BEER

BEER estimates peptide reactivity from Phage Immuno-Precipitation Sequencing (PhIP-seq) data using Bayesian hierarchical models to quantify antibody responses.

Key Features:

• Bayesian Hierarchical Model: Implements a hierarchical Bayesian model that computes posterior probabilities for peptide reactivity, enabling detection of weakly reactive peptides.
• Fold Change Estimation: Provides point estimates of relative fold changes by comparing samples against negative controls.
• edgeR Integration: Offers an alternative inference method using the edgeR package for faster computation with a potential trade-off in sensitivity for weakly reactive peptides.

Scientific Applications:

• Large-scale cohort PhIP-seq analysis: Quantifies antibody binding across extensive peptide libraries to support high-throughput profiling of peptide reactivity.
• Immune response and exposure assessment: Facilitates detection of weak peptide reactivities relevant to studying environmental exposures and immune-response dynamics.

Methodology:

Computational methods explicitly include a hierarchical Bayesian model to calculate posterior probabilities for peptide reactivity, point estimation of relative fold changes versus negative controls, and an alternative inference route using the edgeR package.