ArticleBMC genomics2022
Detecting antibody reactivities in Phage ImmunoPrecipitation Sequencing data.
Article in BMC genomics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
12 citing papers in PubMed.
- Identification of antibody targets associated with lower HIV viral load and viremic control.PloS one · 2024Trial
- Comprehensive profiling of pre-infection antibodies identifies HIV targets associated with viremic control and viral load.Frontiers in immunology · 2023Trial
- Distributed clonal deletion prevents autoimmune disease progression.Cell reports · 2026Article
- Distributed Clonal Deletion Prevents Autoimmune Disease Progression.bioRxiv : the preprint server for biology · 2026Article
- Immunogenicity assessment and epitope mapping of the ASFV proteome by profiling serum antibodies with ASFV antigen phage libraries.Communications biology · 2026Article
- PhIP-Seq: unveiling the complexity of antibody repertoires in health and disease.Frontiers in immunology · 2026Review
- Targets of the human antibody response to the variant surface glycoprotein ofbioRxiv : the preprint server for biology · 2025Article
- The S2 subunit of spike encodes diverse targets for functional antibody responses to SARS-CoV-2.PLoS pathogens · 2024Article
- Efficient encoding of large antigenic spaces by epitope prioritization with Dolphyn.Nature communications · 2024Article
- PhIP-Seq: methods, applications and challenges.Frontiers in bioinformatics · 2024Review
- phippery: a software suite for PhIP-Seq data analysis.Bioinformatics (Oxford, England) · 2023Article
- Detecting and quantifying antibody reactivity in PhIP-Seq data with BEER.Bioinformatics (Oxford, England) · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
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).
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.