Knight, Louise ORCID: https://orcid.org/0000-0003-1431-197X
2017.
Co-evolving protein sites: their identification using novel,
highly-parallel algorithms, and their use in classifying
hazardous genetic mutations.
PhD Thesis,
Cardiff University.
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Abstract
Algorithms for detecting molecular co-evolution have until now been applied only to individual protein families, but not to the human proteome. Linked to this is the problem that performing the computations for identifying co-evolving sites in the human proteome would take a prohibitively long time using the serial algorithms already in use. In addition, co-evolving sites have not been pursued as a possible way of classifying mutations according to their likelihood to cause disease. The main contributions of this thesis are as follows: identification of three suitable methods for detecting molecular co-evolution by comparing the performance of published state-of-the-art methods on simulated data; implementation of these methods in the parallel architecture CUDA, and evaluation of these methods’ performance in comparison to serial implementations of the same methods; and identification of co-evolving sites across the entire human proteome, and analysis of these sites according to what is already known about disease-causing mutations. Beyond demonstrating the effectiveness of CUDA for implementing molecular co-evolution detection algorithms, we derive insights into techniques for efficient implementation of algorithms in CUDA (particularly algorithms which require tree-based structures, such as parametric methods), and our results provide preliminary insights into the relationship between co-evolving sites and mutation pathogenicity.
Item Type: | Thesis (PhD) |
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Date Type: | Completion |
Status: | Unpublished |
Schools: | Computer Science & Informatics |
Date of First Compliant Deposit: | 7 February 2018 |
Last Modified: | 03 Nov 2022 10:42 |
URI: | https://orca.cardiff.ac.uk/id/eprint/108951 |
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