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Alignator:一款基于 GPU 的软件包,用于对冷冻倾转系列进行强大的无基准标记配准。

Alignator: a GPU powered software package for robust fiducial-less alignment of cryo tilt-series.

机构信息

European Molecular Biology Laboratory, Meyerhofstr. 1, 69117 Heidelberg, Germany.

出版信息

J Struct Biol. 2010 Apr;170(1):117-26. doi: 10.1016/j.jsb.2010.01.014. Epub 2010 Feb 1.

Abstract

The robust alignment of tilt-series collected for cryo-electron tomography in the absence of fiducial markers, is a problem that, especially for tilt-series of vitreous sections, still represents a significant challenge. Here we present a complete software package that implements a cross-correlation-based procedure that tracks similar image features that are present in several micrographs and explores them implicitly as substitutes for fiducials like gold beads and quantum dots. The added value compared to previous approaches, is that the algorithm explores a huge number of random positions, which are tracked on several micrographs, while being able to identify trace failures, using a cross-validation procedure based on the 3D marker model of the tilt-series. Furthermore, this method allows the reliable identification of areas which behave as a rigid body during the tilt-series and hence addresses specific difficulties for the alignment of vitreous sections, by correcting practical caveats. The resulting alignments can attain sub-pixel precision at the local level and is able to yield a substantial number of usable tilt-series (around 60%). In principle, the algorithm has the potential to run in a fully automated fashion, and could be used to align any tilt-series directly from the microscope. Finally, we have significantly improved the user interface and implemented the source code on the graphics processing unit (GPU) to accelerate the computations.

摘要

在没有基准标记的情况下,对冷冻电子断层扫描采集的倾斜系列进行稳健的对齐,是一个问题,特别是对于玻璃体部分的倾斜系列,仍然是一个重大挑战。在这里,我们提出了一个完整的软件包,该软件包实现了一种基于互相关的程序,该程序可以跟踪在多个显微照片中存在的类似图像特征,并将其作为金珠和量子点等基准的替代物进行隐式探索。与以前的方法相比,该算法的附加价值在于,该算法可以探索大量随机位置,这些位置在多个显微照片上进行跟踪,同时能够使用基于倾斜系列的 3D 标记模型的交叉验证过程来识别跟踪失败。此外,这种方法允许可靠地识别在倾斜系列过程中表现为刚体的区域,从而通过纠正实际注意事项来解决玻璃体部分对齐的具体困难。生成的对齐可以在局部达到亚像素精度,并能够产生大量可用的倾斜系列(约 60%)。原则上,该算法有可能以全自动的方式运行,并可用于直接从显微镜对齐任何倾斜系列。最后,我们显著改进了用户界面,并在图形处理单元 (GPU) 上实现了源代码,以加速计算。

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