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Novel Methods for the Computational Analysis of Codon Usage Bias

Deng, Yun (2019) Novel Methods for the Computational Analysis of Codon Usage Bias. Doctor of Philosophy (PhD) thesis, University of Kent,. (KAR id:80473)

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The genetic code encodes the same amino acid with multiple codon choices, but in a biased fashion. This phenomenon is called the codon usage bias (CUB). There have been significant research efforts trying to quantify codon usage bias and probe into its origins. Understanding CUB is important for at least two reasons. Firstly, it is connected with gene expression, and thus of fundamental importance for our understanding of life. Secondly it is important for the optimisation of heterologous gene expression in industrial bioproduction including the pharmaceutical industry. This thesis makes three main contributions to the understanding of CUB: (1) It proposes a novel measure of codon usage bias which does not require any context information other than the nature of the coding sequences themselves. The proposed measure is capable of quantifying codon usage bias at different levels of an individual sequence, a particular amino acid type, and a whole genome, and also capable to provide comprehensive and desired CUB information for the correlation study about specific CUB related factors by constructing high dimensional CUB feature spaces. (2) It derives a stochastic thermodynamic based model to investigate what the evolutionary drivers of codon usage bias are from a macroscopic perspective. (3) It applies the proposed methods to extensive genomic data. Our main conclusions derived from the applications to real organisms include (a) codon usage bias and gene lengths cooperate together to satisfy different protein requirements in the cells; (b) codon usage bias correlates with phylogenetic distances among remote groups of species; (c) codon usage bias cannot be explained solely by selection pressures that act on the genome-wide codon frequencies, but also includes pressures that act at the level of individual genes.

Item Type: Thesis (Doctor of Philosophy (PhD))
Thesis advisor: Chu, Dominique
Thesis advisor: von der Haar, Tobias
Uncontrolled keywords: codon usage bias, machine learning, stochastic thermodynamics
Subjects: Q Science > QA Mathematics (inc Computing science)
Divisions: Divisions > Division of Computing, Engineering and Mathematical Sciences > School of Computing
SWORD Depositor: System Moodle
Depositing User: System Moodle
Date Deposited: 16 Mar 2020 12:28 UTC
Last Modified: 09 Dec 2022 17:07 UTC
Resource URI: (The current URI for this page, for reference purposes)

University of Kent Author Information

Deng, Yun.

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