High-throughput sequencing of B and T cell receptors is routinely being applied in studies of adaptive immunity. The Adaptive Immune Receptor Repertoire (AIRR) Community was formed in 2015 to address issues in AIRR sequencing studies, including the development of reporting standards for the sharing of data sets.
This is a preview of subscription content, access via your institution
Relevant articles
Open Access articles citing this article.
-
Seven-chain adaptive immune receptor repertoire analysis in rheumatoid arthritis reveals novel features associated with disease and clinically relevant phenotypes
Genome Biology Open Access 11 March 2024
-
A robust deep learning workflow to predict CD8 + T-cell epitopes
Genome Medicine Open Access 13 September 2023
-
KA-Search, a method for rapid and exhaustive sequence identity search of known antibodies
Scientific Reports Open Access 18 July 2023
Access options
Access Nature and 54 other Nature Portfolio journals
Get Nature+, our best-value online-access subscription
$29.99 / 30 days
cancel any time
Subscribe to this journal
Receive 12 print issues and online access
$209.00 per year
only $17.42 per issue
Rent or buy this article
Prices vary by article type
from$1.95
to$39.95
Prices may be subject to local taxes which are calculated during checkout
References
Litman, G.W., Cannon, J.P. & Dishaw, L.J. Nat. Rev. Immunol. 5, 866–879 (2005).
Tonegawa, S. Nature 302, 575–581 (1983).
Davis, M.M. & Bjorkman, P.J. Nature 334, 395–402 (1988).
Liu, X.S. & Mardis, E.R. Cell 168, 600–612 (2017).
Hou, D., Chen, C., Seely, E.J., Chen, S. & Song, Y. Front. Immunol. 7, 336 (2016).
Georgiou, G. et al. Nat. Biotechnol. 32, 158–168 (2014).
Wardemann, H. & Busse, C.E. Trends Immunol. 38, 471–482 (2017).
Burel, J.G., Apte, S.H. & Doolan, D.L. Trends Immunol. 37, 53–67 (2016).
Friedensohn, S., Khan, T.A. & Reddy, S.T. Trends Biotechnol. 35, 203–214 (2017).
Yaari, G. & Kleinstein, S.H. Genome Med. 7, 121 (2015).
Greiff, V., Miho, E., Menzel, U. & Reddy, S.T. Trends Immunol. 36, 738–749 (2015).
Breden, F. et al. Front. Immunol. http://dx.doi.org/10.3389/fimmu.2017.01418 (2017).
Toby, I.T. et al. BMC Bioinformatics 17, 333 (2016).
Gupta, N.T. et al. Bioinformatics 31, 3356–3358 (2015).
Gadala-Maria, D., Yaari, G., Uduman, M. & Kleinstein, S.H. Proc. Natl. Acad. Sci. USA 112, E862–E870 (2015).
Corcoran, M.M. et al. Nat. Commun. 7, 13642 (2016).
Taylor, C.F. et al. Nat. Biotechnol. 26, 889–896 (2008).
Brazma, A. et al. Nat. Genet. 29, 365–371 (2001).
Brazma, A. ScientificWorldJournal 9, 420–423 (2009).
Bhattacharya, S. et al. Immunol. Res. 58, 234–239 (2014).
Nakamura, Y., Cochrane, G. & Karsch-Mizrachi, I. Nucleic Acids Res. 41, D21–D24 (2013).
Contreras, J.L. Trends Genet. 31, 55–57 (2015).
European Commission. H2020 Program: Guidelines on FAIR Data Management in Horizon 2020, Version 3.0. (European Commission, 2016).
Wilkinson, M.D. et al. Sci. Data 3, 160018 (2016).
Author information
Authors and Affiliations
Consortia
Corresponding authors
Ethics declarations
Competing interests
The authors declare no competing financial interests.
Supplementary information
Supplementary Text and Figures
Supplementary Note 1 (PDF 56 kb)
Rights and permissions
About this article
Cite this article
Rubelt, F., Busse, C., Bukhari, S. et al. Adaptive Immune Receptor Repertoire Community recommendations for sharing immune-repertoire sequencing data. Nat Immunol 18, 1274–1278 (2017). https://doi.org/10.1038/ni.3873
Published:
Issue Date:
DOI: https://doi.org/10.1038/ni.3873
This article is cited by
-
Adaptive immune receptor repertoire analysis
Nature Reviews Methods Primers (2024)
-
Improving generalization of machine learning-identified biomarkers using causal modelling with examples from immune receptor diagnostics
Nature Machine Intelligence (2024)
-
Dandelion uses the single-cell adaptive immune receptor repertoire to explore lymphocyte developmental origins
Nature Biotechnology (2024)
-
Seven-chain adaptive immune receptor repertoire analysis in rheumatoid arthritis reveals novel features associated with disease and clinically relevant phenotypes
Genome Biology (2024)
-
A robust deep learning workflow to predict CD8 + T-cell epitopes
Genome Medicine (2023)