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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 [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 | 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 [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. Bayes' law uniquely determines how prior knowledge and experimental data are to be combined, so that the optimal linear filter is derived from the data, without the need for arbitrary decisions or user expertise. The resulting algorithm is '''highly effective in limiting overfitting'''. | ||
The underlying theory of MAP refinement is given in [[Scheres (2011)]]. If RELION is useful in your work, please cite this paper. | The underlying theory of MAP refinement is given in [[Scheres (2011)]]. If RELION is useful in your work, please cite this paper. | ||
Revision as of 10:50, 29 September 2011
RELION
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. Bayes' law uniquely determines how prior knowledge and experimental data are to be combined, so that the optimal linear filter is derived from the data, without the need for arbitrary decisions or user expertise. The resulting algorithm is highly effective in limiting overfitting.
The underlying theory of MAP refinement is given in Scheres (2011). If RELION is useful in your work, please cite this paper.