PLoS Computational Biology (Jul 2024)

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

  • Gisela Gabernet,
  • Susanna Marquez,
  • Robert Bjornson,
  • Alexander Peltzer,
  • Hailong Meng,
  • Edel Aron,
  • Noah Y Lee,
  • Cole G Jensen,
  • David Ladd,
  • Mark Polster,
  • Friederike Hanssen,
  • Simon Heumos,
  • nf-core community,
  • Gur Yaari,
  • Markus C Kowarik,
  • Sven Nahnsen,
  • Steven H Kleinstein

DOI
https://doi.org/10.1371/journal.pcbi.1012265
Journal volume & issue
Vol. 20, no. 7
p. e1012265

Abstract

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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.