Kymobutler, a deep learning software for automated kymograph analysis
Jakobs MAH, Dimitracopoulos A, Franze K (2019)
Publication Type: Journal article
Publication year: 2019
Journal
Book Volume: 8
Article Number: e42288
DOI: 10.7554/eLife.42288
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 (http://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.
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APA:
Jakobs, M.A.H., Dimitracopoulos, A., & Franze, K. (2019). Kymobutler, a deep learning software for automated kymograph analysis. eLife, 8. https://dx.doi.org/10.7554/eLife.42288
MLA:
Jakobs, Maximilian A. H., Andrea Dimitracopoulos, and Kristian Franze. "Kymobutler, a deep learning software for automated kymograph analysis." eLife 8 (2019).
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