The DOI matches its Crossref record.
IMGT(R), the international ImMunoGeneTics information system(R)
Open accessAbstract
IMGT, the international ImMunoGeneTics information system (http://www.imgt.org), was created in 1989 by Marie-Paule Lefranc, Laboratoire d'ImmunoGénétique Moléculaire LIGM (Université Montpellier 2 and CNRS) at Montpellier, France, in order to standardize and manage the complexity of immunogenetics data. The building of a unique ontology, IMGT-ONTOLOGY, has made IMGT the global reference in immunogenetics and immunoinformatics. IMGT is a high-quality integrated knowledge resource specialized in the immunoglobulins or antibodies, T cell receptors, major histocompatibility complex, of human and other vertebrate species, proteins of the IgSF and MhcSF, and related proteins of the immune systems of any species. IMGT provides a common access to standardized data from genome, proteome, genetics and 3D structures. IMGT consists of five databases (IMGT/LIGM-DB, IMGT/GENE-DB, IMGT/3Dstructure-DB, etc.), fifteen interactive online tools for sequence, genome and 3D structure analysis, and more than 10,000 HTML pages of synthesis and knowledge. IMGT is used in medical research (autoimmune diseases, infectious diseases, AIDS, leukemias, lymphomas and myelomas), veterinary research, biotechnology related to antibody engineering (phage displays, combinatorial libraries, chimeric, humanized and human antibodies), diagnostics (clonalities, detection and follow-up of residual diseases) and therapeutical approaches (graft, immunotherapy, vaccinology). IMGT is freely available at http://www.imgt.org.
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
Cited by
The Immune Landscape of Cancer
Thorsson, V., Gibbs, D. L., Brown, S. D., Wolf, D., Bortone, D. S., Ou Yang, T.-H., Porta-Pardo, E., Gao, G. F., Plaisier, C. L., Eddy, J. A., et al. (2018) · Immunity · doi:10.1016/j.immuni.2018.03.023 · Cited by 5,870
Self-citationPotent neutralizing antibodies against multiple epitopes on SARS-CoV-2 spike
Liu, L., Wang, P., Nair, M. S., Yu, J., Rapp, M., Wang, Q., Luo, Y., Chan, J. F.-W., Sahi, V., Figueroa, A., et al. (2020) · Nature · doi:10.1038/s41586-020-2571-7 · Cited by 1,349
Rogers, T. F., Zhao, F., Huang, D., Beutler, N., Burns, A., He, W.-T., Limbo, O., Smith, C., Song, G., Woehl, J., et al. (2020) · Science · doi:10.1126/science.abc7520 · Cited by 1,230
Corti, D., Voss, J., Gamblin, S. J., Codoni, G., Macagno, A., Jarrossay, D., Vachieri, S. G., Pinna, D., Minola, A., Vanzetta, F., et al. (2011) · Science · doi:10.1126/science.1205669 · Cited by 1,203
Naturally enhanced neutralizing breadth against SARS-CoV-2 one year after infection
Wang, Z., Muecksch, F., Schaefer-Babajew, D., Finkin, S., Viant, C., Gaebler, C., Hoffmann, H. H., Barnes, C. O., Cipolla, M., Ramos, V., et al. (2021) · Nature · doi:10.1038/s41586-021-03696-9 · Cited by 721
Brain regulatory T cells suppress astrogliosis and potentiate neurological recovery
Ito, M., Komai, K., Mise-Omata, S., Iizuka-Koga, M., Noguchi, Y., Kondo, T., Sakai, R., Matsuo, K., Nakayama, T., Yoshie, O., et al. (2019) · Nature · doi:10.1038/s41586-018-0824-5 · Cited by 784
SAbDab: the structural antibody database
Dunbar, J., Krawczyk, K., Leem, J., Baker, T., Fuchs, A., Georges, G., Shi, J., & Deane, C. M. (2014) · Nucleic Acids Research · doi:10.1093/nar/gkt1043 · Cited by 674
c-MAF-dependent regulatory T cells mediate immunological tolerance to a gut pathobiont
Xu, M., Pokrovskii, M., Ding, Y., Yi, R., Au, C., Harrison, O. J., Galan, C., Belkaid, Y., Bonneau, R., & Littman, D. R. (2018) · Nature · doi:10.1038/nature25500 · Cited by 498
The Inhibitory Receptor PD-1 Regulates IgA Selection and Bacterial Composition in the Gut
Kawamoto, S., Tran, T. H., Maruya, M., Suzuki, K., Doi, Y., Tsutsui, Y., Kato, L. M., & Fagarasan, S. (2012) · Science · doi:10.1126/science.1217718 · Cited by 451
T cell fate and clonality inference from single-cell transcriptomes
(2016) · doi:10.1038/nmeth.3800 · Cited by 434
A new clustering of antibody CDR loop conformations
North, B., Lehmann, A., & Dunbrack, R. L. (2011) · Journal of Molecular Biology · doi:10.1016/j.jmb.2010.10.030 · Cited by 380
Reprogramming of CTLs into natural killer–like cells in celiac disease
Meresse, B., Curran, S. A., Ciszewski, C., Orbelyan, G., Setty, M., Bhagat, G., Lee, L., Tretiakova, M., Semrad, C., Kistner, E., et al. (2006) · The Journal of Experimental Medicine · doi:10.1084/jem.20060028 · Cited by 322
Landscape of tumor-infiltrating T cell repertoire of human cancers
Li, B., Li, T., Pignon, J.-C., Wang, B., Wang, J., Shukla, S. A., Dou, R., Chen, Q., Hodi, F. S., Choueiri, T. K., et al. (2016) · Nature Genetics · doi:10.1038/ng.3581 · Cited by 306
Wang, G. C., Dash, P., McCullers, J. A., Doherty, P. C., & Thomas, P. G. (2012) · Science Translational Medicine · doi:10.1126/scitranslmed.3003647 · Cited by 238
David vs. Goliath: The Structure, Function, and Clinical Prospects of Antibody Fragments
Bates, A., & Power, C. A. (2019) · Antibodies · doi:10.3390/antib8020028 · Cited by 237
Dominant protection from HLA-linked autoimmunity by antigen-specific regulatory T cells
Ooi, J. D., Petersen, J., Tan, Y. H., Huynh, M., Willett, Z. J., Ramarathinam, S. H., Eggenhuizen, P. J., Loh, K. L., Watson, K. A., Gan, P. Y., et al. (2017) · Nature · doi:10.1038/nature22329 · Cited by 218
Predicting antibody developability profiles through early stage discovery screening
Bailly, M., Mieczkowski, C., Juan, V., Metwally, E., Tomazela, D., Baker, J., Uchida, M., Kofman, E., Raoufi, F., Motlagh, S., et al. (2020) · mAbs · doi:10.1080/19420862.2020.1743053 · Cited by 228
RosettaAntibodyDesign (RAbD): a general framework for computational antibody design
Adolf-Bryfogle, J., Kalyuzhniy, O., Kubitz, M., Weitzner, B. D., Hu, X., Adachi, Y., Schief, W. R., & Dunbrack, R. L. (2018) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1006112 · Cited by 227
Analysis of allelic expression patterns in clonal somatic cells by single-cell RNA–seq
Reinius, B., Mold, J. E., Ramsköld, D., Deng, Q., Johnsson, P., Michaëlsson, J., Frisén, J., & Sandberg, R. (2016) · Nature Genetics · doi:10.1038/ng.3678 · Cited by 149
Production of α2,6-sialylated IgG1 in CHO cells
Raymond, C., Robotham, A., Spearman, M., Butler, M., Kelly, J., & Durocher, Y. (2015) · mAbs · doi:10.1080/19420862.2015.1029215 · Cited by 106
Prediction of site-specific interactions in antibody–antigen complexes: the proABC method and server
Olimpieri, P. P., Chailyan, A., Tramontano, A., & Marcatili, P. (2013) · Bioinformatics · doi:10.1093/bioinformatics/btt369 · Cited by 90
AbDb: antibody structure database-a database of PDB-derived antibody structures
Ferdous, S., & Martin, A. C. R. (2018) · Database · doi:10.1093/database/bay040 · Cited by 87
Deutsch, E. W., Sun, Z., Campbell, D., Kusebauch, U., Chu, C. S., Mendoza, L., Shteynberg, D., Omenn, G. S., & Moritz, R. L. (2015) · Journal of Proteome Research · doi:10.1021/acs.jproteome.5b00500 · Cited by 80
The clinical significance of allergen-specific IgG4 in allergic diseases
Qin, L., Tang, L.-F., Cheng, L., & Wang, H.-Y. (2022) · Frontiers in Immunology · doi:10.3389/fimmu.2022.1032909 · Cited by 71
Vincents, B., Guentsch, A., Kostolowska, D., von Pawel‐Rammingen, U., Eick, S., Potempa, J., & Abrahamson, M. (2011) · The FASEB Journal · doi:10.1096/fj.11-187799 · Cited by 64
Rubelt, F., Sievert, V., Knaust, F., Diener, C., Lim, T. S., Skriner, K., Klipp, E., Reinhardt, R., Lehrach, H., & Konthur, Z. (2012) · PLoS ONE · doi:10.1371/journal.pone.0049774 · Cited by 37
Quast, N. P., Abanades, B., Guloglu, B., Karuppiah, V., Harper, S., Raybould, M. I. J., & Deane, C. M. (2025) · Communications Biology · doi:10.1038/s42003-025-07708-6 · Cited by 22
ParaSurf: a surface-based deep learning approach for paratope–antigen interaction prediction
Papadopoulos, A.-M., Axenopoulos, A., Iatrou, A., Stamatopoulos, K., Alvarez, F., & Daras, P. (2025) · Bioinformatics · doi:10.1093/bioinformatics/btaf062 · Cited by 9
Ruiz, F., Foreman, W. B., Lilly, M., Baharani, V. A., Depierreux, D. M., Chohan, V., Taylor, A. L., Guenthoer, J., Ralph, D., Matsen IV, F. A., et al. (2024) · PLOS Pathogens · doi:10.1371/journal.ppat.1012650 · Cited by 7
Learning antibody sequence constraints from allelic inclusion
Jagota, M., Hsu, C., Mazumder, T., Sung, K., DeWitt, W. S., Listgarten, J., Matsen, F. A., Ye, C. J., & Song, Y. S. (2024) · doi:10.1101/2024.10.22.619760 · Cited by 2
Metadata
- Authors
- Lefranc, M.-P., Giudicelli, V., Ginestoux, C., Jabado-Michaloud, J., Folch, G., Bellahcene, F., Wu, Y., Gemrot, E., Brochet, X., Lane, J., Regnier, L., Ehrenmann, F., Lefranc, G., & Duroux, P.
- Venue
- Nucleic Acids Research
- Publisher
- Oxford University Press (OUP)
- Published
- 2009-01-01
- Volume
- 37
- Issue
- Database
- Pages
- D1006-D1012
- ISSN
- 0305-1048 (ISSN-L)
- 1362-4962
- OpenAlex
- W2105273832
- Cited by
- 1,119
- References
- 0
- FWCI
- 25.68
- 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
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.
