Reconstruction via Random Conical Tilt (RCT)

In EMAN2 an initial model can be constructed using RCT and a stack of untilted and tilted particles whose set element relationship is on-to-one. Typically the tilt angle between the unilted and tilted is at least 45 degrees, but usually 60 degrees. A larger tilt angle is desireable because RCT produces reconstructions with a missing cone, in Fourier space, and a larger tilt angle reduces the cone volume. In some cases -45 and 45 degree tilt data are collected to remove the missing cone altogether. To do a RCT reconstruction in EMAN2:

1. Pick untilted/tilted particle pairs using e2RCTboxer.py. While WEB/JWEB or DoG tiltpicker can be used, it is recommended to use e2RCTboxer.py ensuring that image attributes will be correct and avoiding file conversion (which can be a bit of a headache)
2. Run: e2refine2d.py on the untilted particle stack. Examine the class averages, and note the rubbish ones. These will be excluded from the rct step.
3. Run: e2rct.py using the data and options described below.

e2rct.py options

Where options are:

• --path: directory that the RCT results will be placed
• --untiltdata: The stack of untilted particle images (usually this will be in the database)
• --tiltdata: The stack of tilted particle images (usually this will be in the database)
• --classavg: The stack of classaverages created by e2refine2d.py (usually this will be in the database)
• --minproj: The minimum number of projections required for a RCT recon
• --sym: RCT recon symmetry
• --stagetilt: The angle used to tilt the stage during data collection
• --careject: A comma delimted list of class averages to reject
• --align: Center the tilted images before RCT reconstruction (impoves recon quality) (Boolean toggle switch)
• --maxshift: Maximum to shift the tilted images during centering
• --tiltaxis: Do a per micrograph tilt axis correction, which improves quality if there is large tiltaxis variation. (only works for e2RCTboxer.py data) (Boolean toggle switch)
• --avgrcts: Align in 3D and average each of the RCT recons, from each classaverage (Boolean toggle switch)
• --preprocess: Preprocess the RCT recons before 3D alignment usign an EMAN2 processor
• --alignran: Finess of the 3D alignment search
• --weightrecons: During averaging weight the RCT recons by number of tilted images used to create each one
• --verbose: Set the verbosity level

e2rct.py algorithm

e2rct.py reads in the class averages from e2refine2d.py and extracts the particles making up each class average. The in plane rotation for each untilted particle in each class is used in conjuction with the stage tilt, and optionally tiltaxis, to insert the tilted particle into the RCT recon, after optional centering. The net result is a RCT recon for each class average. These RCT recons are then, optioanlly preprocessed and aligned, and averaged.

Strategies for success

• Set --minproj to a reasonable level, at least 30. If the number of projection in a RCT recon is too small then the RCT recon will be very poor quality
• If 3D alignment is chosen, then preprocess the RCT recon by aggressively lowpass filteration to boost the signal to noise ratio, enabling a decent 3D alignment
• Reject any rubbish looking class averages (smeary, not CTF rings, etc). Binning the images into self simiar bins is critical, if the images ina classaverage are random/junk, then your RCT will be random/junk
• Unless your tilted particles are precentered, use --align