qFADDpy

qFADDpy implements the Q-FADD method to quantify recruitment kinetics of fluorescently labeled DNA repair proteins to laser-induced DNA damage sites for analysis of diffusion and accumulation dynamics.

Key Features:

• Q-FADD implementation: Implements the Quantitation of Fluorescence Accumulation after DNA Damage (Q-FADD) approach to extract kinetic parameters from fluorescence accumulation data.
• Automated preprocessing: Performs preprocessing including corrections for nuclear drift to improve accuracy of time-series fluorescence measurements.
• Automated grid-search fitting: Uses an automated grid-search algorithm to identify best-fit model parameters for protein recruitment dynamics.
• Simulation of replicates: Integrates automated simulation of replicates within the grid-search to support statistical assessment of fits.
• Monte Carlo diffusion modeling: Applies Monte Carlo diffusion models to interpret recruitment timescales and diffusion-related behavior of repair proteins.

Scientific Applications:

• DNA damage response kinetics: Quantifies accumulation, action, and dissipation timescales of repair proteins at sites of DNA damage, including examples such as PARP1.
• Protein recruitment and diffusion analysis: Compares recruitment kinetics and diffusion properties of repair proteins to elucidate interactions within genome maintenance networks.

Methodology:

Automated preprocessing with nuclear drift correction, automated grid-search fitting incorporating simulated replicates, and Monte Carlo diffusion models.