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Error correction of next-generation sequencing data and reliable estimation of HIV quasispecies

Zagordi, Osvaldo ; Klein, Rolf ; Däumer, Martin ; Beerenwinkel, Niko

In: Nucleic Acids Research, 2010, vol. 38, no. 21, p. 7400-7409

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    Titre
    • Error correction of next-generation sequencing data and reliable estimation of HIV quasispecies
    Auteur
    • Zagordi, Osvaldo. Department of Biosystems Sciences and Engineering, ETH Zurich, Mattenstrasse 26, 4058 Basel, SIB - Swiss Institute of Bioinformatics, Switzerland and Institut für Immunologie und Genetik, Pfaffplatz 10, 67655 Kaiserslautern, Germany
    • Klein, Rolf. Department of Biosystems Sciences and Engineering, ETH Zurich, Mattenstrasse 26, 4058 Basel, SIB - Swiss Institute of Bioinformatics, Switzerland and Institut für Immunologie und Genetik, Pfaffplatz 10, 67655 Kaiserslautern, Germany
    • Däumer, Martin. Department of Biosystems Sciences and Engineering, ETH Zurich, Mattenstrasse 26, 4058 Basel, SIB - Swiss Institute of Bioinformatics, Switzerland and Institut für Immunologie und Genetik, Pfaffplatz 10, 67655 Kaiserslautern, Germany
    • Beerenwinkel, Niko. Department of Biosystems Sciences and Engineering, ETH Zurich, Mattenstrasse 26, 4058 Basel, SIB - Swiss Institute of Bioinformatics, Switzerland and Institut für Immunologie und Genetik, Pfaffplatz 10, 67655 Kaiserslautern, Germany
    Type de document
    • Postprint
    Langue
    • Anglais
    Publié dans
    • Nucleic Acids Research, 2010, vol. 38, no. 21, p. 7400-7409. Oxford University Press
    Autre version électronique
    Identifiant OAI-PMH
    • oai:doc.rero.ch:301516
    Summary
    Next-generation sequencing technologies can be used to analyse genetically heterogeneous samples at unprecedented detail. The high coverage achievable with these methods enables the detection of many low-frequency variants. However, sequencing errors complicate the analysis of mixed populations and result in inflated estimates of genetic diversity. We developed a probabilistic Bayesian approach to minimize the effect of errors on the detection of minority variants. We applied it to pyrosequencing data obtained from a 1.5‐kb-fragment of the HIV-1 gag/pol gene in two control and two clinical samples. The effect of PCR amplification was analysed. Error correction resulted in a two- and five-fold decrease of the pyrosequencing base substitution rate, from 0.05% to 0.03% and from 0.25% to 0.05% in the non-PCR and PCR-amplified samples, respectively. We were able to detect viral clones as rare as 0.1% with perfect sequence reconstruction. Probabilistic haplotype inference outperforms the counting-based calling method in both precision and recall. Genetic diversity observed within and between two clinical samples resulted in various patterns of phenotypic drug resistance and suggests a close epidemiological link. We conclude that pyrosequencing can be used to investigate genetically diverse samples with high accuracy if technical errors are properly treated