Detecting Amino Acid Preference Shifts with Codon-Level Mixture Models
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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.
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Copyright © 2018 the author(s). Theses may be used for non-commercial research, educational, or related academic purposes only. Such uses include personal study, research, scholarship, and teaching. Theses may only be shared by linking to Carleton University Institutional Repository and no part may be used without proper attribution to the author. No part may be used for commercial purposes directly or indirectly via a for-profit platform; no adaptation or derivative works are permitted without consent from the copyright owner.
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- 2018
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kazmi-detectingaminoacidpreferenceshiftswithcodonlevel.pdf | 2023-05-05 | Public | Download |