Live Analysis and Reconstruction of Single-Particle Cryo-Electron Microscopy Data with CryoFLARE
- 1 April 2020
- journal article
- research article
- Published by American Chemical Society (ACS) in Journal of Chemical Information and Modeling
- Vol. 60 (5), 2561-2569
- https://doi.org/10.1021/acs.jcim.9b01102
Abstract
Efficient, reproducible and accountable single-particle cryo-electron microscopy structure determination is tedious and often impeded by the lack of a standardized procedure for data analysis and processing. To address this issue, we have developed the FMI Live Analysis and Reconstruction Engine (CryoFLARE). CryoFLARE is a modular open-source platform offering easy integration of new processing algorithms developed by the cryo-EM community. It provides a user-friendly interface that allows fast setup of standardized workflows, serving the need of pharmaceutical industry and academia alike who need to optimize throughput of their microscope. To consistently document how data is processed, CryoFLARE contains an integrated reporting facility to create reports. Live analysis and processing parallel to data acquisition are used to monitor and optimize data quality. Problems at the level of the sample preparation (heterogeneity, ice thickness, sparse particles, areas selected for acquisition, etc.) or misalignments of the microscope optics can quickly be detected and rectified before data collection is continued. Interfacing with automated data collection software for retrieval of acquisition metadata reduces user input needed for analysis, and with it minimizes potential sources of errors and workflow inconsistencies. Local and remote export support in Relion-compatible job and data format allows seamless integration into the refinement process. The support for non-linear workflows and fine-grained scheduling for mixed workflows with separate CPU and GPU based calculation steps ensures optimal use of processing hardware. CryoFLARE’s flexibility allows it to be used for all types of image acquisitions, ranging from sample screening to high-resolution data collection, and offers a new alternative for setting up image processing workflows. It can be used without modifications of the hardware/software delivered by the microscope supplier. As it is running on a server in parallel to the hardware used for acquisition, it can easily be set up for remote display connections and fast control of the acquisition status.Funding Information
- Schweizerischer Nationalfonds zur F?rderung der Wissenschaftlichen Forschung (179541)
- H2020 European Research Council (666068)
This publication has 21 references indexed in Scilit:
- Real-time cryo-electron microscopy data preprocessing with WarpNature Methods, 2019
- Structure Determination by Single-Particle Cryo-Electron Microscopy: Only the Sky (and Intrinsic Disorder) is the LimitInternational Journal of Molecular Sciences, 2019
- Using Scipion for stream image processing at Cryo-EM facilitiesJournal of Structural Biology, 2018
- Focus: The interface between data collection and data processing in cryo-EMJournal of Structural Biology, 2017
- Advances in the field of single-particle cryo-electron microscopy over the last decadeNature Protocols, 2017
- FEI’s direct electron detector developments: Embarking on a revolution in cryo-TEMJournal of Structural Biology, 2015
- Influence of electron dose rate on electron counting images recorded with the K2 cameraJournal of Structural Biology, 2013
- Initial evaluation of a direct detection device detector for single particle cryo-electron microscopyJournal of Structural Biology, 2011
- Automated electron microscope tomography using robust prediction of specimen movementsJournal of Structural Biology, 2005
- Leginon: An Automated System for Acquisition of Images from Vitreous Ice SpecimensJournal of Structural Biology, 2000