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RELION (for '''RE'''gularised '''LI'''kelihood '''O'''ptimisatio'''N''') is a stand-alone computer program for ''Maximum A Posteriori'' (MAP) refinement of (multiple) 3D reconstructions or 2D class averages in cryo-electron microscopy. It is developed in the research group of [http://www2.mrc-lmb.cam.ac.uk/groups/scheres/ Sjors Scheres] at the [http://www2.mrc-lmb.cam.ac.uk MRC Laboratory of Molecular Biology]. Briefly, the ill-posed problem of 3D-reconstruction is regularised by incorporating prior knowledge: the fact that macromolecular structures are smooth, i.e. they have limited power in the Fourier domain. | RELION (for '''RE'''gularised '''LI'''kelihood '''O'''ptimisatio'''N''') is a stand-alone computer program for ''Maximum A Posteriori'' (MAP) refinement of (multiple) 3D reconstructions or 2D class averages in cryo-electron microscopy. It is developed in the research group of [http://www2.mrc-lmb.cam.ac.uk/groups/scheres/ Sjors Scheres] at the [http://www2.mrc-lmb.cam.ac.uk MRC Laboratory of Molecular Biology]. Briefly, the ill-posed problem of 3D-reconstruction is regularised by incorporating prior knowledge: the fact that macromolecular structures are smooth, i.e. they have limited power in the Fourier domain. In the Bayesian framework, many parameters of the statistical model are iteratively learned from the data themselves, which leads to objective and high-quality results without the need for user expertise. | ||
The underlying theory of MAP refinement is given in [http://dx.doi.org/10.1016/j.jmb.2011.11.010 Scheres (2011) JMB]. If RELION is useful in your work, please cite this paper. | The underlying theory of MAP refinement is given in [http://dx.doi.org/10.1016/j.jmb.2011.11.010 Scheres (2011) JMB]. If RELION is useful in your work, please cite this paper. | ||
Revision as of 12:10, 19 July 2012
RELION (for REgularised LIkelihood OptimisatioN) is a stand-alone computer program for Maximum A Posteriori (MAP) refinement of (multiple) 3D reconstructions or 2D class averages in cryo-electron microscopy. It is developed in the research group of Sjors Scheres at the MRC Laboratory of Molecular Biology. Briefly, the ill-posed problem of 3D-reconstruction is regularised by incorporating prior knowledge: the fact that macromolecular structures are smooth, i.e. they have limited power in the Fourier domain. In the Bayesian framework, many parameters of the statistical model are iteratively learned from the data themselves, which leads to objective and high-quality results without the need for user expertise.
The underlying theory of MAP refinement is given in Scheres (2011) JMB. If RELION is useful in your work, please cite this paper.
Note that this page is currently under reconstruction and may already reflect some changes related to the soon-to-be-released version 1.1. Contact Sjors if you'd like to beta-test this release.
Getting started
- Download & install
- Conventions & File formats
- Running RELION
- A fully-guided 3D-Classification example
- Useful tricks and FAQs
How To
- Preprocess images
- Prepare input files
- Calculate 2D class averages
- Classify 3D structural heterogeneity
- Refine a structure to high-resolution
- Analyse results
- Minimise computational costs