Quantification of HTLV-1 Clonality and TCR Diversity.

D. J. Laydon, A. Melamed, A. Sim, N. Gillet, K. Sim, S. Darko, J. S. Kroll, D. C. Douek, D. A. Price, C. R. M. Bangham, B. Asquith

Research output: Contribution to journalArticlepeer-review


[en] 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 textquotedblspeciestextquotedbl 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.
Original languageEnglish
Pages (from-to)e1003646
JournalPLOS Computational Biology
Issue number6
Publication statusPublished - 2014
Externally publishedYes


  • Sciences de la santé humaine => Immunologie maladie infectieuse

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