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.
Highlights
Classifying genes into distinct functional groups through Gene Ontology (GO) is a commonly used and powerful tool for understanding functional genomics and the underlying molecular pathways
By considering the influences of all expressed genes in functional genomics, even those with low levels of expression, we increased the accuracy of GO term analysis
Because of the additional computational expense associated with the analysis of the GO distribution of all expressed genes within a genome, significant memory and processing resources were required by the Apache web server
Summary
Classifying genes into distinct functional groups through Gene Ontology (GO) is a commonly used and powerful tool for understanding functional genomics and the underlying molecular pathways. GO analysis commonly begins with enrichment carried out on a short list of genes with statistically significant differential expression [1,2,3]. In this method, GO term frequencies in the differentially expressed list of genes are compared to a background control, either GO term frequencies of the whole genome, or another list of genes. GO term frequencies in the differentially expressed list of genes are compared to a background control, either GO term frequencies of the whole genome, or another list of genes This comparison is usually performed using a one sided Fisher-Exact test or a Hypergeometric distribution. This method is called over-representation analysis (ORA) and is implemented nearly in all current GO analysis tools [3,4,5,6]
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