Computational Methods to Advance Phylogenomic Workflows
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Phylogenomics refers to the use of genome-scale data in phylogenetic analysis. There are several methods for acquiring genome-scale, phylogenetically-useful data from an organism that avoid sequencing the entire genome, thus reducing cost and effort, and enabling one to sequence many more individuals. In this dissertation we focus on one method in particular — RNA sequencing — and the concomitant use of assembled protein-coding transcripts in phylogeny reconstruction. Phylogenomic workflows involve tasks that are algorithmically and computationally demanding, in part due to the large amount of sequence data typically included in such analyses. This dissertation applies techniques from computer science to improve methodology and performance associated with phylogenomic workflow tasks such as sequence classification, transcript assembly, orthology determination, and phylogenetic analysis. While the majority of the methods developed in this dissertation can be applied to the analysis of diverse organismal groups, we primarily focus on the analysis of transcriptome data from Lepidoptera (moths and butterflies), generated as part of a collaboration known as “Leptree”.