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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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Autores principales: 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
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2014
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4063693/
https://www.ncbi.nlm.nih.gov/pubmed/24945836
http://dx.doi.org/10.1371/journal.pcbi.1003646
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author 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
author_facet 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
author_sort Laydon, Daniel J.
collection PubMed
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.
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spelling pubmed-40636932014-06-25 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 A. Bangham, Charles R. M. Asquith, Becca PLoS Comput Biol Research Article 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. Public Library of Science 2014-06-19 /pmc/articles/PMC4063693/ /pubmed/24945836 http://dx.doi.org/10.1371/journal.pcbi.1003646 Text en https://creativecommons.org/publicdomain/zero/1.0/ This is an open-access article distributed under the terms of the Creative Commons Public Domain declaration, which stipulates that, once placed in the public domain, this work may be freely reproduced, distributed, transmitted, modified, built upon, or otherwise used by anyone for any lawful purpose.
spellingShingle Research Article
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
Quantification of HTLV-1 Clonality and TCR Diversity
title Quantification of HTLV-1 Clonality and TCR Diversity
title_full Quantification of HTLV-1 Clonality and TCR Diversity
title_fullStr Quantification of HTLV-1 Clonality and TCR Diversity
title_full_unstemmed Quantification of HTLV-1 Clonality and TCR Diversity
title_short Quantification of HTLV-1 Clonality and TCR Diversity
title_sort quantification of htlv-1 clonality and tcr diversity
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4063693/
https://www.ncbi.nlm.nih.gov/pubmed/24945836
http://dx.doi.org/10.1371/journal.pcbi.1003646
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