bioRxivBioinformaticsPreprint
AbSolution: interactive exploration of sequence-derived features in AIRR-seq repertoires
Rodrigo García-ValienteiD, Charisios TriantafyllouiD, Barbera D. C. van SchaikiD, Aldo JongejaniD, Sabrina PollastroiD, Dornatien Chuo AnangiD, Jeroen E. J. GuikemaiD, Niek de VriesiD, Huub C. J. HoefslootiD, Antoine H. C. van KampeniD
Abstract
High-throughput sequencing of B-cell and T-cell immune receptor repertoires provides unprecedented insight into adaptive immune responses. The data produced are structured by clonal relationships and somatic mutation signatures, and yield extremely rich information in sequence-derived features, including physicochemical properties and compositional patterns. However, integrated analysis across datasets, conditions, and time points remains challenging. Current analytical tools typically focus only on certain features within individual repertoires, without enabling integrated, multivariable comparisons across datasets, conditions, and time points to address their diversity and variability. Here we present AbSolution, a user-friendly and flexible interactive application for comprehensive exploration of immune repertoires and their sequence-based properties. AbSolution enables multiscale analysis of thousands of sequence-derived features across receptor regions, while accounting for V(D)J usage, clonal composition and experimental groupings. We demonstrate its utility by identifying distinct sequence-based profiles associated with dominant (highly abundant) and non-dominant B-cell clones in peripheral blood BCR repertoires from patients with idiopathic inflammatory myopathies, and with antigen-responsive T-cell populations over time in a longitudinal in vitro antigen-stimulation dataset. Through interactive, interlinked visualizations, statistical feature selection and multi-sample comparisons, AbSolution facilitates integrated feature profiling that supports the interpretation of immune selection processes and enables systematic analysis of complex repertoire datasets.
- organism: human
- assay: CITE-seq
- research focus: cell type annotation
- research focus: differential expression
- research focus: gene regulatory network inference
- research focus: database / resource
- approach: statistical model
- approach: machine learning (classical)
- Citations
- 0
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