The adaptive B cell response is driven by the expansion, somatic hypermutation, and selection of B cell clonal lineages. A high number of clonal lineages in a B cell population indicates a highly diverse repertoire, while clonal size distribution and sequence diversity reflect antigen selective pressure. Identifying clonal lineages is fundamental to many repertoire studies, including repertoire comparisons, clonal tracking, and statistical analysis. Several methods have been developed to group sequences from high-throughput B cell repertoire data. Current methods use clustering algorithms to group clonally-related sequences based on their similarities or distances. Such approaches create groups by optimizing a single objective that typically minimizes intra-clonal distances. However, optimizing several objective functions can be advantageous and boost the algorithm convergence rate. Here we propose MobiLLe, a new method based on multi-objective clustering. Our approach requires V(D)J annotations to obtain the initial groups and iteratively applies two objective functions that optimize cohesion and separation within clonal lineages simultaneously. We show that our method greatly improves clonal lineage grouping on simulated benchmarks with varied mutation rates compared to other tools. When applied to experimental repertoires generated from high-throughput sequencing, its clustering results are comparable to the most performing tools and can reproduce the results of previous publications. The method based on multi-objective clustering can accurately identify clonally-related antibody sequences and presents the lowest running time among state-of-art tools. All these features constitute an attractive option for repertoire analysis, particularly in the clinical context. MobiLLe can potentially help unravel the mechanisms involved in developing and evolving B cell malignancies.

IMGT®, the international ImMunoGeneTics information system®, has advanced its comprehensive platform for the analysis of immunoglobulin (IG) and T-cell receptor (TR) genes through the development of new automated and scalable tools. This article presents major updates aligned with IMGT’s three axes of research. Axis I introduces dynamic resources such as IMGT/GeneTables, IMGT/AssemblyComparison, IMGT/CDRLengths, IMGT/MultiGenomeViewer, enabling real-time access to annotated genomic data, and IMGT/StatAssembly to assess quality of IG/TR loci in assemblies. Axis II enhances repertoire analysis with a redesigned IMGT/GeneFrequency tool and new customization features in IMGT/V-QUEST, supporting flexible exploration of IG and TR gene expression. Axis III improves the accurate prediction of peptide–MHC thanks to IMGT/RobustpMHC. Additionally, the IMGT knowledge graph and its therapeutic extension, IMGT/mAb-KG, provide semantically structured access to >100 million immunogenetic triplets, integrating IMGT databases and linking IMGT content to external biomedical resources. These developments promote standardization, interoperability, and integrative analysis across immunogenetics and clinical applications, reinforcing IMGT’s (accessible at https://www.imgt.org) role as a core reference in the era of FAIR data and personalized medicine.

Adaptive immune receptor repertoire sequencing (AIRR-seq) enables large-scale profiling of B- and T-cell receptor diversity and has become a cornerstone of modern computational immunology. However, AIRR-seq provides only a partial and lossy molecular snapshot of immune dynamics, lacking explicit ground truth for clonal ancestry, lineage trajectories, antigen specificity, and longitudinal immune evolution. This limitation complicates benchmarking, method validation, and mechanistic interpretation of repertoire analysis pipelines. Here, we introduce UnivAIRRse, a unified hierarchical framework that organizes AIRR simulators within a shared conceptual coordinate system spanning five operational levels, from observed sequence data to the theoretical generative potential of the adaptive immune system. By explicitly distinguishing sequence-, clonal-, specificity-, repertoire-, and generative-level representations, UnivAIRRse enables systematic comparison of simulator assumptions, biological scope, abstraction level, and application focus. To our knowledge, this is the first review to formalize such a unified structure across biological, computational, and functional layers of AIRR simulation. Using this framework, we review how simulation supports benchmarking, strengthens computational inference, and enables multi-scale investigation of immune repertoire formation and evolution. We identify persistent limitations in existing simulators, including incomplete biological context, limited modularity, restricted interoperability, and overreliance on AIRR-seq as a molecular proxy for complex spatiotemporal immune processes. To operationalize this framework, we provide an interactive web-based AIRR Simulation Landscape Explorer (publicly available at https://www.imgt.org/AIRR-Simulator/ ) that enables dynamic filtering and comparison of simulators across biological scope, abstraction level, output fidelity, and application focus. Finally, we outline emerging directions toward digital-twin–ready immune simulation, emphasizing modular architectures, longitudinal multi-omic integration, uncertainty quantification, and dynamic model updating. By providing a coherent conceptual and operational coordinate system, UnivAIRRse establishes a foundation for reproducible, interpretable, and clinically actionable modeling of adaptive immune repertoires, bridging current simulation practices with the next generation of predictive and personalized immunological modeling. Abstract Figure:

A bstract: High-throughput B-cell receptor sequencing has transformed the analysis of adaptive immunity, but benchmarking clonal grouping and lineage reconstruction methods remains limited by the absence of datasets with known evolutionary histories. Here we present Ancestra, a lineage-explicit simulator of B-cell receptor heavy-chain affinity maturation. Ancestra models stochastic V(D)J recombination, context-dependent somatic hypermutation, affinity-based selection and clonal expansion while recording complete parent-child relationships and mutation events. The framework generates BCR heavy-chain sequence datasets together with their corresponding ground-truth lineage trees, enabling direct benchmarking of lineage-aware analytical methods. Across simulations, Ancestra recapitulates key properties of human repertoires, including complementarity-determining region 3 length distributions, amino-acid usage patterns, junctional mutation patterns consistent with IMGT criteria and heterogeneous branching topologies. Simulated lineages also reveal multi-label lineage trees, in which identical nucleotide sequences can arise independently along distinct evolutionary paths. Ancestra provides a practical foundation for rigorous benchmarking of lineage-aware immune repertoire analysis.

Analyse du répertoire des récepteurs des lymphocytes B dans un contexte clinique : nouvelles approches pour le regroupement clonal, les études de diversité intra-clonale et la visualisation du répertoire: Le séquençage de nouvelle génération a permis aux chercheurs de réaliser des analyses approfondies du paysage du répertoire immunologique. Cependant, une préoccupation importante dans ces études est le coût informatique de l'analyse de millions de séquences avec une complexité, une variabilité et une capacité de mutation inhérentes, imposant des défis informatiques et nécessitant le développement de méthodes efficaces. Ce défi est encore plus évident dans le contexte clinique qui n'a pas nécessairement accès à des professionnels ayant des compétences informatiques ou des ressources informatiques robustes. Ainsi, l'objectif principal de cette thèse est de développer un ensemble d'outils dédiés qui seront utilisés dans l'environnement clinique, pour le diagnostic médical et les soins aux patients, et dans l'environnement de recherche, pour effectuer une analyse approfondie et à grande échelle du répertoire. Nous avons conçu et implémenté de multiples algorithmes et les avons rassemblés dans un pipeline interactif de visualisation du répertoire BCR afin de faciliter le processus d'intégration de l'analyse du répertoire BCR dans les pratiques médicales.