Computational strategies for dissecting the high-dimensional complexity of adaptive immune repertoires
Open accessAbstract
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.
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The recent decade saw the advent of next generation sequencing (NGS) of B-cell receptor (antibody) repertoires.
“Specifically, the recent decade saw the advent of next generation sequencing (NGS) of B-cell receptor (antibody) repertoires [22].”
From Computational approaches to therapeutic antibody design: established methods and emerging trends (2020) · cites this paper as [22]
Current bioinformatic analyses of NGS repertoires focus on large-scale decoding of immune responses, with several potential applications for therapeutic design.
“Current bioinformatic analyses of NGS repertoires focus on large-scale decoding of the immune responses, with several potential applications for therapeutic design [22, 25, 214].”
From Computational approaches to therapeutic antibody design: established methods and emerging trends (2020) · cites this paper as [22]
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A Database of Human Immune Receptor Alleles Recovered from Population Sequencing Data
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Systematic comparative study of computational methods for T-cell receptor sequencing data analysis
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Flagged first, then by citations
Research works printed but not linked: 3
- [64]Nouri N Kleinstein SH Performance-optimized partitioning of clonotypes from high-throughput immunoglobulin repertoire sequencing data. bioRxiv (2017). 10.1101/175315 · DOI
- [94]Miho E Greiff V Roskar R Reddy ST The fundamental principles of antibody repertoire architecture revealed by large-scale network analysis. bioRxiv (2017). 10.1101/124578 · DOI
- [108]Barabási A-L Network science. Boston, USA: Cambridge University Press (2016).
Cited by
Sorted by citations
Kovaltsuk, A., Leem, J., Kelm, S., Snowden, J., Deane, C. M., & Krawczyk, K. (2018) · The Journal of Immunology · doi:10.4049/jimmunol.1800708 · Cited by 247 · see citing works
Computational approaches to therapeutic antibody design: established methods and emerging trends
Norman, R. A., Ambrosetti, F., Bonvin, A. M. J. J., Colwell, L. J., Kelm, S., Kumar, S., & Krawczyk, K. (2020) · Briefings in Bioinformatics · doi:10.1093/bib/bbz095 · Cited by 219 · see citing works
“Specifically, the recent decade saw the advent of next generation sequencing (NGS) of B-cell receptor (antibody) repertoires [22].”
BackgroundLarge-scale network analysis reveals the sequence space architecture of antibody repertoires
Miho, E., Roškar, R., Greiff, V., & Reddy, S. T. (2019) · Nature Communications · doi:10.1038/s41467-019-09278-8 · Cited by 138 · see citing works
Self-citationCurrent Challenges in Vaccinology
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T‐cell receptor and B‐cell receptor repertoire profiling in adaptive immunity
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Brown, A. J., Snapkov, I., Akbar, R., Pavlović, M., Miho, E., Sandve, G. K., & Greiff, V. (2019) · Molecular Systems Design & Engineering · doi:10.1039/c9me00071b · Cited by 59 · see citing works
Self-citationLooking for therapeutic antibodies in next generation sequencing repositories
Krawczyk, K., Raybould, M., Kovaltsuk, A., & Deane, C. M. (2019) · doi:10.1101/572958 · Cited by 29 · see citing works
- Authors
- Miho, E., Yermanos, A., Weber, C. R., Berger, C. T., Reddy, S. T., & Greiff, V.
- Affiliations
- ETH Zurich (CH)
- University Hospital of Basel (CH)
- University of Oslo (NO)
- Corresponding author
- Reddy, Sai T.
- Greiff, Victor
- ORCID
- Venue
- Frontiers in Immunology
- Publisher
- Frontiers Media SA
- Published
- 2018-02-21
- Volume
- 9
- Pages
- 224-224
- ISSN
- 1664-3224 (ISSN-L)
- PMC
- PMC5826328
- OpenAlex
- W2774014537
- Cited by
- 189
- References
- 214
- Citations by year
- 2026: 16 · 2025: 12 · 2024: 22 · 2023: 18 · 2022: 30 · 2021: 23 · 2020: 29 · 2019: 27 · 2018: 12
- FWCI
- 2.29
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- Frontiers in Immunology (publishedVersion, cc-by)
- Europe PMC (PubMed Central) (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- NORA - Norwegian Open Research Archives (submittedVersion, cc-by)
- Repository for Publications and Research Data (ETH Zurich) (submittedVersion, cc-by)
- arXiv (Cornell University)
- ETH Zürich Research Collection (cc-by)
- Funders
- Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung (31003A_170110)
- European Research Council (679403)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › vaccines and immunoinformatics approaches
- Life Sciences › Immunology and Microbiology › Immunology › T-cell and B-cell Immunology
- Physical Sciences › Engineering › Biomedical Engineering › Artificial Immune Systems Applications
- Keywords
- antibody diversity, T cell receptor diversity, phylogenetic analysis, vaccine design, immunodiagnosis
- MeSH terms
- Machine Learning, Animals, Autoimmune Diseases, Data Interpretation, Statistical, Humans, Neoplasms, Receptors, Immunologic, Software, Signal Transduction, HIV Infections, Computational Biology, Adaptive Immunity, High-Throughput Nucleotide Sequencing
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
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