Lessons learned from organizing and teaching virtual phylogenetics workshops

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Exploring model-based approaches for simulating and analyzing cophylogenetic data

When two lineages with an intimate ecological association have a repeated history of cospeciation (i.e., both speciate simultaneously), their evolutionary histories can become correlated through time. This correlated diversification can result in perfect concordance between pairs of phylogenies, a pattern often evoked in hosts and obligate symbionts. Yet, many of these presumed cases of cophylogeny do not show a perfect correspondence between host and symbiont phylogenies despite observations of obligate relationships between hosts and symbiont in nature. Previous researchers have primarily used model-free approaches to investigate cophylogenetics. My thesis focuses on moving cophylogenetics closer towards a model-based framework which will allow for more statistically rigorous quantification of cophylogenetic events and further understanding of how these assemblages of hosts and symbionts evolve. Review contrasting the assumptions of and outcomes of cophylogenetic methods Since previous reviews on this topic, new methods have been introduced to test hypotheses of codiversification, and existing methods have been applied in novel ways to understand the parallel histories of associated lineages. Moreover, researchers are now addressing these problems using larger phylogenetic datasets while also applying phylogenetic comparative methods to integrate ecological interactions into macroevolution. I outline different types of ecological interactions that can potentially leave signatures in the branching histories of host-symbiont systems. I contrast how different datasets may violate the assumptions of cophylogenetic methods and how this may limit what conclusions can be drawn about the processes driving species interactions. Developing a generative model for cophylogenetic data Simulation is a necessary step when testing and examining phylogenetic methods. Despite this, there was no available simulation software for cophylogenetic data. I developed the cophylogenetic birth-death model and software to simulate under this model to address this gap. The cophylogenetic birth-death model simulates a pair of phylogenies, the host and the symbiont phylogenies, and their ecological interactions. This model uses independent speciation and extinction for the host and the symbiont and parameters that affect both phylogenies, including cospeciation and host-switching. Using machine learning to estimate cophylogenetic parameters Without a straightforward means to calculate a likelihood for the cophylogenetic birth-death model, I pursued alternative means to estimate the parameters of this model. In particular, I used deep learning to estimate cophylogenetic events. I trained this deep neural network using data simulated under my cophylogenetic birth-death model and then tested the performance of this method. I provide a case study of using this deep neural network on empirical data of figs and fig wasps. Using biogeographic models for examining cophylogenetic data Recently probabilistic models such as the dispersal, extinction, and cladogenesis (DEC) have begun to be used in a phylogenetic context to estimate biogeographic parameters such as dispersal and extirpation. These models have a natural analogy to cophylogenetic data where the host phylogeny becomes the area cladogram, the symbiont phylogeny becomes the species tree, and the host-symbiont interaction matrix becomes the matrix representing the host ranges. As there is currently no method for estimating under the cophylogenetic birth-death model, I tested a Bayesian implementation of the DEC model to estimate symbiont dispersal to new hosts. To do this, I simulated data using treeducken to examine the DEC model's ability to estimate host dispersal

Exploring model-based approaches for simulating and analyzing cophylogenetic data

When two lineages with an intimate ecological association have a repeated history of cospeciation (i.e., both speciate simultaneously), their evolutionary histories can become correlated through time. This correlated diversification can result in perfect concordance between pairs of phylogenies, a pattern often evoked in hosts and obligate symbionts. Yet, many of these presumed cases of cophylogeny do not show a perfect correspondence between host and symbiont phylogenies despite observations of obligate relationships between hosts and symbiont in nature. Previous researchers have primarily used model-free approaches to investigate cophylogenetics. My thesis focuses on moving cophylogenetics closer towards a model-based framework which will allow for more statistically rigorous quantification of cophylogenetic events and further understanding of how these assemblages of hosts and symbionts evolve. Review contrasting the assumptions of and outcomes of cophylogenetic methods Since previous reviews on this topic, new methods have been introduced to test hypotheses of codiversification, and existing methods have been applied in novel ways to understand the parallel histories of associated lineages. Moreover, researchers are now addressing these problems using larger phylogenetic datasets while also applying phylogenetic comparative methods to integrate ecological interactions into macroevolution. I outline different types of ecological interactions that can potentially leave signatures in the branching histories of host-symbiont systems. I contrast how different datasets may violate the assumptions of cophylogenetic methods and how this may limit what conclusions can be drawn about the processes driving species interactions. Developing a generative model for cophylogenetic data Simulation is a necessary step when testing and examining phylogenetic methods. Despite this, there was no available simulation software for cophylogenetic data. I developed the cophylogenetic birth-death model and software to simulate under this model to address this gap. The cophylogenetic birth-death model simulates a pair of phylogenies, the host and the symbiont phylogenies, and their ecological interactions. This model uses independent speciation and extinction for the host and the symbiont and parameters that affect both phylogenies, including cospeciation and host-switching. Using machine learning to estimate cophylogenetic parameters Without a straightforward means to calculate a likelihood for the cophylogenetic birth-death model, I pursued alternative means to estimate the parameters of this model. In particular, I used deep learning to estimate cophylogenetic events. I trained this deep neural network using data simulated under my cophylogenetic birth-death model and then tested the performance of this method. I provide a case study of using this deep neural network on empirical data of figs and fig wasps. Using biogeographic models for examining cophylogenetic data Recently probabilistic models such as the dispersal, extinction, and cladogenesis (DEC) have begun to be used in a phylogenetic context to estimate biogeographic parameters such as dispersal and extirpation. These models have a natural analogy to cophylogenetic data where the host phylogeny becomes the area cladogram, the symbiont phylogeny becomes the species tree, and the host-symbiont interaction matrix becomes the matrix representing the host ranges. As there is currently no method for estimating under the cophylogenetic birth-death model, I tested a Bayesian implementation of the DEC model to estimate symbiont dispersal to new hosts. To do this, I simulated data using treeducken to examine the DEC model's ability to estimate host dispersal

Exploring model-based approaches for simulating and analyzing cophylogenetic data

When two lineages with an intimate ecological association have a repeated history of cospeciation (i.e., both speciate simultaneously), their evolutionary histories can become correlated through time. This correlated diversification can result in perfect concordance between pairs of phylogenies, a pattern often evoked in hosts and obligate symbionts. Yet, many of these presumed cases of cophylogeny do not show a perfect correspondence between host and symbiont phylogenies despite observations of obligate relationships between hosts and symbiont in nature. Previous researchers have primarily used model-free approaches to investigate cophylogenetics. My thesis focuses on moving cophylogenetics closer towards a model-based framework which will allow for more statistically rigorous quantification of cophylogenetic events and further understanding of how these assemblages of hosts and symbionts evolve. Review contrasting the assumptions of and outcomes of cophylogenetic methods Since previous reviews on this topic, new methods have been introduced to test hypotheses of codiversification, and existing methods have been applied in novel ways to understand the parallel histories of associated lineages. Moreover, researchers are now addressing these problems using larger phylogenetic datasets while also applying phylogenetic comparative methods to integrate ecological interactions into macroevolution. I outline different types of ecological interactions that can potentially leave signatures in the branching histories of host-symbiont systems. I contrast how different datasets may violate the assumptions of cophylogenetic methods and how this may limit what conclusions can be drawn about the processes driving species interactions. Developing a generative model for cophylogenetic data Simulation is a necessary step when testing and examining phylogenetic methods. Despite this, there was no available simulation software for cophylogenetic data. I developed the cophylogenetic birth-death model and software to simulate under this model to address this gap. The cophylogenetic birth-death model simulates a pair of phylogenies, the host and the symbiont phylogenies, and their ecological interactions. This model uses independent speciation and extinction for the host and the symbiont and parameters that affect both phylogenies, including cospeciation and host-switching. Using machine learning to estimate cophylogenetic parameters Without a straightforward means to calculate a likelihood for the cophylogenetic birth-death model, I pursued alternative means to estimate the parameters of this model. In particular, I used deep learning to estimate cophylogenetic events. I trained this deep neural network using data simulated under my cophylogenetic birth-death model and then tested the performance of this method. I provide a case study of using this deep neural network on empirical data of figs and fig wasps. Using biogeographic models for examining cophylogenetic data Recently probabilistic models such as the dispersal, extinction, and cladogenesis (DEC) have begun to be used in a phylogenetic context to estimate biogeographic parameters such as dispersal and extirpation. These models have a natural analogy to cophylogenetic data where the host phylogeny becomes the area cladogram, the symbiont phylogeny becomes the species tree, and the host-symbiont interaction matrix becomes the matrix representing the host ranges. As there is currently no method for estimating under the cophylogenetic birth-death model, I tested a Bayesian implementation of the DEC model to estimate symbiont dispersal to new hosts. To do this, I simulated data using treeducken to examine the DEC model's ability to estimate host dispersal

Exploring model-based approaches for simulating and analyzing cophylogenetic data

When two lineages with an intimate ecological association have a repeated history of cospeciation (i.e., both speciate simultaneously), their evolutionary histories can become correlated through time. This correlated diversification can result in perfect concordance between pairs of phylogenies, a pattern often evoked in hosts and obligate symbionts. Yet, many of these presumed cases of cophylogeny do not show a perfect correspondence between host and symbiont phylogenies despite observations of obligate relationships between hosts and symbiont in nature. Previous researchers have primarily used model-free approaches to investigate cophylogenetics. My thesis focuses on moving cophylogenetics closer towards a model-based framework which will allow for more statistically rigorous quantification of cophylogenetic events and further understanding of how these assemblages of hosts and symbionts evolve. Review contrasting the assumptions of and outcomes of cophylogenetic methods Since previous reviews on this topic, new methods have been introduced to test hypotheses of codiversification, and existing methods have been applied in novel ways to understand the parallel histories of associated lineages. Moreover, researchers are now addressing these problems using larger phylogenetic datasets while also applying phylogenetic comparative methods to integrate ecological interactions into macroevolution. I outline different types of ecological interactions that can potentially leave signatures in the branching histories of host-symbiont systems. I contrast how different datasets may violate the assumptions of cophylogenetic methods and how this may limit what conclusions can be drawn about the processes driving species interactions. Developing a generative model for cophylogenetic data Simulation is a necessary step when testing and examining phylogenetic methods. Despite this, there was no available simulation software for cophylogenetic data. I developed the cophylogenetic birth-death model and software to simulate under this model to address this gap. The cophylogenetic birth-death model simulates a pair of phylogenies, the host and the symbiont phylogenies, and their ecological interactions. This model uses independent speciation and extinction for the host and the symbiont and parameters that affect both phylogenies, including cospeciation and host-switching. Using machine learning to estimate cophylogenetic parameters Without a straightforward means to calculate a likelihood for the cophylogenetic birth-death model, I pursued alternative means to estimate the parameters of this model. In particular, I used deep learning to estimate cophylogenetic events. I trained this deep neural network using data simulated under my cophylogenetic birth-death model and then tested the performance of this method. I provide a case study of using this deep neural network on empirical data of figs and fig wasps. Using biogeographic models for examining cophylogenetic data Recently probabilistic models such as the dispersal, extinction, and cladogenesis (DEC) have begun to be used in a phylogenetic context to estimate biogeographic parameters such as dispersal and extirpation. These models have a natural analogy to cophylogenetic data where the host phylogeny becomes the area cladogram, the symbiont phylogeny becomes the species tree, and the host-symbiont interaction matrix becomes the matrix representing the host ranges. As there is currently no method for estimating under the cophylogenetic birth-death model, I tested a Bayesian implementation of the DEC model to estimate symbiont dispersal to new hosts. To do this, I simulated data using treeducken to examine the DEC model's ability to estimate host dispersal

Exploring model-based approaches for simulating and analyzing cophylogenetic data

When two lineages with an intimate ecological association have a repeated history of cospeciation (i.e., both speciate simultaneously), their evolutionary histories can become correlated through time. This correlated diversification can result in perfect concordance between pairs of phylogenies, a pattern often evoked in hosts and obligate symbionts. Yet, many of these presumed cases of cophylogeny do not show a perfect correspondence between host and symbiont phylogenies despite observations of obligate relationships between hosts and symbiont in nature. Previous researchers have primarily used model-free approaches to investigate cophylogenetics. My thesis focuses on moving cophylogenetics closer towards a model-based framework which will allow for more statistically rigorous quantification of cophylogenetic events and further understanding of how these assemblages of hosts and symbionts evolve. Review contrasting the assumptions of and outcomes of cophylogenetic methods Since previous reviews on this topic, new methods have been introduced to test hypotheses of codiversification, and existing methods have been applied in novel ways to understand the parallel histories of associated lineages. Moreover, researchers are now addressing these problems using larger phylogenetic datasets while also applying phylogenetic comparative methods to integrate ecological interactions into macroevolution. I outline different types of ecological interactions that can potentially leave signatures in the branching histories of host-symbiont systems. I contrast how different datasets may violate the assumptions of cophylogenetic methods and how this may limit what conclusions can be drawn about the processes driving species interactions. Developing a generative model for cophylogenetic data Simulation is a necessary step when testing and examining phylogenetic methods. Despite this, there was no available simulation software for cophylogenetic data. I developed the cophylogenetic birth-death model and software to simulate under this model to address this gap. The cophylogenetic birth-death model simulates a pair of phylogenies, the host and the symbiont phylogenies, and their ecological interactions. This model uses independent speciation and extinction for the host and the symbiont and parameters that affect both phylogenies, including cospeciation and host-switching. Using machine learning to estimate cophylogenetic parameters Without a straightforward means to calculate a likelihood for the cophylogenetic birth-death model, I pursued alternative means to estimate the parameters of this model. In particular, I used deep learning to estimate cophylogenetic events. I trained this deep neural network using data simulated under my cophylogenetic birth-death model and then tested the performance of this method. I provide a case study of using this deep neural network on empirical data of figs and fig wasps. Using biogeographic models for examining cophylogenetic data Recently probabilistic models such as the dispersal, extinction, and cladogenesis (DEC) have begun to be used in a phylogenetic context to estimate biogeographic parameters such as dispersal and extirpation. These models have a natural analogy to cophylogenetic data where the host phylogeny becomes the area cladogram, the symbiont phylogeny becomes the species tree, and the host-symbiont interaction matrix becomes the matrix representing the host ranges. As there is currently no method for estimating under the cophylogenetic birth-death model, I tested a Bayesian implementation of the DEC model to estimate symbiont dispersal to new hosts. To do this, I simulated data using treeducken to examine the DEC model's ability to estimate host dispersal

Phylogeny and multiple independent whole-genome duplication events in the Brassicales

Premise: Whole-genome duplications (WGDs) are prevalent throughout the evolutionary history of plants. For example, dozens of WGDs have been phylogenetically localized across the order Brassicales, specifically, within the family Brassicaceae. A WGD event has also been identified in the Cleomaceae, the sister family to Brassicaceae, yet its placement, as well as that of WGDs in other families in the order, remains unclear. Methods: Phylo-transcriptomic data were generated and used to infer a nuclear phylogeny for 74 Brassicales taxa. Genome survey sequencing was also performed on 66 of those taxa to infer a chloroplast phylogeny. These phylogenies were used to assess and confirm relationships among the major families of the Brassicales and within Brassicaceae. Multiple WGD inference methods were then used to assess the placement of WGDs on the nuclear phylogeny. Results: Well-supported chloroplast and nuclear phylogenies for the Brassicales and the putative placement of the Cleomaceae-specific WGD event Th-ɑ are presented. This work also provides evidence for previously hypothesized WGDs, including a well-supported event shared by at least two members of the Resedaceae family, and a possible event within the Capparaceae. Conclusions: Phylogenetics and the placement of WGDs within highly polyploid lineages continues to be a major challenge. This study adds to the conversation on WGD inference difficulties by demonstrating that sampling is especially important for WGD identification and phylogenetic placement. Given its economic importance and genomic resources, the Brassicales continues to be an ideal group for assessing WGD inference methods

Isavuconazole treatment for rare fungal diseases and for invasive aspergillosis in patients with renal impairment: Challenges and lessons of the VITAL trial

Invasive fungal disease (IFD) confers a substantial risk for morbidity and mortality to immunocompromised patients. Invasive aspergillosis (IA) is the most common IFD caused by moulds but the prevalence of other rare mould diseases, such as mucormycosis, hyalohyphomycosis and phaeohyphomycosis, may be increasing. Treatments are available for IA, but evidence to support efficacy and safety of antifungal agents for rare IFDs, or for IFDs in special patient populations, is limited or lacking. The VITAL trial was conducted to assess the efficacy and safety of isavuconazole for the treatment of patients with IA and renal impairment, or with IFDs caused by rare moulds, yeasts or dimorphic fungi. These patients stand to benefit most from a new treatment option but are unlikely to be included in a randomised, controlled trial. In this article, we review the challenges faced in the design and conduct of the VITAL trial. We also review the findings of VITAL, which included evidence of the efficacy and safety of isavuconazole. Finally, we consider the importance of trials such as VITAL to inform therapeutic decision making for clinicians faced with the challenge of treating patients with rare IFDs and as one paradigm of how to determine efficacy and safety of new drugs for rare and resistant infections without a suitable comparator