Cryo-RAlib - Accelrating alignment in cryo-EM

This is the repository that contains GPU-accelerated multireference alignment code for cryo-EM image processing. The code is used in the 2020 NCHC GPU Hackathon.

Team member: Szu Chi Chung, Cheng-Yu Hung, Huei-Lun Siao, Hung-Yi Wu, Institute of Statistical Science, Academia Sinica

Mentor: Ryan Jeng, Nvidia

• Multireference alignment (Class averages)

• Reference-free alignment (Average of the first 10 iterations)

Manuscript:

Cryo-RALib -- a modular library for accelerating alignment in cryo-EM. Szu-Chi Chung, Cheng-Yu Hung, Huei-Lun Siao, Hung-Yi Wu, Wei-Hau Chang, I-Ping Tu. https://arxiv.org/abs/2011.05755

Benchmark

• Multireference alignment We compared the CPU implementation from EMAN2 and used Ribosome 80s benchmark dataset but downsampling to 90 pixels. The following chart is running on TWCC c.super instance. The xr, yr, ou, maxit is set to 3,3,36,6 respectively.

The speedup is 22x~37x with different reference number.

• Reference-free alignment

The following chart is running on TWCC c.super instance. The ts, ou, maxit is set to 1,36,6 respectively.

The speedup is 2.4x~9.4x with different 2D shifts.

How to use the library

1. Install

• Install EMAN2 and Sphire:
  • Please install EMAN2.31.
  • If you would like to use the version that uses CuPy, please install according to here.

Note you may need to relink the nvrtc library ln -s /usr/local/cuda/lib64/libnvrtc-builtins.so.10.0.130 /usr/local/EMAN2/lib/libnvrtc-builtins.so

2. Setup

• $ ./install.sh
• You may need to modify the shebang line in the python scripts to point out the location of the EMAN2 library.

3. Test

Test data can be downloaded from here. See the Example Notebook for more details.

• Multi-reference alignment

  • mpirun -np 4 test_mref_cheng_yu_bdb_cuda.py rib80s_ori_bin.hdf rib80s_ref.hdf out --ou=36 --xr=3 --yr=3
  • mpirun -np 4 test_mref_gpu_align.py rib80s_ori_bin.hdf rib80s_ref.hdf out --ou=36 --xr=3 --yr=3

• Reference-free alignment

  • mpirun -np 4 test_reffree_gpu_align.py rib80s_ori_bin.hdf out --ou=36 --xr=3 --yr=3 --ts=1

Notebook

The python environment exposed by EMAN2 can be couple with CuPy and other libraries for drop-in acceleration and visualization. See the Example Notebook where we accelerate the rotation and shift operations by five-fold and visualize the results.

The library can be used for exploratory data analysis as demonstrated in the notebook. You will need the utils_ralib.py and scikit-learn for analysis.

License

Cryo-RAlib is open source software released under the GNU General Public License, Version 3.

Credit

Some of the code is built upon Cuda code from gpu_isac 2.3.2.