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

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.