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Coalescent inference using serially sampled, high-throughput sequencing data from HIV infected patients

Kevin Dialdestoro, Jonas Andreas Sibbesen, Lasse Maretty, Jayna Raghwani, Astrid Gall, Paul Kellam, Oliver G. Pybus, Jotun Hein, Paul A. Jenkins
doi: https://doi.org/10.1101/020552
Kevin Dialdestoro
aDepartment of Statistics, University of Oxford, Oxford, United Kingdom
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Jonas Andreas Sibbesen
bThe Bioinformatics Centre, Department of Biology, University of Copenhagen, Copenhagen, Denmark
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Lasse Maretty
bThe Bioinformatics Centre, Department of Biology, University of Copenhagen, Copenhagen, Denmark
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Jayna Raghwani
cDepartment of Zoology, University of Oxford, Oxford, United Kingdom
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Astrid Gall
dWellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge, United Kingdom
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Paul Kellam
dWellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge, United Kingdom
eUCL/MRC Centre for Medical Molecular Virology, Division of Infection and Immunity, University College London, United Kingdom
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Oliver G. Pybus
cDepartment of Zoology, University of Oxford, Oxford, United Kingdom
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Jotun Hein
aDepartment of Statistics, University of Oxford, Oxford, United Kingdom
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Paul A. Jenkins
fDepartment of Statistics, University of Warwick, Coventry, United Kingdom
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  • For correspondence: p.jenkins@warwick.ac.uk
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Posted June 07, 2015.
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Coalescent inference using serially sampled, high-throughput sequencing data from HIV infected patients
Kevin Dialdestoro, Jonas Andreas Sibbesen, Lasse Maretty, Jayna Raghwani, Astrid Gall, Paul Kellam, Oliver G. Pybus, Jotun Hein, Paul A. Jenkins
bioRxiv 020552; doi: https://doi.org/10.1101/020552
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Coalescent inference using serially sampled, high-throughput sequencing data from HIV infected patients
Kevin Dialdestoro, Jonas Andreas Sibbesen, Lasse Maretty, Jayna Raghwani, Astrid Gall, Paul Kellam, Oliver G. Pybus, Jotun Hein, Paul A. Jenkins
bioRxiv 020552; doi: https://doi.org/10.1101/020552

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