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eman2:e2tomosmall [2025/08/05 02:41] – [New Deep-Learning 3-D Picker (recommended approach)] steveludtkeeman2:e2tomosmall [2025/08/05 11:06] (current) – [Particle extraction (~2 min (a few manual) - hours (a couple of thousand))] steveludtke
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 This is the easiest approach by far, and is basically a single step process, but is only available in recent (2022+) snapshots of EMAN2. This is the easiest approach by far, and is basically a single step process, but is only available in recent (2022+) snapshots of EMAN2.
   * You should easily be able to select ~500 particles per tomogram, potentially more, but 500 is more than sufficient for the tutorial   * You should easily be able to select ~500 particles per tomogram, potentially more, but 500 is more than sufficient for the tutorial
-  * See: [[EMAN2:e2tomo_more|Automated Particle Selection]] Section for the original instructions, but they are updated on this page. 
  
 {{https://blake.bcm.edu/dl/EMAN2/sptboxer_convnet.png}} {{https://blake.bcm.edu/dl/EMAN2/sptboxer_convnet.png}}
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   - When complete, skip ahead to the next major section **Particle Extraction**   - When complete, skip ahead to the next major section **Particle Extraction**
  
 +<note>
 +See: [[EMAN2:e2tomo_more|Automated Particle Selection]] Section for the original instructions for this tool, but they may be a bit dated.
 +</note>
 ==== Tomogram annotation (GPU recommended) ==== ==== Tomogram annotation (GPU recommended) ====
 This is an older strategy using the deep-learning based tomogram annotation program to find particles. It still works, but the new deep-learning picker in the section above will generally work better. This is an older strategy using the deep-learning based tomogram annotation program to find particles. It still works, but the new deep-learning picker in the section above will generally work better.
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   * If you did the previous optional annotation step above, you will be able to see the selected particles here, and if you like, manually update them.   * If you did the previous optional annotation step above, you will be able to see the selected particles here, and if you like, manually update them.
  
-===== Particle extraction (~2 min (a few manual) - hours (a couple of thousand)) =====+===== Particle extraction (~2 min (a few manual) - over an hour (a couple of thousand, large tutorial)) =====
 Note that this step will be vastly different resource-wise if you are only extracting a few manually selected particles for purposes of later template matching or if you already selected hundreds of particles from each tomogram.  Note that this step will be vastly different resource-wise if you are only extracting a few manually selected particles for purposes of later template matching or if you already selected hundreds of particles from each tomogram. 
 +
 +<note>
 +Depending on how you selected particles you may have a significant number of particles which contain fiducials or artifacts from the fiducials (bright spots). Having these in your particle data can cause significant problems, and could lead to getting bad initial models and thus bad refinements. For a small data set like this, it might be a good idea to open the manual boxer for each tomogram and delete any particles with fiducials in them before completing this step (particle extraction).
 +</note>
  
 The reduced 1k x 1k (or 2k) tomograms are used only as a reference to identify the location of the objects to be averaged. Now that we have particle locations, the software returns to the original tilt-series, extracts a per-particle tilt-series, and reconstructs each particle in 3-D independently at full resolution. Since this is performing a full resolution reconstruction of each particle it is somewhat resource intensive. The reduced 1k x 1k (or 2k) tomograms are used only as a reference to identify the location of the objects to be averaged. Now that we have particle locations, the software returns to the original tilt-series, extracts a per-particle tilt-series, and reconstructs each particle in 3-D independently at full resolution. Since this is performing a full resolution reconstruction of each particle it is somewhat resource intensive.
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     * set //boxsz_unbin// to 128 (Small) or 256 (Large).     * set //boxsz_unbin// to 128 (Small) or 256 (Large).
       * If you had the correct size in the previous step this should be the same as leaving the default -1       * If you had the correct size in the previous step this should be the same as leaving the default -1
-    * enter the label you used when picking particles ("initribo" if you manually boxed, "tomobox" if you used the deep learning picker)+    * enter the label you used when picking particles ("initribo" if you manually boxed, "ribo" or "tomobox" if you used the deep learning picker)
     * //threads// = value for your machine     * //threads// = value for your machine
     * Launch     * Launch
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 Once it gets past 3-4 iterations, you can use the browser to look in //sptsgd_00//, and double-click on //output_cls0.hdf//. This file will change after each iteration completes. It contains the results of the most recent iteration. When you are satisfied with the quality of the initial model, you can kill it with the task manager in e2projectmanager. Once it gets past 3-4 iterations, you can use the browser to look in //sptsgd_00//, and double-click on //output_cls0.hdf//. This file will change after each iteration completes. It contains the results of the most recent iteration. When you are satisfied with the quality of the initial model, you can kill it with the task manager in e2projectmanager.
 +
 +Note that while the program converges pretty quickly, it won't always get the correct structure, particularly if you have some bad particles (like fiducials) included in the particle set. It is important to look at the resulting starting map and make sure it looks at least vaguely like you expect. If not you may wish to try running this step again. This will produce //sptsgd_01//, //02//, ...
  
 <note tip> <note tip>
eman2/e2tomosmall.1754361680.txt.gz · Last modified: by steveludtke