ArticleImmunoinformatics (Amsterdam, Netherlands)2025
AnalyzAIRR: A user-friendly guided workflow for AIRR data analysis.
Article in Immunoinformatics (Amsterdam, Netherlands), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed.
- Revised Adaptive Immune Receptor Data in the Immune Epitope Database.bioRxiv : the preprint server for biology · 2026Article
- Divergent B-cell repertoire remodelling by mRNA, DNA and live attenuated vaccines in fish.NPJ vaccines · 2025Article
- Contrasted modifications of IgM and IgT repertoires induced by high- and low-virulent infectious pancreatic necrosis virus strains in rainbow trout (Frontiers in immunology · 2025Article
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Authors and funding
9 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The analysis of bulk adaptive immune receptor repertoires (AIRR) enables the understanding of immune responses in both normal and pathological conditions. However, the complexity of AIRR calls for advanced, specialized methods to extract meaningful biological insights. These sophisticated approaches often present challenges for researchers with limited bioinformatics expertise, hindering access to comprehensive immune system analysis. To address this challenge, we developed AnalyzAIRR, an AIRR-compliant R package enabling advanced bulk AIRR sequencing data. The tool integrates state-of-the-art statistical and visualization methods applicable at various levels of granularity. It offers a platform for general data exploration, filtering and manipulation, and in-depth cross-comparisons of AIRR datasets, aimed at answering specific biological questions. We illustrate AnalyzAIRR functionalities using a published murine dataset of 18 T-cell receptor repertoires from three diferrent T cell subsets. We first detected and removed a major contaminant in a group of samples, before proceeding with the comparative analysis. Subsequent cross-sample analysis revealed differences in repertoire diversity that aligned with the respective cell phenotypes, and in repertoire convergence among the studied subsets. AnalyzAIRR's set of analytical metrics is integrated into a Shiny web application and complemented with a tutorial to help users in their analytical strategy, making it user-friendly for biologists with little or no background in bioinformatics.
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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.