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Run-on and sequencing assays like GRO-seq, PRO-seq, and ChRO-seq allow for joint profiling of transcription activity of transcriptional regulatory elements (TREs), i.e., promoters and active enhancers, and target genes. Variation in biological conditions, such as treated vs. control, results in changes in the activity of transcription factors (TFs), which induces concerted changes in TREs and target genes. By modeling the differences between two biological conditions, we developed the computational pipeline known as tfTarget that predicts a set of putative TREs and target genes responding to each TF under the biological condition of interest. In this chapter, we demonstrate the use of the new web-based tfTarget in mapping transcription regulation using run-on sequencing data., (© 2023. The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature.)

The transcription factor p53 controls a gene expression program with pleiotropic effects on cell biology including cell cycle arrest and apoptosis. Identifying direct p53 target genes within this network and determining how they influence cell fate decisions downstream of p53 activation is a prerequisite for designing therapeutic approaches that target p53 to effectively kill cancer cells. Here we describe a comprehensive multi-omics approach for identifying genes that are direct transcriptional targets of p53. We provide detailed procedures for measuring global RNA polymerase activity, defining p53 binding sites across the genome, and quantifying changes in steady-state mRNA in response to p53 activation.

Next generation sequencing (NGS) is routinely used in gene expression analyses. In particular, RNA-seq has been the method of choice for highly sensitive genome-wide quantification of RNA expression. The method can be used in a wide variety of model systems, including studies to gain insight into underlying mechanisms of toxicologic processes and disease development induced by environmental toxicants. RNA-seq has also been coupled to many other molecular biology protocols to monitor specific aspects of the gene expression process. Here, we describe two such coupling-(a) global run-on sequencing (GRO-seq) that coupled it to the nuclear run-on (NRO), and (b) polysome profiling that coupled it to sucrose-gradient-based polysome isolation. Simultaneous RNA-seq, GRO-seq, and polysome profiling analyses enabled genome-wide analysis of the mode of stability control of individual RNA molecules.

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.

The advent of next-generation sequencing (NGS) technologies has revolutionized the way we do research on gene expression. High-throughput transcriptomics became possible with the development of microarray technology, but its widespread application only occurred after the emergence of massive parallel sequencing. Especially, RNA sequencing (RNA-seq) has greatly increased our knowledge about the genome and led to the identification and annotation of novel classes of RNAs in different species. However, RNA-seq measures the steady-state level of a given RNA, which is the equilibrium between transcription, processing, and degradation. In recent years, a number of dedicated RNA-seq technologies were developed to measure specifically transcription events. Global run-on sequencing (GRO-seq) is the most widely used method to measure nascent RNA, and in recent years, it has been applied successfully to study the function and mechanism of action of noncoding RNAs. Here, we describe a detailed protocol of GRO-seq that can be readily applied to investigate different aspects of RNA biology in human cells.