Detecting Amino Acid Preference Shifts with Codon-Level Mixture Models

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Creator: 

Kazmi, Shan Omar

Date: 

2018

Abstract: 

In recent years, increasing attention has been placed on the development of phylogeny-based statistical methodologies for uncovering site-specific changes in amino acid fitness profiles over time. The few available random-effects approaches, modelling across-site variation in amino acid profiles as random variables drawn from a statistical law, either lack a mechanistic codon-level formulation, or pose significant computational challenges. Here, we explore a simple and fast method based on a predefined finite mixture of amino acid profiles within a codon-level mutation-selection substitution model. Our study detects shifts of amino acid profiles over a known sub-clade of a tree, using simulations with and without shifts over the sub-clade to study the properties of the method. We apply the approach to a real data set, previously studied with other methods: lists of sites identified as having undergone a change in amino acid profile have obvious overlap between methods, while also showing notable differences.

Subject: 

Bioinformatics

Language: 

English

Publisher: 

Carleton University

Thesis Degree Name: 

Master of Science: 
M.Sc.

Thesis Degree Level: 

Master's

Thesis Degree Discipline: 

Biology

Parent Collection: 

Theses and Dissertations

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