ProtPeptigram 1.0.0

Mapping peptides to source protein for immunopeptidiomics data analysis

ProtPeptigram

Visualization of Immunopeptides Mapped to Source Proteins Across Multiple Samples

ProtPeptigram provides a comprehensive visualization platform for mapping immunopeptides to their source proteins across different biological samples. This tool can enables to identify peptide coverage patterns, analyze density distributions, and compare peptide presentations between experimental conditions.

Features

• Intuitive Peptide Visualization: Map peptides to their source proteins with detailed positional information
• Multi-Sample Support: Compare peptide presentation across different experimental conditions
• Intensity-Based Coloring: Visualize peptide abundance with customizable color schemes
• Automatic Highlighting: Identify regions of interest with dense peptide coverage
• Publication-Quality Outputs: Generate high-resolution figures suitable for scientific publications
• Customizable Visualizations: Adjust color schemes, highlighting, and display options to suit your needs

Installation

From PyPI (Recommended)

pip install protpeptigram

From TestPyPI

pip install -i https://test.pypi.org/simple/ --extra-index-url https://pypi.org/simple/ protpeptigram

From Source

git clone https://github.com/Sanpme66/ProtPeptigram.git
cd ProtPeptigram
pip install -e .

Requirements

• Python ≥ 3.8
• pandas
• matplotlib
• numpy
• Biopython
• rich

Quick Start

Command Line Usage

# Basic usage with minimal options
protpeptigram -i data/peptides.csv -f data/proteome.fasta -o output_directory

# Specify top 10 proteins by peptide count
protpeptigram -i data/peptides.csv -f data/proteome.fasta -o output_dir -tp 10

# Visualize specific proteins
protpeptigram -i data/peptides.csv -f data/proteome.fasta -o output_dir -pl protein_list.txt

# Apply intensity threshold
protpeptigram -i data/peptides.csv -f data/proteome.fasta -o output_dir -th 1000

Python API Usage

from ProtPeptigram.DataProcessor import PeptideDataProcessor
from ProtPeptigram.viz import ImmunoViz

# Initialize data processor
processor = PeptideDataProcessor()

# Load data
processor.load_peaks_data("data/peptides.csv")
processor.load_protein_sequences("data/proteome.fasta")

# Process data
formatted_data = processor.filter_and_format_data(
    filter_contaminants=True,
    intensity_threshold=1000,
    min_samples=2
)

# Create visualizations
viz = ImmunoViz(formatted_data)
fig, _ = viz.plot_peptigram(
    protein_ids=["P20152", "P32261"],
    group_by="Sample",
    color_by="protein",
    title="HLA Peptide Visualization"
)

# Save visualization
fig.savefig("protein_visualization.png", dpi=300, bbox_inches="tight")

Running on Google Colab

You can quickly try out ProtPeptigram on Google Colab without installing anything locally. Click the link below to open the example notebook:

Documentation

For detailed documentation including API reference, tutorials, and examples:

• API Documentation
• Tutorial
• Example Gallery

Input Data Format

ProtPeptigram accepts peptide data in CSV format from PEAKS software with the following columns:

• Peptide sequence
• Protein accession
• Intensity values for each sample

For protein sequences, standard FASTA format files are supported.

Citation

If you use ProtPeptigram in your research, please cite:

Krishna S, Li C, et al. (2024). ProtPeptigram: Visualization tool for mapping peptides to source proteins.
bioRxiv. https://www.monash.edu/research/compomics/

Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

1. Fork the repository
2. Create your feature branch (git checkout -b feature/amazing-feature)
3. Commit your changes (git commit -m 'Add some amazing feature')
4. Push to the branch (git push origin feature/amazing-feature)
5. Open a Pull Request

License

This project is licensed under the MIT License - see the LICENSE file for details.

Acknowledgments

• Developed at Li Lab/Purcell Lab, Monash University, Australia
• Inspired by the need for better visualization tools in immunopeptidomics research

Contact

Sanjay Krishna - GitHub

Project Link: https://github.com/Sanpme66/ProtPeptigram