title: A novel hypothesis-unbiased method for Gene Ontology enrichment based on transcriptome data.
abstract: Gene 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.
authors.affiliation: School of Biological Sciences, The University of Adelaide, Adelaide, South Australia, Australia.|School of Agriculture, Food and Wine, The University of Adelaide, Adelaide, South Australia, Australia.
authors.forename: Mario
authors.initials: M
authors.lastname: Fruzangohar
authors.affiliation: Australian Centre for Antimicrobial Resistance Ecology, School of Animal and Veterinary Sciences, The University of Adelaide, Adelaide, South Australia, Australia.|School of Medicine, Faculty of Health Sciences, The University of Adelaide, Adelaide, Australia.|School of Information Technology and Mathematical Sciences, Division of Information Technology, Engineering and the Environment, University of South Australia, Adelaide, Australia.|School of Biological Sciences, Faculty of Science and Engineering, Flinders University, Adelaide, Australia.
authors.forename: Esmaeil
authors.initials: E
authors.lastname: Ebrahimie
authors.affiliation: School of Biological Sciences, The University of Adelaide, Adelaide, South Australia, Australia.|Zhendong Australia - China Centre for Molecular Chinese Medicine, The University of Adelaide, Adelaide, South Australia, Australia.
authors.forename: David L
authors.initials: DL
authors.lastname: Adelson
mesh_terms: D000544:Alzheimer Disease / Q000235:genetics* / Q000378:metabolism; D030561:Databases, Nucleic Acid; D015964:Gene Expression Regulation, Bacterial; D063990:Gene Ontology; D006801:Humans; D012477:Salmonella enteritidis / Q000235:genetics* / Q000378:metabolism; D059467:Transcriptome