Ghost buster procedure for tip complex reconstructions
1. Follow the reconstruction procedure to create a map of ∆ipaB. Create two separate reconstructions –
one where the bad classes have been removed (good_classes) and one where they haven’t
(all_classes).
IMPORTANT: After each step in the reconstruction of all_classes, save the alignment parameters.
After
running
program
one
rename
the
alignment
parameters
file
as
align_parameters_afterprog1.dat, after program two rename as align_parameters_afterprog2.dat and
after tkmanualshift.py rename as shift_param_from_taka_program.dat.
2. Use the volume tracer and colour zone tools in Chimera to remove the identified ghost density from
the good_classes. Save this map as an .mrc map and then use proc2d to convert it to .spi format.
This will be the reference map.
3. Make projections of the reference map about the needle axis every 2°, through 360 °. These will be
your reference images.
4.
Now, we want to apply the alignment parameters (align_parameters_afterprog1.dat,
align_parameters_afterprog2.dat and shift_param_from_taka_program.dat) to the original data set
stack. Create a new directory and copy the following files to it:
a. align_parameters_afterprog1.dat,
align_parameters_afterprog2.dat
and
shift_param_from_taka_program.dat
b. Original data set stack file – rename this dataset_lp8hp200
c. sel***.dat files from the classification (output files from spider cl km) which denote which
images contribute to which class
d. program_to_apply_shift_param_to_stack.spi
Run:
Output:
spider spi/dat @program_to_apply_sift_param_to_stack
final_shifted_raw_images****.dat
AVERAGE_of_shifted_images.dat
The final_shifted_raw_images****.dat files are stacks of raw images contributing to each class.
AVERAGE_of_shifted_images.dat are the class averages from these separate stacks. Compare
AVERAGE_of_shifted_images.dat to the class averages obtained from the classification conducted
for the reconstruction; they should look identical (if not, something has gone wrong…).
5. The data set will now be divided according to azimuthal orientation, with images within 2° wedges
forming new classes. Create a new directory and copy the following files to it:
a. align_new00*.dat – this is the alignment parameters files from program 3 of the
reconstruction procedure. Choose the file that the best iteration (i.e. if iter5 gave the bet
reconstruction, copy align_new005.dat)
b. final_shifted_raw_images****.dat – from previous step
c. divid2data.spi
d. directory ‘source’
e. cp.spi, cp.py and ave.spi
Run:
Output:
spider spi/dat @divid2data
average***.dat
average_all.dat
data***
The average***.dat files are the new class averages for each 2° angle. Average_all.dat is a stack of
all the class averages. The folders ‘data***’ correspond to each class average.
6. Open average_all.dat and the reference images from step 3 and compare them. You want to first
look at the reference images and identify the projections where you can see the removed density.
The removed density will be most visible when it is facing forward, and it will also be visible +/- 20°
either side. It may also be visible at other angles, so be sure to check thoroughly. Next, compare
these images with average_all.dat and identify the equivalent class averages i.e. the class averages
that look the same except for the extra bit of ghost density. Make a record of these classes.
7. The classes that you’ve chosen should contain misaligned images that are causing the ghost
density. To remove the misaligned images, vector analysis will be used to re-classify the images into
three new classes, using the factor showing strongest variance at the ghost density position. For
each ‘data***’ directory that was identified as containing misaligned images, copy the stack.dat file
into the ‘source’ subdirectory and rename it stack.spi.
Run:
Output:
spider dam/spi @tipdeterm
 Program will ask “Aligned input file?” Type ‘stack’
 A box will appear showing a class average. Use the mouse to draw a box around
the TC portion
 Click “Select”
 The first 10 eigenvectors will be shown. Choose the factor(s) that show the greatest
variance at the ghost density position
 Close the program. It will ask you “Input first eigen number?” then “Input end eigen
number?”. Input the first and last factors you have chosen.
 It will ask “Input class numbers”. Type “3”.
/cls/clkm_imc_class_634_001.spi
clkm_imc_class_634_002.spi
clkm_imc_class_634_003.spi (three new sub-classes are formed)
8. A new interface will open that shows the total class average on the left and the class averages from
the new sub-classes on the right. Cycle through the three new sub-classes and identify the sub-class
that shows the ghost density. You can choose either 1 or 2 classes depending on which ones show
ghost density (removing 3 will remove the entire class). Record which classes contain ghost density.
9. If
you
identified
1
sub-class
to
remove,
copy
the
program
single_prog_create_remove_images_list_and_stack.spi to the ‘data***/cls’ directory. Use a text
editor to open this program and where it say [cls1]=*, change * to the sub-class number to be
removed. Change [set]=*** to the data set number (i.e. from data***). If you wish to remove two subclasses, copy program double_prog_create_remove_images_list_and_stack.spi instead and change
[cls1]=* and [cls2]=* to the two sub-classes to be removed. Also, copy /data***/stack.dat to the ‘cls’
directory (note: there is no program to remove three sub-classes as this will remove the entire class).
Run:
Output:
spider spi/dat @single_prog_create_remove_images_list_and_stack
or
spider spi/dat @double_prog_create_remove_images_list_and_stack
clkm_imc_class_634_00*.dat
remove_images_list_***.dat (list of images to removed)
remove_images_from_data***.dat (data set of images to be removed)
10. Having repeated steps 7-9 for each ‘data***’ directory containing misaligned images, consolidate all
the removed_images_from_data***.dat files into one directory. Then, use proc2d to combine all the
images into a single stack:

proc2d removed_images_from_data***.dat all_removed_stack.dat spider
Keep typing this line into the console, changing *** each time to correspond to the
remove_images_from_data***.dat files.
11. Create a new directory and copy the following files to it:
a. all_removed_stack.dat (from step 10)
b. APSH.spi
c. The original data set stack of all the images
Run:
spider spi/dat @ APSH
Output:
align_param.dat
This procedure will align each image in all_removed_stack.dat with the images in the original data
set stack and will identify which images need to be removed from the stack.
12. Each image identified in step 11 will be replaced with a blank image. This will remove the image from
the stack and thus will not contribute to the final reconstruction. However, this procedure does not
physically remove the image from the stack, meaning the image numbers will remain unchanged.
Create a new directory and copy the following:
a. align_param.dat (from step 11)
b. blank_stack.dat
c. prog_copy_blank_to_remove_images.spi
Run:
Output:
spider spi/dat @prog_copy_blank_to_remove_images
Bminus_ghost_images_removed.dat (of course, the output filename can be changed to
whatever you want in the program file)
13. We
want
to
apply
the
alignment
parameters
(align_parameters_afterprog1.dat,
align_parameters_afterprog2.dat and shift_param_from_taka_program.dat) to the new data set stack
from step 12. Create a new directory and copy the following:
a. align_parameters_afterprog1.dat,
align_parameters_afterprog2.dat
and
shift_param_from_taka_program.dat
b. Bminus_ghost_images_removed.dat (from step 12)
c. sel***.dat files from the classification (output files from spider cl km) which denote which
images contribute to which class
d. program_to_apply_shift_param_to_stack.spi
Run:
Output:
spider spi/dat @program_to_apply_shift_param_to_stack
AVERAGE_of_shifted_images.dat
14. Using this new stack with the mis-aligned images blanked out, we can make a new reconstruction.
Rename AVERAGE_of_shifted_images.dat (from step 13) to stack_rotated_shifted_xy.dat. Create a
new directory and copy the following:
a. 3_recontruction_Refinement.spi
b. stack_rotated_shifted_xy.dat (AVERAGE_of_shifted_images.dat renamed version)
c. make_angle.py
d. syndoc_needle.dat
e. reference_volume.dat (copy and rename Cordes_yflip.dat)
15. Analyse the reconstructions from each iteration and determine the best map