The DOI matches its Crossref record; 53 of the 53 references it cites were found in a registry or an index.
PIRD: Pan Immune Repertoire Database
Open accessPublished version of a preprint
Abstract
MOTIVATION: T and B cell receptors (TCRs and BCRs) play a pivotal role in the adaptive immune system by recognizing an enormous variety of external and internal antigens. Understanding these receptors is critical for exploring the process of immunoreaction and exploiting potential applications in immunotherapy and antibody drug design. Although a large number of samples have had their TCR and BCR repertoires sequenced using high-throughput sequencing in recent years, very few databases have been constructed to store these kinds of data. To resolve this issue, we developed a database. RESULTS: We developed a database, the Pan Immune Repertoire Database (PIRD), located in China National GeneBank (CNGBdb), to collect and store annotated TCR and BCR sequencing data, including from Homo sapiens and other species. In addition to data storage, PIRD also provides functions of data visualization and interactive online analysis. Additionally, a manually curated database of TCRs and BCRs targeting known antigens (TBAdb) was also deposited in PIRD. AVAILABILITY AND IMPLEMENTATION: PIRD can be freely accessed at https://db.cngb.org/pird.
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.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
In the citing paper
“Efforts have been made to create standardized, publicly available repositories for these sequencing data [8,9].”
From Computational and artificial intelligence-based methods for antibody development (2023) · cites this paper as [9]
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
Basic local alignment search tool
Altschul, S. F., Gish, W., Miller, W., Myers, E. W., & Lipman, D. J. (1990) · Journal of Molecular Biology · doi:10.1006/jmbi.1990.9999 · Cited by 98,669
Tumor and Microenvironment Evolution during Immunotherapy with Nivolumab
Riaz, N., Havel, J. J., Makarov, V., Desrichard, A., Urba, W. J., Sims, J. S., Hodi, F. S., Martín-Algarra, S., Mandal, R., Sharfman, W. H., et al. (2017) · Cell · doi:10.1016/j.cell.2017.09.028 · Cited by 2,401
Landscape of Infiltrating T Cells in Liver Cancer Revealed by Single-Cell Sequencing
Zheng, C., Zheng, L., Yoo, J.-K., Guo, H., Zhang, Y., Guo, X., Kang, B., Hu, R., Huang, J. Y., Zhang, Q., et al. (2017) · Cell · doi:10.1016/j.cell.2017.05.035 · Cited by 2,152
MiXCR: software for comprehensive adaptive immunity profiling
Bolotin, D. A., Poslavskii, S., Mitrophanov, I., Shugay, M., Mamedov, I. Z., Putintseva, E. V., & Chudakov, D. M. (2015) · Nature Methods · doi:10.1038/nmeth.3364 · Cited by 1,730
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
Comprehensive assessment of T-cell receptor β-chain diversity in αβ T cells
Robins, H. S., Campregher, P. V., Srivastava, S. K., Wacher, A., Turtle, C. J., Kahsai, O., Riddell, S. R., Warren, E. H., & Carlson, C. S. (2009) · Blood · doi:10.1182/blood-2009-04-217604 · Cited by 1,122
Identifying specificity groups in the T cell receptor repertoire
Glanville, J., Huang, H., Nau, A., Hatton, O., Wagar, L. E., Rubelt, F., Ji, X., Han, A., Krams, S. M., Pettus, C., et al. (2017) · Nature · doi:10.1038/nature22976 · Cited by 1,026
Quantifiable predictive features define epitope-specific T cell receptor repertoires
Dash, P., Fiore-Gartland, A. J., Hertz, T., Wang, G., Sharma, S., Souquette, A. H., Crawford, J. C., Clemens, E. B., Nguyen, T. H. O., Kedzierska, K., et al. (2017) · Nature · doi:10.1038/nature22383 · Cited by 904
Change-O: a toolkit for analyzing large-scale B cell immunoglobulin repertoire sequencing data
Gupta, N. T., Heiden, J. A. V., Uduman, M., Gadala-Maria, D., Yaari, G., & Kleinstein, S. H. (2015) · Bioinformatics · doi:10.1093/bioinformatics/btv359 · Cited by 869
BLAST: improvements for better sequence analysis
Ye, J., McGinnis, S., & Madden, T. (2006) · Nucleic Acids Research · doi:10.1093/nar/gkl164 · Cited by 774
Cited by
Sorted by citations
Best practices for single-cell analysis across modalities
Heumos, L., Schaar, A. C., Lance, C., Litinetskaya, A., Drost, F., Zappia, L., Lücken, M. D., Strobl, D. C., Henao, J., Curion, F., et al. (2023) · Nature Reviews Genetics · doi:10.1038/s41576-023-00586-w · Cited by 1,051
Olsen, T. H., Boyles, F., & Deane, C. M. (2022) · Protein Science · doi:10.1002/pro.4205 · Cited by 319
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
Can we predict T cell specificity with digital biology and machine learning?
Hudson, D., Fernandes, R. A., Basham, M., Ogg, G. S., & Koohy, H. (2023) · Nature reviews. Immunology · doi:10.1038/s41577-023-00835-3 · Cited by 209
Computational and artificial intelligence-based methods for antibody development
Kim, J., McFee, M., Fang, Q., Abdin, O., & Kim, P. M. (2023) · Trends in Pharmacological Sciences · doi:10.1016/j.tips.2022.12.005 · Cited by 178
Metadata
- Authors
- Zhang, W., Wang, L., Liu, K., Wei, X., Yang, K., Du, W., Wang, S., Guo, N., Ma, C., Luo, L., Wu, J., Lin, L., Yang, F., Gao, F., Wang, X., Li, T., Zhang, R., Saksena, N. K., Yang, H., Wang, J., Fang, L., Hou, Y., Xu, X., & Liu, X.
- Affiliations
- BGI Group (China) (CN)
- Corresponding author
- Liu, Xiao
- ORCID
- Venue
- Bioinformatics
- Publisher
- Oxford University Press (OUP)
- Published
- 2019-08-02
- Volume
- 36
- Issue
- 3
- Pages
- 897-903
- ISSN
- 1367-4803 (ISSN-L)
- 1367-4811
- OpenAlex
- W2965921152
- Cited by
- 166
- References
- 53
- Citations by year
- 2026: 19 · 2025: 34 · 2024: 38 · 2023: 34 · 2022: 17 · 2021: 15 · 2020: 8 · 2019: 1
- FWCI
- 4.07
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- academic.oup.com/journals/pages/open_access/funder_policies/chorus/standard_publication_model (vor)
- Locations
- Bioinformatics (publishedVersion)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion)
- PubMed (publishedVersion)
- Research at the University of Copenhagen (University of Copenhagen) (submittedVersion, cc-by)
- Funders
- Shenzhen Municipal Government of China (JCYJ20160510141910129)
- Topics
- 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
- Physical Sciences › Computer Science › Artificial Intelligence › Artificial Immune Systems Applications
- Keywords
- T cell receptor repertoire, B cell receptor repertoire, adaptive immunity, immunotherapy, data visualization, human
- MeSH terms
- Antigens, Humans, Immunotherapy, Receptors, Antigen, T-Cell, Databases, Factual, 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 ›
Are you an author, or do you see a problem with this page? Request a review