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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 [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]
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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].”
From Computational approaches to therapeutic antibody design: established methods and emerging trends (2020) · cites this paper as [17]
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Cites
FLASH: fast length adjustment of short reads to improve genome assemblies
Magoč, T., & Salzberg, S. L. (2011) · Bioinformatics · doi:10.1093/bioinformatics/btr507 · Cited by 15,366
Database resources of the National Center for Biotechnology Information: update
Wheeler, D. L. (2003) · Nucleic Acids Research · doi:10.1093/nar/gkh073 · Cited by 10,695
Identification of common molecular subsequences
Smith, T. F., & Waterman, M. S. (1981) · Journal of Molecular Biology · doi:10.1016/0022-2836(81)90087-5 · Cited by 9,962
Capillary pericytes regulate cerebral blood flow in health and disease
Hall, C. N., Reynell, C., Gesslein, B., Hamilton, N. B., Mishra, A., Sutherland, B. A., O’Farrell, F. M., Buchan, A. M., Lauritzen, M., & Attwell, D. (2014) · Nature · doi:10.1038/nature13165 · Cited by 1,870
IgBLAST: an immunoglobulin variable domain sequence analysis tool
Ye, J., Ma, N., Madden, T., & Ostell, J. M. (2013) · Nucleic Acids Research · doi:10.1093/nar/gkt382 · Cited by 1,161
Co-evolution of a broadly neutralizing HIV-1 antibody and founder virus
Liao, H., Lynch, R. M., Zhou, T., Gao, F., Alam, S. M., Boyd, S. D., Fire, A. Z., Roskin, K. M., Schramm, C. A., Zhang, Z., et al. (2013) · Nature · doi:10.1038/nature12053 · Cited by 986
Lefranc, M., Pommie, C., Ruíz, M., Giudicelli, V., Foulquier, E., Truong, L., Contet, V., & Lefranc, G. (2002) · Developmental & Comparative Immunology · doi:10.1016/s0145-305x(02)00039-3 · Cited by 823
Database Resources of the National Center for Biotechnology Information
Coordinators, N. R. (2017) · Nucleic Acids Research · doi:10.1093/nar/gkw1071 · Cited by 817
Lefranc, M., Pommie, C., Kaas, Q., Duprat, É., Bosc, N., Guiraudou, D., Jean, C., Ruíz, M., da Piedade, I., Rouard, M., et al. (2004) · Developmental & Comparative Immunology · doi:10.1016/j.dci.2004.07.003 · Cited by 808
ImmPort: disseminating data to the public for the future of immunology
Bhattacharya, S., Andorf, S., Gomes, L., Dunn, P. J., Schaefer, H., Pontius, J. U., Berger, P., Desborough, V., Smith, T., Campbell, J. P., et al. (2014) · Immunologic Research · doi:10.1007/s12026-014-8516-1 · Cited by 807
Research works printed but not linked: 2
- Kindt, 2007, Kuby Immunology
- HannonLab, 2014, FASTX toolkit.
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Metadata
- 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
- Life Sciences › Immunology and Microbiology › Immunology › 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.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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