PT  - JOURNAL ARTICLE
AU  - Maximilian A. H. Jakobs
AU  - Andrea Dimitracopoulos
AU  - Kristian Franze
TI  - KymoButler, a Deep Learning software for automated kymograph analysis
AID  - 10.1101/405183
DP  - 2019 Jan 01
TA  - bioRxiv
PG  - 405183
4099  - http://biorxiv.org/content/early/2019/05/13/405183.short
4100  - http://biorxiv.org/content/early/2019/05/13/405183.full
AB  - 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.