Investigating the effect of paralogs on microarray gene-set analysis

Simple item page
dc.contributor.advisorMulder, Nicolaen_ZA
dc.contributor.advisorSeoighe, Cathalen_ZA
dc.contributor.authorFaure, Andréen_ZA
dc.date.accessioned2014-07-30T17:37:33Z
dc.date.available2014-07-30T17:37:33Z
dc.date.issued2008en_ZA
dc.descriptionIncludes abstract.
dc.descriptionIncludes bibliographical references.
dc.description.abstractIn order to interpret the results obtained from a microarray experiment, researchers often shift focus from analysis of individual differentially expressed genes to analyses of sets of genes. These gene-set analysis (GSA) methods use previously accumulated biological knowledge from databases such as the Gene Ontology (GO) or KEGG to group genes into sets based on their annotations. They aim to rank these gene sets in a way that reflects their relative importance in the experimental situation in question. The objective is that this approach reveals sets of genes with subtle but coordinated behaviour implicating specific biological processes or pathways in the response under study. Several GSA methods have been proposed and debates have ensued on the statistical foundations of the different approaches and the various hypothesis tests used. In particular, criticism has been directed at methods that rely on a strict cut-off to determine significant genes and those that assume genes are expressed independently. We show that paralogs, which typically have high sequence identity and similar molecular functions also exhibit high correlation in their expression patterns. This, together with the fact that the calculation of gene-set significance by all GSA methods is influenced by the number of genes in the gene set, means that sets with high numbers of paralogs are ranked in a biased manner that reflects more the redundant and dependent nature of para logs than any biological phenomenon.en_ZA
dc.identifier.apacitationFaure, A. (2008). <i>Investigating the effect of paralogs on microarray gene-set analysis</i>. (Thesis). University of Cape Town ,Faculty of Science ,Department of Molecular and Cell Biology. Retrieved from http://hdl.handle.net/11427/4260en_ZA
dc.identifier.chicagocitationFaure, André. <i>"Investigating the effect of paralogs on microarray gene-set analysis."</i> Thesis., University of Cape Town ,Faculty of Science ,Department of Molecular and Cell Biology, 2008. http://hdl.handle.net/11427/4260en_ZA
dc.identifier.citationFaure, A. 2008. Investigating the effect of paralogs on microarray gene-set analysis. University of Cape Town.en_ZA
dc.identifier.ris TY - Thesis / Dissertation AU - Faure, André AB - In order to interpret the results obtained from a microarray experiment, researchers often shift focus from analysis of individual differentially expressed genes to analyses of sets of genes. These gene-set analysis (GSA) methods use previously accumulated biological knowledge from databases such as the Gene Ontology (GO) or KEGG to group genes into sets based on their annotations. They aim to rank these gene sets in a way that reflects their relative importance in the experimental situation in question. The objective is that this approach reveals sets of genes with subtle but coordinated behaviour implicating specific biological processes or pathways in the response under study. Several GSA methods have been proposed and debates have ensued on the statistical foundations of the different approaches and the various hypothesis tests used. In particular, criticism has been directed at methods that rely on a strict cut-off to determine significant genes and those that assume genes are expressed independently. We show that paralogs, which typically have high sequence identity and similar molecular functions also exhibit high correlation in their expression patterns. This, together with the fact that the calculation of gene-set significance by all GSA methods is influenced by the number of genes in the gene set, means that sets with high numbers of paralogs are ranked in a biased manner that reflects more the redundant and dependent nature of para logs than any biological phenomenon. DA - 2008 DB - OpenUCT DP - University of Cape Town LK - https://open.uct.ac.za PB - University of Cape Town PY - 2008 T1 - Investigating the effect of paralogs on microarray gene-set analysis TI - Investigating the effect of paralogs on microarray gene-set analysis UR - http://hdl.handle.net/11427/4260 ER - en_ZA
dc.identifier.urihttp://hdl.handle.net/11427/4260
dc.identifier.vancouvercitationFaure A. Investigating the effect of paralogs on microarray gene-set analysis. [Thesis]. University of Cape Town ,Faculty of Science ,Department of Molecular and Cell Biology, 2008 [cited yyyy month dd]. Available from: http://hdl.handle.net/11427/4260en_ZA
dc.language.isoengen_ZA
dc.publisher.departmentDepartment of Molecular and Cell Biologyen_ZA
dc.publisher.facultyFaculty of Scienceen_ZA
dc.publisher.institutionUniversity of Cape Town
dc.subject.otherCell Biologyen_ZA
dc.titleInvestigating the effect of paralogs on microarray gene-set analysisen_ZA
dc.typeMaster Thesis
dc.type.qualificationlevelMasters
dc.type.qualificationnameMScen_ZA
uct.type.filetypeText
uct.type.filetypeImage
uct.type.publicationResearchen_ZA
uct.type.resourceThesisen_ZA
Files
Original bundle
Now showing 1 - 1 of 1
Thumbnail Image
Name:
thesis_sci_2008_faure_a.pdf
Size:
4.15 MB
Format:
Adobe Portable Document Format
Description:
Download
Collections
Masters