Evidence map›Paper›PMID 42305137›Full record

ArticleImmunoinformatics (Amsterdam, Netherlands)2025

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

Vanessa Mhanna, Gabriel Pires, Grégoire Bohl-Viallefond, Karim El Soufi, Nicolas Tchitchek, David Klatzmann, Adrien Six, Hang P Pham, Encarnita Mariotti-Ferrandiz

Abstract read
In one paragraph

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.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

3 citing papers in PubMed.

  1. Revised Adaptive Immune Receptor Data in the Immune Epitope Database.bioRxiv : the preprint server for biology · 2026
    Article
  2. Article
  3. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

9 authors.

Vanessa MhannaSorbonne Université, INSERM, Immunology-Immunopathology-Immunotherapy (i3), F-75005 Paris, France.
Gabriel PiresAP-HP, Hôpital Pitié-Salpêtrière, Clinical Investigation Center for Biotherapies (CIC-BTi) and Immunology-Inflammation-Infectiology and Dermatology Department (3iD), Paris, France.
Grégoire Bohl-ViallefondSorbonne Université, INSERM, Immunology-Immunopathology-Immunotherapy (i3), F-75005 Paris, France.
Karim El SoufiAP-HP, Hôpital Pitié-Salpêtrière, Clinical Investigation Center for Biotherapies (CIC-BTi) and Immunology-Inflammation-Infectiology and Dermatology Department (3iD), Paris, France.
Nicolas TchitchekSorbonne Université, INSERM, Immunology-Immunopathology-Immunotherapy (i3), F-75005 Paris, France.
David KlatzmannSorbonne Université, INSERM, Immunology-Immunopathology-Immunotherapy (i3), F-75005 Paris, France.
Adrien SixSorbonne Université, INSERM, Immunology-Immunopathology-Immunotherapy (i3), F-75005 Paris, France.
Hang P PhamILTOO Pharma, Statistics Department, F-75013, Paris, France.
Encarnita Mariotti-FerrandizSorbonne Université, INSERM, Immunology-Immunopathology-Immunotherapy (i3), F-75005 Paris, France.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

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.

Indexed as

AIRRBCRBulk high-throughput sequencingRepertoire convergenceRepertoire diversityStatistical analysesTCR

Identifiers

PMID42305137
PMCPMC7619189

What OpenQuestion holds

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LicenceCC BY-NC-ND
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Registered trials

None linked

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.