Detecting and quantifying antibody reactivity in PhIP-Seq data with BEER

Because of their high abundance, easy accessibility in peripheral blood, and relative stability ex vivo, antibodies serve as excellent records of environmental exposures and immune responses. Phage Immuno-Precipitation Sequencing (PhIP-Seq) is the most efficient technique available for assessing antibody binding to hundreds of thousands of peptides at a cohort scale. PhIP-Seq is a high-throughput approach for assessing antibody reactivity to hundreds of thousands of candidate epitopes. Accurate detection of weakly reactive peptides is particularly important for characterizing the development and decline of antibody responses. Here, we present BEER (Bayesian Enrichment Estimation in R), a software package specifically developed for the quantification of peptide reactivity from PhIP-Seq experiments. BEER implements a hierarchical model and produces posterior probabilities for peptide reactivity and a fold change estimate to quantify the magnitude. BEER also offers functionality to infer peptide reactivity based on the edgeR package, though the improvement in speed is offset by slightly lower sensitivity compared to the Bayesian approach, specifically for weakly reactive peptides.

BEER is implemented in R and freely available from the Bioconductor repository at https://bioconductor.org/packages/release/bioc/html/beer.html.