Tim Baker's lab at University of California, San Diego has made publically available several data sets designed to show people how to use the auto3dem package to generate reconstructions of icosahedral viruses (and other macro-molecular complexes that have icosahedral or 532 point group symmetry). The EMC keeps copies of two of these data sets on an IUB computing cluster disk array: a very well-behaved ~2000 particle Reovirus data set that can be used to generate an ~10 Å resolution reconstruction using three commands (!) and an ~600 particle bacteriophage P22 data set that illustrates the entire reconstruction process in more detail.
These data sets come with all the instructions necessary to use them. Anyone should be able to copy the data sets to a machine that runs the auto3dem package, follow the instructions found with each and produce the correct final results. The additional details provided here are intended to show users how to do the data processing using the IUB computing clusters (specifically the Karst cluster) and to show some ways the EMC has implemented to do additional analysis of the image processing steps and results. In addition, because the auto3dem package is continually changing and the documentation in the various demos does not necessarily reflect the recent changes, the description here provides commands and commentary that work with the current auto3dem release installed on the cluster (version 4.05, as of spring, 2015).
NOTE: The data sets are stored on DCWAN as compressed archives (tar files, aka tarballs) and must be un-compressed before use. All these details are described in the individual sections below, but the linux commands used are only briefly described. Information about the individual linux commands and a brief overall description of how linux works ore offered. Further questions about the use of the computing clusters should be directed to David Morgan at email@example.com. These commands will begin with a $ (the usual linux promote) and any comments about the command will be placed on the same line but separated from it using a # character (the pound sign).
It will be convenient in the discussion below to have a simple of understanding of how auto3dem works and the "units of data/information" that it utilizes. The auto3dem package is an example of one of the many different model-based procedures that are used in cryoTEM single particle reconstruction schemes. Other packages (e.g., EMAN, IMAGIC, SPIDER and XMIPP) use their own type of model-based alignment and reconstruction algorithms, and the details of auto3dem's Polar Fourier Transform (PFT) method will not be discussed here. The initial publication that describes the PFT procedure is a great place to learn the procedure's details, and David Morgan would be happy to share what he knows with anyone who asks.
When such model-based alignment procedures are used, a reference three-dimensional (3d) model is projected over a range of angles, individual images (or class-averages, though the rest of this discussion will use the term "individual images" to refer to either) are aligned to each projection of the 3d model and the "best match" between an individual image and a specific projection of the 3d model is selected. After every individual image has been compared to each of the model projections, the individual images are assigned the projection angles of the best matched 3d model projection and a new 3d model is then generated from the individual images and these projection angles. Every model-based alignment and 3d reconstruction procedure follows this sort of general scheme, though there is considerable variation in the details of how the different steps are accomplished. In the discussions below, the procedures of 3d model projection and their comparison to the individual images will simply be referred to as an alignment or a cycle of aligning/alignment.
The auto3dem package performs exactly this sort of model-based alignment and reconstruction and is designed to be used in an iterative fashion (i.e., once a new 3d model has been generated, a new set of projections are calculated, the individual images are again aligned to these new projections and those results are used to create an even newer 3d model. Iteration occurs for any number of cycles of alignment and 3d model creation, and it is the user's responsibility to determine when enough iterations have been performed. The user can also adjust parameters that control details of the alignment and reconstruction steps, with the goal of getting a better and better 3d model.