Quantification of HTLV-1 clonality and TCR diversity

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 i...

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Veröffentlicht in:PLoS computational biology 2014-06, Vol.10 (6), p.e1003646-e1003646
Hauptverfasser: Laydon, Daniel J, Melamed, Anat, Sim, Aaron, Gillet, Nicolas A, Sim, Kathleen, Darko, Sam, Kroll, J Simon, Douek, Daniel C, Price, David A, Bangham, Charles R M, Asquith, Becca
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container_issue 6
container_start_page e1003646
container_title PLoS computational biology
container_volume 10
creator Laydon, Daniel J
Melamed, Anat
Sim, Aaron
Gillet, Nicolas A
Sim, Kathleen
Darko, Sam
Kroll, J Simon
Douek, Daniel C
Price, David A
Bangham, Charles R M
Asquith, Becca
description 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.
doi_str_mv 10.1371/journal.pcbi.1003646
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subjects Algorithms
Biology and Life Sciences
Biomedical research
Cell research
Cloning
Computational Biology
Databases, Genetic - statistics & numerical data
Estimates
Feces - microbiology
Genetic aspects
Genetic research
Genetic Variation
Health aspects
HTLV-I Infections - virology
Human health sciences
Human T-lymphotropic virus 1 - genetics
Humans
Immune system
Immunologie & maladie infectieuse
Immunology & infectious disease
Infant
Infections
Lymphocytes
Medical research
Microbiota - genetics
Models, Genetic
Population
Receptors, Antigen, T-Cell - genetics
Sample size
Sciences de la santé humaine
Seawater - microbiology
Statistics, Nonparametric
T cell receptors
T cells
Viral infections
title Quantification of HTLV-1 clonality and TCR diversity
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