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KymoButler, a deep learning software for automated kymograph analysis.

Maximilian A H Jakobs1, Andrea Dimitracopoulos1, Kristian Franze1

  • 1Department of Physiology, Development and Neuroscience, University of Cambridge, Cambridge, United Kingdom.

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|August 14, 2019
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Summary
This summary is machine-generated.

KymoButler, a new Deep Learning software, automatically tracks particle motion in kymographs. This tool provides accurate, unbiased analysis of biological dynamics, matching expert performance.

Keywords:
artificial intelligencecell biologykymogramskymographsmachine learningneuronsnonephysics of living systemstransport

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Area of Science:

  • Cell Biology
  • Biophysics
  • Computational Biology

Background:

  • Kymographs visualize spatial-temporal data, commonly used for tracking biological components like vesicles and organelles.
  • Automated quantitative analysis of kymographs is challenging due to low signal-to-noise ratios (SNRs) and complex trajectories.
  • Existing tools often require manual intervention, limiting throughput and introducing bias.

Purpose of the Study:

  • To develop a Deep Learning-based software, KymoButler, for automated tracking of dynamic processes in kymographs.
  • To provide a user-friendly, one-click application for the scientific community.
  • To enable unbiased and efficient quantitative analysis of biological motion.

Main Methods:

  • Development of a Deep Learning model for kymograph analysis.
  • Implementation of the model into a web-based, 'one-click' application (KymoButler).
  • Validation against expert manual analysis using diverse biological datasets with complex particle trajectories.

Main Results:

  • KymoButler achieves performance comparable to expert manual analysis for kymograph data.
  • The software successfully tracks dynamic processes across various biological systems.
  • Demonstrated significant speed-up in data analysis and reduction of unconscious bias.

Conclusions:

  • KymoButler offers a robust solution for automated, quantitative analysis of kymographs.
  • The tool facilitates the adaptation of Machine Learning in biological research.
  • KymoButler promotes efficient and unbiased analysis of cellular dynamics.