nf-core/airrflow: an adaptive immune receptor repertoire analysis workflow employing the Immcantation framework 9075

Abstract Description Sequencing the Adaptive Immune Receptor Repertoire (AIRR) is a valuable experimental tool for probing the immune state in health and following immune challenges such as infectious diseases, (auto)immune diseases, and cancer. Several tools have been developed to reconstruct B cell receptor (BCR) and T cell receptor (TCR) sequences from AIRRseq data and infer B and T cell clonal relationships. However, existing tools often lack parallelization across samples, scalability or portability to high-performance computing infrastructures. To address this need, we developed nf-core/airrflow (https://nf-co.re/airrflow), a comprehensive bulk and single-cell AIRRseq data processing workflow written in the Nextflow language. The workflow integrates tools from the open-source Immcantation Framework (immcantation.org) following BCR and TCR data analysis best practices. nf-core/airrflow has been applied to a variety of studies, with a growing user community. To showcase its utility for high-throughput AIRRseq data processing, we validated and expanded previously reported findings of convergent BCR responses among six individuals after SARS-CoV-2 infection. Our analysis involved close to 200 subjects, encompassing both bulk and single-cell sequencing datasets. We identified a convergent antibody cluster including 20 additional subjects, highlighting the applicability of nf-core/airrflow to validating findings in small cohorts with reanalysis of large publicly available AIRR datasets. Funding Sources Supported by NIH U01AI184647 and Chan Zuckerberg Initiative EOSS4 [2021-237742] to GG; DFG EXC 2180–390900677 (iFIT), DFG EXC 2124–390838134 (CMFI) and DFG NFDI 49/1 - 501875662 to SN; and NIH R01AI104739 to SHK. Topic Categories Computational and Systems Immunology (COMP)