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e2refine2d

e2refine2d.py runs in much the same way as EMAN1's refine2d.py, though it has been improved in a number of subtle ways

This program will take a set of boxed out particle images and perform iterative reference-free classification to produce a set of representative class-averages. The point of this process is to reduce noise levels, so the overall shape of the particle views present in the data can be better observed. Generally cryo-EM single particles are noisy enough that it is difficult to distinguish subtle, or even not-so-subtle differences between particle images. By aligning and averaging similar particles together, less noisy versions of representative views are created. The class-averages produced by this program are typically used for:

• Direct observation to look for heterogeneity or discover symmetry
• Building initial models for single particle reconstruction
• Separating particles into subgroups for additional analysis

This last point can be used to produce 'population-dynamics' movies of a particle in very close to the same orientation.

This program is quite fast for as many as a few thousand particles and ~100 classes. For most purposes if your data set is large (>10,000) particles you might consider using only a subset of the data for speed, though this clearly isn't appropriate for the 3rd use above.