Release notes: Difference between revisions
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== Release 2.0 (January 2017) == | == Release 2.0 (January 2017) == | ||
# GPUs can be used for the most demanding calculations (relion_refine and relion_autopick). One GPU card can equal the computational capacity of approximately 80-100 CPU cores, so this | # GPUs can be used for the most demanding calculations (relion_refine and relion_autopick). One GPU card can equal the computational capacity of approximately 80-100 CPU cores, so this will probably transform the way you process images (or at least how much it costs). This work was done in the group of Erik Lindahl at the Scilife lab in Stockholm, Sweden. PhD students Bjorn Forsberg and Dari Kimanius did most of the hard work. A paper describing these benefits has been published in [http://dx.doi.org/10.7554/eLife.18722 eLife], which also details the specific methods employed to use the GPU hardware architecture for highly parallel tasks. | ||
will probably transform the way you process images (or at least how much it costs). This work was done in the group of Erik Lindahl at the Scilife lab in Stockholm, Sweden. PhD students Bjorn Forsberg and Dari Kimanius did most of the hard work. A paper describing these benefits has been published in [http://dx.doi.org/10.7554/eLife.18722 eLife], which also details the specific methods employed to use the GPU hardware architecture for highly parallel tasks. | # Functionality to process helical assemblies. This work was done by Shaoda He, a PhD student in the group of Sjors Scheres at LMB. The workflow of helical processing closely follows that of single-particles, so existing relion users should find this familiar. New parameters and tricks are explained on the [[Helical processing]] page. A paper about this work has been submitted to J.Struct. Biol., and is meanwhile available on [https://doi.org/10.1101/095034 bioRxiv]. | ||
# Functionality to process helical assemblies. This work was done by Shaoda He, a PhD student in the group of Sjors Scheres at LMB. The workflow of helical processing closely follows that of | |||
single-particles, so existing relion users should find this familiar. New parameters and tricks are explained on the [[Helical processing]] page. A paper about this work has been submitted to J.Struct. Biol., and is meanwhile available on [https://doi.org/10.1101/095034 bioRxiv]. | |||
# A new GUI which acts as a pipeline for image processing has been made. This will automatically log everything you do, and allow on-the-fly image processing during data acquisition. Details on how to use this are in the new tutorial, and a dedicated manuscript was accepted in the 2016 CCPEM Spring symposium special issue of [https://doi.org/10.1107/S2059798316019276 Acta Cryst D]. | # A new GUI which acts as a pipeline for image processing has been made. This will automatically log everything you do, and allow on-the-fly image processing during data acquisition. Details on how to use this are in the new tutorial, and a dedicated manuscript was accepted in the 2016 CCPEM Spring symposium special issue of [https://doi.org/10.1107/S2059798316019276 Acta Cryst D]. | ||
Revision as of 10:13, 30 January 2017
Below are the main new features for each release.
Release 2.0 (January 2017)
- GPUs can be used for the most demanding calculations (relion_refine and relion_autopick). One GPU card can equal the computational capacity of approximately 80-100 CPU cores, so this will probably transform the way you process images (or at least how much it costs). This work was done in the group of Erik Lindahl at the Scilife lab in Stockholm, Sweden. PhD students Bjorn Forsberg and Dari Kimanius did most of the hard work. A paper describing these benefits has been published in eLife, which also details the specific methods employed to use the GPU hardware architecture for highly parallel tasks.
- Functionality to process helical assemblies. This work was done by Shaoda He, a PhD student in the group of Sjors Scheres at LMB. The workflow of helical processing closely follows that of single-particles, so existing relion users should find this familiar. New parameters and tricks are explained on the Helical processing page. A paper about this work has been submitted to J.Struct. Biol., and is meanwhile available on bioRxiv.
- A new GUI which acts as a pipeline for image processing has been made. This will automatically log everything you do, and allow on-the-fly image processing during data acquisition. Details on how to use this are in the new tutorial, and a dedicated manuscript was accepted in the 2016 CCPEM Spring symposium special issue of Acta Cryst D.
Release 1.4 (September 2015)
- Full classification and auto-refine capabilities for sub-tomogram averaging (Bharat et al, Structure 2015)
- Focused classification with signal subtraction (see preprint on bioRxiv)
- 10x faster movie processing, and more efficient threads implementation
- Possibility to compile in single-precision (saves 50% RAM)
Release 1.3 (June 2014)
- a movie-processing procedure for small (sub-Megadalton) particles (Scheres, eLife 2014)
- a template-matching based (semi-)automated particle picking procedure (Scheres, J. Struct. Biol. 2015)
- a new GUI with image-displaying functionalities. Just click your way through the entire image processing pipeline.
Release 1.2 (June 2013)
- a statistical movie-processing procedure (Bai et al, eLife 2013)
- a semi-automated post-processing procedure (for improved resolution estimates after masking, MTF correction and B-factor sharpening; Chen et al, Ultramicroscopy 2013 )
Release 1.1 (September 2012)
- convenient preprocessing procedures
- a fully automated refinement procedure (3D auto-refine) for homogeneous data sets, employing "gold-standard" FSC calculations (Scheres&Chen, Nat. Methods 2012, Scheres, J. Struct. Biol. 2012)
Release 1.0 (November 2011)
- 2D and 3D classification procedures. The latter can also be used for high-resolution refinement (Scheres, J. Mol. Biol. 2012)