Pause and change point detection in single-molecule motor trajectories by BIC-based model selection

Detecting pause events in single-molecule trajectories is essential for extracting kinetic information but remains challenging in noisy measurements. Here, we present a pause-detection method based on model selection using the Bayesian Information Criterion. The approach operates directly on raw trajectories, requires no preprocessing, and does not rely on user-defined parameter tuning. We provide a rigorous analysis of detection sensitivity that quantifies the probability of missing events under given experimental conditions, and the method naturally accommodates missing data in trajectories. Benchmarking shows that the approach outperforms commonly used pause-detection and change-point-detection strategies across simulated and experimental datasets. The method provides a robust and general framework for pause and change-point detection in single-molecule trajectories and is broadly applicable to single-molecule measurements obtained with magnetic tweezers, optical tweezers, and single-particle tracking.