Filling in the GUI

For 3D refinements, select the run-type of 3D reconstruction from the drop-down menu at the top of the GUI.

I/O tab

The pixel size (in Angstrom) should be the same as the one used to estimate the CTF parameters.

If the reference was not reconstructed from the input images in either XMIPP or RELION, you may assume it is not on the absolute greyscale.

Note there are various settings for icosahedral symmetry, also see the Conventions. Make sure your input map is in the one you provide here.

If no CTF correction is to be performed, make sure you phase-flipped your data during preprocessing. See the Prepare input files page.

Normalisation correction is robust and therefore recommended in the general case.

Intensity correction corrects for distinct grey-scale intensities among the signal in the data, e.g. because due to distinct SNRs among the micrographs. This option is only effective if the data is provided in a STAR file that contains multiple unique strings for the rlnMicrographName label (see the Prepare input files page.

Successful classification often requires starting from a very strongly low-pass filtered map. If your input map is not low-pass filtered, it may be filtered internally using the Initial low-pass filter option. Typically, one filters as much as possible, i.e. before the reference becomes a feature-less blob that can no longer be refined.

Often 25-50 iterations are necessary before the refinement converges to a stable solution. Note there is currently no convergence criterion implemented, so the user is responsible for monitoring the convergence. Jobs may be killed if they converge before their maximum number of iterations has been reached, or if the opposite happens a previous run may be continued.

The number of classes is the most important parameter. Often one performs multiple calculations with different values.

The regularisation parameter determines the relative weight between the experimental data and the prior. Bayes' law dictates it should be 1, but better results are often obtained using slightly higher values (e.g. 2-4), especially when dealing with cryo-data.

The particle diameter (in Angstroms) serves to define a soft spherical mask that will be applied to the experimental images to reduce their background noise. If solvent flattening is set to Yes, then also the references will be masked using the same spherical mask (or using a user-provided one under the solvent mask option).

CPU requirement will increase rapidly with increased angular samplings (but in contrast to ML3D/MLF3D implementations memory requirements will not!). Therefore, 3D classification is often performed at relatively coarse angular sampling, e.g. 7.5 degrees. Ultimately this will however depend on the nature of the heterogeneity one wants to classify.

In some cases tilt angle searches may be restricted (e.g. because one knows there are only side views in the data set).

For 3D classification with relatively coarse angular one typically does not perform local angular searches.

Translational search ranges may depend on how well-centered the particles were picked, but often 10 pixels will do the job (translational searches in subsequent iterations are centered at the optimal translation in the previous one, so that particles may "move" much more than the original search range during the course of an entire refinement. Note that pre-centering prior to RELION refinement is not necessary, and also not recommended (it often messes up the Gaussian distribution of origin offsets).

If available memory becomes a limitation (memory is mainly needed to store the oversampled Fourier transforms of the references) then running multiple threads on multi-core machines are a good option. Otherwise, MPI parallelisation is probably more efficient.