Detecting And Quantifying Antibody Reactivity In Phip-Seq Data With BEER

Author(s): Athena Chen, Kai Kammers, H Benjamin Larman, Robert B Scharpf, Ingo Ruczinski

Affiliation(s): Johns Hopkins University

Twitter: @athena_chen

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. While several multiplexed methods have been developed to assess antibody binding specificities, the recently developed Phage Immuno-Precipitation Sequencing (PhIP-Seq) is the most efficient technique available for assessing antibody binding to hundreds of thousands of peptides at cohort scale. Here, we present BEER (Bayesian Enrichment Estimation in R), a software package specifically developed for quantification of peptide reactivity from PhIP-Seq experiments. BEER provides two approaches for this purpose. The first approach, for which the package is named after, was developed specifically for PhIP-Seq experiments and uses a Bayesian framework to estimate enriched antibody responses. BEER is more sensitive for detecting peptides with smaller degrees of enrichment than other methods designed for count data. The second approach, which takes considerably less time but is less sensitive for detecting weakly enriched peptides, modifies the edgeR pipeline for identifying differential genes from RNA-Seq data.