EMAN2 is the successor to EMAN1. It is a broadly based greyscale scientific image processing suite with a primary focus on processing data from transmission electron microscopes. EMAN's original purpose was performing single particle reconstructions (3-D volumetric models from 2-D cryo-EM images) at the highest possible resolution, but the suite now also offers support for single particle cryo-ET, and tools useful in many other subdisciplines such as helical reconstruction, 2-D crystallography and whole-cell tomography. Image processing in a suite like EMAN differs from consumer image processing packages like Photoshop in that pixels in images are represented as floating-point numbers rather than small (8-16 bit) integers. In addition, image compression is avoided entirely, and there is a focus on quantitative analysis rather than qualitative image display.
Author: Prof. Steve Ludtke
EMAN2 requires some environment variables to be set. The simplest way is to use environment modules:
module load eman2
For those adventurous users who prefer to use bleeding edge versions, the daily release is available using modules as well:
module load eman2/daily
Create a PBS batch script along the following lines:
#!/bin/bash #PBS -N EMAN2 #PBS -m be #PBS -k oe # empty the local /scratch area, just in case clearscratch # change to the current working directory from which the script was submitted. cd $PBS_O_WORKDIR # set the environment properly module load eman2 # Run refinement. Make sure to replace the input, output, and reference files, # as well as any options needed. This command is designed to run on four c16 nodes, using 8 # threads each and storing temporary files in /scratch. e2refine.py \ --parallel=mpi:32:/scratch \ --input=bdb:sets#set2-allgood_phase_flipped-hp \ --mass=1200.0 \ --apix=2.9 \ --automask3d=0.7,24,9,9,24 \ --iter=1 \ --sym=c1 \ --model=bdb:refine_02#threed_filt_05 \ --path=refine_sge \ --orientgen=eman:delta=3:inc_mirror=0 \ --projector=standard \ --simcmp=frc:snrweight=1:zeromask=1 \ --simalign=rotate_translate_flip \ --simaligncmp=ccc \ --simralign=refine \ --simraligncmp=frc:snrweight=1 \ --twostage=2 \ --classcmp=frc:snrweight=1:zeromask=1 \ --classalign=rotate_translate_flip \ --classaligncmp=ccc \ --classralign=refine \ --classraligncmp=frc:snrweight=1 \ --classiter=1 \ --classkeep=1.5 \ --classnormproc=normalize.edgemean \ --classaverager=ctf.auto \ --sep=5 \ --m3diter=2 \ --m3dkeep=0.9 \ --recon=fourier \ --m3dpreprocess=normalize.edgemean \ --m3dpostprocess=filter.lowpass.gauss:cutoff_freq=.1 \ --pad=256 \ --lowmem \ --classkeepsig \ --classrefsf \ --m3dsetsf -v 2 e2bdb.py -cF
Then submit the job, allocating the appropriate number of processors. In this case, we are running 32 threads, and therefore use a four c16 nodes (the c16 nodes are running 'hyperthreading', so that it appears to have twice as many cpus).
$ qsub -l nodes=4:c16 EMAN2.sh
See Parallel Processing in EMAN2 for more information about running EMAN2 in parallel.
See the EMAN2 main page