Thank you so much for your detailed response, Carlos! I completely agree with your points, especially regarding the fact that no single workflow can be universally applied to all datasets. It truly depends on the specific characteristics of the data and the experimental goals.
In my work, I have also found that each dataset requires a slightly different approach. For instance, I typically use particles from bin2 or bin3 for 2-3 rounds of 2D classification because I find 2D classification to be more efficient for rapid screening and visualization. If the data contains significant heterogeneity, I tend to be more cautious about whether to proceed with 3D classification to avoid over-screening and losing useful information.
Regarding ab-initio, I fully agree with your point that it is a time-consuming but essential step. In my experience, ab-initio plays a crucial role in generating initial models, especially when dealing with complex or flexible particles. I often adjust certain parameters to make the calculation more “strict.” While this increases the runtime (sometimes exceeding 24 hours), it also helps me better generate high-quality volume maps.
In practice, my workflow is as follows:
- After completing 2D classification, I proceed directly to ab-initio to generate several initial model classes.
- Then, I perform hetero-refine on each class to gradually remove noisy particles and异构体 while optimizing the map quality.
- After a few rounds of “ab-initio → hetero-refine,” I select the best-performing class and move on to nu-refine.
This process has become somewhat of a habit for me. Following these steps, I typically proceed with 3D classification, 3DVA, 3DFlex, etc., to organize different dynamic or conformational states and attempt to further improve resolution. Of course, local refinement is also a critical step in this workflow.
However, recently, I noticed that my new colleague follows a slightly different approach: after completing 2D classification, he directly performs ab-initio and then applies nu-refine to each class before proceeding with hetero-refine. This is quite different from what I have learned so far, which makes me wonder if there are other diverse workflows for data processing.
In my view, the key to successful data analysis lies in understanding the function of each step and applying them flexibly based on the specific characteristics of the dataset and the experimental goals. No single method can guarantee perfect results for all datasets, so trial and error are inevitable. These are just some of my current thoughts and experiences—I’d love to hear more insights and suggestions from everyone!
Cheers!
Zhe