Shino Murakami
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  3. Venkat S. Malladi
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  4. W. Lee Kraus Ph.D.
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Corresponding author

Correspondence to W. Lee Kraus Ph.D. .

Editor information

Editors and Affiliations

  1. Max Planck Institute Molecular Genetics, Berlin, Germany

    Ulf Andersson Ørom

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Nagari, A., Murakami, S., Malladi, V.S., Kraus, W.L. (2017). Computational Approaches for Mining GRO-Seq Data to Identify and Characterize Active Enhancers. In: Ørom, U. (eds) Enhancer RNAs. Methods in Molecular Biology, vol 1468. Humana Press, New York, NY. https://doi.org/10.1007/978-1-4939-4035-6_10

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  • DOI: https://doi.org/10.1007/978-1-4939-4035-6_10

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  • Publisher Name: Humana Press, New York, NY

  • Print ISBN: 978-1-4939-4033-2

  • Online ISBN: 978-1-4939-4035-6

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