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ProjectionMatchingRefinement

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Adrian Quintana edited this page Dec 11, 2017 · 1 revision

Projection Matching Angular Refinement

  • Mask and filter reference volume
  • Perform a single iteration of projection matching (may use defocus groups see bellow for details)
  • In order to remove model bias from the refinement procedure, perform a 2D re-alignment for the images assigned to each reference projection direction
  • Perform a 3D reconstruction with the aligned images.
  • Estimate the resolution limit of the reconstructed volume, by calculating the Fourier Shell Correlation
  • Repeat previous steps using as new reference voume the reconstruction
  • Iterate until the resolution does not improve anymore.

Example Data

  • Input
    • gallery of images
    • user defined mask
    • reference volume

File proj_match.tar.gz contains all the images, the reference volume, the mask and the xmipp_protocol_projmatch.py script with suitable parameters. This example consists of about 5,000 simulated ribosome images (130x130 pixels) classified by ML3DClassification protocol. These images belong to the class with the elongation factor G (EFG).

  • Output
    • 3D reconstruction
    • Document file with assigned angles

File output_proj_match.tar.gz contains the results of executing theProjection Matching script. In order to reduce the size of the tar.gz file only has been included the two first (1 and 2) and the two last iteration (9 and 10). Only the first iteration has all the output files, so is the only one in which the projection matching library and aligned classes could be visualized.

Pressanalyze results button for visualizing the results of angular refinement.

Projection matching library and averages of the aligned classes (top-right), 3D intial reference (bottom-left), 3D reconstructed map of the last iteration (bottom-middle) and plot of the fourier shell correlation of the 3D reconstructed map in some iterations (bottom-right).
## Defocus Groups

Starting with Xmipp version 2.0.2, this protocols may process defocus groups. The strategy followed is:

  • create gallery of reference images
  • on the fly multiply the reference images by the adequate CTF
  • search for correlation maxima
  • apply Wiener filtration to the average class
  • reconstruct using all "corrected" proyections

How to convert defocus groups from Spider to Xmipp

If images have been processed using Xmipp the programxmipp_ctf_group will help you to create the defocuss groups but if this is not the case some auxiliary files are needed. you may create them following these steps:

(i) create filedefocus.docwith average defocus per group

1 1 -17721.

2 1 -19632.

3 1 -21939.

4 1 -22738.

(ii) create filestandard.ctfparam with the following lines

   sampling_rate=        2.8
   voltage=              200
   spherical_aberration= 2.26
   Q0=                   -0.1

(iii) create sel file per defocus group. In the following wxample each group was in a diferent directory that started with df

for i in df*; do xmipp_selfile_create  "$PWD/$i/*.spi" > $i.sel; done

(iv) create a sel sel file with selfiles of defocus groups

xmipp_selfile_create  "$PWD/Images/*.sel" > defocus.sel

(v) createctf.param xmipp files

xmipp_ctf_create_ctfdat -i defocus.sel -o CTF/ctf -param standard.ctfparam -doc defocus.doc

(vi) sel file with all images

xmipp_selfile_create  "$PWD/Images/df???/*.spi" > all_images.sel
xmipp_ctf_group  -i all_images.sel -ctfdat CTF/ctf.ctfdat  -phase_flipped -pad 1.5 -wiener -o CTF/group

Note:B00.xmp you may find a spider batch file that unstacks stack files

How to use this protocols if data has not been procesed with xmipp protocols:

If you have used xmipp_protocols to process your data this is not needed but if this is not the case then you need the following files:

create file defocus.doc with average defocus     1 1 -17721.          2 1 -19632.          3 1 -21939.          4 1 -22738.

create minimal CTF file called standard.ctfparam    sampling_rate=        2.8    voltage=              200    spherical_aberration= 2.26    Q0=                   -0.1

create sel file per defocus group, I am assuming that each defocus group is in a different directory starting with df but this is not really needed

for i in df*; do xmipp_selfile_create  "$PWD/$i/*.spi" > $i.sel; done

sel file with selfiles of defocus groups (I assume that the images are in directory Images and subdirectory dfXXXX)

xmipp_selfile_create  "$PWD/Images/*.sel" > defocus.sel

#create ctf.param xmipp files xmipp_ctf_create_ctfdat -i defocus.sel -o CTF/ctf -param standard.ctfparam -doc defocus.doc

#sel file with all images xmipp_selfile_create  "$PWD/Images/df???/*.spi" > all_images.sel #xmipp_ctf_group  -i all_images.sel -ctfdat CTF/ctf.ctfdat  -phase_flipped -pad 1.5 -wiener -o CTF/group xmipp_ctf_group  -i all_images.sel -ctfdat CTF/ctf.ctfdat  -phase_flipped -pad 1.0 -wiener -o CTF/group (this last step is only needed so you can see if there is aliasing in the wiener filters, if there is you should pad) cd CTF/ copy and edit  xmipp_protocol_projmatch.py

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