Genome-wide association study of Pseudomonas aeruginosa adaptations to the cystic fibrosis lung

Pseudomonas aeruginosais an opportunistic pathogen that chronically infects the lungs of patients with cystic fibrosis. We know that these bacteria evolve within the lung environment, and have an idea of some of the changes that occur. I performed a genome-wide association study inP. aeruginosausing a variety of analysis methods and several datasets of gene and SNP presence/absence. I determined that the machine learning algorithms random forest and support vector machines performed well on gene presence/absence and core SNP datasets, respectively, when compared with current methods (PLINK and treeWAS). Genes and SNPs already associated with adaptation to the CF lung environment, such asmucA,gyrA, andmexgenes, were found with these methods. Some hypothetical and probable proteins were also recovered, and are good candidates for future research.

Name thesis:degree_name

Master of Science (M.Sc.)

Level thesis:degree_level

Master's

Discipline thesis:degree_discipline

Biology

Grantor dc:publisher

Carleton University

Year dc:date.issued

2019

Author dc:creator

• Noah, Katherine Elizabeth

Statement dc:rights

• Copyright © 2019 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.

Language dc:language.iso

en

DOI

10.22215/etd/2019-13815

OAI identifier oai:identifier

oai:carleton.scholaris.ca:20.500.14718/40346