Browsing by Author "Jones, Graham"

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    BEAST 2.5: An advanced software platform for Bayesian evolutionary analysis
    (Public Library of Science, 2019) Bouckaert, Remco; Vaughan, Timothy G.; Barido-Sottani, Joëlle; Duchêne, Sebastián; Fourment, Mathieu; Gavryushkina, Alexandra; Heled, Joseph; Jones, Graham; Kühnert, Denise; De Maio, Nicola; Matschiner, Michael; Mendes, Fábio K.; Müller, Nicola F.; Ogilvie, Huw A.; du Plessis, Louis; Popinga, Alex; Rambaut, Andrew; Rasmussen, David; Siveroni, Igor; Suchard, Marc A.; Wu, Chieh-His; Xie, Dong; Zhang, Chi; Stadler, Tanja; Drummond, Alexei J.
    Elaboration of Bayesian phylogenetic inference methods has continued at pace in recent years with major new advances in nearly all aspects of the joint modelling of evolutionary data. It is increasingly appreciated that some evolutionary questions can only be adequately answered by combining evidence from multiple independent sources of data, including genome sequences, sampling dates, phenotypic data, radiocarbon dates, fossil occurrences, and biogeographic range information among others. Including all relevant data into a single joint model is very challenging both conceptually and computationally. Advanced computational software packages that allow robust development of compatible (sub-)models which can be composed into a full model hierarchy have played a key role in these developments. Developing such software frameworks is increasingly a major scientific activity in its own right, and comes with specific challenges, from practical software design, development and engineering challenges to statistical and conceptual modelling challenges. BEAST 2 is one such computational software platform, and was first announced over 4 years ago. Here we describe a series of major new developments in the BEAST 2 core platform and model hierarchy that have occurred since the first release of the software, culminating in the recent 2.5 release.
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    Embracing heterogeneity: coalescing the Tree of Life and the future of phylogenomics
    (PeerJ, 2019) Bravo, Gustavo A.; Antonelli, Alexandre; Bacon, Christine D.; Bartoszek, Krzysztof; Blom, Mozes P.K.; Huynh, Stella; Jones, Graham; Knowles, L. Lacey; Lamichhaney, Sangeet; Marcussen, Thomas; Morlon, Hélène; Nakhleh, Luay K.; Oxelman, Bengt; Pfeil, Bernard; Schliep, Alexander; Wahlberg, Niklas; Werneck, Fernanda P.; Wiedenhoeft, John; Willows-Munro, Sandi; Edwards, Scott V.
    Building the Tree of Life (ToL) is a major challenge of modern biology, requiring advances in cyberinfrastructure, data collection, theory, and more. Here, we argue that phylogenomics stands to benefit by embracing the many heterogeneous genomic signals emerging from the first decade of large-scale phylogenetic analysis spawned by high-throughput sequencing (HTS). Such signals include those most commonly encountered in phylogenomic datasets, such as incomplete lineage sorting, but also those reticulate processes emerging with greater frequency, such as recombination and introgression. Here we focus specifically on how phylogenetic methods can accommodate the heterogeneity incurred by such population genetic processes; we do not discuss phylogenetic methods that ignore such processes, such as concatenation or supermatrix approaches or supertrees. We suggest that methods of data acquisition and the types of markers used in phylogenomics will remain restricted until a posteriori methods of marker choice are made possible with routine whole-genome sequencing of taxa of interest. We discuss limitations and potential extensions of a model supporting innovation in phylogenomics today, the multispecies coalescent model (MSC). Macroevolutionary models that use phylogenies, such as character mapping, often ignore the heterogeneity on which building phylogenies increasingly rely and suggest that assimilating such heterogeneity is an important goal moving forward. Finally, we argue that an integrative cyberinfrastructure linking all steps of the process of building the ToL, from specimen acquisition in the field to publication and tracking of phylogenomic data, as well as a culture that values contributors at each step, are essential for progress.