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Data processing and analysis with the autoPROC toolbox
A typical diffraction experiment will generate many images and data sets from different crystals in a very short time. This creates a challenge for the high-throughput operation of modern synchrotron beamlines as well as for the subsequent data processing. Novice users in particular may feel overwhe...
Autores principales: | , , , , , , , |
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Formato: | Texto |
Lenguaje: | English |
Publicado: |
International Union of Crystallography
2011
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3069744/ https://www.ncbi.nlm.nih.gov/pubmed/21460447 http://dx.doi.org/10.1107/S0907444911007773 |
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author | Vonrhein, Clemens Flensburg, Claus Keller, Peter Sharff, Andrew Smart, Oliver Paciorek, Wlodek Womack, Thomas Bricogne, Gérard |
author_facet | Vonrhein, Clemens Flensburg, Claus Keller, Peter Sharff, Andrew Smart, Oliver Paciorek, Wlodek Womack, Thomas Bricogne, Gérard |
author_sort | Vonrhein, Clemens |
collection | PubMed |
description | A typical diffraction experiment will generate many images and data sets from different crystals in a very short time. This creates a challenge for the high-throughput operation of modern synchrotron beamlines as well as for the subsequent data processing. Novice users in particular may feel overwhelmed by the tables, plots and numbers that the different data-processing programs and software packages present to them. Here, some of the more common problems that a user has to deal with when processing a set of images that will finally make up a processed data set are shown, concentrating on difficulties that may often show up during the first steps along the path of turning the experiment (i.e. data collection) into a model (i.e. interpreted electron density). Difficulties such as unexpected crystal forms, issues in crystal handling and suboptimal choices of data-collection strategies can often be dealt with, or at least diagnosed, by analysing specific data characteristics during processing. In the end, one wants to distinguish problems over which one has no immediate control once the experiment is finished from problems that can be remedied a posteriori. A new software package, autoPROC, is also presented that combines third-party processing programs with new tools and an automated workflow script that is intended to provide users with both guidance and insight into the offline processing of data affected by the difficulties mentioned above, with particular emphasis on the automated treatment of multi-sweep data sets collected on multi-axis goniostats. |
format | Text |
id | pubmed-3069744 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2011 |
publisher | International Union of Crystallography |
record_format | MEDLINE/PubMed |
spelling | pubmed-30697442011-04-07 Data processing and analysis with the autoPROC toolbox Vonrhein, Clemens Flensburg, Claus Keller, Peter Sharff, Andrew Smart, Oliver Paciorek, Wlodek Womack, Thomas Bricogne, Gérard Acta Crystallogr D Biol Crystallogr Research Papers A typical diffraction experiment will generate many images and data sets from different crystals in a very short time. This creates a challenge for the high-throughput operation of modern synchrotron beamlines as well as for the subsequent data processing. Novice users in particular may feel overwhelmed by the tables, plots and numbers that the different data-processing programs and software packages present to them. Here, some of the more common problems that a user has to deal with when processing a set of images that will finally make up a processed data set are shown, concentrating on difficulties that may often show up during the first steps along the path of turning the experiment (i.e. data collection) into a model (i.e. interpreted electron density). Difficulties such as unexpected crystal forms, issues in crystal handling and suboptimal choices of data-collection strategies can often be dealt with, or at least diagnosed, by analysing specific data characteristics during processing. In the end, one wants to distinguish problems over which one has no immediate control once the experiment is finished from problems that can be remedied a posteriori. A new software package, autoPROC, is also presented that combines third-party processing programs with new tools and an automated workflow script that is intended to provide users with both guidance and insight into the offline processing of data affected by the difficulties mentioned above, with particular emphasis on the automated treatment of multi-sweep data sets collected on multi-axis goniostats. International Union of Crystallography 2011-04-01 2011-03-18 /pmc/articles/PMC3069744/ /pubmed/21460447 http://dx.doi.org/10.1107/S0907444911007773 Text en © Vonrhein et al. 2011 http://creativecommons.org/licenses/by/2.0/uk/ This is an open-access article distributed under the terms of the Creative Commons Attribution Licence, which permits unrestricted use, distribution, and reproduction in any medium, provided the original authors and source are cited. |
spellingShingle | Research Papers Vonrhein, Clemens Flensburg, Claus Keller, Peter Sharff, Andrew Smart, Oliver Paciorek, Wlodek Womack, Thomas Bricogne, Gérard Data processing and analysis with the autoPROC toolbox |
title | Data processing and analysis with the autoPROC toolbox |
title_full | Data processing and analysis with the autoPROC toolbox |
title_fullStr | Data processing and analysis with the autoPROC toolbox |
title_full_unstemmed | Data processing and analysis with the autoPROC toolbox |
title_short | Data processing and analysis with the autoPROC toolbox |
title_sort | data processing and analysis with the autoproc toolbox |
topic | Research Papers |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3069744/ https://www.ncbi.nlm.nih.gov/pubmed/21460447 http://dx.doi.org/10.1107/S0907444911007773 |
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