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||<35%><<TableOfContents>>|| e2refine2d.py runs in much the same way as refine2d.py, though it has beein improved in a number of subtle ways in [[EMAN1]] |
e2refine2d.py runs in much the same way as EMAN1's refine2d.py, though it has been improved in a number of subtle ways |
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<<Anchor(args)>> === Command Line Arguments === ||<style="background-color: #E0E0FF;"> Parm ||<style="background-color: #E0E0FF;"> Description ||<style="background-color: #E0E0FF;"> Default || ||path||Path to store results||automatic|| ||input||File containing particle stack||(required)|| ||iter||Number of iterations to perform. 5-10 is typical|| ||ncls||Number of classes to generate. Only approximate, sometimes fewer will be produced. Typical min ~10 ptcl/class. Very slow if >~128|| ||naliref||Number of alignment references for each iteration. May be as few as 1-3 if you have very similar looking particles, 10-20 may be appropriate for more varied particles|| ||nbasisfp|| ==== General parameters ==== |
| Command line arguments | Check functionality | e2refine FAQ |
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.