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The field of time-resolved macromolecular crystallography has been expanding rapidly after free electron lasers for hard x rays (XFELs) became available. Techniques to collect and process data from XFELs spread to synchrotron light sources. Although time-scales and data collection modalities can differ substantially between these types of light sources, the analysis of the resulting x-ray data proceeds essentially along the same pathway. At the base of a successful time-resolved experiment is a difference electron density (DED) map that contains chemically meaningful signal. If such a difference map cannot be obtained, the experiment has failed. Here, a practical approach is presented to calculate DED maps and use them to determine structural models.
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The Pixel Anomaly Detection Tool : a user-friendly GUI for classifying detector frames using machine-learning approaches
Data collection at X-ray free electron lasers has particular experimental challenges, such as continuous sample delivery or the use of novel ultrafast high-dynamic-range gain-switching X-ray detectors. This can result in a multitude of data artefacts, which can be detrimental to accurately determining structure-factor amplitudes for serial crystallography or single-particle imaging experiments. Here, a new data-classification tool is reported that offers a variety of machine-learning algorithms to sort data trained either on manual data sorting by the user or by profile fitting the intensity distribution on the detector based on the experiment. This is integrated into an easy-to-use graphical user interface, specifically designed to support the detectors, file formats and software available at most X-ray free electron laser facilities. The highly modular design makes the tool easily expandable to comply with other X-ray sources and detectors, and the supervised learning approach enables even the novice user to sort data containing unwanted artefacts or perform routine data-analysis tasks such as hit finding during an experiment, without needing to write code.
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- Award ID(s):
- 1231306
- PAR ID:
- 10587052
- Publisher / Repository:
- Journal of Applied Crystallography
- Date Published:
- Journal Name:
- Journal of Applied Crystallography
- Volume:
- 57
- Issue:
- 2
- ISSN:
- 1600-5767
- Page Range / eLocation ID:
- 529 to 538
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1107/s1600576724000116
