Observed antibody space: a resource for data mining next-generation sequencing of antibody repertoires
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Abstract
Abs are immune system proteins that recognize noxious molecules for elimination. Their sequence diversity and binding versatility have made Abs the primary class of biopharmaceuticals. Recently, it has become possible to query their immense natural diversity using next-generation sequencing of Ig gene repertoires (Ig-seq). However, Ig-seq outputs are currently fragmented across repositories and tend to be presented as raw nucleotide reads, which means nontrivial effort is required to reuse the data for analysis. To address this issue, we have collected Ig-seq outputs from 55 studies, covering more than half a billion Ab sequences across diverse immune states, organisms (primarily human and mouse), and individuals. We have sorted, cleaned, annotated, translated, and numbered these sequences and make the data available via our Observed Antibody Space (OAS) resource at http://antibodymap.org The data within OAS will be regularly updated with newly released Ig-seq datasets. We believe OAS will facilitate data mining of immune repertoires for improved understanding of the immune system and development of better biotherapeutics.
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The increasing number of structural, sequence and experimental data on antibodies being deposited in the public domain provides the necessary foundation for improving data-driven methods.
“The increasing number of structural [16], sequence [17] and experimental data [18–21] on antibodies being deposited in the public domain provides the necessary foundation for improving such data-driven methods.”
From Computational approaches to therapeutic antibody design: established methods and emerging trends (2020) · cites this paper as [17]
The field currently benefits from a steady stream of new data from NGS of B-cell Receptors and increasing resources for downloading such data.
“In this respect, the field currently benefits from a steady stream of new data from NGS of B-cell Receptors (BCRs, to be used as proxy for antibodies) [212, 213] and there is an increasing number of resources available for downloading such data [17], which are being used to analyze therapeutic antibodies [13].”
From Computational approaches to therapeutic antibody design: established methods and emerging trends (2020) · cites this paper as [17]
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CoV-AbDab: the coronavirus antibody database
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“In this respect, the field currently benefits from a steady stream of new data from NGS of B-cell Receptors (BCRs, to be used as proxy for antibodies) [212, 213] and there is an increasing number of resources available for downloading such data [17], which are being used to analyze therapeutic antibodies [13].”
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Self-citationHumanization of antibodies using a machine learning approach on large-scale repertoire data
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Self-citationxTrimoPGLM: Unified 100B-Scale Pre-trained Transformer for Deciphering the Language of ProteinPreprint
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Modern Hopfield Networks and Attention for Immune Repertoire ClassificationPreprint
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In silico proof of principle of machine learning-based antibody design at unconstrained scale
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Self-citationAb-Ligity: identifying sequence-dissimilar antibodies that bind to the same epitope
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Self-citationCurrent strategies for detecting functional convergence across B-cell receptor repertoires
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Self-citationA dataset comprised of binding interactions for 104,972 antibodies against a SARS-CoV-2 peptide
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- Authors
- Kovaltsuk, A., Leem, J., Kelm, S., Snowden, J., Deane, C. M., & Krawczyk, K.
- Affiliations
- University of Oxford (GB)
- UCB Pharma (United Kingdom) (GB)
- Corresponding author
- Deane, Charlotte M
- Krawczyk, Konrad
- ORCID
- Venue
- The Journal of Immunology
- Publisher
- Oxford University Press (OUP)
- Published
- 2018-10
- Volume
- 201
- Issue
- 8
- Pages
- 2502-2509
- ISSN
- 0022-1767 (ISSN-L)
- 1550-6606
- OpenAlex
- W2950118240
- Cited by
- 247
- References
- 96
- Citations by year
- 2026: 34 · 2025: 46 · 2024: 48 · 2023: 33 · 2022: 33 · 2021: 21 · 2020: 17 · 2019: 12 · 2018: 3
- FWCI
- 9.15
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Locations
- The Journal of Immunology (publishedVersion, other-oa)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by)
- PubMed (publishedVersion)
- Funders
- Directorate for Biological Sciences
- Biotechnology and Biological Sciences Research Council (1801486, BB/M011224, BB/M011224/1, M011224)
- UCB Pharma
- Topics
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Monoclonal and Polyclonal Antibodies Research
- Life Sciences › Immunology and Microbiology › Immunology › T-cell and B-cell Immunology
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › vaccines and immunoinformatics approaches
- Keywords
- antibody repertoire, biologic therapy
- MeSH terms
- Animals, Antibodies, Antibody Diversity, Humans, Immunoglobulins, Immunotherapy, Databases, Genetic, Mice, Immunity, Humoral, Data Mining, Molecular Sequence Annotation, 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.
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