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Detecting antibody reactivities in Phage ImmunoPrecipitation Sequencing data

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 impo...

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Autores principales: Chen, Athena, Kammers, Kai, Larman, H Benjamin, Scharpf, Robert B., Ruczinski, Ingo
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9476399/
https://www.ncbi.nlm.nih.gov/pubmed/36109689
http://dx.doi.org/10.1186/s12864-022-08869-y
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author Chen, Athena
Kammers, Kai
Larman, H Benjamin
Scharpf, Robert B.
Ruczinski, Ingo
author_facet Chen, Athena
Kammers, Kai
Larman, H Benjamin
Scharpf, Robert B.
Ruczinski, Ingo
author_sort Chen, Athena
collection PubMed
description 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). SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12864-022-08869-y.
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spelling pubmed-94763992022-09-15 Detecting antibody reactivities in Phage ImmunoPrecipitation Sequencing data Chen, Athena Kammers, Kai Larman, H Benjamin Scharpf, Robert B. Ruczinski, Ingo BMC Genomics Research 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). SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12864-022-08869-y. BioMed Central 2022-09-15 /pmc/articles/PMC9476399/ /pubmed/36109689 http://dx.doi.org/10.1186/s12864-022-08869-y Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data.
spellingShingle Research
Chen, Athena
Kammers, Kai
Larman, H Benjamin
Scharpf, Robert B.
Ruczinski, Ingo
Detecting antibody reactivities in Phage ImmunoPrecipitation Sequencing data
title Detecting antibody reactivities in Phage ImmunoPrecipitation Sequencing data
title_full Detecting antibody reactivities in Phage ImmunoPrecipitation Sequencing data
title_fullStr Detecting antibody reactivities in Phage ImmunoPrecipitation Sequencing data
title_full_unstemmed Detecting antibody reactivities in Phage ImmunoPrecipitation Sequencing data
title_short Detecting antibody reactivities in Phage ImmunoPrecipitation Sequencing data
title_sort detecting antibody reactivities in phage immunoprecipitation sequencing data
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9476399/
https://www.ncbi.nlm.nih.gov/pubmed/36109689
http://dx.doi.org/10.1186/s12864-022-08869-y
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