Enhancer RNA profiling predicts transcription factor activity.

Transcription factors (TFs) exert their regulatory influence through the binding of enhancers, resulting in coordination of gene expression programs. Active enhancers are often characterized by the presence of short, unstable transcripts termed enhancer RNAs (eRNAs). While their function remains unclear, we demonstrate that eRNAs are a powerful readout of TF activity. We infer sites of eRNA origination across hundreds of publicly available nascent transcription data sets and show that eRNAs initiate from sites of TF binding. By quantifying the colocalization of TF binding motif instances and eRNA origins, we derive a simple statistic capable of inferring TF activity. In doing so, we uncover dozens of previously unexplored links between diverse stimuli and the TFs they affect.

A generative model for the behavior of RNA polymerase.

MOTIVATION: Transcription by RNA polymerase is a highly dynamic process involving multiple distinct points of regulation. Nascent transcription assays are a relatively new set of high throughput techniques that measure the location of actively engaged RNA polymerase genome wide. Hence, nascent transcription is a rich source of information on the regulation of RNA polymerase activity. To fully dissect this data requires the development of stochastic models that can both deconvolve the stages of polymerase activity and identify significant changes in activity between experiments. RESULTS: We present a generative, probabilistic model of RNA polymerase that fully describes loading, initiation, elongation and termination. We fit this model genome wide and profile the enzymatic activity of RNA polymerase across various loci and following experimental perturbation. We observe striking correlation of predicted loading events and regulatory chromatin marks. We provide principled statistics that compute probabilities reminiscent of traveler's and divergent ratios. We finish with a systematic comparison of RNA Polymerase activity at promoter versus non-promoter associated loci. AVAILABILITY AND IMPLEMENTATION: Transcription Fit (Tfit) is a freely available, open source software package written in C/C ++ that requires GNU compilers 4.7.3 or greater. Tfit is available from GitHub (https://github.com/azofeifa/Tfit). CONTACT: robin.dowell@colorado.eduSupplementary information: Supplementary data are available at Bioinformatics online.

RNA Pol II transcription model and interpretation of GRO-seq data.

A mixture model and statistical method is proposed to interpret the distribution of reads from a nascent transcriptional assay, such as global run-on sequencing (GRO-seq) data. The model is annotation agnostic and leverages on current understanding of the behavior of RNA polymerase II. Briefly, it assumes that polymerase loads at key positions (transcription start sites) within the genome. Once loaded, polymerase either remains in the initiation form (with some probability) or transitions into an elongating form (with the remaining probability). The model can be fit genome-wide, allowing patterns of Pol II behavior to be assessed on each distinct transcript. Furthermore, it allows for the first time a principled approach to distinguishing the initiation signal from the elongation signal; in particular, it implies a data driven method for calculating the pausing index, a commonly used metric that informs on the behavior of RNA polymerase II. We demonstrate that this approach improves on existing analyses of GRO-seq data and uncovers a novel biological understanding of the impact of knocking down the Male Specific Lethal (MSL) complex in Drosophilia melanogaster.

Prolonged Cre expression driven by the α-myosin heavy chain promoter can be cardiotoxic.

Studying the importance of genetic factors in a desired cell type or tissue necessitates the use of precise genetic tools. With the introduction of bacteriophage Cre recombinase/loxP mediated DNA editing and promoter-specific Cre expression, it is feasible to generate conditional knockout mice in which particular genes are disrupted in a cell type-specific manner in vivo. In cardiac myocytes, this is often achieved through α-myosin heavy chain promoter (αMyHC)-driven Cre expression in conjunction with a loxP-site flanked gene of interest. Recent studies in other cell types demonstrate toxicity of Cre expression through induction of DNA damage. However, it is unclear to what extent the traditionally used αMyHC-Cre line [1] may exhibit cardiotoxicity. Further, the genotype of αMyHC-Cre(+/-) is not often included as a control group in cardiac myocyte-specific knockout studies. Here we present evidence that these αMyHC-Cre(+/-) mice show molecular signs of cardiac toxicity by 3months of age and exhibit decreased cardiac function by 6months of age compared to wild-type littermates. Hearts from αMyHC-Cre(+/-) mice also display evidence of fibrosis, inflammation, and DNA damage. Interestingly, some of the early functional changes observed in αMyHC-Cre(+/-) mice are sexually dimorphic. Given the high level of Cre recombinase expression resulting from expression from the αMyHC promoter, we asked if degenerate loxP-like sites naturally exist in the mouse genome and if so, whether they are affected by Cre in the absence of canonical loxP-sites. Using a novel bioinformatics search tool, we identified 619 loxP-like sites with 4 or less mismatches to the canonical loxP-site. 227 sites overlapped with annotated genes and 55 of these genes were expressed in cardiac muscle. Expression of ~26% of the 27 genes tested was disrupted in αMyHC-Cre(+/-) mice indicating potential targeting by Cre. Taken together, these results highlight both the importance of using αMyHC-Cre mice as controls in conditional knockout studies as well as the need for a less cardiotoxic Cre driver for the field.

An Annotation Agnostic Algorithm for Detecting Nascent RNA Transcripts in GRO-Seq.

We present a fast and simple algorithm to detect nascent RNA transcription in global nuclear run-on sequencing (GRO-seq). GRO-seq is a relatively new protocol that captures nascent transcripts from actively engaged polymerase, providing a direct read-out on bona fide transcription. Most traditional assays, such as RNA-seq, measure steady state RNA levels which are affected by transcription, post-transcriptional processing, and RNA stability. GRO-seq data, however, presents unique analysis challenges that are only beginning to be addressed. Here, we describe a new algorithm, Fast Read Stitcher (FStitch), that takes advantage of two popular machine-learning techniques, hidden Markov models and logistic regression, to classify which regions of the genome are transcribed. Given a small user-defined training set, our algorithm is accurate, robust to varying read depth, annotation agnostic, and fast. Analysis of GRO-seq data without a priori need for annotation uncovers surprising new insights into several aspects of the transcription process.