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PLoS Comput Biol. 2019 Oct 16;15(10):e1007425. doi: 10.1371/journal.pcbi.1007425. eCollection 2019 Oct.

Computational design and interpretation of single-RNA translation experiments.

Author information

1
Department of Chemical and Biological Engineering, Colorado State University Fort Collins, Colorado, United States of America.
2
School of Biomedical Engineering, Colorado State University Fort Collins, Colorado, United States of America.
3
Department of Biochemistry and Molecular Biology and Institute for Genome Architecture and Function, Colorado State University, Fort Collins, Colorado, United States of America.
4
Cell Biology Unit, Institute of Innovative Research, Tokyo Institute of Technology, Nagatsuta-cho 4259, Midori-ku, Yokohama, Kanagawa, Japan.

Abstract

Advances in fluorescence microscopy have introduced new assays to quantify live-cell translation dynamics at single-RNA resolution. We introduce a detailed, yet efficient sequence-based stochastic model that generates realistic synthetic data for several such assays, including Fluorescence Correlation Spectroscopy (FCS), ribosome Run-Off Assays (ROA) after Harringtonine application, and Fluorescence Recovery After Photobleaching (FRAP). We simulate these experiments under multiple imaging conditions and for thousands of human genes, and we evaluate through simulations which experiments are most likely to provide accurate estimates of elongation kinetics. Finding that FCS analyses are optimal for both short and long length genes, we integrate our model with experimental FCS data to capture the nascent protein statistics and temporal dynamics for three human genes: KDM5B, β-actin, and H2B. Finally, we introduce a new open-source software package, RNA Sequence to NAscent Protein Simulator (rSNAPsim), to easily simulate the single-molecule translation dynamics of any gene sequence for any of these assays and for different assumptions regarding synonymous codon usage, tRNA level modifications, or ribosome pauses. rSNAPsim is implemented in Python and is available at: https://github.com/MunskyGroup/rSNAPsim.git.

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