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phippery: a software suite for PhIP-Seq data analysis.

Jared G Galloway¹, Kevin Sung¹, Samuel S Minot²

¹Computational Biology, Public Health Sciences Division, Fred Hutchinson Cancer Center, Seattle, WA 98109, USA.

Bioinformatics (Oxford, England)

|September 23, 2023

Summary

This summary is machine-generated.

The phippery software suite offers automated analysis of phage display immunoprecipitation sequencing (PhIP-Seq) data. This suite streamlines processing, normalization, and visualization of antibody-peptide binding interactions.

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Area of Science:

Computational Biology
Immunology
Bioinformatics

Background:

Phage display is a powerful technique for identifying peptide binders to antibodies.
Analyzing immunoprecipitation and deep sequencing (PhIP-Seq) data requires specialized bioinformatics tools.
Existing workflows for PhIP-Seq data analysis can be complex and lack end-to-end automation.

Purpose of the Study:

To introduce the phippery software suite for comprehensive analysis of PhIP-Seq data.
To provide a modular and integrated solution for processing, analyzing, and visualizing antibody-peptide binding data.
To enhance reproducibility and accessibility of PhIP-Seq data analysis through open-source tools.

Main Methods:

Developed phip-flow, a Nextflow pipeline for automated processing of raw sequencing data into a standardized format.
Created phippery, a Python API for performing key analytical tasks including count normalization and enrichment calculations.
Implemented phip-viz, a Streamlit application for interactive visualization of PhIP-Seq data, including heatmap generation.

Main Results:

The phippery suite provides a complete workflow from raw sequencing reads to interactive data visualization.
The modular components (phip-flow, phippery API, phip-viz) can be used independently or together.
The software facilitates reproducible analysis of antibody-peptide binding affinities derived from PhIP-Seq experiments.

Conclusions:

The phippery software suite significantly simplifies and standardizes the analysis of PhIP-Seq data.
This integrated toolkit empowers researchers to efficiently explore antibody-peptide interactions.
All components are open-source and available under the MIT License, promoting wider adoption and collaboration.