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Modeling Enzyme Processivity Reveals that RNA-Seq Libraries Are Biased in Characteristic and Correctable Ways

Archer, Nathan; Walsh, Mark D.; Shahrezaei, Vahid; Hebenstreit, Daniel

Modeling Enzyme Processivity Reveals that RNA-Seq Libraries Are Biased in Characteristic and Correctable Ways Thumbnail


Authors

Mark D. Walsh

Vahid Shahrezaei

Daniel Hebenstreit



Abstract

© 2016 The Author(s) Experimental procedures for preparing RNA-seq and single-cell (sc) RNA-seq libraries are based on assumptions regarding their underlying enzymatic reactions. Here, we show that the fairness of these assumptions varies within libraries: coverage by sequencing reads along and between transcripts exhibits characteristic, protocol-dependent biases. To understand the mechanistic basis of this bias, we present an integrated modeling framework that infers the relationship between enzyme reactions during library preparation and the characteristic coverage patterns observed for different protocols. Analysis of new and existing (sc)RNA-seq data from six different library preparation protocols reveals that polymerase processivity is the mechanistic origin of coverage biases. We apply our framework to demonstrate that lowering incubation temperature increases processivity, yield, and (sc)RNA-seq sensitivity in all protocols. We also provide correction factors based on our model for increasing accuracy of transcript quantification in existing samples prepared at standard temperatures. In total, our findings improve our ability to accurately reflect in vivo transcript abundances in (sc)RNA-seq libraries.

Citation

Archer, N., Walsh, M. D., Shahrezaei, V., & Hebenstreit, D. (2016). Modeling Enzyme Processivity Reveals that RNA-Seq Libraries Are Biased in Characteristic and Correctable Ways. Cell Systems, 3(5), 467-479. https://doi.org/10.1016/j.cels.2016.10.012

Journal Article Type Article
Acceptance Date Oct 13, 2016
Online Publication Date Nov 10, 2016
Publication Date Nov 23, 2016
Deposit Date Mar 18, 2019
Publicly Available Date Apr 3, 2019
Journal Cell Systems
Electronic ISSN 2405-4712
Publisher Cell Press
Peer Reviewed Peer Reviewed
Volume 3
Issue 5
Pages 467-479
DOI https://doi.org/10.1016/j.cels.2016.10.012
Keywords RNA-seqprocessivitycoveragebiasmathematical modelingMarkov Chain Monte CarloBayesian frameworkenzymepolymerasereverse transcriptase
Public URL https://nottingham-repository.worktribe.com/output/1661760
Publisher URL https://www.sciencedirect.com/science/article/pii/S2405471216303313?via%3Dihub

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