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

Johannes Stigler1

  • 1Gene Center and Department of Biochemistry, Ludwig-Maximilians-Universität München, 81377 Munich, Germany.

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|June 8, 2026
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Summary

This study introduces a new method for detecting pause events in single-molecule trajectories using the Bayesian Information Criterion. The approach accurately identifies pauses in noisy data without needing parameter tuning, improving kinetic analysis.

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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.