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KymoButler, a Deep Learning software for automated kymograph analysis

View ORCID ProfileMaximilian A. H. Jakobs, View ORCID ProfileAndrea Dimitracopoulos, View ORCID ProfileKristian Franze
doi: https://doi.org/10.1101/405183
Maximilian A. H. Jakobs
Department of Physiology, Development and Neuroscience, University of Cambridge, Cambridge, UK
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  • ORCID record for Maximilian A. H. Jakobs
  • For correspondence: mj455@cam.ac.uk kf284@cam.ac.uk
Andrea Dimitracopoulos
Department of Physiology, Development and Neuroscience, University of Cambridge, Cambridge, UK
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Kristian Franze
Department of Physiology, Development and Neuroscience, University of Cambridge, Cambridge, UK
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  • For correspondence: mj455@cam.ac.uk kf284@cam.ac.uk
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Abstract

Kymographs are graphical representations of spatial position over time, which are often used in biology to visualise the motion of fluorescent particles, molecules, vesicles, or organelles moving along a predictable path. Although in kymographs tracks of individual particles are qualitatively easily distinguished, their automated quantitative analysis is much more challenging. Kymographs often exhibit low signal-to-noise-ratios (SNRs), and available tools that automate their analysis usually require manual supervision. Here we developed KymoButler, a Deep Learning-based software to automatically track dynamic processes in kymographs. We demonstrate that KymoButler performs as well as expert manual data analysis on kymographs with complex particle trajectories from a variety of different biological systems. The software was packaged in a web-based “one-click” application for use by the wider scientific community. Our approach significantly speeds up data analysis, avoids unconscious bias, and represents another step towards the widespread adaptation of Machine Learning techniques in biological data analysis.

Footnotes

  • The manuscript now contains a new approach that allows to analyse dense bidirectional kymgraphs.

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The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC-ND 4.0 International license.
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Posted May 13, 2019.
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KymoButler, a Deep Learning software for automated kymograph analysis
Maximilian A. H. Jakobs, Andrea Dimitracopoulos, Kristian Franze
bioRxiv 405183; doi: https://doi.org/10.1101/405183
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KymoButler, a Deep Learning software for automated kymograph analysis
Maximilian A. H. Jakobs, Andrea Dimitracopoulos, Kristian Franze
bioRxiv 405183; doi: https://doi.org/10.1101/405183

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