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1. Denoise

Denoising can vastly improve particle picking by helping to increase signal to noise in low-dose micrographs. Different denoising algorithms exist, include deep denoisers Topaz (Bepler et al., 2020) and Janni (Wagner and Raunser., 2020) and various Gaussian and fourier space denoisers. PartiNet implements a modified heuristic Wiener filter denoiser based on the method from CryoSegNet (Gyawali et al., 2024). PartiNet's implementation introduces multiprocessing, allowing for high-throughput denoising of large datasets, as well as saving in .mrc format if you prefer to perform picking on denoised micrographs in RELION or CryoSPARC.

denoise workflow

Parameters​

Required Parameters​

ParameterDescriptionExample
--sourceDirectory containing motion-corrected micrographs in MRC format/data/partinet_picking/motion_corrected
--projectParent project directory where all outputs will be saved/data/partinet_picking

Optional Parameters​

ParameterTypeDefaultDescription
--num_workersintmax available CPUsNumber of CPU workers for processing
--img_formatstringpngOutput format for denoised images (png, jpg, mrc)

Input Requirements​

Motion-Corrected Micrographs​

Your motion-corrected micrographs should ideally meet these criteria in order for the denoising to work correctly:

  • Format: single-slice MRC files from RELION or CryoSPARC
  • Motion correction: Total full frame motion should be less than 100 pixels
  • CTF estimation: CTF fit resolution should be less than 10 Angstroms
  • Convergence: Motion correction and CTF estimation should have converged appropriately

Quality Control Check​

In CryoSPARC, you can verify micrograph quality using the Manually Curate Exposures job:

  • Navigate to: Processing → Exposure Curation → Interactive Job: Manually Curate Exposures
  • Check motion and CTF fit parameters for each micrograph
  • Remove micrographs that don't meet quality criteria

Directory Structure​

Your motion-corrected directory should contain:

motion_corrected/
├── micrograph_001_fractions_patch_aligned.mrc
├── micrograph_002_fractions_patch_aligned.mrc
├── micrograph_003_fractions_patch_aligned.mrc
└── ...

Setup Instructions​

1. Create Project Directory​

mkdir partinet_picking
cd partinet_picking
mkdir motion_corrected

2. Transfer Motion-Corrected Micrographs​

From CryoSPARC:

# Using symbolic links (faster, saves space)
ln -s /path/to/cryosparc/project/job_number/motioncorrected/*_fractions_patch_aligned.mrc motion_corrected/

# Using rsync (copies files)
rsync /path/to/cryosparc/project/job_number/motioncorrected/*_fractions_patch_aligned.mrc motion_corrected/

From RELION:

# Link motion-corrected micrographs
ln -s /path/to/relion/project/MotionCorr/job_number/Micrographs/*.mrc motion_corrected/

3. Run Denoising​

Apptainer/Singularity
apptainer exec --nv --no-home \
    -B /data oras://ghcr.io/wehi-researchcomputing/partinet:main-singularity partinet denoise \
    --source /data/partinet_picking/motion_corrected \
    --project /data/partinet_picking
Docker
docker run --gpus all -v /data:/data \
    ghcr.io/wehi-researchcomputing/partinet:main partinet denoise \
    --source /data/partinet_picking/motion_corrected \
    --project /data/partinet_picking
Local Installation
partinet denoise \
    --source /data/partinet_picking/motion_corrected \
    --project /data/partinet_picking

Output​

Directory Structure​

After denoising, your project directory will contain:

partinet_picking/
├── motion_corrected/
│   └── [original MRC files]
├── denoised/
│   ├── micrograph_001_fractions_patch_aligned.png
│   ├── micrograph_002_fractions_patch_aligned.png
│   ├── micrograph_003_fractions_patch_aligned.png
│   └── ...
└── partinet_denoise.log

Log File Output​

The partinet_denoise.log file provides detailed processing information:

2025-07-15 17:37:03,807 - Using 48 workers out of 96 available CPUs.
2025-07-15 17:37:03,807 - Processing raw micrographs in /data/partinet_picking/motion_corrected
2025-07-15 17:37:03,807 - Saving denoised micrographs in /data/partinet_picking/denoised
2025-07-15 17:37:03,812 - Directory ready: /data/partinet_picking/denoised
2025-07-15 17:37:49,774 - Processed image micrograph_001_fractions_patch_aligned.mrc to dest. micrograph_001_fractions_patch_aligned.png
2025-07-15 17:37:49,956 - Processed image micrograph_002_fractions_patch_aligned.mrc to dest. micrograph_002_fractions_patch_aligned.png

Congratulations! You have prepared your micrographs for picking, you can now move to particle picking with Detect.

Advanced Usage​

Custom CPU Configuration​

Number of CPUs used by PartiNet is controlled with --num_workers. These CPUs are automatically split between tasks to optimize for CPU utilization during denoising:

  • Processing CPUs: Half of available CPUs used for denoising
  • I/O CPUs: Remaining CPUs reserved for file operations
  • Resource efficiency: Achieves close to 100% CPU utilization

Example with 64 CPUs:

--num_workers 64
  • 32 CPUs for denoising operations
  • 32 CPUs for I/O operations (reading/writing micrographs)

Example in project:

partinet denoise \
    --source /data/partinet_picking/motion_corrected \
    --project /data/partinet_picking \
    --num_workers 32

Different Output Formats​

By default PartiNet outputs denoised images in png format. This is necessary for compatibility with the detection architecture. png is a lossless compression, however micrographs are normalised from 32 bit depth mrc files to 8 bit png. jpg is also available (eg for making figures) but is not recommended for use due to lossy compression.

# JPEG format (smaller file size, lossy compression)
partinet denoise \
    --source /data/partinet_picking/motion_corrected \
    --project /data/partinet_picking \
    --img_format jpg

# PNG format (default, best for PartiNet pipeline, lossless compression)
partinet denoise \
    --source /data/partinet_picking/motion_corrected \
    --project /data/partinet_picking \
    --img_format png

For use with other particle pickers (RELION, CryoSPARC, Topaz, crYOLO) mrc is also available:

partinet denoise \
    --source /data/partinet_picking/motion_corrected \
    --project /data/partinet_picking \
    --img_format mrc

What's Next​

References​

Bepler, T., Kelley, K., Noble, A. J., & Berger, B. (2020). Topaz-Denoise: General deep denoising models for cryoEM and cryoET. Nature Communications, 11(1), 5208. https://doi.org/10.1038/s41467-020-18952-1

Wagner, T., & Raunser, S. (2020). The evolution of SPHIRE-crYOLO particle picking and its application in automated cryo-EM processing workflows. Communications Biology, 3(1), 61. https://doi.org/10.1038/s42003-020-0790-y

Gyawali, R., Dhakal, A., Wang, L., & Cheng, J. (2024). CryoSegNet: Accurate cryo-EM protein particle picking by integrating the foundational AI image segmentation model and attention-gated U-Net. Briefings in Bioinformatics, 25(4), bbae282. https://doi.org/10.1093/bib/bbae282