Public server:
Summary:Machine learning-based analysis and classification of adaptive immune receptors and repertoires (AIRR).


  • immuneML is an open-source collaborative ecosystem for machine learning-based (ML) analyses of adaptive immune receptors and repertoires (AIRR).
  • Does not assume previous knowledge of machine learning.
  • immuneML enables the ML study of both experimental and synthetic AIRR-seq data that are labeled on the repertoire-level (e.g., immune state, sex, age, or any other metadata) or sequence-level (e.g., antigen binding), all the way from preprocessing to model training and model interpretation.
  • The core functionality is available in two tool interface forms: one form based on intuitive selection boxes, and a second form allowing full control of all analysis details by providing a yaml-based analysis specification.
  • immuneML’s compliance with AIRR community software and sequence annotation standards ensures straightforward integration with third-party tools for AIRR data preprocessing and AIRR ML results’ downstream analysis, several of which are already integrated at the Galaxy server.

User Support


  • The Galaxy interface is intended to make it easy for users to try out immuneML quickly, but for large-scale analyses, please install immuneML locally or on a private server. (It is available as a Docker image.)



  • The immuneML team is part of the Sandve Lab and Greiff Lab in the Department of Informatics, the Centre for Bioinformatics and the Department of immunology at the University of Oslo. The public web server of immuneML has been developed with support from the University Center for Information Technology at the University of Oslo and ELIXIR Norway.
  • immuneML is supported by the SUURPh programme, the Helmsley Charitable Trust, UiO World-Leading Research Community, UiO:LifeSciences Convergence Environment, EU Horizon 2020 iReceptor Plus (#825821), Research Council of Norway (#300740, #311341).