- Step 25/29 - Train for Polishing
- Step 26/29 - Polishing
- Step 27/29 - Refine Polished Model
- Step 28/29 - Post Process of Polished Model
- Step 29/29 - Local-Resolution Estimation
This is the final stage in the simplified tutorial - polishing. Here we will polish our refined model, and it will be ready for the real use in particle detection.
| Tutorial Unit | 8.1 Bayesian polishing - Running in training mode |
|---|---|
| Previous Step | ~(2) MotionCorr/own |
| ~(23) CtfRefine/ctfrefine | |
| (24) CtfRefine/ctfrefine | |
| Step Alias | (25) Polish/train |
| Next Step | ~(25) Polish/polish |
We are going to polish our model with the Bayesian approach to beam-induced motion correction. On this step, we will train optimal params for polishing that will be performed on the next step.
| Field name | Field value |
|---|---|
| I/O | I/O |
| Micrographs (from MotionCorr) | MotionCorr/job002/corrected_micrographs.star |
| Particles (from Refine3D or CtfRefine) | CtfRefine/job024/particles_ctf_refine.star |
| Postprocess STAR file | PostProcess/job023/postprocess.star |
| First movie frame | 1 |
| Last movie frame | -1 |
| Train | Train |
| Train optimal parameters? | Yes |
| Fraction of Fourier pixels for testing | 0.5 |
| Use this many particles | 5000 |
| Polish | Polish |
| Perform particle polishing? | No |
The optimal parameters will be stored in Polish/train/opt_params.txt once the job is done.
References
| Tutorial Unit | 8.2 Bayesian polishing - Running in polishing mode |
|---|---|
| Previous Steps | ~(2) MotionCorr/own |
| ~(23) PostProcess/first3dref | |
| Step Alias | (24) CtfRefine/ctfrefine |
| Next Steps | ~(25) Polish/train |
| (26) Polish/polish | |
| (27) Refine3D/polished |
Now, we will perform Bayesian polishing with optimal parameters calculated on the previous step.
| Field name | Field value |
|---|---|
| I/O | I/O |
| Micrographs (from MotionCorr) | MotionCorr/job002/corrected_micrographs.star |
| Particles (from Refine3D or CtfRefine) | CtfRefine/job024/particles_ctf_refine.star |
| Postprocess STAR file | PostProcess/job023/postprocess.star |
| First movie frame | 1 |
| Last movie frame | -1 |
| Train | Train |
| Train optimal parameters? | No |
| Polish | Polish |
| Perform particle polishing? | Yes |
| Optimised parameter file | Polish/job025/opt_params.txt |
| OR use your own parameters? | No |
| Minimum resolution for B-factor fit (A) | 20 |
| Maximum resolution for B-factor fit (A) | -1 |
This job produces shiny.star file that contains polished particles. logfile.pdf file includes graphs of the scale and B-factors used for the radiation-damage weighting and visualization of the movement of refined particles for each micrograph.
You can realize that almost all particles on the micrographs moving and have similar traces. To make a correct understanding: consider a point, that represents a particle on the graph, as an initial position of a particle. In other words: all particles move from the top right to bottom left during the movie.
| Tutorial Unit | 8.3 Bayesian polishing - Analysing the results |
|---|---|
| Previous Steps | (21) Refine3D/first3dref |
| (22) MaskCreate/first3dref | |
| (26) Polish/polish | |
| Step Alias | (27) Refind3D/polished |
| Next Steps | (28) PostProcess/polished |
| (29) LocalRes/polished |
Applying 3D refine to the polished particles.
| Field name | Field value |
|---|---|
| I/O | I/O |
| Input images | Polish/job026/shiny.star |
| Reference map | Refine3D/job021/run_class001.mrc |
| Reference mask (optional) | MaskCreate/job022/mask.mrc |
| Reference | Reference |
| Ref. map is on absolute greyscale? | Yes |
| Initial low-pass filter (A) | 50 |
| Symmetry | D2 |
| CTF | CTF |
| Do CTF-correction? | Yes |
| Has reference been CTF-corrected? | Yes |
| Have data been phase-flipped? | No |
| Ignore CTFs until first peak? | No |
| Optimization | Optimization |
| Mask diameter (A) | 200 |
| Mask individual particles with zeros? | Yes |
| Use solvent-flattened FSCs? | Yes |
| Auto-sampling | Auto-sampling |
| Initial angular sampling | 7.5 degrees |
| Initial offset range (pix) | 5 |
| Initial offset step (pix) | 1 |
| Local searches from auto-sampling | 1.8 degrees |
HINT: to make the model more detailed in Chimera - set "Step" value to 1.
| Tutorial Unit | 8.3 Bayesian polishing - Analysing the results |
|---|---|
| Previous Steps | (22) MaskCreate/first3dref |
| (27) PostProcess/polished | |
| Step Alias | (28) PostProcess/polished |
Now we are applying the post-processing to the polished particles.
| Field name | Field value |
|---|---|
| I/O | I/O |
| One of the 2 unfiltered half-maps | Refine3D/job027/run_half1_class001_unfil.mrc |
| Solvent mask | MaskCreate/job022/mask.mrc |
| Calibrated pixel size (A) | 1.244 |
| Sharpen | Sharpen |
| MTF of the detector (STAR file) | mtf_k2_300kV.star |
| Estimate B-factor automatically? | Yes |
| Lowest resolution for auto-B fit (A) | 10 |
| Use your own B-factor? | No |
| Filter | Filter |
| Skip FSC-weighting? | No |
| Tutorial Unit | 9 Local-resolution estimation |
|---|---|
| Previous Step | (27) Refine3D/polished |
| Step Alias | (29) LocalRes/polished |
At the final step of the tutorial, we are going to perform the local-resolution estimation. RELION provides the application that can estimate local variations in the model resolution that is the opposite to global estimation in post-processing step. It will help us to differentiate the noise from the final model.
| Field name | Field value |
|---|---|
| I/O | I/O |
| One of the 2 unfiltered half-maps | Refine3D/job027/run_half1_class001_unfil.mrc |
| Calibrated pixel size (A) | 1.244 |
| ResMap | ResMap |
| Use ResMap? | No |
| Relion | Relion |
| User-provided B-factor | -100 |
| MTF of the detector (STAR file) | mtf_k2_300kV.star |
Let’s open chimera with the following settings.
- Open
Postprocess/polished/postprocess.mrcfile. - Open “Tools” → “Volume data” → “Surface color”.
- Select coloring by “volume data value” and select
LocalRes/polished/relion_locres.mrcfile and press “Color” button.
You will see that the model became colored in different colors depending on the B-factor value.
Now we can see that the only red part of the final model could be associated with the particle.
There is also the LocalRes/polished/relion_locres_filtered.mrc file that contains the locally-filtered and sharpened model, which may be useful to describe the overall variations in model quality in a single model.
The particle model is ready now!
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