Computational Methods for Inference of Species/Gene Trees and Trait Evolution
| dc.contributor.advisor | Nakhleh, Luay K. | en_US |
| dc.creator | Wang, Yaxuan | en_US |
| dc.date.accessioned | 2020-12-02T22:39:13Z | en_US |
| dc.date.available | 2021-06-01T05:01:12Z | en_US |
| dc.date.created | 2020-12 | en_US |
| dc.date.issued | 2020-12-01 | en_US |
| dc.date.submitted | December 2020 | en_US |
| dc.date.updated | 2020-12-02T22:39:13Z | en_US |
| dc.description.abstract | Phylogenetic trees play a central role in almost all of biology. These trees have emerged as a powerful paradigm in the post-genomic era to elucidate the processes that shaped the evolutionary history. Species trees model how species split and diversify from their most recent common ancestors. Gene trees model how individual recombination-free loci within a set of genomes evolve from the ancestor. Phylogenetic trees also play a significant role in other comparative evolutionary biology studies such as trait evolution. Therefore, deriving accurate phylogenetic trees and developing an appropriate adaptation of the phylogenetic inference methodology to support trait evolution is the major endeavor. The contribution of this thesis comes from three aspects. Firstly, it provides a heuristic framework for species/gene tree co-estimation. By iteratively inferring the species tree and gene tree, the topological inference of phylogenetic trees become accurate and efficient. I implemented the framework in the multispecies coalescent model while it can be applied in various evolutionary models. By taking the advantage of gene tree discordance, this framework is able to derive reliable and efficient phylogenetic inference. Secondly, I presented a novel proposal strategy to improve the scalability of Bayesian sampling estimation by appropriately reducing unnecessary search space. In my thesis, I applied this idea to empower Bayesian sampling approach of phylogenetic inference. More importantly, this idea reveals a feasible direction to fix the poor fixing problem in Bayesian sampling scenarios. Thirdly, adaptive phenotypic convergence is considered as key evidence of convergent evolution However, gene tree discordance can also generate convergent trait patterns. To determine if the horizontal gene transfer plays a role in the trait evolution when the trait is incongruent with the species phylogeny, I presented a statistical factor from the perspective of phylogenomics. I revealed the impact of ignoring the introgression in the phylogenomic and trait evolution analysis. I implemented the methods and models in the publicly available software package PhyloNet. The contributions of my thesis not only empower effective and practical phylogenomic analysis but also reveal the significance of embracing gene heterogeneity in the post-genomic era. | en_US |
| dc.embargo.terms | 2021-06-01 | en_US |
| dc.format.mimetype | application/pdf | en_US |
| dc.identifier.citation | Wang, Yaxuan. "Computational Methods for Inference of Species/Gene Trees and Trait Evolution." (2020) Diss., Rice University. <a href="https://hdl.handle.net/1911/109597">https://hdl.handle.net/1911/109597</a>. | en_US |
| dc.identifier.uri | https://hdl.handle.net/1911/109597 | en_US |
| dc.language.iso | eng | en_US |
| dc.rights | Copyright is held by the author, unless otherwise indicated. Permission to reuse, publish, or reproduce the work beyond the bounds of fair use or other exemptions to copyright law must be obtained from the copyright holder. | en_US |
| dc.subject | Phylogenetic inference | en_US |
| dc.subject | Trait evolution | en_US |
| dc.subject | Multispecies coalescent | en_US |
| dc.subject | Bayesian sampling | en_US |
| dc.subject | Markov chain Monte Carlo | en_US |
| dc.title | Computational Methods for Inference of Species/Gene Trees and Trait Evolution | en_US |
| dc.type | Thesis | en_US |
| dc.type.material | Text | en_US |
| thesis.degree.department | Computer Science | en_US |
| thesis.degree.discipline | Engineering | en_US |
| thesis.degree.grantor | Rice University | en_US |
| thesis.degree.level | Doctoral | en_US |
| thesis.degree.name | Doctor of Philosophy | en_US |
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