Computational strategies for dissecting the high-dimensional complexity of adaptive immune repertoires
- 1Department of Biosystems Science and Engineering, ETH Zurich, Switzerland
- 2University Hospital of Basel, Switzerland
- 3Department of Immunology, University of Oslo, Norway
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 in order 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 towards coupling AIRR-seq with the computational discovery of immunotherapeutics, vaccines, and immunodiagnostics.
Keywords: Systems Immunology, Computational Biology, Antibody Diversity, T cell receptor, machine learning, networks, mathematical ecology, phylogenetics, B cell, immunogenomics, immunoproteomics, graph theory, bayesian statistics, artificial intelligence
Received: 22 Nov 2017;
Accepted: 26 Jan 2018.
Edited by:Jacob Glanville, Distributed Bio, United States
Reviewed by:Claude-Agnes Reynaud, Institut National de la Santé et de la Recherche Médicale (INSERM), France
Benny Chain, University College London, United Kingdom
Copyright: © 2018 Miho, Yermanos, Weber, Berger, Reddy and Greiff. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Prof. Victor Greiff, University of Oslo, Department of Immunology, Oslo, Norway, email@example.com