Detecting antibody reactivities in Phage ImmunoPrecipitation Sequencing data.

Autor: Chen A; Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA., Kammers K; Department of Oncology, Johns Hopkins University School of Medicine, Baltimore, USA., Larman HB; Department of Pathology and the Institute for Cell Engineering, Johns Hopkins University School of Medicine, Baltimore, USA., Scharpf RB; Department of Oncology, Johns Hopkins University School of Medicine, Baltimore, USA., Ruczinski I; Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA. ingo@jhu.edu.
Jazyk: angličtina
Zdroj: BMC genomics [BMC Genomics] 2022 Sep 15; Vol. 23 (1), pp. 654. Date of Electronic Publication: 2022 Sep 15.
DOI: 10.1186/s12864-022-08869-y
Abstrakt: Phage ImmunoPrecipitation Sequencing (PhIP-Seq) is a recently developed technology to assess antibody reactivity, quantifying antibody binding towards hundreds of thousands of candidate epitopes. The output from PhIP-Seq experiments are read count matrices, similar to RNA-Seq data; however some important differences do exist. In this manuscript we investigated whether the publicly available method edgeR (Robinson et al., Bioinformatics 26(1):139-140, 2010) for normalization and analysis of RNA-Seq data is also suitable for PhIP-Seq data. We find that edgeR is remarkably effective, but improvements can be made and introduce a Bayesian framework specifically tailored for data from PhIP-Seq experiments (Bayesian Enrichment Estimation in R, BEER).
(© 2022. The Author(s).)
Databáze: MEDLINE
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