Instructions for processing Single particle crystals

1 Setup directories:

   mkdir doc
   mkdir patch
   mkdir coran
   mkdir classavg

2 Convert from mrc to spider file format with em2em

3 Invert the density with the spider command neg:

   
  spider wjr/spd
   neg
   <enter name of input file>
   <enter name of output file>
   en                 ; quit

4 Setup directories:

   mkdir doc
   mkdir patch
   mkdir coran
   mkdir classavg

5 Make a docfile that covers the entire area

   spider wjr/spd @make_grid

6 Optionally, low-pass filter the input file:

   spider wjr/spd
   fq
   <enter name of inpptu file>
   <enter name of output file>
    3                       (Gaussian lowpass filter type)
   0.2                      (Gaussian lowpass filter radius. 0.5=Nyquist. Use appropriate value)
   en            ; quit

7 Window the files:

   spider wjr/spd @window_grid

8 Do principal component analysis of the windowed files:

   spider wjr/spd
   ca s
   patch/patch****      <name of input files>
   1-1024               <particle numbers - use all>
   *                    <mask file - no mask>
   20                   <number of factors to calculate. Calculate many, don't need to use all>
   i                    <iterative pca>
   coran/coran          <output file prefix>
   en                 ; quit

9 Check Eigenvectors:

   gs EIGENVALUES.eps

10 Calculate eigenimages:

    spider wjr/spd @make_eigenimages

11 View these in Web, using montage command.

12 With a combination of viewing the images and values, choose the vectors you want to use

13 Do Hierarchical clustering using these vectors:

    spider wjr/spd
    cl hc
    coran/coran_IMC              [ input file]
    1-10                          [enter vector numbers determined above]
    0                             [equal weight for all factors]
    5                             [Ward's method of clustering]
    N                             [no postscript plot]
    Y                             [make a dendogram doc file]
    docdendro                     [output dendogram>]
    en                                ; end

14 Using Web, view the dendogram ("dendogram" command) and determine an appropriate cutoff

15 Make class files for the desired cutoff

    spider wjr/spd
    cl he
    0.3               [cutoff level determined above]
    docdendro         [dendogram doc file]
    doc/seldoc_**     [output class files]

16 Make average, variance files for the above classes and also calculate frc for each (estimate resolution)

    spider wjr/spd @frc_classes

17 Choose the best class as a reference for searching the original file

18 Search the raw file for correlation peaks with this best class.

    spider wjr/spd @corav1

19 Window out these peaks:

• Edit the script window2.wjr
• The script will automatically eliminate boxes that overlap the edge of the original image.

20 Do principal component analysis of these peaks

• Same as step 8, but enter newly windowed files for the input, and enter doc/goodpartpeak as the input selection docfile (rather than simply numbers)
• Similarly, repeat steps 9-16 for the new set of images.

21 View the chosen areas

Scripts

• single_particle_crystal.zip: spider scripts needed for this process

• Set ALLOWTOPICVIEW =

-- BillRice - 06 Feb 2008

Instructions for processing Single particle crystals

1 Setup directories:

   mkdir doc
   mkdir patch
   mkdir coran
   mkdir classavg

2 Convert from mrc to spider file format with em2em

3 Invert the density with the spider command neg:

   
  spider wjr/spd
   neg
   <enter name of input file>
   <enter name of output file>
   en                 ; quit

4 Setup directories:

   mkdir doc
   mkdir patch
   mkdir coran
   mkdir classavg

5 Make a docfile that covers the entire area

   spider wjr/spd @make_grid

6 Optionally, low-pass filter the input file:

   spider wjr/spd
   fq
   <enter name of inpptu file>
   <enter name of output file>
    3                       (Gaussian lowpass filter type)
   0.2                      (Gaussian lowpass filter radius. 0.5=Nyquist. Use appropriate value)
   en            ; quit

7 Window the files:

   spider wjr/spd @window_grid

8 Do principal component analysis of the windowed files:

   spider wjr/spd
   ca s
   patch/patch****      <name of input files>
   1-1024               <particle numbers - use all>
   *                    <mask file - no mask>
   20                   <number of factors to calculate. Calculate many, don't need to use all>
   i                    <iterative pca>
   coran/coran          <output file prefix>
   en                 ; quit

9 Check Eigenvectors:

   gs EIGENVALUES.eps

10 Calculate eigenimages:

    spider wjr/spd @make_eigenimages

11 View these in Web, using montage command.

12 With a combination of viewing the images and values, choose the vectors you want to use

13 Do Hierarchical clustering using these vectors:

    spider wjr/spd
    cl hc
    coran/coran_IMC              [ input file]
    1-10                          [enter vector numbers determined above]
    0                             [equal weight for all factors]
    5                             [Ward's method of clustering]
    N                             [no postscript plot]
    Y                             [make a dendogram doc file]
    docdendro                     [output dendogram>]
    en                                ; end

14 Using Web, view the dendogram ("dendogram" command) and determine an appropriate cutoff

15 Make class files for the desired cutoff

    spider wjr/spd
    cl he
    0.3               [cutoff level determined above]
    docdendro         [dendogram doc file]
    doc/seldoc_**     [output class files]

16 Make average, variance files for the above classes and also calculate frc for each (estimate resolution)

    spider wjr/spd @frc_classes

17 Choose the best class as a reference for searching the original file

18 Search the raw file for correlation peaks with this best class.

    spider wjr/spd @corav1

19 Window out these peaks:

• Edit the script window2.wjr
• The script will automatically eliminate boxes that overlap the edge of the original image.

20 Do principal component analysis of these peaks

• Same as step 8, but enter newly windowed files for the input, and enter doc/goodpartpeak as the input selection docfile (rather than simply numbers)
• Similarly, repeat steps 9-16 for the new set of images.

21 View the chosen areas

    drawallboxes.wjr

• Set ALLOWTOPICVIEW =

-- BillRice - 06 Feb 2008

Instructions for processing Single particle crystals

1 Setup directories:

   mkdir doc
   mkdir patch
   mkdir coran
   mkdir classavg

2 Convert from mrc to spider file format with em2em

3 Invert the density with the spider command neg:

   
  spider wjr/spd
   neg
   <enter name of input file>
   <enter name of output file>
   en                 ; quit

4 Setup directories:

   mkdir doc
   mkdir patch
   mkdir coran
   mkdir classavg

5 Make a docfile that covers the entire area

   spider wjr/spd @make_grid

6 Optionally, low-pass filter the input file:

   spider wjr/spd
   fq
   <enter name of inpptu file>
   <enter name of output file>
    3                       (Gaussian lowpass filter type)
   0.2                      (Gaussian lowpass filter radius. 0.5=Nyquist. Use appropriate value)
   en            ; quit

7 Window the files:

   spider wjr/spd @window_grid

8 Do principal component analysis of the windowed files:

   spider wjr/spd
   ca s
   patch/patch****      <name of input files>
   1-1024               <particle numbers - use all>
   *                    <mask file - no mask>
   20                   <number of factors to calculate. Calculate many, don't need to use all>
   i                    <iterative pca>
   coran/coran          <output file prefix>
   en                 ; quit

9 Check Eigenvectors:

   gs EIGENVALUES.eps

10 Calculate eigenimages:

    spider wjr/spd @make_eigenimages

11 View these in Web, using montage command.

12 With a combination of viewing the images and values, choose the vectors you want to use

13 Do Hierarchical clustering using these vectors:

    spider wjr/spd
    cl hc
    coran/coran_IMC              [ input file]
    1-10                          [enter vector numbers determined above]
    0                             [equal weight for all factors]
    5                             [Ward's method of clustering]
    N                             [no postscript plot]
    Y                             [make a dendogram doc file]
    docdendro                     [output dendogram>]
    en                                ; end

14 Using Web, view the dendogram ("dendogram" command) and determine an appropriate cutoff

15 Make class files for the desired cutoff

    spider wjr/spd
    cl he
    0.3               [cutoff level determined above]
    docdendro         [dendogram doc file]
    doc/seldoc_**     [output class files]

16 Make average, variance files for the above classes and also calculate frc for each (estimate resolution)

    spider wjr/spd @frc_classes

17 Choose the best class as a reference for searching the original file

18 Search the raw file for correlation peaks with this best class.

    spider wjr/spd @corav1

19 Window out these peaks:

• Edit the script window2.wjr
• The script will automatically eliminate boxes that overlap the edge of the original image.

20 Do principal component analysis of these peaks

• Same as step 8, but enter newly windowed files for the input, and enter doc/goodpartpeak as the input selection docfile (rather than simply numbers)
• Similarly, repeat steps 9-16 for the new set of images.

21 View the chosen areas

    drawallboxes.wjr

• Set ALLOWTOPICVIEW =

-- BillRice - 06 Feb 2008

Instructions for processing Single particle crystals

1 Setup directories:

   mkdir doc
   mkdir patch
   mkdir coran
   mkdir classavg

2 Convert from mrc to spider file format with em2em

   
  spider wjr/spd
   neg
   <enter name of input file>
   <enter name of output file>
   en                 ; quit

4 Setup directories:

   mkdir doc
   mkdir patch
   mkdir coran
   mkdir classavg

5 Make a docfile that covers the entire area

   spider wjr/spd @make_grid

6 Optionally, low-pass filter the input file:

   spider wjr/spd
   fq
   <enter name of inpptu file>
   <enter name of output file>
    3                       (Gaussian lowpass filter type)
   0.2                      (Gaussian lowpass filter radius. 0.5=Nyquist. Use appropriate value)
   en            ; quit

7 Window the files:

   spider wjr/spd @window_grid

8 Do principal component analysis of the windowed files:

   spider wjr/spd
   ca s
   patch/patch****      <name of input files>
   1-1024               <particle numbers - use all>
   *                    <mask file - no mask>
   20                   <number of factors to calculate. Calculate many, don't need to use all>
   i                    <iterative pca>
   coran/coran          <output file prefix>
   en                 ; quit

9 Check Eigenvectors:

   gs EIGENVALUES.eps

10 Calculate eigenimages:

    spider wjr/spd @make_eigenimages

11 View these in Web, using montage command.

12 With a combination of viewing the images and values, choose the vectors you want to use

13 Do Hierarchical clustering using these vectors:

    spider wjr/spd
    cl hc
    coran/coran_IMC              [ input file]
    1-10                          [enter vector numbers determined above]
    0                             [equal weight for all factors]
    5                             [Ward's method of clustering]
    N                             [no postscript plot]
    Y                             [make a dendogram doc file]
    docdendro                     [output dendogram>]
    en                                ; end

14 Using Web, view the dendogram ("dendogram" command) and determine an appropriate cutoff

15 Make class files for the desired cutoff

    spider wjr/spd
    cl he
    0.3               [cutoff level determined above]
    docdendro         [dendogram doc file]
    doc/seldoc_**     [output class files]

16 Make average, variance files for the above classes and also calculate frc for each (estimate resolution)

    spider wjr/spd @frc_classes

17 Choose the best class as a reference for searching the original file

18 Search the raw file for correlation peaks with this best class.

    spider wjr/spd @corav1

19 Window out these peaks:

• Edit the script window2.wjr
• The script will automatically eliminate boxes that overlap the edge of the original image.

20 Do principal component analysis of these peaks

• Same as step 8, but enter newly windowed files for the input, and enter doc/goodpartpeak as the input selection docfile (rather than simply numbers)
• Similarly, repeat steps 9-16 for the new set of images.

21 View the chosen areas

    drawallboxes.wjr

• Set ALLOWTOPICVIEW =

-- BillRice - 06 Feb 2008

Instructions for processing Single particle crystals

• Set ALLOWTOPICVIEW =

-- BillRice - 06 Feb 2008

Instructions for processing Single particle crystals

Setup directories:

1. Convert from mrc to spider file format with em2em

1. Invert the density with the spider command neg: spider wjr/spd neg en ; quit

1. Setup directories: mkdir doc mkdir patch mkdir coran mkdir classavg

1. Make a docfile that covers the entire area Edit script make_grid.wjr - to match size of image and box size

spider wjr/spd @make_grid

1. Optionally, low-pass filter the input file: spider wjr/spd fq 3 (Gaussian lowpass filter type)
2. 2 (Gaussian lowpass filter radius. 0.5=Nyquist. Use appropriate value) en ; quit

1 Window the files: Edit the script window_grid.wjr to match the file input name. Work on the filtered file if desired

Run it spider wjr/spd @window_grid

8) Do principal component analysis of the windowed files:

spider wjr/spd ca s patch/patch****

1. -1024

1. i coran/coran
   en ; quit

9) Check Eigenvectors: run the script /cryoem/script/eigendoc.pl input file: coran/coran_EIG.spd output file: eigen1.txt View the eigenvector values: gs EIGENVALUES.eps

10) Calculate eigenimages: Edit the script make_eigenimages.wjr and run spider wjr/spd @make_eigenimages

11) View these in Web, using montage command.

12) With a combination of viewing the images and values, choose the vectors you want to use

13) Do Hierarchical clustering using these vectors: spider wjr/spd cl hc coran/coran_IMC < input file> 1-10 0 5 <Ward's method of clustering> N Y docdendro

en ; end

14) Using Web, view the dendogram ("dendogram" command) and determine an appropriate cutoff

15) Make class files for the desired cutoff spider wjr/spd cl he 0.3 < cutoff level determined above> docdendro doc/seldoc_**

16) Make average, variance files for the above classes and also calculate frc for each (estimate resolution) edit script frc_classes - number of classes, selction doc filename, output class averages spider wjr/spd @frc_classes

17) Choose the best class as a reference for searching the original file

18) Search the raw file for correlation peaks with this best class. Edit the script corav1.wjr run it spider wjr/spd @corav1 Choose a reasonable number of peaks.

19) Window out these peaks: Edit the script window2.wjr The script will automatically eliminate boxes that overlap the edge of the original image.

20) Do principal component analysis of these peaks Same as step 8, but enter newly windowed files for the input, and enter doc/goodpartpeak as the input selection docfile (rather than simply numbers) Similarly, repeat steps 9-16 for the new set of images.

21) To view the choses anreas, edit and use the script drawallboxes.wjr This will show you the locations of the boxes on the original file.

subsub level topic

• Set ALLOWTOPICVIEW =

-- BillRice - 06 Feb 2008

Instructions for processing Single particle crystals

1. Convert from mrc to spider file format with em2em

1. Invert the density with the spider command neg: spider wjr/spd neg en ; quit

1. Setup directories: mkdir doc mkdir patch mkdir coran mkdir classavg

1. Make a docfile that covers the entire area Edit script make_grid.wjr - to match size of image and box size

spider wjr/spd @make_grid

1. Optionally, low-pass filter the input file: spider wjr/spd fq 3 (Gaussian lowpass filter type)
2. 2 (Gaussian lowpass filter radius. 0.5=Nyquist. Use appropriate value) en ; quit

1 Window the files: Edit the script window_grid.wjr to match the file input name. Work on the filtered file if desired

Run it spider wjr/spd @window_grid

8) Do principal component analysis of the windowed files:

spider wjr/spd ca s patch/patch****

1. -1024

1. i coran/coran
   en ; quit

9) Check Eigenvectors: run the script /cryoem/script/eigendoc.pl input file: coran/coran_EIG.spd output file: eigen1.txt View the eigenvector values: gs EIGENVALUES.eps

10) Calculate eigenimages: Edit the script make_eigenimages.wjr and run spider wjr/spd @make_eigenimages

11) View these in Web, using montage command.

12) With a combination of viewing the images and values, choose the vectors you want to use

13) Do Hierarchical clustering using these vectors: spider wjr/spd cl hc coran/coran_IMC < input file> 1-10 0 5 <Ward's method of clustering> N Y docdendro

en ; end

14) Using Web, view the dendogram ("dendogram" command) and determine an appropriate cutoff

15) Make class files for the desired cutoff spider wjr/spd cl he 0.3 < cutoff level determined above> docdendro doc/seldoc_**

16) Make average, variance files for the above classes and also calculate frc for each (estimate resolution) edit script frc_classes - number of classes, selction doc filename, output class averages spider wjr/spd @frc_classes

17) Choose the best class as a reference for searching the original file

18) Search the raw file for correlation peaks with this best class. Edit the script corav1.wjr run it spider wjr/spd @corav1 Choose a reasonable number of peaks.

19) Window out these peaks: Edit the script window2.wjr The script will automatically eliminate boxes that overlap the edge of the original image.

20) Do principal component analysis of these peaks Same as step 8, but enter newly windowed files for the input, and enter doc/goodpartpeak as the input selection docfile (rather than simply numbers) Similarly, repeat steps 9-16 for the new set of images.

21) To view the choses anreas, edit and use the script drawallboxes.wjr This will show you the locations of the boxes on the original file.

subsub level topic

• Set ALLOWTOPICVIEW =

-- BillRice - 06 Feb 2008

Instructions for processing Single particle crystals

1. Setup directories: mkdir doc mkdir patch mkdir coran mkdir classavg

1. Convert from mrc to spider file format with em2em

1. Invert the density with the spider command neg: spider wjr/spd neg en ; quit

1. Setup directories: mkdir doc mkdir patch mkdir coran mkdir classavg

1. Make a docfile that covers the entire area Edit script make_grid.wjr - to match size of image and box size

spider wjr/spd @make_grid

1. Optionally, low-pass filter the input file: spider wjr/spd fq 3 (Gaussian lowpass filter type)
2. 2 (Gaussian lowpass filter radius. 0.5=Nyquist. Use appropriate value) en ; quit

1 Window the files: Edit the script window_grid.wjr to match the file input name. Work on the filtered file if desired

Run it spider wjr/spd @window_grid

8) Do principal component analysis of the windowed files:

spider wjr/spd ca s patch/patch****

1. -1024

1. i coran/coran
   en ; quit

9) Check Eigenvectors: run the script /cryoem/script/eigendoc.pl input file: coran/coran_EIG.spd output file: eigen1.txt View the eigenvector values: gs EIGENVALUES.eps

10) Calculate eigenimages: Edit the script make_eigenimages.wjr and run spider wjr/spd @make_eigenimages

11) View these in Web, using montage command.

12) With a combination of viewing the images and values, choose the vectors you want to use

13) Do Hierarchical clustering using these vectors: spider wjr/spd cl hc coran/coran_IMC < input file> 1-10 0 5 <Ward's method of clustering> N Y docdendro

en ; end

14) Using Web, view the dendogram ("dendogram" command) and determine an appropriate cutoff

15) Make class files for the desired cutoff spider wjr/spd cl he 0.3 < cutoff level determined above> docdendro doc/seldoc_**

16) Make average, variance files for the above classes and also calculate frc for each (estimate resolution) edit script frc_classes - number of classes, selction doc filename, output class averages spider wjr/spd @frc_classes

17) Choose the best class as a reference for searching the original file

18) Search the raw file for correlation peaks with this best class. Edit the script corav1.wjr run it spider wjr/spd @corav1 Choose a reasonable number of peaks.

19) Window out these peaks: Edit the script window2.wjr The script will automatically eliminate boxes that overlap the edge of the original image.

20) Do principal component analysis of these peaks Same as step 8, but enter newly windowed files for the input, and enter doc/goodpartpeak as the input selection docfile (rather than simply numbers) Similarly, repeat steps 9-16 for the new set of images.

21) To view the choses anreas, edit and use the script drawallboxes.wjr This will show you the locations of the boxes on the original file.

subsub level topic

• Set ALLOWTOPICVIEW =

-- BillRice - 06 Feb 2008