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eman2:e2tomo_atpsyn [2026/06/03 20:22] – muyuancheneman2:e2tomo_atpsyn [2026/06/04 20:47] (current) – muyuanchen
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 ===== All tomogram reconstruction ===== ===== All tomogram reconstruction =====
  
-Reconstruct all tilt series using the same parameters and the tilt axis estimated by the handedness check. Note that in the full dataset, tilt series of different sessions in EMPIAR-11830 may have different tilt step and pixel size in their header. Run the **--alltiltseries** command with caution when processing large datasets. The 5 tilt series in this tutorial are from the same session and have similar conditions.+Reconstruct all tilt series using the same parameters and the tilt axis estimated by the handedness check. Note that in the full dataset, tilt series of different sessions in EMPIAR-11830 may have different tilt step and pixel size in their header. Run the **--alltiltseries** command with caution when processing large datasets. The 5 tilt series in this tutorial are from the same session and have similar conditions. While the selection makes the processing simpler, it limits the final resolution because the 5 tilt series are collected at similar (and relatively high) defocus. 
  
 <code> <code>
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 To make sure the operations on particle lists are done properly, compare the Euler angles of the lists by clicking "Plot2D".  To make sure the operations on particle lists are done properly, compare the Euler angles of the lists by clicking "Plot2D". 
 +
 {{http://blake.bcm.edu/dl/EMAN2/atpsyn_euler_view.png| Euler angle comparison |width="600"}} {{http://blake.bcm.edu/dl/EMAN2/atpsyn_euler_view.png| Euler angle comparison |width="600"}}
  
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 </code> </code>
  
-The resolution should improve to ~10Å at this point. Next, we can focus the refinement on the rotation of F1 head. Make a mask using FilterTool that covers the F1 head only, and name it mask_f1.hdf. Here we use the deep learning based alignment to recover the large scale rotation. +The resolution should get slightly better than 10Å at this point. Next, we can focus the refinement on the rotation of F1 head. Make a mask using FilterTool that covers the F1 head only, and name it mask_f1.hdf. Here we first run one iteration of the deep learning based alignment to recover the large scale rotation, followed by 3 iterations of the direct alignment from the deep learning result. 
  
 <code> <code>
-e2gmm_spt_refine_iter.py gmm_00/threed_03.hdf --initpts spt_02/threed_07_seg.pdb --startres 10 --maskpp mask_01.hdf --mask mask_f1.hdf --align_mlp+e2gmm_spt_refine_iter.py gmm_00/threed_03.hdf --initpts spt_03/threed_07_seg.pdb --startres 15 --maskpp mask_01.hdf --mask mask_f1.hdf --align_mlp --niter 1 
 +e2gmm_spt_refine_iter.py gmm_01/threed_01.hdf --initpts spt_03/threed_07_seg.pdb --startres 10 --maskpp mask_01.hdf --mask mask_f1.hdf
 </code> </code>
  
-This should improve the structure features at the F1 head domain, and slightly improve the FSC resolution. Because the even/odd half set only are only aligned to the "neutral" struture of their half-set and never see each other, there is a possiblity that they converge to slightly different states, and the FSC resolution decrease even though the feature in each half-set improves. This is less of a problem in datasets with more particles since the "neutral" state would be better defined, but here there are some uncertainties with only 5 tomograms...+This should improve the structure features at the F1 head domain, but the FSC resolution does not necessarily improve here. Because the even/odd half set only are only aligned to the "neutral" struture of their half-set and never see each other, there is a possiblity that they converge to slightly different states, and the FSC resolution decrease even though the feature in each half-set improves. This is less of a problem in datasets with more particles since the "neutral" state would be better defined, but here there are some uncertainties with only 5 tomograms... 
 + 
 +{{http://blake.bcm.edu/dl/EMAN2/atpsyn_cmp_focus_refine.png| Focus refinement comparison | width="600"}}
  
 To visualize the dynamics, run the following.  To visualize the dynamics, run the following. 
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 This only shows the motion of the even set, and the same can be done to the odd half. Since the deep learning models for the two half-sets are trained independently, visualizing the motion in the combined dataset without breaking the "gold-standard" validation is impossible. Still, the rotation movement should be visible already even with the small dataset. This only shows the motion of the even set, and the same can be done to the odd half. Since the deep learning models for the two half-sets are trained independently, visualizing the motion in the combined dataset without breaking the "gold-standard" validation is impossible. Still, the rotation movement should be visible already even with the small dataset.
  
-{{http://blake.bcm.edu/dl/EMAN2/atpsyn_bad_ptcls.png| Bad particle removal | width="600"}} +{{http://blake.bcm.edu/dl/EMAN2/atpsyn_f1_motion.gif | F1 head motion | width="600"}}
- +
-Finally, we can refine the local motion of the F1 domain a bit more, without the neural network part. Depending on the particle count and the type of motion, this sometimes improve the resolution of the target domain.  +
-<code> +
-e2gmm_spt_refine_iter.py gmm_01/threed_03.hdf --initpts spt_02/threed_07_seg.pdb --startres 10 --maskpp mask_01.hdf --mask mask_f1.hdf +
-</code> +
- +
-{{http://blake.bcm.edu/dl/EMAN2/atpsyn_cmp_focus_refine.png| Focus refinement comparison | width="600"}}+
  
  
  
eman2/e2tomo_atpsyn.1780518145.txt.gz · Last modified: by muyuanchen