稳健而自动的光束止影异常点剔除:在半监督学习策略下将晶体学统计与现代聚类相结合。

IF 2.6 4区 生物学 Q2 BIOCHEMICAL RESEARCH METHODS
Yunyun Gao, Helen M Ginn, Andrea Thorn
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引用次数: 0

摘要

在晶体学衍射实验的自动处理过程中,光束止点的阴影经常被忽略或仅被部分遮挡。因此,异常反射强度会被整合进来,这是一个已知的问题。传统的统计诊断方法在识别这些异常值(这里称为未排除-未掩蔽-异常值(NEMOs))方面效果有限。诊断工具 AUSPEX 可以对 NEMOs 进行目视检查,NEMOs 在这里形成一种典型模式:在 AUSPEX 强度或振幅与分辨率关系图的低分辨率端形成群集。为了自动检测 NEMO,我们开发了一种新算法,将数据统计与基于密度的聚类方法相结合。这种方法在检测合并数据集中的 NEMO 方面表现出良好的性能,而且不会破坏现有的数据还原管道。再提纯结果表明,排除已识别的 NEMO 可有效提高后续结构确定步骤的质量。该方法提供了一种评估光束阻挡掩膜有效性的前瞻性自动化手段,同时也突出了现代模式识别技术在数据处理过程中自动排除离群点的潜力,便于未来适应不断发展的实验策略。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Robust and automatic beamstop shadow outlier rejection: combining crystallographic statistics with modern clustering under a semi-supervised learning strategy.

During the automatic processing of crystallographic diffraction experiments, beamstop shadows are often unaccounted for or only partially masked. As a result of this, outlier reflection intensities are integrated, which is a known issue. Traditional statistical diagnostics have only limited effectiveness in identifying these outliers, here termed Not-Excluded-unMasked-Outliers (NEMOs). The diagnostic tool AUSPEX allows visual inspection of NEMOs, where they form a typical pattern: clusters at the low-resolution end of the AUSPEX plots of intensities or amplitudes versus resolution. To automate NEMO detection, a new algorithm was developed by combining data statistics with a density-based clustering method. This approach demonstrates a promising performance in detecting NEMOs in merged data sets without disrupting existing data-reduction pipelines. Re-refinement results indicate that excluding the identified NEMOs can effectively enhance the quality of subsequent structure-determination steps. This method offers a prospective automated means to assess the efficacy of a beamstop mask, as well as highlighting the potential of modern pattern-recognition techniques for automating outlier exclusion during data processing, facilitating future adaptation to evolving experimental strategies.

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来源期刊
Acta Crystallographica. Section D, Structural Biology
Acta Crystallographica. Section D, Structural Biology BIOCHEMICAL RESEARCH METHODSBIOCHEMISTRY &-BIOCHEMISTRY & MOLECULAR BIOLOGY
CiteScore
4.50
自引率
13.60%
发文量
216
期刊介绍: Acta Crystallographica Section D welcomes the submission of articles covering any aspect of structural biology, with a particular emphasis on the structures of biological macromolecules or the methods used to determine them. Reports on new structures of biological importance may address the smallest macromolecules to the largest complex molecular machines. These structures may have been determined using any structural biology technique including crystallography, NMR, cryoEM and/or other techniques. The key criterion is that such articles must present significant new insights into biological, chemical or medical sciences. The inclusion of complementary data that support the conclusions drawn from the structural studies (such as binding studies, mass spectrometry, enzyme assays, or analysis of mutants or other modified forms of biological macromolecule) is encouraged. Methods articles may include new approaches to any aspect of biological structure determination or structure analysis but will only be accepted where they focus on new methods that are demonstrated to be of general applicability and importance to structural biology. Articles describing particularly difficult problems in structural biology are also welcomed, if the analysis would provide useful insights to others facing similar problems.
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