KymoButler, a deep learning software for automated kymograph analysis.
eLife Sciences Publications Ltd
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Jakobs, M., Dimitracopoulos, A., & Franze, K. (2019). KymoButler, a deep learning software for automated kymograph analysis.. eLife, 8 https://doi.org/10.7554/elife.42288
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 (kymobutler.deepmirror.ai). 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.
Kymography, Software, Automation, Laboratory, Deep Learning
Wellcome Trust (Research Grant 109145/Z/15/Z to M.A.H.J.), the Herchel Smith Foundation (Fellowship to A.D.), Isaac Newton Trust (Research Grant 17.24(p) to K.F.), UK BBSRC (Research Project Grant BB/N006402/1 to K.F.), and the ERC (Consolidator Award 772426 to K.F.).
Isaac Newton Trust (17.24(p))
European Commission Horizon 2020 (H2020) ERC (772426)
WELLCOME TRUST (109145/Z/15/Z)
Embargo Lift Date
External DOI: https://doi.org/10.7554/elife.42288
This record's URL: https://www.repository.cam.ac.uk/handle/1810/294816
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