In an effort to simplify the analysis of kymographs, Jakobs et al. Manually annotating kymographs is tedious, time-consuming and prone to the researcher’s unconscious bias. Unfortunately, tracing the lines that represent movement in kymographs of biological particles is hard to do automatically, so currently this analysis is done by hand. Kymographs are images that represent the movement of particles in time and space. Studying these movements is important to understand many biological processes, including the development of the brain or the spread of viruses. Many molecules and structures within cells have to move about to do their job. 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. The software was packaged in a web-based ‘one-click’ application for use by the wider scientific community ( ).
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. Here we developed KymoButler, a Deep Learning-based software to automatically track dynamic processes in kymographs.
Kymographs often exhibit low signal-to-noise-ratios (SNRs), and available tools that automate their analysis usually require manual supervision. Although in kymographs tracks of individual particles are qualitatively easily distinguished, their automated quantitative analysis is much more challenging. 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.