Comments (7)
That part of the protocol (GoodScoringModelSelector.py) is superseded by @iecheverria and @ichem001 's new methods for selecting models for analysis. So not sure if it is worth investing a lot of time in revamping this script. Only the actin tutorial (and perhaps a couple of older application papers?) use this. Perhaps the actin tutorial should be updated to include the new analysis protocol?
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@saltzberg -
Instead of writing one giant RMF file per sample - maybe we could write one small RMF and a big DCD file for each sample - and this will also make deposition to Zenodo almost automatic since we need DCD files at the end of the day - might kill two birds with one stone -
We could either link both DCD files to the ensembles or concatenate the DCD file with catDCD from the VMD/NAMD group.
what do you think?
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Instead of writing one giant RMF file per sample - maybe we could write one small RMF and a big DCD file for each sample
This is essentially what happens internally anyway - everything is converted to a monstrous numpy array of coordinates, which is about as efficient as it can be. I don't much like DCD as a long-term solution since you lose all of the topology information and can only store coordinates. I'd rather overhaul RMF to make it more efficient at storing multiple conformations (on my lengthy list of things to fix).
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@ichem001
The single large RMF that I am talking about are replacing the ./analysis/sample_A/tons_of_one_frame.rmf3s
, not the final output.
Reading individual RMF files with rmf_slice
is exceedingly slow...almost half of the total time for clustering. The PMI_analysis run_extract_models.py
step can be changed to output two RMF files (sample_A and sample_B) for each cluster. These can be read into imp-sampcon an order of magnitude faster than individual RMFs for each model.
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Related Issues (20)
- Debug print outs need to be removed
- feature request - more options for subunit seection in RMSD HOT 4
- Consolidate duplicated code HOT 1
- Ambiguity and alignment does not work. HOT 12
- pyRMSD's pairwiseRMSDMatrix not taking ambiguity into account HOT 5
- Parallelizing the clustering step to improve efficiency
- EM map and localization densities are not aligned HOT 1
- Parallelize the I/O step before clustering
- Multi-element symm groups
- Domain densities unavailable
- Allow for complexes while setting permutations for ambiguity
- selection of ranges in pdb files? HOT 1
- Large memory usage with many cores in clustering
- Add extra metadata to cluster centroid RMF to aid in mmCIF generation
- rmf_slice output in select_good_scoring_models is long and not helpful HOT 1
- select_good_scoring_models does not create scoreX.txt files
- Handle multi-state models HOT 1
- Handle ambiguity HOT 1
- Merge into IMP proper HOT 3
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