2026-09-19T14:39:54.297323600Zhttps://opus.uleth.ca/server/oai/requestoai:opus.uleth.ca:10133/33912019-02-07T06:11:17Zcom_10133_3346com_10133_1com_10133_296col_10133_3347col_10133_298
ULethbridge OPUS Repository
supervisor
Thomas, James E.
supervisor
Taboada, Eduardo N.
author
Kruczkiewicz, Peter
2014-04-08T18:08:00Z
2014-04-08T18:08:00Z
2013
https://hdl.handle.net/10133/3391
Although increased genome sequencing e orts have increased our understanding of genomic
variability within many bacterial species, there has been limited application of this knowledge
towards assessing current molecular typing methods and developing novel molecular typing
methods. This thesis reports a novel in silico comparative genomic framework where the
performance of typing methods is assessed on the basis of the discriminatory power of the
method as well as the concordance of the method with a whole-genome phylogeny. Using
this framework, we designed a comparative genomic ngerprinting (CGF) assay for Listeria
monocytogenes through optimized molecular marker selection. In silico validation and assessment
of the CGF assay against two other molecular typing methods for L. monocytogenes (multilocus
sequence typing (MLST) and multiple virulence locus sequence typing (MVLST)) revealed that
the CGF assay had better performance than these typing methods. Hence, optimized molecular
marker selection can be used to produce highly discriminatory assays with high concordance to
whole-genome phylogenies. The framework described in this thesis can be used to assess current
molecular typing methods against whole-genome phylogenies and design the next generation of
high-performance molecular typing methods from whole-genome sequence data.
en_CA
Bacterial genomes -- Identification -- Computer simulation
Bacteria -- Identification -- Computer simulation
Gene mapping -- Computer simulation
Listeria monocytogenes -- Identification -- Technique
Dissertations, Academic
A comparative genomic framework for the in silico design and assessment of molecular typing methods using whole-genome sequence data with application to Listeria monocytogenes
Thesis
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