Network-based contextualisation of LC-MS/MS proteomics data

[摘要] ENGLISH ABSTRACT: This thesis explores the use of networks as a means to visualise, interpret andmine MS-based proteomics data.A network-based approach was applied to a quantitative, cross-species LCMS/MS dataset derived from two yeast species, namely Saccharomyces cere-visiae strain VIN13 and Saccharomyces paradoxus strain RO88.In order to identify and quantify proteins from the mass spectra, a workflowconsisting of both custom-built and existing programs was assembled. Networkswhich place the identifed proteins in several biological contexts werethen constructed. The contexts included sequence similarity to other proteins,ontological descriptions, proteins-protein interactions, metabolic pathways andcellular location.The contextual, network-based representations of the proteins proved effectivefor identifying trends and patterns in the data that may otherwise havebeen obscured. Moreover, by bringing the experimentally derived data togetherwith multiple, extant biological resources, the networks represented thedata in a manner that better represents the interconnected biological systemfrom which the samples were derived. Both existing and new hypotheses basedon proteins relating to the yeast cell wall and proteins of putative oenologicalpotential were investigated. These proteins were investigated in light oftheir differential expression between the two yeast species. Examples of proteinsthat were investigated included cell wall proteins such as GGP1 and SCW4. Proteins with putative oenological potential included haze protectionfactor proteins such as HPF2. Furthermore, differences in capacity for maloethanolicfermentation between the two strains were also investigated in lightof the protein data. The network-based representations also allowed new hypothesesto be formed around proteins that were identified in the dataset, butwere of unknown function.

[发布机构] Stellenbosch University