PRGMiner Tutorial

A comprehensive guide to using PRGMiner for plant resistance gene prediction

Homepage

The homepage provides an overview of PRGMiner and quick access to all major features.

  • Navigation bar with links to all major sections
  • Quick introduction to PRGMiner's capabilities
  • Direct access to prediction tools
  • Latest updates and announcements
PRGMiner homepage

Making Predictions

Learn how to submit sequences and get predictions using PRGMiner.

Input Methods

Accession ID

Enter a valid protein accession ID from NCBI or UniProt to fetch and analyze the sequence.

FASTA File

Upload a FASTA file containing one or multiple protein sequences for analysis.

Paste Sequence

Directly paste FASTA-formatted sequences into the text area.

Prediction page showing the three input methods

Submission Process

  1. Choose your preferred input method
  2. Click "Run Prediction" to start the analysis
  3. Wait for the analysis to complete

Understanding Results

Learn how to interpret and download your prediction results.

Results Table

  • Sequence ID and basic information
  • Prediction outcome (R-gene or Non-R-gene)
  • Confidence scores for predictions
  • Detailed classification for R-genes
Results table showing prediction outcomes

Download Options

Complete Results

Download all results in CSV, JSON, or FASTA format, including sequences and predictions.

Filtered Results

Download results for specific R-gene classes or confidence thresholds.

Download options and file format selection

Documentation and Help

Access comprehensive documentation and get help when needed.

Documentation

Detailed technical documentation covering:

  • Installation guide
  • API reference
  • File format specifications
  • Best practices

Help Center

Get assistance through:

  • FAQs
  • Email support
  • GitHub issues
  • Community forums
Help center and documentation resources

Local Installation

Instructions for downloading and running PRGMiner locally.

System Requirements

  • Python 3.7 or higher
  • Required dependencies (listed in requirements.txt)
  • Sufficient RAM for large datasets
  • GPU support (optional, for faster processing)
Downloads page with available resources

Important Note

Local installation is recommended for:

  • Processing large datasets (>10,000 sequences)
  • Integration with existing pipelines
  • Customized analysis workflows
  • Offline usage