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High throughput sequencing of adaptive immune receptor repertoire (AIRR-seq) has provided numerous human immunoglobulin (IG) sequences allowing specific B cell receptor (BCR) studies such as the antigen-driven evolution of antibodies (soluble forms of the membrane-bound IG part of the BCR). AIRR-seq data allows researchers to examine intraclonal differences caused primarily by somatic hypermutations in IG genes and affinity maturation. Exploring this essential adaptive immunity process could help elucidate the generation of antibodies with high affinity or broadly neutralizing activities. Retracing their evolutionary history could also help to clarify how vaccines or pathogen exposition drive the humoral immune response. Computational methods are necessary for large-scale analysis of AIRR-seq properties. However, there is no efficient and interactive tool for analyzing intraclonal diversity, permitting users to explore adaptive immune receptor repertoires in biological and clinical applications. Here we developed ViCloD, a web server for large-scale visual analysis of repertoire clonality and intraclonal diversity. ViCloD uses data preprocessed by IMGT/HighV-QUEST and performs clonal grouping and evolutionary analyses, producing a collection of useful plots. The web server presents diverse functionalities, including repertoire navigation, clonal abundance analysis, and intraclonal evolutionary tree reconstruction. Users can download the analyzed data in different table formats and save the generated plots as images. ViCloD is a simple, versatile, and user-friendly tool that can help researchers and clinicians to analyze B cell intraclonal diversity. Moreover, its pipeline is optimized to process hundreds of thousands of sequences within a few minutes, allowing an efficient investigation of large and complex repertoires.Availability and implementationThe web server is available athttp://www.lcqb.upmc.fr/viclod/. The pipeline is available at github and allows users to run analyses locallyhttps://github.com/julibinho/ViCLoD

The adaptive B cell response is driven by the expansion, somatic hypermutation, and selection of B cell clones. A high number of clones in a B cell population indicates a highly diverse repertoire, while clonal size distribution and sequence diversity within clones can be related to antigen’s selective pressure. Identifying clones 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 a new method based on multi-objective clustering. Our approach requires V(D)J annotations to obtain the initial clones and iteratively applies two objective functions that optimize cohesion and separation within clones simultaneously. We show that under simulations with varied mutation rates, our method greatly improves clonal grouping as compared to other tools. When applied to experimental repertoires generated from high-throughput sequencing, its clustering results are comparable to the most performing tools. The method based on multi-objective clustering can accurately identify clone members, has fewer parameter settings and presents the lowest running time among existing tools. All these features constitute an attractive option for repertoire analysis, particularly in the clinical context to unravel the mechanisms involved in the development and evolution of B cell malignancies.