Use this URL to cite or link to this record in EThOS: https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.738342
Title: Co-evolving protein sites : their identification using novel, highly-parallel algorithms, and their use in classifying hazardous genetic mutations
Author: Knight, Louise
ISNI:       0000 0004 7228 8496
Awarding Body: Cardiff University
Current Institution: Cardiff University
Date of Award: 2017
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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.
Supervisor: Not available Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID: uk.bl.ethos.738342  DOI: Not available
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