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

Area of Science:

• Biophysics
• Biochemistry
• Data Analysis

Background:

• Accurate detection of pause events in single-molecule trajectories is crucial for understanding molecular kinetics.
• Existing methods struggle with noisy data and often require manual parameter tuning.

Purpose of the Study:

• To develop a robust and automated method for detecting pause events in single-molecule trajectories.
• To improve the accuracy and reliability of kinetic information extraction from single-molecule data.

Main Methods:

• A novel pause-detection algorithm based on model selection using the Bayesian Information Criterion (BIC).
• The method operates directly on raw trajectory data, requiring no preprocessing or user-defined parameters.
• Analysis of detection sensitivity and accommodation of missing data points within trajectories.

Main Results:

• The proposed method demonstrates superior performance compared to existing pause-detection and change-point-detection strategies.
• Benchmarking on simulated and experimental datasets confirms the method's effectiveness.
• The approach is robust to noise and naturally handles missing data in trajectories.

Conclusions:

• The Bayesian Information Criterion-based method offers a reliable and generalizable framework for pause and change-point detection.
• This technique enhances the analysis of single-molecule measurements from various experimental setups.
• The method facilitates more accurate kinetic information extraction in biophysical studies.