A novel hypothesis-unbiased method for gene ontology enrichment based on transcriptome data

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dc.contributor.authorFruzangohar, M.
dc.contributor.authorEbrahimie, E.
dc.contributor.authorAdelson, D.
dc.contributor.editorWang, J.
dc.date.issued2017
dc.description.abstractGene Ontology (GO) classification of statistically significantly differentially expressed genes is commonly used to interpret transcriptomics data as a part of functional genomic analysis. In this approach, all significantly expressed genes contribute equally to the final GO classification regardless of their actual expression levels. Gene expression levels can significantly affect protein production and hence should be reflected in GO term enrichment. Genes with low expression levels can also participate in GO term enrichment through cumulative effects. In this report, we have introduced a new GO enrichment method that is suitable for multiple samples and time series experiments that uses a statistical outlier test to detect GO categories with special patterns of variation that can potentially identify candidate biological mechanisms. To demonstrate the value of our approach, we have performed two case studies. Whole transcriptome expression profiles of Salmonella enteritidis and Alzheimer's disease (AD) were analysed in order to determine GO term enrichment across the entire transcriptome instead of a subset of differentially expressed genes used in traditional GO analysis. Our result highlights the key role of inflammation related functional groups in AD pathology as granulocyte colony-stimulating factor receptor binding, neuromedin U binding, and interleukin were remarkably upregulated in AD brain when all using all of the gene expression data in the transcriptome. Mitochondrial components and the molybdopterin synthase complex were identified as potential key cellular components involved in AD pathology.
dc.description.statementofresponsibilityMario Fruzangohar, Esmaeil Ebrahimie, David L. Adelson
dc.identifier.citationPLoS ONE, 2017; 12(2):e0170486-1-e0170486-14
dc.identifier.doi10.1371/journal.pone.0170486
dc.identifier.issn1932-6203
dc.identifier.issn1932-6203
dc.identifier.orcidEbrahimie, E. [0000-0002-4431-2861]
dc.identifier.orcidAdelson, D. [0000-0003-2404-5636]
dc.identifier.urihttp://hdl.handle.net/2440/105044
dc.language.isoen
dc.publisherPublic Library Science
dc.relation.granthttp://purl.org/au-research/grants/nhmrc/1061006
dc.rightsCopyright: © 2017 Fruzangohar et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
dc.source.urihttps://doi.org/10.1371/journal.pone.0170486
dc.subjectAlzheimer disease; transcriptome analysis; gene expression; gene ontologies; bacterial pathogens; pathogen motility; salmonella; genomic databases
dc.titleA novel hypothesis-unbiased method for gene ontology enrichment based on transcriptome data
dc.typeJournal article
pubs.publication-statusPublished

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