Dar All,

Exactly based on “Case Study: Yeast U4/U6.U5 tri-snRNP” in the cryosparc tutorial, I have run a 4-class ab-initial reconstruction. Using the output particle and output volume from each class from the ab-initial reconstruction step, I run a homogeneous refinement. For class 0 from the ab-initial reconstruction step, the resolution of the homogeneous refinement step was 6.52. For class 1 from the ab-initial reconstruction step, the resolution of the homogeneous refinement step was 5.77.

Using the output volume from each class from the ab-initial reconstruction step and all particles input for the above mentioned ab-initial reconstruction step, I run a homogeneous refinement. For class 0 from the ab-initial reconstruction step, the resolution of the homogeneous refinement step was 3.71. For class 1 from the ab-initial reconstruction step, the resolution of the homogeneous refinement step was 3.82.

We can see, with the same class volume from the ab-initial reconstruction step, if we used the full stack particles as input for the ab-initial step, the map resolution output from the homogeneous refinement step was much higher.

Then can we conclude that, the model volume used for the homogeneous refinement step, can heavily lead to the output model volume bias?

I am looking forward to getting a reply from you.

Flemming