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Analyzairr: A User-friendly Guided Workflow For Airr Data Analysis.

Home
Analyzairr: A User-friendly Guided Workflow For Airr Data Analysis.

Related Experiment Video

A User-friendly and Powerful R Analysis of Large-scale Datasets

A User-friendly and Powerful R Analysis of Large-scale Datasets

Published on: November 4, 2025

AnalyzAIRR: A user-friendly guided workflow for AIRR data analysis.

Vanessa Mhanna^1,2, Gabriel Pires², Grégoire Bohl-Viallefond¹

¹Sorbonne Université, INSERM, Immunology-Immunopathology-Immunotherapy (i3), F-75005 Paris, France.

Immunoinformatics (Amsterdam, Netherlands)

|June 17, 2026

View abstract on PubMed

Summary

This summary is machine-generated.

AnalyzAIRR is a new R package for analyzing adaptive immune receptor repertoires (AIRR) data. This user-friendly tool simplifies complex bioinformatics, enabling deeper insights into immune responses for researchers.

Keywords:

AIRR BCR Bulk high-throughput sequencing Repertoire convergence Repertoire diversity Statistical analyses TCR

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Area of Science:

Immunology
Bioinformatics
Computational Biology

Background:

Adaptive Immune Receptor Repertoires (AIRR) analysis is crucial for understanding immune responses in health and disease.
Complex AIRR data analysis requires specialized bioinformatics skills, posing a barrier for many researchers.
Existing tools may lack the integrated statistical and visualization methods needed for comprehensive AIRR data exploration.

Purpose of the Study:

To develop an accessible R package, AnalyzAIRR, for advanced bulk AIRR sequencing data analysis.
To provide researchers with user-friendly tools for data exploration, filtering, manipulation, and cross-comparison of AIRR datasets.
To facilitate the extraction of meaningful biological insights from complex immune repertoire data.

Main Methods:

Development of AnalyzAIRR, an R package compliant with AIRR standards.
Integration of advanced statistical and visualization methods for AIRR data analysis.
Application of AnalyzAIRR to a murine T-cell receptor repertoire dataset from three distinct T cell subsets.

Main Results:

AnalyzAIRR successfully detected and removed a significant contaminant from the murine dataset.
Comparative analysis revealed distinct repertoire diversity patterns correlating with T cell phenotypes.
The tool demonstrated the ability to analyze repertoire convergence across different T cell subsets.

Conclusions:

AnalyzAIRR enhances accessibility to advanced AIRR data analysis for biologists with limited bioinformatics expertise.
The package provides a comprehensive platform for exploring, comparing, and interpreting immune repertoire data.
AnalyzAIRR, with its integrated Shiny web application and tutorial, empowers researchers to investigate specific biological questions related to immune responses.