Cargando…
Best practices for high data-rate macromolecular crystallography (HDRMX)
In macromolecular crystallography, higher flux, smaller beams, and faster detectors open the door to experiments with very large numbers of very small samples that can reveal polymorphs and dynamics but require re-engineering of approaches to the clustering of images both at synchrotrons and XFELs (...
Autores principales: | , , , , , , , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
American Crystallographic Association
2020
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6952294/ https://www.ncbi.nlm.nih.gov/pubmed/31934601 http://dx.doi.org/10.1063/1.5128498 |
_version_ | 1783486418216026112 |
---|---|
author | Bernstein, Herbert J. Andrews, Lawrence C. Diaz, Jorge A. Jakoncic, Jean Nguyen, Thu Sauter, Nicholas K. Soares, Alexei S. Wei, Justin Y. Wlodek, Maciej R. Xerri, Mario A. |
author_facet | Bernstein, Herbert J. Andrews, Lawrence C. Diaz, Jorge A. Jakoncic, Jean Nguyen, Thu Sauter, Nicholas K. Soares, Alexei S. Wei, Justin Y. Wlodek, Maciej R. Xerri, Mario A. |
author_sort | Bernstein, Herbert J. |
collection | PubMed |
description | In macromolecular crystallography, higher flux, smaller beams, and faster detectors open the door to experiments with very large numbers of very small samples that can reveal polymorphs and dynamics but require re-engineering of approaches to the clustering of images both at synchrotrons and XFELs (X-ray free electron lasers). The need for the management of orders of magnitude more images and limitations of file systems favor a transition from simple one-file-per-image systems such as CBF to image container systems such as HDF5. This further increases the load on computers and networks and requires a re-examination of the presentation of metadata. In this paper, we discuss three important components of this problem—improved approaches to the clustering of images to better support experiments on polymorphs and dynamics, recent and upcoming changes in metadata for Eiger images, and software to rapidly validate images in the revised Eiger format. |
format | Online Article Text |
id | pubmed-6952294 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | American Crystallographic Association |
record_format | MEDLINE/PubMed |
spelling | pubmed-69522942020-01-13 Best practices for high data-rate macromolecular crystallography (HDRMX) Bernstein, Herbert J. Andrews, Lawrence C. Diaz, Jorge A. Jakoncic, Jean Nguyen, Thu Sauter, Nicholas K. Soares, Alexei S. Wei, Justin Y. Wlodek, Maciej R. Xerri, Mario A. Struct Dyn ARTICLES In macromolecular crystallography, higher flux, smaller beams, and faster detectors open the door to experiments with very large numbers of very small samples that can reveal polymorphs and dynamics but require re-engineering of approaches to the clustering of images both at synchrotrons and XFELs (X-ray free electron lasers). The need for the management of orders of magnitude more images and limitations of file systems favor a transition from simple one-file-per-image systems such as CBF to image container systems such as HDF5. This further increases the load on computers and networks and requires a re-examination of the presentation of metadata. In this paper, we discuss three important components of this problem—improved approaches to the clustering of images to better support experiments on polymorphs and dynamics, recent and upcoming changes in metadata for Eiger images, and software to rapidly validate images in the revised Eiger format. American Crystallographic Association 2020-01-09 /pmc/articles/PMC6952294/ /pubmed/31934601 http://dx.doi.org/10.1063/1.5128498 Text en © 2020 Author(s). 2329-7778/2020/7(1)/014302/8 All article content, except where otherwise noted, is licensed under a Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/). |
spellingShingle | ARTICLES Bernstein, Herbert J. Andrews, Lawrence C. Diaz, Jorge A. Jakoncic, Jean Nguyen, Thu Sauter, Nicholas K. Soares, Alexei S. Wei, Justin Y. Wlodek, Maciej R. Xerri, Mario A. Best practices for high data-rate macromolecular crystallography (HDRMX) |
title | Best practices for high data-rate macromolecular crystallography (HDRMX) |
title_full | Best practices for high data-rate macromolecular crystallography (HDRMX) |
title_fullStr | Best practices for high data-rate macromolecular crystallography (HDRMX) |
title_full_unstemmed | Best practices for high data-rate macromolecular crystallography (HDRMX) |
title_short | Best practices for high data-rate macromolecular crystallography (HDRMX) |
title_sort | best practices for high data-rate macromolecular crystallography (hdrmx) |
topic | ARTICLES |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6952294/ https://www.ncbi.nlm.nih.gov/pubmed/31934601 http://dx.doi.org/10.1063/1.5128498 |
work_keys_str_mv | AT bernsteinherbertj bestpracticesforhighdataratemacromolecularcrystallographyhdrmx AT andrewslawrencec bestpracticesforhighdataratemacromolecularcrystallographyhdrmx AT diazjorgea bestpracticesforhighdataratemacromolecularcrystallographyhdrmx AT jakoncicjean bestpracticesforhighdataratemacromolecularcrystallographyhdrmx AT nguyenthu bestpracticesforhighdataratemacromolecularcrystallographyhdrmx AT sauternicholask bestpracticesforhighdataratemacromolecularcrystallographyhdrmx AT soaresalexeis bestpracticesforhighdataratemacromolecularcrystallographyhdrmx AT weijustiny bestpracticesforhighdataratemacromolecularcrystallographyhdrmx AT wlodekmaciejr bestpracticesforhighdataratemacromolecularcrystallographyhdrmx AT xerrimarioa bestpracticesforhighdataratemacromolecularcrystallographyhdrmx |