Detecting antibody reactivities in Phage ImmunoPrecipitation Sequencing data

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Abstract

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).

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The online version contains supplementary material available at 10.1186/s12864-022-08869-y.

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Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2

Michael Love, Wolfgang Huber, Simon Anders (2014)

In comparative high-throughput sequencing assays, a fundamental task is the analysis of count data, such as read counts per gene in RNA-seq, for evidence of systematic changes across experimental conditions. Small replicate numbers, discreteness, large dynamic range and the presence of outliers require a suitable statistical approach. We present DESeq2, a method for differential analysis of count data, using shrinkage estimation for dispersions and fold changes to improve stability and interpretability of estimates. This enables a more quantitative analysis focused on the strength rather than the mere presence of differential expression. The DESeq2 package is available at http://www.bioconductor.org/packages/release/bioc/html/DESeq2.html. Electronic supplementary material The online version of this article (doi:10.1186/s13059-014-0550-8) contains supplementary material, which is available to authorized users.

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limma powers differential expression analyses for RNA-sequencing and microarray studies

Matthew E. Ritchie, Belinda Phipson, Di Wu … (2015)

limma is an R/Bioconductor software package that provides an integrated solution for analysing data from gene expression experiments. It contains rich features for handling complex experimental designs and for information borrowing to overcome the problem of small sample sizes. Over the past decade, limma has been a popular choice for gene discovery through differential expression analyses of microarray and high-throughput PCR data. The package contains particularly strong facilities for reading, normalizing and exploring such data. Recently, the capabilities of limma have been significantly expanded in two important directions. First, the package can now perform both differential expression and differential splicing analyses of RNA sequencing (RNA-seq) data. All the downstream analysis tools previously restricted to microarray data are now available for RNA-seq as well. These capabilities allow users to analyse both RNA-seq and microarray data with very similar pipelines. Second, the package is now able to go past the traditional gene-wise expression analyses in a variety of ways, analysing expression profiles in terms of co-regulated sets of genes or in terms of higher-order expression signatures. This provides enhanced possibilities for biological interpretation of gene expression differences. This article reviews the philosophy and design of the limma package, summarizing both new and historical features, with an emphasis on recent enhancements and features that have not been previously described.

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edgeR: a Bioconductor package for differential expression analysis of digital gene expression data

Mark Robinson, Davis J. McCarthy, Gordon K. Smyth (2009)

Summary: It is expected that emerging digital gene expression (DGE) technologies will overtake microarray technologies in the near future for many functional genomics applications. One of the fundamental data analysis tasks, especially for gene expression studies, involves determining whether there is evidence that counts for a transcript or exon are significantly different across experimental conditions. edgeR is a Bioconductor software package for examining differential expression of replicated count data. An overdispersed Poisson model is used to account for both biological and technical variability. Empirical Bayes methods are used to moderate the degree of overdispersion across transcripts, improving the reliability of inference. The methodology can be used even with the most minimal levels of replication, provided at least one phenotype or experimental condition is replicated. The software may have other applications beyond sequencing data, such as proteome peptide count data. Availability: The package is freely available under the LGPL licence from the Bioconductor web site (http://bioconductor.org). Contact: mrobinson@wehi.edu.au

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Author and article information

Contributors

Ingo Ruczinski: ingo@jhu.edu

Journal

Journal ID (nlm-ta): BMC Genomics

Journal ID (iso-abbrev): BMC Genomics

Title: BMC Genomics

Publisher: BioMed Central (London )

ISSN (Electronic): 1471-2164

Publication date (Electronic): 15 September 2022

Publication date PMC-release: 15 September 2022

Publication date Collection: 2022

Volume: 23

Electronic Location Identifier: 654

Affiliations

[1 ]GRID grid.21107.35, ISNI 0000 0001 2171 9311, Department of Biostatistics, , Johns Hopkins Bloomberg School of Public Health, ; Baltimore, MD USA

[2 ]GRID grid.21107.35, ISNI 0000 0001 2171 9311, Department of Oncology, , Johns Hopkins University School of Medicine, ; Baltimore, USA

[3 ]GRID grid.21107.35, ISNI 0000 0001 2171 9311, Department of Pathology and the Institute for Cell Engineering, , Johns Hopkins University School of Medicine, ; Baltimore, USA

Article

Publisher ID: 8869

DOI: 10.1186/s12864-022-08869-y

PMC ID: 9476399

PubMed ID: 36109689

SO-VID: 0f0ac85b-3f02-4209-aa79-60b19b29a8af

License:

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/. The Creative Commons Public Domain Dedication waiver ( http://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.

History

Date received : 2 February 2022

Date accepted : 31 August 2022

Funding

Funded by: FundRef http://dx.doi.org/10.13039/100000057, National Institute of General Medical Sciences;

Award ID: GM136724

Award Recipient : Athena Chen Kai Kammers H Benjamin Larman Ingo Ruczinski

Funded by: FundRef http://dx.doi.org/10.13039/100000060, National Institute of Allergy and Infectious Diseases;

Award ID: AI095068

Award Recipient : Athena Chen

Funded by: FundRef http://dx.doi.org/10.13039/100000054, National Cancer Institute;

Award ID: CA062924

Award Recipient : Robert B. Scharpf

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ScienceOpen disciplines: Genetics

Keywords: antobodies,bayesian model,peptides,phage immunoprecipitation sequencing,reactivity

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ScienceOpen disciplines: Genetics

Keywords: antobodies, bayesian model, peptides, phage immunoprecipitation sequencing, reactivity

Detecting antibody reactivities in Phage ImmunoPrecipitation Sequencing data

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Most cited references 31

Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2

limma powers differential expression analyses for RNA-sequencing and microarray studies

edgeR: a Bioconductor package for differential expression analysis of digital gene expression data

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