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PLoS Comput Biol. 2014 Jun 19;10(6):e1003646. doi: 10.1371/journal.pcbi.1003646. eCollection 2014 Jun.

Quantification of HTLV-1 clonality and TCR diversity.

Author information

1
Section of Immunology, Wright-Fleming Institute, Imperial College School of Medicine, London, United Kingdom.
2
Centre for Integrative Systems Biology and Bioinformatics, South Kensington Campus, Imperial College, London, United Kingdom.
3
Section of Immunology, Wright-Fleming Institute, Imperial College School of Medicine, London, United Kingdom; Department of Molecular and Cellular Epigenetics, University of Liège, Liège, Belgium.
4
Section of Paediatrics, Wright-Fleming Institute, Imperial College School of Medicine, London, United Kingdom.
5
Vaccine Research Center, National Institutes of Health, Bethesda, Maryland, United States of America.
6
Vaccine Research Center, National Institutes of Health, Bethesda, Maryland, United States of America; Institute of Infection and Immunity, Cardiff University School of Medicine, Cardiff, Wales, United Kingdom.

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.

PMID:
24945836
PMCID:
PMC4063693
DOI:
10.1371/journal.pcbi.1003646
[Indexed for MEDLINE]
Free PMC Article

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