Marigold:一个基于机器学习的网络应用程序,用于跟踪斑马鱼的姿势。

IF 2.9 3区 生物学 Q2 BIOCHEMICAL RESEARCH METHODS
Gregory Teicher, R Madison Riffe, Wayne Barnaby, Gabrielle Martin, Benjamin E Clayton, Josef G Trapani, Gerald B Downes
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引用次数: 0

摘要

背景:高通量行为分析对于药物发现、毒理学研究和神经系统疾病(如自闭症和癫痫)的建模非常重要。斑马鱼的胚胎和幼体是这种应用的理想选择,因为它们大量产卵,发育迅速,具有相对简单的神经系统,并且与许多人类疾病基因同源。然而,现有的基于视频的行为分析软件可能与包含动态背景或外来物体的录音不兼容,缺乏对多井格式的支持,需要昂贵的硬件,并且/或者需要相当的编程专业知识。在这里,我们介绍Marigold,一个免费的开源web应用程序,用于高通量的胚胎和幼体斑马鱼的行为分析。结果:Marigold具有直观的图形用户界面,可跟踪多达10个用户定义的关键点,支持单井和多井格式,除了出版物质量的数据可视化外,还可以导出一系列运动学参数。通过利用高效、定制设计的神经网络架构,即使在缺乏独立图形处理单元的中等功率计算机上,Marigold也能实现合理的训练和推理速度。值得注意的是,作为一个web应用程序,Marigold不需要任何安装,并且可以在ChromeOS, Linux, macOS和Windows上的流行web浏览器中运行。为了证明玛丽戈尔德的效用,我们使用了两组生物实验。首先,我们研究了tnt突变胚胎中触碰诱发逃避反应的新方面,该胚胎包含先前描述的编码神经胶质谷氨酸转运体Eaat2b基因的功能缺失突变。我们确定了触摸位置(头与尾)和基因型之间的差异和相互作用。其次,我们研究了摄食对受精后5天和7天幼虫视觉运动行为的影响。我们发现在这两个时间点,喂食和未喂食的鱼在游泳次数和活力上存在差异,以及发育阶段和喂食方式之间的相互作用。结论:在这里提出的两个生物学实验中,万寿菊的使用促进了新的行为发现。Marigold的易用性、强大的姿态跟踪、对各种实验范例的适应性以及对硬件要求的灵活性使其成为分析斑马鱼行为的强大工具,特别是在资源匮乏的环境中,如基于课程的本科生研究经历。Marigold网站:https://downeslab.github.io/marigold/。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Marigold: a machine learning-based web app for zebrafish pose tracking.

Background: High-throughput behavioral analysis is important for drug discovery, toxicological studies, and the modeling of neurological disorders such as autism and epilepsy. Zebrafish embryos and larvae are ideal for such applications because they are spawned in large clutches, develop rapidly, feature a relatively simple nervous system, and have orthologs to many human disease genes. However, existing software for video-based behavioral analysis can be incompatible with recordings that contain dynamic backgrounds or foreign objects, lack support for multiwell formats, require expensive hardware, and/or demand considerable programming expertise. Here, we introduce Marigold, a free and open source web app for high-throughput behavioral analysis of embryonic and larval zebrafish.

Results: Marigold features an intuitive graphical user interface, tracks up to 10 user-defined keypoints, supports both single- and multiwell formats, and exports a range of kinematic parameters in addition to publication-quality data visualizations. By leveraging a highly efficient, custom-designed neural network architecture, Marigold achieves reasonable training and inference speeds even on modestly powered computers lacking a discrete graphics processing unit. Notably, as a web app, Marigold does not require any installation and runs within popular web browsers on ChromeOS, Linux, macOS, and Windows. To demonstrate Marigold's utility, we used two sets of biological experiments. First, we examined novel aspects of the touch-evoked escape response in techno trousers (tnt) mutant embryos, which contain a previously described loss-of-function mutation in the gene encoding Eaat2b, a glial glutamate transporter. We identified differences and interactions between touch location (head vs. tail) and genotype. Second, we investigated the effects of feeding on larval visuomotor behavior at 5 and 7 days post-fertilization (dpf). We found differences in the number and vigor of swimming bouts between fed and unfed fish at both time points, as well as interactions between developmental stage and feeding regimen.

Conclusions: In both biological experiments presented here, the use of Marigold facilitated novel behavioral findings. Marigold's ease of use, robust pose tracking, amenability to diverse experimental paradigms, and flexibility regarding hardware requirements make it a powerful tool for analyzing zebrafish behavior, especially in low-resource settings such as course-based undergraduate research experiences. Marigold is available at: https://downeslab.github.io/marigold/ .

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来源期刊
BMC Bioinformatics
BMC Bioinformatics 生物-生化研究方法
CiteScore
5.70
自引率
3.30%
发文量
506
审稿时长
4.3 months
期刊介绍: BMC Bioinformatics is an open access, peer-reviewed journal that considers articles on all aspects of the development, testing and novel application of computational and statistical methods for the modeling and analysis of all kinds of biological data, as well as other areas of computational biology. BMC Bioinformatics is part of the BMC series which publishes subject-specific journals focused on the needs of individual research communities across all areas of biology and medicine. We offer an efficient, fair and friendly peer review service, and are committed to publishing all sound science, provided that there is some advance in knowledge presented by the work.
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