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

Abstract

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.