ViCloD, an interactive web tool for visualizing B cell repertoires and analyzing intraclonal diversities: application to human B-cell tumors

Abstract

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 (IG secreted molecules). AIRR-seq data allows researchers to examine intra-clonal differences caused primarily by affinity maturation and somatic hypermutation processes. Understating the diversity of antibodies and how they evolve could help elucidate the generation of antibodies with high affinity or broadly neutralizing activities. Consequently, 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 evaluation of AIRR-seq properties. Currently, there is no efficient and interactive tool for analyzing intra-clonal diversity, permitting users to explore AIRR-seq data and potentially discover discriminating clonal features. We developed ViCloD, a web server for large-scale visual analysis of repertoire clonality and intra-clonal diversity. ViCloD uses data preprocessed by IMGT and performs evolutionary and statistical analyses, producing a set of valuable plots. The web server presents diverse functionalities, including repertoire navigation, clonal abundance analysis, and intra-clonal evolutionary tree reconstruction. Users can download the analyzed data in different table formats and save the generated plots as image files. ViCloD is a versatile analytical tool that can help researchers and clinicians in their investigations involving clonal diversity analysis. It is user-friendly, simple, accessible, and open to all users. Moreover, its pipeline is optimized to process hundreds of thousands of sequences in a few minutes, allowing intra-clonal diversity analysis in large and complex repertoires.

Identification of B cell subsets based on antigen receptor sequences using deep learning

“…To consider lineages undergone active maturations, those containing 10 or more BCR sequences and isotype switching were taken into consideration. Lineages containing more than 200 BCR sequences were down-sampled to prevent excessive computation time required when constructing the phylogenetic tree(41). The phylogenetic tree was analyzed and visualized in R using dowser(42,43) (v1.1.0), and ggtree(44) (v3.6.2) package.…”

Identification of B cell subsets based on antigen receptor sequences using deep learning

“…To consider lineages undergone active maturations, those containing 10 or more BCR sequences and isotype switching were taken into consideration. Lineages containing more than 200 BCR sequences were down-sampled to prevent excessive computation time required when constructing the phylogenetic tree (41). The phylogenetic tree was analyzed and visualized in R using dowser (42, 43) (v1.1.0), and ggtree (44) (v3.6.2) package.…”