Computational approaches for mining GRO-seq data to identify and characterize active enhancers

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

Research output: Contribution to journalArticle

4 Citations (Scopus)

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.

Original languageEnglish (US)
Pages (from-to)121-138
Number of pages18
JournalMethods in Molecular Biology
Volume1468
DOIs
StatePublished - 2017

Fingerprint

Transcription Factors
RNA Polymerase II
DNA-Directed RNA Polymerases
Molecular Biology
Binding Sites
Genome
RNA
Gene Expression
DNA
Genes

Keywords

  • Enhancer
  • Enhancer prediction
  • Enhancer RNAs (eRNAs)
  • Gene regulation
  • GRO-seq
  • GroHMM
  • Looping
  • Motif
  • Motif search
  • Promoter
  • Response element
  • Transcription
  • Transcription factor
  • Transcription unit

ASJC Scopus subject areas

  • Molecular Biology
  • Genetics

Cite this

Computational approaches for mining GRO-seq data to identify and characterize active enhancers. / Nagari, Anusha; Murakami, Shino; Malladi, Venkat S.; Kraus, W. Lee.

In: Methods in Molecular Biology, Vol. 1468, 2017, p. 121-138.

Research output: Contribution to journalArticle

Nagari, Anusha ; Murakami, Shino ; Malladi, Venkat S. ; Kraus, W. Lee. / Computational approaches for mining GRO-seq data to identify and characterize active enhancers. In: Methods in Molecular Biology. 2017 ; Vol. 1468. pp. 121-138.
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