期刊
ELIFE
卷 8, 期 -, 页码 -出版社
ELIFE SCIENCES PUBLICATIONS LTD
DOI: 10.7554/eLife.42288
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资金
- Wellcome Trust [109145/Z/15/Z]
- Herchel Smith Foundation
- Isaac Newton Trust [17.24(p)]
- Biotechnology and Biological Sciences Research Council [BB/N006402/1]
- European Research Council [772426]
- BBSRC [BB/N006402/1] Funding Source: UKRI
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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