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

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Title: Modeling Enzyme Processivity Reveals that RNA-Seq Libraries Are Biased in Characteristic and Correctable Ways
Author(s): Archer, N
Walsh, MD
Shahrezaei, V
Hebenstreit, D
Item Type: Journal Article
Abstract: 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.
Publication Date: 10-Nov-2016
Date of Acceptance: 13-Oct-2016
URI: http://hdl.handle.net/10044/1/49655
DOI: https://dx.doi.org/10.1016/j.cels.2016.10.012
ISSN: 2405-4712
Publisher: Elsevier
Start Page: 467
End Page: +
Journal / Book Title: Cell Systems
Volume: 3
Issue: 5
Copyright Statement: © 2016 The Author(s). Published by Elsevier Inc. Open Access funded by Biotechnology and Biological Sciences Research Council Under a Creative Commons license (https://creativecommons.org/licenses/by/4.0/)
Keywords: Science & Technology
Life Sciences & Biomedicine
Biochemistry & Molecular Biology
Cell Biology
SINGLE-CELL TRANSCRIPTOMICS
NONUNIFORM READ DISTRIBUTION
GENE-EXPRESSION
REVERSE-TRANSCRIPTASE
HIGHLY PARALLEL
SEQUENCING DATA
QUANTIFICATION
GENOME
DEGRADATION
CHALLENGES
Bayesian framework
Markov Chain Monte Carlo
RNA-seq
bias
coverage
enzyme
mathematical modeling
polymerase
processivity
reverse transcriptase
Publication Status: Published
Open Access location: http://www.cell.com/cell-systems/abstract/S2405-4712(16)30331-3?_returnURL=http://linkinghub.elsevier.com/retrieve/pii/S2405471216303313?showall=true
Appears in Collections:Mathematics
Applied Mathematics and Mathematical Physics



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