Ot2Rec:半自动、可扩展、多软件断层重建工作流程。

IF 0.1 1区 艺术学 0 MUSIC REVUE DE MUSICOLOGIE Pub Date : 2023-03-29 eCollection Date: 2023-01-01 DOI:10.1017/S2633903X23000107
Neville B-Y Yee, Elaine M L Ho, Win Tun, Jake L R Smith, Maud Dumoux, Michael Grange, Michele C Darrow, Mark Basham
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引用次数: 0

摘要

电子低温层析成像技术是一种探测纳米级三维结构的成像技术。这种技术已广泛应用于生物医学领域,用于研究蛋白质和其他大分子的复杂结构。随着技术的进步,目前显微镜每天能够生成高达 TB 数据的图像,这给科学家们带来了巨大的挑战,因为图像处理的速度跟不上显微镜越来越高的吞吐量。因此,从单张显微照片到完整的断层图像,自动化是图像处理发展的必经之路。在本文中,我们将介绍 Ot2Rec,这是一款开源流水线工具,旨在帮助科学家以灵活、自动的方式建立自己的处理工作流程。Ot2Rec 的基本构件是插件,这些插件遵循统一的应用编程接口结构,使科学家可以通过添加尚未提供的功能来为 Ot2Rec 做出贡献。在本文中,我们还介绍了三个使用 Ot2Rec 进行图像处理的案例研究,通过这些案例研究,我们展示了使用半自动工作流程比手动工作流程更快的速度、编写和使用自定义(原型)插件的可能性,以及 Ot2Rec 的灵活性,它可以实现插件的混合和匹配。我们还在补充材料中演示了 Ot2Rec 的内置报告功能,该功能可汇总所有运行过程的元数据,并以 Jupyter Notebook 和/或 HTML 格式输出,以便快速查看图像处理质量。Ot2Rec 的网址为 https://github.com/rosalindfranklininstitute/ot2rec。
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Ot2Rec: A semi-automatic, extensible, multi-software tomographic reconstruction workflow.

Electron cryo-tomography is an imaging technique for probing 3D structures with at the nanometer scale. This technique has been used extensively in the biomedical field to study the complex structures of proteins and other macromolecules. With the advancement in technology, microscopes are currently capable of producing images amounting to terabytes of data per day, posing great challenges for scientists as the speed of processing of the images cannot keep up with the ever-higher throughput of the microscopes. Therefore, automation is an essential and natural pathway on which image processing-from individual micrographs to full tomograms-is developing. In this paper, we present Ot2Rec, an open-source pipelining tool which aims to enable scientists to build their own processing workflows in a flexible and automatic manner. The basic building blocks of Ot2Rec are plugins which follow a unified application programming interface structure, making it simple for scientists to contribute to Ot2Rec by adding features which are not already available. In this paper, we also present three case studies of image processing using Ot2Rec, through which we demonstrate the speedup of using a semi-automatic workflow over a manual one, the possibility of writing and using custom (prototype) plugins, and the flexibility of Ot2Rec which enables the mix-and-match of plugins. We also demonstrate, in the Supplementary Material, a built-in reporting feature in Ot2Rec which aggregates the metadata from all process being run, and output them in the Jupyter Notebook and/or HTML formats for quick review of image processing quality. Ot2Rec can be found at https://github.com/rosalindfranklininstitute/ot2rec.

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