KymoButler, a deep learning software for automated kymograph analysis
KymoButler, a deep learning software for automated kymograph analysis
复制标题
DOI：
10.7554/elife.42288
复制
发表时间：
2019-08-13
期刊：
影响因子：
7.7
通讯作者：
Franze, Kristian
中科院分区：
文献类型：
--
作者：
Jakobs, Maximilian A. H.;Dimitracopoulos, Andrea;Franze, Kristian
记波图是空间位置随时间变化的图形表示，在生物学中，记波图通常用于可视化荧光粒子、分子、囊泡或细胞器沿着可预测路径的运动。虽然记波图中单个粒子的轨迹很容易定性区分，但它们的自动定量分析更具挑战性。记波器通常表现出低信噪比（SNR），并且自动化其分析的可用工具通常需要人工监督。在这里，我们开发了KymoButler，这是一款基于深度学习的软件，可以自动跟踪记波图中的动态过程。我们证明，KymoButler执行以及专家手动数据分析与复杂的粒子轨迹从各种不同的生物系统的记波图。该软件被打包成一个基于网络的“一键”应用程序，供更广泛的科学界使用（kymobutler.deepmirror.ai）。我们的方法大大加快了数据分析的速度，避免了无意识的偏见，并代表了机器学习技术在生物数据分析中广泛应用的又一步。
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.