The DOI matches its Crossref record; 16 of the 16 references it cites were found in a registry or an index.
VDJdb in 2019: database extension, new analysis infrastructure and a T-cell receptor motif compendium
Open accessAbstract
Here, we report an update of the VDJdb database with a substantial increase in the number of T-cell receptor (TCR) sequences and their cognate antigens. The update further provides a new database infrastructure featuring two additional analysis modes that facilitate database querying and real-world data analysis. The increased yield of TCR specificity identification methods and the overall increase in the number of studies in the field has allowed us to expand the database more than 5-fold. Furthermore, several new analysis methods are included. For example, batch annotation of TCR repertoire sequencing samples allows for annotating large datasets on-line. Using recently developed bioinformatic methods for TCR motif mining, we have built a reduced set of high-quality TCR motifs that can be used for both training TCR specificity predictors and matching against TCRs of interest. These additions enhance the versatility of the VDJdb in the task of exploring T-cell antigen specificities. The database is available at https://vdjdb.cdr3.net.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Molecular mimicry and autoimmunity
Rojas, M., Restrepo-Jiménez, P., Monsalve, D. M., Pacheco, Y., Acosta-Ampudia, Y., Ramírez-Santana, C., Leung, P. S. C., Ansari, A. A., Gershwin, M. E., & Anaya, J.-M. (2018) · Journal of Autoimmunity · doi:10.1016/j.jaut.2018.10.012 · Cited by 627
VDJdb: a curated database of T-cell receptor sequences with known antigen specificity
Shugay, M., Bagaev, D. V., Zvyagin, I. V., Vroomans, R. M., Crawford, J. C., Dolton, G., Komech, E. A., Sycheva, A. L., Koneva, A. E., Egorov, E. S., et al. (2018) · Nucleic Acids Research · doi:10.1093/nar/gkx760 · Cited by 618
Self-citationMcPAS-TCR: a manually curated catalogue of pathology-associated T cell receptor sequences
Tickotsky, N., Sagiv, T., Prilusky, J., Shifrut, E., & Friedman, N. (2017) · Bioinformatics · doi:10.1093/bioinformatics/btx286 · Cited by 555
Emerson, R. O., DeWitt, W. S., Vignali, M., Gravley, J., Hu, J. K., Osborne, E. J., Desmarais, C., Klinger, M., Carlson, C. S., Hansen, J. A., et al. (2017) · Nature Genetics · doi:10.1038/ng.3822 · Cited by 532
Bentzen, A. K., Marquard, A. M., Lyngaa, R., Saini, S. K., Ramskov, S., Donia, M., Such, L., Furness, A. J. S., McGranahan, N., Rosenthal, R., et al. (2016) · Nature Biotechnology · doi:10.1038/nbt.3662 · Cited by 337
Community, T. A., Rubelt, F., Busse, C. E., Bukhari, S. A. C., Bürckert, J.-P., Mariotti-Ferrandiz, E., Cowell, L. G., Watson, C. T., Marthandan, N., Faison, W. J., et al. (2017) · Nature Immunology · doi:10.1038/ni.3873 · Cited by 178
Using T Cell Receptor Repertoires to Understand the Principles of Adaptive Immune Recognition
Bradley, P. H., & Thomas, P. G. (2019) · Annual Review of Immunology · doi:10.1146/annurev-immunol-042718-041757 · Cited by 162
Detecting T cell receptors involved in immune responses from single repertoire snapshots
Pogorelyy, M. V., Minervina, A. A., Shugay, M., Chudakov, D. M., Lebedev, Y. B., Mora, T., & Walczak, A. M. (2019) · PLoS Biology · doi:10.1371/journal.pbio.3000314 · Cited by 150
Self-citationDeWitt, W. S., Smith, A., Schoch, G. H., Hansen, J. A., Matsen, F. A., IV, & Bradley, P. H. (2018) · eLife · doi:10.7554/elife.38358 · Cited by 149
Jurtz, V. I., Jessen, L. E., Bentzen, A. K., Jespersen, M. C., Mahajan, S., Vita, R., Jensen, K. K., Marcatili, P., Hadrup, S. R., Peters, B., et al. (2018) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/433706 · Cited by 118
Cited by
Multiple early factors anticipate post-acute COVID-19 sequelae
Su, Y., Yuan, D., Chen, D. G., Ng, R. H., Wang, K., Choi, J., Li, S., Hong, S., Zhang, R., Xie, J., et al. (2022) · Cell · doi:10.1016/j.cell.2022.01.014 · Cited by 1,156
Neoantigens: promising targets for cancer therapy
Xie, N., Shen, G., Gao, W., Huang, Z., Huang, C., & Fu, L. (2023) · Signal Transduction and Targeted Therapy · doi:10.1038/s41392-022-01270-x · Cited by 1,020
Huang, H., Wang, C., Rubelt, F., Scriba, T. J., & Davis, M. M. (2020) · Nature Biotechnology · doi:10.1038/s41587-020-0505-4 · Cited by 484
A blood atlas of COVID-19 defines hallmarks of disease severity and specificity
Ahern, D. J., Ai, Z., Ainsworth, M., Allan, C., Allcock, A., Angus, B., Ansari, M. A., Arancibia-Cárcamo, C. V., Aschenbrenner, D., Attar, M., et al. (2022) · Cell · doi:10.1016/j.cell.2022.01.012 · Cited by 328
Deep learning-based prediction of the T cell receptor–antigen binding specificity
Lu, T., Zhang, Z., Zhu, J., Wang, Y., Jiang, P., Xiao, X., Bernatchez, C., Heymach, J. V., Gibbons, D. L., Wang, J., et al. (2021) · Nature Machine Intelligence · doi:10.1038/s42256-021-00383-2 · Cited by 295
Metadata
- Authors
- Bagaev, D. V., Vroomans, R. M. A., Samir, J., Stervbo, U., Rius, C., Dolton, G., Greenshields-Watson, A., Attaf, M., Egorov, E. S., Zvyagin, I. V., Babel, N., Cole, D. K., Godkin, A. J., Sewell, A. K., Kesmir, C., Chudakov, D. M., Luciani, F., & Shugay, M.
- Venue
- Nucleic Acids Research
- Publisher
- Oxford University Press (OUP)
- Published
- 2019-10-07
- Volume
- 48
- Issue
- D1
- Pages
- D1057-D1062
- ISSN
- 0305-1048 (ISSN-L)
- 1362-4962
- OpenAlex
- W2979330275
- Cited by
- 394
- References
- 16
- FWCI
- 11.74
- Type
- journal article
- Language
- en
- Open access
- gold
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 ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: VDJdb in 2019: database extension, new analysis infrastructure and a T-cell receptor motif compendium. https://app.cochat.ai/papers/10.1093/nar/gkz874
- The paper
- Bagaev, D. V., Vroomans, R. M. A., Samir, J., Stervbo, U., Rius, C., Dolton, G., Greenshields-Watson, A., Attaf, M., Egorov, E. S., Zvyagin, I. V., Babel, N., Cole, D. K., Godkin, A. J., Sewell, A. K., Kesmir, C., Chudakov, D. M., Luciani, F., & Shugay, M. (2020). VDJdb in 2019: database extension, new analysis infrastructure and a T-cell receptor motif compendium. Nucleic Acids Research. https://doi.org/10.1093/nar/gkz874
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
