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Quantification of HTLV-1 clonality and TCR diversity

Laydon, Daniel J., Melamed, Anat, Sim, Aaron, Gillet, Nicolas A., Sim, Kathleen, Darko, Sam, Kroll, J. Simon, Douek, Daniel C., Price, David, Bangham, Charles R. M. and Asquith, Becca 2014. Quantification of HTLV-1 clonality and TCR diversity. PLoS Computational Biology 10 (6) , e1003646. 10.1371/journal.pcbi.1003646

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Abstract

Estimation of immunological and microbiological diversity is vital to our understanding of infection and the immune response. For instance, what is the diversity of the T cell repertoire? These questions are partially addressed by high-throughput sequencing techniques that enable identification of immunological and microbiological “species” in a sample. Estimators of the number of unseen species are needed to estimate population diversity from sample diversity. Here we test five widely used non-parametric estimators, and develop and validate a novel method, DivE, to estimate species richness and distribution. We used three independent datasets: (i) viral populations from subjects infected with human T-lymphotropic virus type 1; (ii) T cell antigen receptor clonotype repertoires; and (iii) microbial data from infant faecal samples. When applied to datasets with rarefaction curves that did not plateau, existing estimators systematically increased with sample size. In contrast, DivE consistently and accurately estimated diversity for all datasets. We identify conditions that limit the application of DivE. We also show that DivE can be used to accurately estimate the underlying population frequency distribution. We have developed a novel method that is significantly more accurate than commonly used biodiversity estimators in microbiological and immunological populations.

Item Type: Article
Date Type: Published Online
Status: Published
Schools: Medicine
Systems Immunity Research Institute (SIURI)
Subjects: Q Science > QH Natural history > QH301 Biology
Publisher: Public Library of Science
ISSN: 1553-7358
Date of First Compliant Deposit: 30 March 2016
Last Modified: 06 Feb 2019 16:58
URI: http://orca.cf.ac.uk/id/eprint/76906

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Cited 35 times in Scopus. View in Scopus. Powered By Scopus® Data

Cited 12 times in Web of Science. View in Web of Science.

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