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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
Jonas Andreas Sibbesen
bThe Bioinformatics Centre, Department of Biology, University of Copenhagen, Copenhagen, Denmark
Lasse Maretty
bThe Bioinformatics Centre, Department of Biology, University of Copenhagen, Copenhagen, Denmark
Jayna Raghwani
cDepartment of Zoology, University of Oxford, Oxford, United Kingdom
Astrid Gall
dWellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge, United Kingdom
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
Oliver G. Pybus
cDepartment of Zoology, University of Oxford, Oxford, United Kingdom
Jotun Hein
aDepartment of Statistics, University of Oxford, Oxford, United Kingdom
Paul A. Jenkins
fDepartment of Statistics, University of Warwick, Coventry, United Kingdom
Article usage
Posted June 07, 2015.
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
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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