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.
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 ~]$