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EMAN2 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. EMAN2 is capable of processing large data sets (>100,000 particle) very efficiently.
Please also note that this is not the (related) EMEN2 electronic notebook, but is EMAN2, a scientific image processing suite.
Please Cite (and read)
EMAN is free software, supported by NIH Grants. It is critical that you cite EMAN2 when you use it in a publication in any significant way, to help us document usage when trying to renew our funding. Feel free to cite any of these:
Primary EMAN2 paper:
G. Tang, L. Peng, P.R. Baldwin, D.S. Mann, W. Jiang, I. Rees & S.J. Ludtke. (2007) EMAN2: an extensible image processing suite for electron microscopy. J Struct Biol. 157, 38-46. PMID: 16859925
Subnanometer resolution subtomogram averaging using PPPT methods:
- Chen M, Bell JM, Shi X, Sun SY, Wang Z, Ludtke SJ. Nat Methods. (2019) A complete data processing workflow for cryo-ET and subtomogram averaging, Nat. Methods, 16(11):1161-1168, PMC31611690
Neural Network Based Tomogram Segmentation
- Chen M, Dai W, Sun SY, Jonasch D, He CY, Schmid MF, et al. (2017). Convolutional neural networks for automated annotation of cellular cryo-electron tomograms. Nat. Methods, 14(10), 983–5. PMCID: PMC5623144
Neural Network Particle Picker
- Bell JM, Chen M, Durmaz T, Fluty AC, Ludtke SJ. (2018). New software tools in EMAN2 inspired by EMDatabank map challenge. J. Struct. Biol. Elsevier;, 204(2), 283–90.
EMAN2 high resolution refinement methods:
J.M. Bell, M. Chen, P.R. Baldwin & S.J. Ludtke. (2016) High Resolution Single Particle Refinement in EMAN2.1. Methods. 100, 25-34. PMC4848122
Methods for analysis of conformational and compositional variability:
- Ludtke, S. J. "Single-Particle Refinement and Variability Analysis in EMAN2.1." in Methods Enzymol 579159-189 (Elsevier, United States, 2016). PMC5101015
Tutorials (Full PDF tutorials with data covering many different tasks)
YouTube Tutorials (Archived video tutorials and mini-tutorials)
Advanced Tutorials (A few simple Python-level tutorials for programmers and Power Users)
Clusters (Running EMAN2 on clusters and multi-core workstations)
File Descriptions (Folders and files in an EMAN2 Project)
Standards (File Formats, Symmetry, Box Size, etc.)
Programs (Individual Program Documentation)
GPGPU Computing (use the graphics processor for image processing)
Old Docs (Out of date documentation)
Ask For Help
We prefer to provide assistance via the Google group below, since this archives all discussions and makes them searchable. You must join the group to post, but can browse/search old content anonymously.
http://groups.google.com/group/eman2 (Main discussion list for EMAN2)
http://groups.google.com/group/eman2-developers (Discussions among developers, likely less interesting for users)
FAQ - Please ask your questions in the Google Group which has a searchable archive. This page is somewhat out of date
NOTE - If you are located in a country that blocks Google (China) or prefer not to post publicly, please feel free to email firstname.lastname@example.org directly. The Google Group is used because it creates a persistent searchable archive of past questions, but direct emails are completely acceptable.
Advanced Users & Programmers (Python)
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.
All EMAN2 programs, including GUI programs, are written in the easy-to-learn Python scripting language. This permits knowledgeable end-users to customize any of the code with unprecedented ease. If you aren't an advanced user, you can still make use of the integrated GUI and all of EMAN2's command-line programs.