A topic model based approach to inferring episodic directional selection in protein coding sequences

 

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dc.contributor.advisor Lacerda, Miguel en_ZA
dc.contributor.author Sadiq, Hassan Taiwo en_ZA
dc.date.accessioned 2016-06-10T10:53:55Z
dc.date.available 2016-06-10T10:53:55Z
dc.date.issued 2015 en_ZA
dc.identifier.citation Sadiq, H. 2015. A topic model based approach to inferring episodic directional selection in protein coding sequences. University of Cape Town. en_ZA
dc.identifier.uri http://hdl.handle.net/11427/20013
dc.description.abstract Pathogens, such as HIV and influenza, evolve in response to the selective pressures of their host environments accumulating changes in their genomes that offer fitness benefits. This selective pressure is characterised by three properties: (1.) it is episodic, tracking changes in the adaptive immune response and drug therapy, (2.) it is directional in that only particular amino acid substitutions are favoured and (3.) it varies between genomic loci. Most previous models have ignored or inadequately addressed some of these phenomena. This work extends recent approaches to modelling episodic directional selection acting on protein-coding sequences. We use inference techniques within the topic model framework to identify loci evolving under natural selection. A notable example of such techniques are the variational Bayesian methods. We show that our approach performs well in terms of specificity and power, and demonstrate its utility by applying it to some real datasets of HIV sequences. en_ZA
dc.language.iso eng en_ZA
dc.subject.other Decision Sciences and Analytics en_ZA
dc.title A topic model based approach to inferring episodic directional selection in protein coding sequences en_ZA
dc.type Thesis / Dissertation en_ZA
uct.type.publication Research en_ZA
uct.type.resource Thesis en_ZA
dc.publisher.institution University of Cape Town
dc.publisher.faculty Faculty of Science en_ZA
dc.publisher.department Department of Statistical Sciences en_ZA
dc.type.qualificationlevel Masters en_ZA
dc.type.qualificationname MSc en_ZA
uct.type.filetype Text
uct.type.filetype Image


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