Adaptive immune receptor repertoires (AIRR) are key targets for biomedical research as they record past and ongoing adaptive immune responses. The capacity of machine learning (ML) to identify complex discriminative sequence patterns renders it an ideal approach for AIRR-based diagnostic and therapeutic discovery. So far, widespread adoption of AIRR ML has been inhibited by a lack of reproducibility, transparency and interoperability. immuneML (immuneml.uio.no) addresses these concerns by implementing each step of the AIRR ML process in an extensible, open-source software ecosystem that is based on fully specified and shareable workflows. To facilitate widespread user adoption, immuneML is available as a command-line tool and through an intuitive Galaxy web interface, and extensive documentation of workflows is provided. We demonstrate the broad applicability of immuneML by (1) reproducing a large-scale study on immune state prediction, (2) developing, integrating and applying a novel deep learning method for antigen specificity prediction and (3) showcasing streamlined interpretability-focused benchmarking of AIRR ML.

适应性免疫受体库 (AIRR) 是生物医学研究的关键目标，因为它们记录过去和正在进行的适应性免疫反应。机器学习 (ML) 识别复杂的判别序列模式的能力使其成为基于 AIRR 的诊断和治疗发现的理想方法。到目前为止，AIRR ML 的广泛采用因缺乏可重复性、透明度和互操作性而受到阻碍。immunML (immuneml.uio.no) 通过在基于完全指定和可共享工作流程的可扩展开源软件生态系统中实施 AIRR ML 流程的每个步骤来解决这些问题。为了促进广泛的用户采用，immuneML 可作为命令行工具并通过直观的 Galaxy Web 界面使用，并提供广泛的工作流程文档。