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Introduction

PartiNet is a powerful command-line tool for particle picking on cryo-EM micrographs. It provides a comprehensive three-stage pipeline designed to clean, identify, and prepare particles from experimental data for subsequent processing.

The Three-Stage Pipeline​

PartiNet processes data through three sequential stages, each building on the output of the previous stage:

1. Denoise​

The first stage removes noise and artifacts from your raw data using fast heuristic denoising algorithms. This stage improves signal-to-noise ratios and prepares micrographs for accurate particle detection.

2. Detect​

The detection stage identifies and locates individual particles within your cleaned data. Using a dynamic adaptive architecture, it quickly and accurately identifies particles within micrographs.

3. Star​

The final stage prepares particle data for further processing and provides reports on particle populations in your dataset.

Key Features​

  • Fast picking - Leverages state-of-the-art dynamic deep learning models for accurate particle processing
  • Accurate picking - PartiNet accurately identifies proteins in your micrographs and filters junk prior to further processing
  • Overcome orientation bias - PartiNet identifies rare views of proteins in your dataset
  • Multi-species identification - PartiNet can identify and pick heterogeneous samples without requiring prior estimation of box sizes
  • Batch processing - Process multiple files efficiently with parallel processing capabilities

Use Cases​

PartiNet is ideal for:

  • Identifying rare views
  • Picking on heterogeneous datasets
  • Reporting on particle populations

Next Steps​

  • New to PartiNet? Start with Installation to get up and running
  • Ready to begin? Follow our Getting Started guide for your first analysis
  • Need specific details? Check the individual stage documentation: Denoise, Detect, Star