Use this URL to cite or link to this record in EThOS: http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.627890
Title: Understanding lineage-specific biology through comparative genomics
Author: Li, Yang
Awarding Body: University of Oxford
Current Institution: University of Oxford
Date of Award: 2014
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Abstract:
A major challenge in biology is to identify how different species arose and acquired distinct phenotypic traits. High-throughput sequencing is transforming our understanding of biology by allowing us to study genomes and cellular processes at genome-wide levels. Only a decade subsequent to the publication of the first human genome draft, genome assemblies of hundreds of organisms have been produced. Yet, genome analysis remains challenging and advances have lagged far behind our sequencing abilities and other technological advances. The next generation of comparative genomicists must therefore understand, invent and apply a wide number of computational tools in order to study biology in the most efficient manner and in order to pose the most interesting questions. This thesis spans areas covering evolutionary genomics, gene regulation, and computational methods development. A major aim was to understand how genetic variation contributes to variation in phenotypic traits. This was approached using a large variety of evolutionary and comparative genomics tools. In particular, high-throughput sequencing datasets were analysed to study single-cell transcriptomics, gene duplications, gene architecture evolution, and alternative splicing. Additionally, in cases where off-the-shelf analysis tools were inexistent, novel pipelines and programs were designed and implemented to solve algorithmic problems such as scaffolding genome assemblies and short-read mapping onto small exons.
Supervisor: Ponting, Chris P.; Copley, Richard R. Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID: uk.bl.ethos.627890  DOI: Not available
Keywords: Evolution (zoology) ; Bioinformatics (biochemistry) ; Bioinformatics (life sciences) ; Genomics ; Evolution ; Computational Biology
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