PIRD: pan immune repertoire database
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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.
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Efforts have been made to create standardized, publicly available repositories for antibody sequencing data.
“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]
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Strategies to Overcome Hurdles in Cancer Immunotherapy
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T cell receptor β repertoires in patients with COVID-19 reveal disease severity signatures
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Advances in antibody discovery from human BCR repertoires
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Context-Aware Amino Acid Embedding Advances Analysis of TCR-Epitope InteractionsPreprint
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nuTCRacker: Predicting the Recognition of HLA‐I–Peptide Complexes by αβTCRs for Unseen Peptides
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Personalizing DNA Cancer Vaccines
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Similarity Network Analysis of the Adaptive Immune Response in the Proximal Airway
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epiTCR-KDA: knowledge distillation model on dihedral angles for TCR-peptide prediction
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Neo-epitope identification by weakly-supervised peptide-TCR binding predictionPreprint
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- 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 › Engineering › Biomedical Engineering › 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.
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