Automated data collection and real‐time data analysis suite for serial synchrotron crystallography. (14th December 2018)

• Record Type: Journal Article
• Authors: Basu, Shibom; Kaminski, Jakub W.; Panepucci, Ezequiel; Huang, Chia-Ying; Warshamanage, Rangana; Wang, Meitian; Wojdyla, Justyna Aleksandra
• Is Part Of: Journal of synchrotron radiation. Volume 26:Part 1(2019)
• Page Start: 244
• Page End: 252
• Publication Date: 2018-12-14
• Subjects: serial synchrotron crystallography -- protein crystallography -- online data analysis -- data merging -- data acquisition; Synchrotron radiation -- Periodicals; Free electron lasers -- Periodicals; 539.73505
• DOI: 10.1107/S1600577518016570
• Languages: English
• ISSNs: 0909-0495

Abstract

Abstract : Implementation of the complete synchrotron serial crystallography (SSX) suite for real‐time data collection and processing available at the Swiss Light Source macromolecular beamlines is reported. A detailed description of the latest software extensions, i.e. the SSX user interface, as well as online data processing and merging routines, is also given. Abstract : At the Swiss Light Source macromolecular crystallography (MX) beamlines the collection of serial synchrotron crystallography (SSX) diffraction data is facilitated by the recent DA+ data acquisition and analysis software developments. The SSX suite allows easy, efficient and high‐throughput measurements on a large number of crystals. The fast continuous diffraction‐based two‐dimensional grid scan method allows initial location of microcrystals. The CY+ GUI utility enables efficient assessment of a grid scan's analysis output and subsequent collection of multiple wedges of data (so‐called minisets) from automatically selected positions in a serial and automated way. The automated data processing ( adp ) routines adapted to the SSX data collection mode provide near real time analysis for data in both CBF and HDF5 formats. The automatic data merging ( adm ) is the latest extension of the DA+ data analysis software routines. It utilizes the sxdm (SSX data merging) package, which provides automatic online scaling and merging of minisets and allows identification of a minisets subset resulting in the best quality of the final merged data. The results of both adp and adm are sent to the MX MongoDB database and displayed in the web‐based tracker, which provides the user with on‐the‐fly feedback about the experiment.