{"doi":"10.1371/journal.pcbi.1012265","title":"nf-core/airrflow: An adaptive immune receptor repertoire analysis workflow employing the Immcantation framework","abstract":"Adaptive Immune Receptor Repertoire sequencing (AIRR-seq) is a valuable experimental tool to study 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 and T cell receptor sequences from AIRR-seq data and infer B and T cell clonal relationships. However, currently available tools offer limited parallelization across samples, scalability or portability to high-performance computing infrastructures. To address this need, we developed nf-core/airrflow, an end-to-end bulk and single-cell AIRR-seq processing workflow which integrates the Immcantation Framework following BCR and TCR sequencing data analysis best practices. The Immcantation Framework is a comprehensive toolset, which allows the processing of bulk and single-cell AIRR-seq data from raw read processing to clonal inference. nf-core/airrflow is written in Nextflow and is part of the nf-core project, which collects community contributed and curated Nextflow workflows for a wide variety of analysis tasks. We assessed the performance of nf-core/airrflow on simulated sequencing data with sequencing errors and show example results with real datasets. To demonstrate the applicability of nf-core/airrflow to the high-throughput processing of large AIRR-seq datasets, we validated and extended previously reported findings of convergent antibody responses to SARS-CoV-2 by analyzing 97 COVID-19 infected individuals and 99 healthy controls, including a mixture of bulk and single-cell sequencing datasets. Using this dataset, we extended the convergence findings to 20 additional subjects, highlighting the applicability of nf-core/airrflow to validate findings in small in-house cohorts with reanalysis of large publicly available AIRR datasets.","journal":"PLoS Computational Biology","year":2024,"id":425619,"datarank":0.0,"base_score":0.0,"endowment":0.0,"self_citation_contribution":0.0,"citation_network_contribution":0.0,"self_endowment_contribution":0.0,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":19,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.9283,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2024-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":1019707,"name":"Susanna Marquez","orcid":"0000-0003-1946-7957","position":1,"is_corresponding":false},{"id":35207,"name":"Robert Bjornson","orcid":null,"position":2,"is_corresponding":false},{"id":552661,"name":"Alexander Peltzer","orcid":"0000-0002-6503-2180","position":3,"is_corresponding":false},{"id":671312,"name":"Hailong Meng","orcid":"0000-0001-5311-9716","position":4,"is_corresponding":false},{"id":1174322,"name":"E Aron","orcid":"0000-0002-8683-4772","position":5,"is_corresponding":false},{"id":1223786,"name":"Noah Y. Lee","orcid":"0000-0001-9309-8585","position":6,"is_corresponding":false},{"id":1223787,"name":"Cole G. Jensen","orcid":"0000-0001-9236-4853","position":7,"is_corresponding":false},{"id":1224385,"name":"David Ladd","orcid":null,"position":8,"is_corresponding":false},{"id":1224386,"name":"M Polster","orcid":null,"position":9,"is_corresponding":false},{"id":1223788,"name":"Friederike Hanssen","orcid":"0009-0001-9875-5262","position":10,"is_corresponding":false},{"id":24594,"name":"Simon Heumos","orcid":"0000-0003-3326-817X","position":11,"is_corresponding":false},{"id":1224387,"name":"nf-core community","orcid":null,"position":12,"is_corresponding":false},{"id":357047,"name":"Gur Yaari","orcid":"0000-0001-9311-9884","position":13,"is_corresponding":false},{"id":346810,"name":"Markus C. 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