Detecting antibody reactivities in Phage ImmunoPrecipitation Sequencing data
Athena Chen1, Kai Kammers2, H Benjamin Larman3
1Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA.
BMC Genomics
|September 15, 2022
Summary
Phage ImmunoPrecipitation Sequencing (PhIP-Seq) analysis can be improved using a new Bayesian method called BEER. This tool enhances the analysis of antibody reactivity data generated by PhIP-Seq experiments.
Area of Science:
- Immunology
- Bioinformatics
- Genomics
Background:
- Phage ImmunoPrecipitation Sequencing (PhIP-Seq) is a novel technology for assessing antibody reactivity against numerous epitopes.
- PhIP-Seq generates read count matrices, analogous to RNA-Seq data, necessitating appropriate analytical methods.
Purpose of the Study:
- To evaluate the suitability of the existing RNA-Seq analysis tool, edgeR, for PhIP-Seq data.
- To develop an improved analytical framework specifically designed for PhIP-Seq data.
Main Methods:
- Comparative analysis of edgeR performance on PhIP-Seq data.
- Development and implementation of a Bayesian framework, Bayesian Enrichment Estimation in R (BEER), tailored for PhIP-Seq.
Main Results:
- The existing edgeR method demonstrates effectiveness for PhIP-Seq data normalization and analysis.
- The newly developed BEER framework offers specific improvements for analyzing PhIP-Seq data.
Conclusions:
- While edgeR is a viable option, the BEER framework provides a more tailored and potentially superior approach for PhIP-Seq data analysis.
- BEER is recommended for enhanced antibody reactivity assessment using PhIP-Seq.
