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| Single Particle Tomography in EMAN2 | = Single Particle Tomography in EMAN2 = |
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| === Wed PM Practical === SPT (single particle tomography) capabilities are relatively new in EMAN2. They were inspired by Michael Schmid's studies on sub-volume averaging (mostly on viruses), and a stubborn student insisting on doing extensive sub-volume averaging on chaperons. |
Unfortunately, this technique SPT is very computationally intensive (both in terms of memory and processing speed). 3GB of RAM is the bare minimum recommended to get through the tutorial. For more realistic SPT on full 3D alignments on large sets of large particles particles, 8GB of memory and the use of multiple processing units is advised. |
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| This session will cover the beta version of a small fraction of the SPT capabilities EMAN2 will eventually make available. | |
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| The session will cover: | == E2SPT USERS' GUIDE == |
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| * 1) SPT processing through EMAN2's workflow: e2workflow.py * 2) Sub-volume extraction from tomograms using e2tomoboxer.py * 3) "Preparation" of extracted particles for alignment {{{#!wiki warning |
* Single particle tomography USER'S GUIDE (updated on June 2012; under major refactoring do to extensive changes in e2spt capabilities; look for an extensively different version soon) |
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| For a myriad of reasons, it is NOT recommendable to align and average sub-volumes directly after extraction. }}} * 4) Reference based alignment and averaging |
[[attachment:e2spt_users_guide_06_2012.pdf| e2spt_users_guide_06_2012.pdf|&do=get]] |
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| All necessary software is provided as part of EMAN2. If you don't have EMAN2 installed, you can download the most updated version (for your specific platform, Windows, Linux or Mac), from here: http://ncmi.bcm.edu/ncmi/software/counter_222/software_86 | == DATA == Epsilon 15 virus data, used since the EMAN2 Workshop in 2010. |
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| We have prepared sample data for this tutorial. The tomogram in the link below comes from a tilt series of epsilon15 virus particles in vitro, recorded using Zernike phase-plate technology: | [[http://blake.grid.bcm.edu/wikifiles/SPT/e2spt_data.zip|e2spt_data.zip]] |
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| [[attachment:e15phaseplate.rec]] | TRiC chapeornin data (NOT READY). |
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| The following tomogram also comes from a tilt series of epsilon15 viruses in vitro but was recorded under conventional cryoEM imaging conditions: | [[attachment:e2spt_data_apoTRiC.zip| e2spt_data_apoTRiC.zip|&do=get]] |
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| [[attachment:e15normal.rec]] | == COMMANDS (for easy "copy-pasting" into the command line == |
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| {{attachment:imagefile.png|text describing image|width=100}} | === Monstrous command for alignment with e2spt_classaverage.py (used to be e2classaverage3d.py) === e2spt_classaverage.py --input=e15pp_set1_stack.hdf --output=e15pp_set1_aligned.hdf --ref=e15ref_prep_icos_bin2.hdf --npeakstorefine=1 -v 3 --mask=mask.sharp:outer_radius=48 --lowpass=filter.lowpass.gauss:cutoff_freq=.02:apix=4.401 --align=rotate_translate_3d:search=10:delta=8:dphi=8:verbose=1:sym=icos --parallel=thread:2 --ralign=refine_3d_grid:delta=3:range=9:search=2 --averager=mean.tomo --aligncmp=ccc.tomo --raligncmp=ccc.tomo --shrink=3 --shrinkrefine=3 --savesteps --saveali --iter=8 --normproc=normalize --sym=c1 --keep=0.8 --path=whatever_folder_I_want === Monstrous command for alignment with e2spt_hac.py (used to be e2tomoallvsall.py) === e2spt_hac.py -v 1 --path=AVSAs087 --input=CENTEREDvsD8aliVSapo_s087.hdf --shrink=3 --shrinkrefine=2 --iter=87 --mask=mask.sharp:outer_radius=36 --npeakstorefine=16 --preprocess=filter.lowpass.gauss:cutoff_freq=.02:apix=4.401 --align=rotate_translate_3d:search=4:dphi=12:delta=12 --parallel=thread:24 --ralign=refine_3d_grid:delta=3:range=12:search=2 --averager=mean.tomo --aligncmp=ccc.tomo --raligncmp=ccc.tomo --saveali --savesteps -v 2 --postprocess=filter.lowpass.gauss:cutoff_freq=.02:apix=4.401 --autocenter --exclusive_class_min=8 --normproc=normalize === Not-so-monstrous command for e2spt_simulation.py (used to be e2tomosim.py) === e2spt_simulation.py --input=../groRef_scaled_bin2.hdf --snr=5 --nptcls=2 --tiltstep=5 --tiltrange=60 --transrange=10 --saveprjs --noiseproc=math.addnoise === Semi monstrous command for e2spt_resolutionplot.py === e2spt_resolutionplot.py --vol1=half1avg.hdf --vol2=half2avg.hdf --output=whatever3.txt --npeakstorefine=1 --verbose=3 --shrink=3 --shrinkrefine=2 --mask=mask.sharp:outer_radius=36 --preprocess=filter.lowpass.gauss:cutoff_freq=.02:apix=4.401 --align=rotate_translate_3d:search=4:dphi=30:delta=30:sym=icos --parallel=thread:8 --ralign=refine_3d_grid:delta=15:range=30:search=2 --aligncmp=ccc.tomo --raligncmp=ccc.tomo --postprocess=filter.lowpass.gauss:cutoff_freq=.02:apix=4.401 --normproc=normalize --sym=icos === Decent command for e2spt_rotationalplot.py === e2spt_rotationalplot.py --input=initModel.hdf --output=toAs129avsaAVG.txt --daz=1 --shrink=1 --dalt=180 --mask=mask.sharp:outer_radius=28 === Command for e2spt_radialdensityplot.py === e2spt_radialdensityplot.py --vols=volA_aligned.hdf,volB_aligned.hdf --normproc=normalize.edgemean --lowpass=filter.lowpass.gauss:cutoff_freq=0.02:apix=4.401 --singleplot --output=volAali_VS_volBali.png |
Single Particle Tomography in EMAN2
Unfortunately, this technique SPT is very computationally intensive (both in terms of memory and processing speed). 3GB of RAM is the bare minimum recommended to get through the tutorial. For more realistic SPT on full 3D alignments on large sets of large particles particles, 8GB of memory and the use of multiple processing units is advised.
E2SPT USERS' GUIDE
* Single particle tomography USER'S GUIDE (updated on June 2012; under major refactoring do to extensive changes in e2spt capabilities; look for an extensively different version soon)
DATA
Epsilon 15 virus data, used since the EMAN2 Workshop in 2010.
TRiC chapeornin data (NOT READY).
COMMANDS (for easy "copy-pasting" into the command line
Monstrous command for alignment with e2spt_classaverage.py (used to be e2classaverage3d.py)
e2spt_classaverage.py --input=e15pp_set1_stack.hdf --output=e15pp_set1_aligned.hdf --ref=e15ref_prep_icos_bin2.hdf --npeakstorefine=1 -v 3 --mask=mask.sharp:outer_radius=48 --lowpass=filter.lowpass.gauss:cutoff_freq=.02:apix=4.401 --align=rotate_translate_3d:search=10:delta=8:dphi=8:verbose=1:sym=icos --parallel=thread:2 --ralign=refine_3d_grid:delta=3:range=9:search=2 --averager=mean.tomo --aligncmp=ccc.tomo --raligncmp=ccc.tomo --shrink=3 --shrinkrefine=3 --savesteps --saveali --iter=8 --normproc=normalize --sym=c1 --keep=0.8 --path=whatever_folder_I_want
Monstrous command for alignment with e2spt_hac.py (used to be e2tomoallvsall.py)
e2spt_hac.py -v 1 --path=AVSAs087 --input=CENTEREDvsD8aliVSapo_s087.hdf --shrink=3 --shrinkrefine=2 --iter=87 --mask=mask.sharp:outer_radius=36 --npeakstorefine=16 --preprocess=filter.lowpass.gauss:cutoff_freq=.02:apix=4.401 --align=rotate_translate_3d:search=4:dphi=12:delta=12 --parallel=thread:24 --ralign=refine_3d_grid:delta=3:range=12:search=2 --averager=mean.tomo --aligncmp=ccc.tomo --raligncmp=ccc.tomo --saveali --savesteps -v 2 --postprocess=filter.lowpass.gauss:cutoff_freq=.02:apix=4.401 --autocenter --exclusive_class_min=8 --normproc=normalize
Not-so-monstrous command for e2spt_simulation.py (used to be e2tomosim.py)
e2spt_simulation.py --input=../groRef_scaled_bin2.hdf --snr=5 --nptcls=2 --tiltstep=5 --tiltrange=60 --transrange=10 --saveprjs --noiseproc=math.addnoise
Semi monstrous command for e2spt_resolutionplot.py
e2spt_resolutionplot.py --vol1=half1avg.hdf --vol2=half2avg.hdf --output=whatever3.txt --npeakstorefine=1 --verbose=3 --shrink=3 --shrinkrefine=2 --mask=mask.sharp:outer_radius=36 --preprocess=filter.lowpass.gauss:cutoff_freq=.02:apix=4.401 --align=rotate_translate_3d:search=4:dphi=30:delta=30:sym=icos --parallel=thread:8 --ralign=refine_3d_grid:delta=15:range=30:search=2 --aligncmp=ccc.tomo --raligncmp=ccc.tomo --postprocess=filter.lowpass.gauss:cutoff_freq=.02:apix=4.401 --normproc=normalize --sym=icos
Decent command for e2spt_rotationalplot.py
e2spt_rotationalplot.py --input=initModel.hdf --output=toAs129avsaAVG.txt --daz=1 --shrink=1 --dalt=180 --mask=mask.sharp:outer_radius=28
Command for e2spt_radialdensityplot.py
e2spt_radialdensityplot.py --vols=volA_aligned.hdf,volB_aligned.hdf --normproc=normalize.edgemean --lowpass=filter.lowpass.gauss:cutoff_freq=0.02:apix=4.401 --singleplot --output=volAali_VS_volBali.png