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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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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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.</description><identifier>ISSN: 1553-7358</identifier><identifier>ISSN: 1553-734X</identifier><identifier>EISSN: 1553-7358</identifier><identifier>DOI: 10.1371/journal.pcbi.1003646</identifier><identifier>PMID: 24945836</identifier><language>eng</language><publisher>United States: Public Library of Science</publisher><subject>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</subject><ispartof>PLoS computational biology, 2014-06, Vol.10 (6), p.e1003646-e1003646</ispartof><rights>COPYRIGHT 2014 Public Library of Science</rights><rights>2014</rights><rights>2014 Public Library of Science. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited: Citation: Laydon DJ, Melamed A, Sim A, Gillet NA, Sim K, et al. (2014) Quantification of HTLV-1 Clonality and TCR Diversity. 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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.</description><subject>Algorithms</subject><subject>Biology and Life Sciences</subject><subject>Biomedical research</subject><subject>Cell research</subject><subject>Cloning</subject><subject>Computational Biology</subject><subject>Databases, Genetic - statistics & numerical data</subject><subject>Estimates</subject><subject>Feces - microbiology</subject><subject>Genetic aspects</subject><subject>Genetic research</subject><subject>Genetic Variation</subject><subject>Health aspects</subject><subject>HTLV-I Infections - virology</subject><subject>Human health sciences</subject><subject>Human T-lymphotropic virus 1 - genetics</subject><subject>Humans</subject><subject>Immune system</subject><subject>Immunologie & maladie infectieuse</subject><subject>Immunology & infectious disease</subject><subject>Infant</subject><subject>Infections</subject><subject>Lymphocytes</subject><subject>Medical research</subject><subject>Microbiota - genetics</subject><subject>Models, Genetic</subject><subject>Population</subject><subject>Receptors, Antigen, T-Cell - genetics</subject><subject>Sample size</subject><subject>Sciences de la santé humaine</subject><subject>Seawater - microbiology</subject><subject>Statistics, Nonparametric</subject><subject>T cell receptors</subject><subject>T cells</subject><subject>Viral infections</subject><issn>1553-7358</issn><issn>1553-734X</issn><issn>1553-7358</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2014</creationdate><recordtype>article</recordtype><sourceid>EIF</sourceid><sourceid>DOA</sourceid><recordid>eNqVkk2P0zAQhiMEYpfCP0AQiQscWuz4I_YFaVUBW6kCsRSuluOMi6s0LnFSsf-eadNdbSUuKFLGmjzvOzPxZNlLSmaUlfT9Jg5da5vZzlVhRglhkstH2SUVgk1LJtTjB-eL7FlKG2SE0vJpdlFwzYVi8jLj3wbb9sEHZ_sQ2zz6_Hq1_DmluWsi2of-Nrdtna_mN3kd9tAlzDzPnnjbJHhxipPsx6ePq_n1dPn182J-tZy6krN-6qUg4KVXgumqdBVYUgFgKAVnTjtWKq2EtKoS1Ne8FoKXlXVCuUJhr4RNstej766JyZwGToYiJ6TGFxKLkaij3ZhdF7a2uzXRBnNMxG5tbNcH14CxtSxLRxj1tOa8oIrSoiJAgQpJqXDo9eFUbai2UDto-842Z6bnX9rwy6zj3nAimdQMDdho0ARYAxavgtkXR-HxPDTYjTMVmKKQylCpBT-M8PZUtou_B0i92YbkoGlsC3E4TMtw1KLQGtE3I7q2OFBofcQ-3AE3V0wxwkuNYZLN_kHhU8M2uNiCD5g_E7w7EyDTw59-bYeUzOL7zX-wX85ZPrKuiyl14O__JSXmsMN3V2oOO2xOO4yyVw_v4V50t7TsLyn06m8</recordid><startdate>20140601</startdate><enddate>20140601</enddate><creator>Laydon, Daniel J</creator><creator>Melamed, Anat</creator><creator>Sim, Aaron</creator><creator>Gillet, Nicolas A</creator><creator>Sim, Kathleen</creator><creator>Darko, Sam</creator><creator>Kroll, J Simon</creator><creator>Douek, Daniel C</creator><creator>Price, David A</creator><creator>Bangham, Charles R M</creator><creator>Asquith, Becca</creator><general>Public Library of Science</general><general>Public Library of Science (PLoS)</general><scope>CGR</scope><scope>CUY</scope><scope>CVF</scope><scope>ECM</scope><scope>EIF</scope><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>ISN</scope><scope>ISR</scope><scope>7X8</scope><scope>Q33</scope><scope>5PM</scope><scope>DOA</scope></search><sort><creationdate>20140601</creationdate><title>Quantification of HTLV-1 clonality and TCR diversity</title><author>Laydon, Daniel J ; 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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.</abstract><cop>United States</cop><pub>Public Library of Science</pub><pmid>24945836</pmid><doi>10.1371/journal.pcbi.1003646</doi><oa>free_for_read</oa></addata></record> |
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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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