Novel scalable approaches for multiple sequence alignment and phylogenomic reconstruction

dc.contributor.advisorPingali, Keshaven
dc.contributor.advisorWarnow, Tandy, 1955-en
dc.contributor.committeeMemberHillis, Daviden
dc.contributor.committeeMemberGosh, Joydeepen
dc.contributor.committeeMemberBerger, Bonnieen
dc.contributor.committeeMemberMooney, Rayen
dc.creatorMir arabbaygi, Siavashen 2015en
dc.description.abstractThe amount of biological sequence data is increasing rapidly, a promising development that would transform biology if we can develop methods that can analyze large-scale data efficiently and accurately. A fundamental question in evolutionary biology is building the tree of life: a reconstruction of relationships between organisms in evolutionary time. Reconstructing phylogenetic trees from molecular data is an optimization problem that involves many steps. In this dissertation, we argue that to answer long-standing phylogenetic questions with large-scale data, several challenges need to be addressed in various steps of the pipeline. One challenges is aligning large number of sequences so that evolutionarily related positions in all sequences are put in the same column. Constructing alignments is necessary for phylogenetic reconstruction, but also for many other types of evolutionary analyses. In response to this challenge, we introduce PASTA, a scalable and accurate algorithm that can align datasets with up to a million sequences. A second challenge is related to the interesting fact that various parts of the genome can have different evolutionary histories. Reconstructing a species tree from genome-scale data needs to account for these differences. A main approach for species tree reconstruction is to first reconstruct a set of ``gene trees'' from different parts of the genome, and to then summarize these gene trees into a single species tree. We argue that this approach can suffer from two challenges: reconstruction of individual gene trees from limited data can be plagued by estimation error, which translates to errors in the species tree, and also, methods that summarize gene trees are not scalable or accurate enough under some conditions. To address the first challenge, we introduce statistical binning, a method that re-estimates gene trees by grouping them into bins. We show that binning improves gene tree accuracy, and consequently the species tree accuracy. To address the second challenge, we introduce ASTRAL, a new summary method that can run on a thousand genes and a thousand species in a day and has outstanding accuracy. We show that the development of these methods has enabled biological analyses that were otherwise not possible.en
dc.description.departmentComputer Sciencesen
dc.subjectMulti-species coalescent modelen
dc.subjectMultiple sequence alignmenten
dc.subjectStatistical binningen
dc.subjectSpecies tree estimationen
dc.titleNovel scalable approaches for multiple sequence alignment and phylogenomic reconstructionen
dc.typeThesisen Sciencesen scienceen University of Texas at Austinen of Philosophyen

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