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

IEEE/ACM Transactions on Computational Biology and Bioinformatics
Joseph G AzofeifaRobin D Dowell

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.

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Citations

May 5, 2016·Journal of Mathematical Biology·Manuel E LladserRobin D Dowell
Mar 6, 2019·Scientific Data·Andrea A PerreaultBryan J Venters
Nov 1, 2016·Bioinformatics·Joseph G Azofeifa, Robin D Dowell
May 12, 2018·Molecules : a Journal of Synthetic Chemistry and Natural Product Chemistry·Ignacio J TripodiRobin D Dowell
May 17, 2021·Genome Biology·Jason P SmithNathan C Sheffield

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