Particle picking and generation of data set
1. Particles are selected from micrographs using the helixboxer tool of EMAN2, with
box sizes of 120 x 215 px. The needle is aligned along the box vertical axis and
the top of the TC is placed 30 px from the top of the box (guided by the Mac app
“Rulers”).
EMAN2 program: e2helixboxer.py
Terminal command: e2helixboxer.py <input>
- Input:
micrograph <*.tiff>
- Output: Images <*.spi>
2. Images of selected particles are individually examined by eye and those
displaying structural degradation or incorrect positioning of the TCs are removed.
3. Images are trimmed/padded to the correct size (120 px x 215 px).
Matlab program: Trimming
- Copy images <*.spi> to same directory as trimming program
Matlab command: T=prog;
- Input:
*.spi
- Output: Stack file <stacker.dat>
4. Images in stacker.dat are normalized and ramped in SPIDER
SPIDER program: normalise_stack.spi
- Copy stacker.dat to same directory as normalse_stack.spi
Terminal command: spider spi/dat @normalise_stack
- Input: stacker.dat
- Output: dataset_ramp_norm.dat
5. Images are aligned in the X and Y axes
Matlab program: 2D_align
- Copy dataset_ramp_norm.dat to same directory as 2D_align program
Matlab command: main;
- Input: dataset_ramp_norm.dat
-
Output: aligned.dat
6. Images are padded/clipped to 200 x 200 px and a tight mask is applied to reduce
the background
SPIDER program: clip_to_200.spi and tightmask.spi
- Copy aligned.dat to same directory as programs
Terminal command: spider spi/dat @clip_to_200.spi
spider spi/dat @tightmask.spi
- Input: aligned.dat
- Output: datasest_200clip.dat
Correspondence analysis and generation of class averages
7. Correspondence analysis of images in data set
SPIDER operation: CA S
SPIDER commands: ca s
dataset_200clip@******
1-n
(change to number of images)
*
50
C
5
coran
8. Reconstitution of eigenvectors
SPIDER operation: CA SRE
SIPDER commands: do lb1 [k]=1,50
ca sre
coran
[k]
eigen@****[k]
lb1
-
Output: eigen.dat
9. Assess eigenvectors in eigen.dat and choose vectors which display strong helical
signal
10. Classification by k-means clustering
SPIDER operation: CL KM
SPIDER commands: cl km
coran_IMC
n
n,n,n
0
2129
sel***
obj
(usually make this 20 images per class)
(change to selected factors in step 9)
11. Create class averages
SPIDER operation: AS
SPIDER commands: do lb1 [k]=1,n (change to number of classes selected)
as
dataset_200clip@*****
sel***[k]
A
avg@***[k]
_1
lb1
12. Inspect the class averages and remove classes that do no display helical
information from the needle or that show structural degradation/loss of the TC
(.e.g using EMAN)
13. Apply a band-pass filter to the data set of low-pass = 8, high-pass = 200
Terminal command: proc2d avg.dat avg_temp.dat lp=8 hp=200 apix=2.12 spider
14. Copy avg_temp.dat to a new stack called stackfinal.dat
3D reconstruction
15. Correct in-plane rotations of images
SPIDER program: 1_rotation_tip_Refinement.spi
SIPDER commands: spider spi/dat @1_rotation_tip_Refinement.spi
- Input: stackfinal.dat, make_angle.py, symdoc_needle.dat,
reference_volume.dat
- Output: stack_rotated.dat
16. Correct X and Y shifts of class averages by aligning class averages with mirrored
class average
SPIDER program: mirror_av.spi
2_xyshift_tip_Refinement.spi
Terminal commands: spider spi/dat @mirror_av
spider spi/dat @2_xyshift_tip_Refinement
- Input: stack_rotated.dat,
- Output: stack_mirror_av.dat, stack_rotated_shifted.dat
17. Correct X and Y shifts of class averages using semi-interactive adjust program
Python program: tkmanualshift.py
Terminal command: python tkmanualshift.py
- Input: stack_rotated_shifted.dat
- Output: shift_param.dat
18. Apply shifts to class averages
SPIDER program: takashift.spi
Terminal command: spider spi/dat @takashift
- Input: stack_rotated_shifted.dat, shift_param.dat
- Output: stack_rotated_shifted_xy.dat
19. Copy stack_rotated_shifted_xy.dat to a new stack called
stack_rotated_shifted_xy_final.dat
20. Create 3D volume
SPIDER program: 3_reconstruction_Refinement.spi
Terminal command: spider spi/dat @3_reconstruction_Refinement
- Input: stack_rotated_shifted_xy.dat, make_angle.py,
symdoc_needle.dat, reference_volume.dat
- Output: vol_raw_iter00n.dat, vol_raw_iter00n_hel.dat