phippery

phippery processes PhIP-Seq high-throughput sequencing data and provides components for transforming raw reads into compact multidimensional datasets, performing count normalization and enrichment calculation, applying multidimensional scaling, and producing visualizations for antibody–peptide interaction analysis.

Key Features:

• phip-flow (Nextflow pipeline): Processes raw sequencing data into a compact, multidimensional dataset format and enables reproducible, automated end-to-end workflows.
• phippery Python API: Implements count normalization, enrichment calculation, and multidimensional scaling for analytical processing of PhIP-Seq data.
• phip-viz visualization: Generates heatmaps and other flexible visualization formats for interpreted PhIP-Seq results.

Scientific Applications:

• Immunology research: Enables detailed analysis of antibody–peptide interactions from PhIP-Seq datasets.
• Vaccine epitope discovery: Supports identification of potential peptide epitopes through enrichment analysis and visualization.
• Therapeutic antibody development: Facilitates exploration of candidate antibody targets and recognition patterns using normalized counts and multidimensional scaling.

Methodology:

Uses a Nextflow pipeline to convert raw sequencing reads into a compact multidimensional dataset and applies Python-based count normalization, enrichment calculation, and multidimensional scaling with modular components that can be used independently.