We provide a protocol for precision nuclear run-on sequencing (PRO-seq) and its variant, PRO-cap, which map the location of active RNA polymerases (PRO-seq) or transcription start sites (TSSs) (PRO-cap) genome-wide at high resolution. The density of RNA polymerases at a particular genomic locus directly reflects the level of nascent transcription at that region. Nuclei are isolated from cells and, under nuclear run-on conditions, transcriptionally engaged RNA polymerases incorporate one or, at most, a few biotin-labeled nucleotide triphosphates (biotin-NTPs) into the 3′ end of nascent RNA. The biotin-labeled nascent RNA is used to prepare sequencing libraries, which are sequenced from the 3′ end to provide high-resolution positional information for the RNA polymerases. PRO-seq provides much higher sensitivity than ChIP-seq, and it generates a much larger fraction of usable sequence reads than ChIP-seq or NET-seq (native elongating transcript sequencing). Similarly to NET-seq, PRO-seq maps the RNA polymerase at up to base-pair resolution with strand specificity, but unlike NET-seq it does not require immunoprecipitation. With the protocol provided here, PRO-seq (or PRO-cap) libraries for high-throughput sequencing can be generated in 4–5 working days. The method has been applied to human, mouse, Drosophila melanogaster and Caenorhabditis elegans cells and, with slight modifications, to yeast.

Inferring single-cell compositions and their contributions to global gene expression changes from bulk RNA sequencing (RNA-seq) datasets is a major challenge in oncology. Here we develop Bayesian cell proportion reconstruction inferred using statistical marginalization (BayesPrism), a Bayesian method to predict cellular composition and gene expression in individual cell types from bulk RNA-seq, using patient-derived, scRNA-seq as prior information. We conduct integrative analyses in primary glioblastoma, head and neck squamous cell carcinoma and skin cutaneous melanoma to correlate cell type composition with clinical outcomes across tumor types, and explore spatial heterogeneity in malignant and nonmalignant cell states. We refine current cancer subtypes using gene expression annotation after exclusion of confounding nonmalignant cells. Finally, we identify genes whose expression in malignant cells correlates with macrophage infiltration, T cells, fibroblasts and endothelial cells across multiple tumor types. Our work introduces a new lens to accurately infer cellular composition and expression in large cohorts of bulk RNA-seq data.

Gene expression is precisely controlled in time and space through the integration of signals that act at gene promoters and gene-distal enhancers. Classically, promoters and enhancers are considered separate classes of regulatory elements, often distinguished by histone modifications. However, recent studies have revealed broad similarities between enhancers and promoters, blurring the distinction: active enhancers often initiate transcription, and some gene promoters have the potential to enhance transcriptional output of other promoters. Here, we propose a model in which promoters and enhancers are considered a single class of functional element, with a unified architecture for transcription initiation. The context of interacting regulatory elements and the surrounding sequences determine local transcriptional output as well as the enhancer and promoter activities of individual elements.

Our genomes encode a wealth of transcription initiation regions (TIRs) that can be identified by their distinctive patterns of actively elongating RNA polymerase. We previously introduced dREG to identify TIRs using PRO-seq data. Here, we introduce an efficient new implementation of dREG that uses PRO-seq data to identify both uni-and bidirectionally transcribed TIRs with 70% improvement in accuracy, three-to fourfold higher resolution, and >100-fold increases in computational efficiency. Using a novel strategy to identify TIRs based on their statistical confidence reveals extensive overlap with orthogonal assays, yet also reveals thousands of additional weakly transcribed TIRs that were not identified by H3K27ac ChIP-seq or DNase-seq. Novel TIRs discovered by dREG were often associated with RNA polymerase III initiation, bound by pioneer transcription factors, or located in broad domains marked by repressive chromatin modifications. Our results suggest that transcription initiation can be a powerful tool for expanding the catalog of functional elements.

BackgroundGlobal run-on coupled with deep sequencing (GRO-seq) provides extensive information on the location and function of coding and non-coding transcripts, including primary microRNAs (miRNAs), long non-coding RNAs (lncRNAs), and enhancer RNAs (eRNAs), as well as yet undiscovered classes of transcripts. However, few computational tools tailored toward this new type of sequencing data are available, limiting the applicability of GRO-seq data for identifying novel transcription units.ResultsHere, we present groHMM, a computational tool in R, which defines the boundaries of transcription units de novo using a two state hidden-Markov model (HMM). A systematic comparison of the performance between groHMM and two existing peak-calling methods tuned to identify broad regions (SICER and HOMER) favorably supports our approach on existing GRO-seq data from MCF-7 breast cancer cells. To demonstrate the broader utility of our approach, we have used groHMM to annotate a diverse array of transcription units (i.e., primary transcripts) from four GRO-seq data sets derived from cells representing a variety of different human tissue types, including non-transformed cells (cardiomyocytes and lung fibroblasts) and transformed cells (LNCaP and MCF-7 cancer cells), as well as non-mammalian cells (from flies and worms). As an example of the utility of groHMM and its application to questions about the transcriptome, we show how groHMM can be used to analyze cell type-specific enhancers as defined by newly annotated enhancer transcripts.ConclusionsOur results show that groHMM can reveal new insights into cell type-specific transcription by identifying novel transcription units, and serve as a complete and useful tool for evaluating functional genomic elements in cells.Electronic supplementary materialThe online version of this article (doi:10.1186/s12859-015-0656-3) contains supplementary material, which is available to authorized users.