IsoNet: Isotropic Reconstruction of Electron Tomograms with Deep Learning

IsoNet is a deep learning-based software package that iteratively reconstructs the missing-wedge information and increases signal-to-noise ratio, using the knowledge learned from raw tomograms. Without the need for sub-tomogram averaging, IsoNet generates tomograms with significantly reduced resolution anisotropy. Applications of IsoNet to three representative types of cryoET data demonstrate greatly improved structural interpretability: resolving lattice defects in immature HIV particles, establishing architecture of the paraflagellar rod in Eukaryotic flagella, and identifying heptagon-containing clathrin cages inside a neuronal synapse of cultured cells. detect AMR genes from thirteen genomes of Pseudomonas strains.


References:

Documentation
Important Notes

Interactive job
Interactive jobs should be used for debugging, graphics, or applications that cannot be run as batch jobs.

Allocate an interactive session and run the program. Sample session:

[user@biowulf]$ sinteractive 
[user@cn3107 ~]$ module load IsoNet
[+] Loading singularity  3.10.5  on cn4183
[+] Loading CUDA Toolkit  10.2.89  ...
[+] Loading cuDNN/7.6.5/CUDA-10.2 libraries...
[+] Loading IsoNet  0.2.1
[user@cn3107 ~]$ isonet.py -h 
INFO: Showing help with the command 'isonet.py -- --help'.

NAME
    isonet.py - ISONET: Train on tomograms and restore missing-wedge

SYNOPSIS
    isonet.py -

DESCRIPTION
    for detail discription, run one of the following commands:

    isonet.py prepare_star -h
    isonet.py prepare_subtomo_star -h
    isonet.py deconv -h
    isonet.py make_mask -h
    isonet.py extract -h
    isonet.py refine -h
    isonet.py predict -h
    isonet.py resize -h
    isonet.py gui -h

[user@cn3107 ~]$ isonet.py gui 

End the interactive session:
[user@cn3107 ~]$ exit
salloc.exe: Relinquishing job allocation 46116226
[user@biowulf ~]$