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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. |
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| This session will cover the beta version of a small fraction of the SPT | 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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| == OUTLINE == * 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. [Details later. For a myriad of reasons, it is NOT recommendable to align and average sub-volumes directly after extraction without "preparing" them first]. * 4) Reference based alignment and averaging |
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| == SOFTWARE == 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]] |
== TUTORIAL DOCUMENT == |
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| == RAW DATA == 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: |
[[attachment:e2spt_users_guide_06_2012.pdf| e2spt_users_guide_06_2012.pdf|&do=get]] |
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| [[attachment:e15phaseplate.rec]] | == DATA == Epsilon 15 virus data, used since the EMAN2 Workshop in 2010. |
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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: | [[http://blake.grid.bcm.edu/wikifiles/SPT/e2spt_data.zip|e2spt_data.zip]] |
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| [[attachment:e15normal.rec]] | TRiC chapeornin data (NOT READY). |
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| == BOXING == You have two options for opening the tomogram for purposes of boxing it. |
[[attachment:e2spt_data_apoTRiC.zip| e2spt_data_apoTRiC.zip|&do=get]] |
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| 1) Directly, by typing e2tomoboxer.py followed my the path to the tomogram file at the commandline. | == Monstrous command for alignment with e2spt_classaverage.py (used to be e2classaverage3d.py) == |
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| 2)Or you can launch e2workflow.py from the commandline and access a tomogram through the browser in the tomographic menu. | 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 --preprocess=filter.lowpass.gauss:cutoff_freq=.02 --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 |
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| [In theory, you can open a tomogram for contemplation purposes by typing: "e2display.py <my_tomogram_path_name_goes_here>" at the commandline. | |
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| /!\ This is NOT recommendable, unless you have a grossly large (VERY, VERY large) amount of virtual memory on your computer; otherwise, catastrophe WILL befall upon you]. | |
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| Let's explore the FIRST APPROACH. | == Monstrous command for alignment with e2spt_hac.py (used to be e2tomoallvsall.py) == |
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| === OPENING A TOMOGRAM DIRECTLY WITH e2tomoboxer.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.
TUTORIAL DOCUMENT
DATA
Epsilon 15 virus data, used since the EMAN2 Workshop in 2010.
TRiC chapeornin data (NOT READY).
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 --preprocess=filter.lowpass.gauss:cutoff_freq=.02 --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