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The diversity of an organism's B- and T-cell repertoires is both clinically important and a key measure of immunological complexity. However, diversity is hard to estimate by current methods, because of inherent uncertainty in the number of B- and T-cell clones that will be missing from a blood or tissue sample by chance (the missing-species problem), inevitable sampling bias, and experimental noise. To solve this problem, we developed Recon, a modified maximum-likelihood method that outputs the overall diversity of a repertoire from measurements on a sample. Recon outputs accurate, robust estimates by any of a vast set of complementary diversity measures, including species richness and entropy, at fractional repertoire coverage. It also outputs error bars and power tables, allowing robust comparisons of diversity between individuals and over time. We apply Recon to in silico and experimental immune-repertoire sequencing data sets as proof of principle for measuring diversity in large, complex systems.

Original publication

DOI

10.1038/ncomms11881

Type

Journal article

Journal

Nat Commun

Publication Date

15/06/2016

Volume

7

Keywords

Algorithms, B-Lymphocytes, Computer Simulation, Genetic Variation, High-Throughput Nucleotide Sequencing, Humans, Reference Standards, Reproducibility of Results, T-Lymphocytes