(Although we know that this sample has D2 symmetry, it is often a good idea to perform an initial classification without any symmetry, so bad particles, which are not symmetric, can get separated from proper ones, and the symmetry can be verified in the reconstructed maps.) (One should NOT use high-resolution starting models as they may introduce bias into the refinement process.Īs also explained in, one should filter the initial map as much as one can.įor ribosome we often use 70 Å, for smaller particles we typically use values of 40-60 Å.) Symmetry: (Given that this map was reconstructed from this data set, it is already on the correct greyscale.Īny map that is not reconstructed from the same data in relion should probably be considered as not being on the correct greyscale.) Initial low-pass filter (A): This introduces the least bias into the classification.) If left empty, a spherical mask with the particle diameter given on the Optimisation tab will be used. This is the place where we for example provided large/small-subunit masks for our focussed ribosome refinements. However, already being in the right symmetry setting is more convenient later on.) Reference mask (optional): You can either type it’s name into the entry field, or first import the map using the Import jobtype.Īlso note that, because we wil be running in symmetry C1, we could have also chosen to use the non-symmetric InitialModel/job015/run_it150_class001.mrc. (Note that this map does not appear in the Browse button as it is not part of the pipeline. InitialModel/symC1/run_it150_class001_symD2.mrc Select/class2d_template/particles.star Reference map: On the I/O tab set: Input images STAR file: Unsupervised 3D classifcation may be run from the 3D classification job-type. relion’s 3D multi-reference refinement procedure provides a powerful unsupervised 3D classification approach. All data sets are heterogeneous! The question is how much you are willing to tolerate.
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