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Front Immunol. 2018 Feb 21;9:224. doi: 10.3389/fimmu.2018.00224. eCollection 2018.

Computational Strategies for Dissecting the High-Dimensional Complexity of Adaptive Immune Repertoires.

Author information

1
Department for Biosystems Science and Engineering, ETH Zürich, Basel, Switzerland.
2
aiNET GmbH, ETH Zürich, Basel, Switzerland.
3
Department of Biomedicine, University Hospital Basel, Basel, Switzerland.
4
Department of Internal Medicine, Clinical Immunology, University Hospital Basel, Basel, Switzerland.
5
Department of Immunology, University of Oslo, Oslo, Norway.

Abstract

The adaptive immune system recognizes antigens via an immense array of antigen-binding antibodies and T-cell receptors, the immune repertoire. The interrogation of immune repertoires is of high relevance for understanding the adaptive immune response in disease and infection (e.g., autoimmunity, cancer, HIV). Adaptive immune receptor repertoire sequencing (AIRR-seq) has driven the quantitative and molecular-level profiling of immune repertoires, thereby revealing the high-dimensional complexity of the immune receptor sequence landscape. Several methods for the computational and statistical analysis of large-scale AIRR-seq data have been developed to resolve immune repertoire complexity and to understand the dynamics of adaptive immunity. Here, we review the current research on (i) diversity, (ii) clustering and network, (iii) phylogenetic, and (iv) machine learning methods applied to dissect, quantify, and compare the architecture, evolution, and specificity of immune repertoires. We summarize outstanding questions in computational immunology and propose future directions for systems immunology toward coupling AIRR-seq with the computational discovery of immunotherapeutics, vaccines, and immunodiagnostics.

KEYWORDS:

B-cell receptor; T-cell receptor; antibody discovery; artificial intelligence; immunogenomics; networks; phylogenetics; systems immunology

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