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downloads:sphire_1_3 [2019/09/29 10:28]
twagner 		downloads:sphire_1_3 [2019/09/30 09:34]
shaikh [What's new]
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 ==== What's new ==== ==== What's new ====
 [{{ :downloads:logo_helical.png?200|Helical processing with SPHIRE 1.3}}] [{{ :downloads:defocus_refinement.png?200|Per particle CTF refinement. The image shows a refinement of appoferritin dataset with tilted stage. Each point represent a particle on the micrograph. The refined defocus values (left side) clearly shows the tilt axis.  The right side is the a error estimate for every defocus value.}}] [{{ :downloads:cryolo_logo.jpg?200|crYOLO can now directly used from SPHIRE 1.3}}] [{{ :downloads:logo_helical.png?200|Helical processing with SPHIRE 1.3}}] [{{ :downloads:defocus_refinement.png?200|Per particle CTF refinement. The image shows a refinement of appoferritin dataset with tilted stage. Each point represent a particle on the micrograph. The refined defocus values (left side) clearly shows the tilt axis.  The right side is the a error estimate for every defocus value.}}] [{{ :downloads:cryolo_logo.jpg?200|crYOLO can now directly used from SPHIRE 1.3}}]
-  * We add support for [[howto:sphire_helical|processing helical specimens]] (Beta).+  * Support for [[howto:sphire_helical|processing helical specimens]] (beta).
  
  
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   *  Per particle CTF refinement with error estimation. When you reach a resolution of about 4Å you can refine your CTF on a per-particle basis. This can improve the resolution by 0.3Å or more.   *  Per particle CTF refinement with error estimation. When you reach a resolution of about 4Å you can refine your CTF on a per-particle basis. This can improve the resolution by 0.3Å or more.
  
-  * Integrated [[pipeline:window:cryolo|crYOLO 1.5]] for particle picking.  crYOLO is based on convolutional neural networks and can accurately pick about ten micrographs per second.+  * Integration of [[pipeline:window:cryolo|crYOLO 1.5]] for particle picking.  crYOLO is based on convolutional neural networks and can accurately pick about ten micrographs per second.
  
   * Integration of automatic 2D class selection with [[:auto_2d_class_selection|Cinderella]]. This tool is based on a deep learning network to separate 2D classes from .hdf / .mrcs files into good and bad classes. It uses the same deep neural network as crYOLO and comes pre-trained on a set of good and bad classes. Cinderella was written to automate cryo-EM data processing.   * Integration of automatic 2D class selection with [[:auto_2d_class_selection|Cinderella]]. This tool is based on a deep learning network to separate 2D classes from .hdf / .mrcs files into good and bad classes. It uses the same deep neural network as crYOLO and comes pre-trained on a set of good and bad classes. Cinderella was written to automate cryo-EM data processing.
  
-  * The new [[pipeline:viper:sp_proj_compare|compare re-projections tool]] can now be used to produce side-by-side comparisons of the 2D class averages with the respective projections of the initial 3D model.+  * The new [[pipeline:viper:sp_proj_compare|compare re-projections tool]] can now be used to produce side-by-side comparisons of the 2D class averages with the respective projections of a 3D model.
  
   * Visualization of 2D power spectra in CTER.   * Visualization of 2D power spectra in CTER.
downloads/sphire_1_3.txt · Last modified: 2020/09/28 15:43 by twagner

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