数据缩减
计算机科学
探测器
光学
算法
物理
衍射
可视化
贝叶斯概率
数据处理
数据挖掘
人工智能
电信
操作系统
作者
Zbyszek Otwinowski,W. Minor
出处
期刊:Methods in Enzymology
日期:1997-01-01
卷期号:: 307-326
被引量:31493
标识
DOI:10.1016/s0076-6879(97)76066-x
摘要
X-ray data can be collected with zero-, one-, and two-dimensional detectors, zero-dimensional (single counter) being the simplest and two-dimensional the most efficient in terms of measuring diffracted X-rays in all directions. To analyze the single-crystal diffraction data collected with these detectors, several computer programs have been developed. Two-dimensional detectors and related software are now predominantly used to measure and integrate diffraction from single crystals of biological macromolecules. Macromolecular crystallography is an iterative process. To monitor the progress, the HKL package provides two tools: (1) statistics, both weighted (χ2) and unweighted (R-merge), where the Bayesian reasoning and multicomponent error model helps obtain proper error estimates and (2) visualization of the process, which helps an operator to confirm that the process of data reduction, including the resulting statistics, is correct and allows the evaluation of the problems for which there are no good statistical criteria. Visualization also provides confidence that the point of diminishing returns in data collection and reduction has been reached. At that point, the effort should be directed to solving the structure. The methods presented in the chapter have been applied to solve a large variety of problems, from inorganic molecules with 5 Å unit cell to rotavirus of 700 Å diameters crystallized in 700 × 1000 × 1400 Å cell.
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