rSNAPsim

rSNAPsim simulates single-molecule translation dynamics at single-RNA resolution to generate synthetic fluorescence-microscopy assay data for studies of translation and nascent protein dynamics.

Key Features:

• Sequence-based stochastic model: Implements a sequence-based stochastic model for single-molecule translation dynamics at single-RNA resolution.
• Assay simulation: Simulates synthetic data for Fluorescence Correlation Spectroscopy (FCS), ribosome Run-Off Assays (ROA) after Harringtonine treatment, and Fluorescence Recovery After Photobleaching (FRAP).
• Sequence-level factors: Accounts for synonymous codon usage, tRNA level modifications, and ribosome pauses in translation simulations.
• Imaging and gene scope: Simulates experiments under diverse imaging conditions across thousands of human genes.
• Experimental evaluation: Performs extensive simulations to evaluate experimental setups for estimating elongation kinetics and identifies FCS analyses as optimal for both short and long genes.
• Integration with experimental data: Integrates simulations with experimental data to capture nascent protein statistics and temporal dynamics.
• Demonstrated genes: Applied to human genes KDM5B, β-actin, and H2B.
• Implementation: Implemented in Python.

Scientific Applications:

• Synthetic data generation: Producing realistic synthetic datasets for FCS, ROA (post-Harringtonine), and FRAP experiment planning and analysis.
• Experimental design and evaluation: Assessing which imaging assays and setups most accurately estimate elongation kinetics across gene lengths.
• Mechanistic modeling: Modeling the effects of synonymous codon usage, tRNA level modifications, and ribosome pauses on translation dynamics.
• Data integration analysis: Analyzing nascent protein statistics and temporal dynamics by combining simulations with experimental measurements.
• Genome-scale simulation: Simulating translation dynamics across thousands of human genes, including KDM5B, β-actin, and H2B.

Methodology:

Uses a sequence-based stochastic model to simulate translation dynamics accounting for synonymous codon usage, tRNA level modifications, and ribosome pauses, performs extensive simulations to evaluate experimental setups, and is implemented in Python.