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Quantification of HTLV-1 clonality and TCR diversity.
- Source :
-
PLoS computational biology [PLoS Comput Biol] 2014 Jun 19; Vol. 10 (6), pp. e1003646. Date of Electronic Publication: 2014 Jun 19 (Print Publication: 2014). - Publication Year :
- 2014
-
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.
- Subjects :
- Computational Biology
Databases, Genetic statistics & numerical data
Feces microbiology
HTLV-I Infections virology
Humans
Infant
Microbiota genetics
Models, Genetic
Seawater microbiology
Statistics, Nonparametric
Algorithms
Genetic Variation
Human T-lymphotropic virus 1 genetics
Receptors, Antigen, T-Cell genetics
Subjects
Details
- Language :
- English
- ISSN :
- 1553-7358
- Volume :
- 10
- Issue :
- 6
- Database :
- MEDLINE
- Journal :
- PLoS computational biology
- Publication Type :
- Academic Journal
- Accession number :
- 24945836
- Full Text :
- https://doi.org/10.1371/journal.pcbi.1003646