title: 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.
authors.forename: Joseph G
authors.initials: JG
authors.lastname: Azofeifa
authors.forename: Mary A
authors.initials: MA
authors.lastname: Allen
authors.forename: Manuel E
authors.initials: ME
authors.lastname: Lladser
authors.forename: Robin D
authors.initials: RD
authors.lastname: Dowell
mesh_terms: D000465:Algorithms; D019295:Computational Biology / Q000379:methods*; D030541:Databases, Genetic; D006801:Humans; D008390:Markov Chains; D058977:Molecular Sequence Annotation / Q000379:methods*; D012313:RNA / Q000032:analysis / Q000235:genetics*; D017423:Sequence Analysis, RNA / Q000379:methods*
chemical_list: D012313:RNA
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