immuneML: an ecosystem for machine learning analysis of adaptive immune receptor repertoires

Pavlovic, M.; Scheffer, L.; Motwani, K.; Kanduri, C.; Kompova, R.; Vazov, N.; Waagan, K.; Bernal, F. L. M.; Costa, A. A.; Corrie, B.; Akbar, R.; Al Hajj, G. S.; Balaban, G.; Brusko, T. M.; Chernigovskaya, M.; Christley, S.; Cowell, L. G.; Frank, R.; Grytten, I.; Gundersen, S.; Hobaek Haff, I.; Hochreiter, S.; Hovig, E.; Hsieh, P.-H.; Klambauer, G.; Kuijjer, M. L.; Lund-Andersen, C.; Martini, A.; Minotto, T.; Pensar, J.; Rand, K.; Riccardi, E.; Robert, P. A.; Rocha, A.; Slabodkin, A.; Snapkov, I.; Sollid, L. M.; Titov, D.; Weber, C. R.; Widrich, M.; Yaari, G.; Greiff, V.; Sandve, G. K.

doi:10.1101/2021.03.08.433891

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bioRxiv · 10.1101/2021.03.08.433891

immuneML: an ecosystem for machine learning analysis of adaptive immune receptor repertoires

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Abstract

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. To date, 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 (i) reproducing a large-scale study on immune state prediction, (ii) developing, integrating, and applying a novel method for antigen specificity prediction, and (iii) showcasing streamlined interpretability-focused benchmarking of AIRR ML.

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Pavlovic, M., Scheffer, L., Motwani, K., Kanduri, C., Kompova, R., Vazov, N., Waagan, K., Bernal, F. L. M., Costa, A. A., Corrie, B., Akbar, R., Al Hajj, G. S., Balaban, G., Brusko, T. M., Chernigovskaya, M., Christley, S., Cowell, L. G., Frank, R., Grytten, I., Gundersen, S., Hobaek Haff, I., Hochreiter, S., Hovig, E., Hsieh, P.-H., Klambauer, G., Kuijjer, M. L., Lund-Andersen, C., Martini, A., Minotto, T., Pensar, J., Rand, K., Riccardi, E., Robert, P. A., Rocha, A., Slabodkin, A., Snapkov, I., Sollid, L. M., Titov, D., Weber, C. R., Widrich, M., Yaari, G., Greiff, V., Sandve, G. K.. 2021-03-09. immuneML: an ecosystem for machine learning analysis of adaptive immune receptor repertoires. https://doi.org/10.1101/2021.03.08.433891

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