ISAC (Iterative Stable Alignment and Clustering) is a 2D classification algorithm. It sorts a given stack of cryo-EM particles into different classes that share the same view of a target protein. ISAC is based around iterations of alternating equal size k-means clustering and repeated 2D alignment routines.

• ISAC is the initial version as described in the original paper. At this point this implementation is obsolete and has been replaced by ISAC2 and GPU ISAC (see below).

• ISAC2 is an improved version of ISAC and used by default tool to produce 2D class averages in the SPHIRE (git) software package and the TranSPHIRE automated pipeline for processing cryo-EM data. ISAC2 is a CPU-only implementation and was developed to run on a computer cluster.

• GPU ISAC was developed to run ISAC2 on a single workstation by outsourcing its computationally expensive bottleneck calculations to any available GPUs, while simultaneously keeping its MPI-based CPU parallelization otherwise intact. GPU ISAC is currently only provided as an add-on to SPHIRE that can be installed manually (see below).

• GPU ISAC is currently developed as a manually installed add-on for SPHIRE and distributed as a .zip file that can be found here: GPU ISAC download link.

Before you start, make sure your SPHIRE environment is activated.

GPU ISAC comes with a handy installation script that can be used as follows:

1. Extract the archive to your chosen GPU ISAC installation folder.
2. Open a terminal and navigate to your installation folder.
3. Run the installation script:

   ./install.sh

All done!

An example call to use GPU ISAC looks as follows:

mpirun /path/to/sxisac2_gpu.py bdb:path/to/stack path/to/output --CTF -–radius=160 --target_radius=29 --target_nx=76 --img_per_grp=100 --minimum_grp_size=60 --thld_err=0.7 --center_method=0 --gpu_devices=0,1

Using the following mix of both mandatory and optional parameters (see below to learn which is which):

mpirun /path/to/sxisac2_gpu.py
bdb:path/to/stack
path/to/output
--CTF 
-–radius=160
--target_radius=29
--target_nx=76
--img_per_grp=100
--minimum_grp_size=60
--thld_err=0.7
--center_method=0
--gpu_devices=0,1

[ ! ] - Mandatory parameters in the GPU ISAC call:

• mpirun is not a GPU ISAC parameter, but is required to launch GPU ISAC using MPI parallelization (GPU ISAC uses both CPU/MPI and GPU/CUDA parallelization).
• /path/to/sxisac2_gpu.py is the path to your sxisac2_gpu.py file. If you followed these instructions it should be your/installation/path/gpu_isac_2.2/bin/sxisac2_gpu.py.
• path/to/stack is the path to your input .bdb stack. If you prefer to use an .hdf stack, simply remove the bdb: prefix.
• path/to/output is the path to your preferred output directory.
• --radius=160 is the radius of your target particle (in pixels) and has to be set accordingly.

[?] - Optional parameters in the GPU ISAC call:

• Using --gpu_devices you can set what GPUs to use. This example uses two GPUs with id values 0 and 1, respectively. You can check the id values of your available GPUs by executing nvidia-smi in your terminal (GPUs are sorted by capability, with 0 being your strongest GPU).
• You can also use --img_per_grp to limit the maximum size of individual classes. Empirically, a class size of 100-200 (30-50 for negative stain) particles has been proven successful when dealing with around 100,000 particles.
• Similarly, you can also use --minimum_grp_size to limit the minimum size of individual classes. In general, this value should be around 50-60% of your maximum class size.

GPU ISAC produces a multitude of output files that can be used to analyze the success of running the program, even while it is still ongoing. These include the following:

• Main iteration folders: As GPU ISAC is running, it performs multiple “main iterations” and “generations” that are stored within the output folder structure. New class averages are produced during every iteration / generation and can be looked at during runtime without having to wait for the overall process to conclude. This can help to quickly gauge the quality of a data set. Check path/to/output/mainXXX/generationYYY for the .hdf files to that contain any newly produced class averages.
• In both the main iteration folders and the base output folder you will find processed_images.txt files. These contain the indices of all processed particles and can be used to determine how many particles GPU ISAC did account for during classification.
• The final averages are stored in path/to/output/ordered_class_averages.hdf.