The primary means of classifying new functions for genes and proteins relies on Gene Ontology (GO), which defines genes/proteins using a controlled vocabulary in terms of their Molecular Function, Biological Process and Cellular Component. The challenge is to present this information to researchers to compare and discover patterns in multiple datasets using visually comprehensible and user-friendly statistical reports. Importantly, while there are many GO resources available for eukaryotes, there are none suitable for simultaneous, graphical and statistical comparison between multiple datasets. In addition, none of them supports comprehensive resources for bacteria. By using Streptococcus pneumoniae as a model, we identified and collected GO resources including genes, proteins, taxonomy and GO relationships from NCBI, UniProt and GO organisations. Then, we designed database tables in PostgreSQL database server and developed a Java application to extract data from source files and loaded into database automatically. We developed a PHP web application based on Model-View-Control architecture, used a specific data structure as well as current and novel algorithms to estimate GO graphs parameters. We designed different navigation and visualization methods on the graphs and integrated these into graphical reports. This tool is particularly significant when comparing GO groups between multiple samples (including those of pathogenic bacteria) from different sources simultaneously. Comparing GO protein distribution among up- or down-regulated genes from different samples can improve understanding of biological pathways, and mechanism(s) of infection. It can also aid in the discovery of genes associated with specific function(s) for investigation as a novel vaccine or therapeutic targets.Availability http://turing.ersa.edu.au/BacteriaGO.

BackgroundGene Ontology (GO) classification of statistically significant over/under expressed genes is a common method for interpreting transcriptomics data as a first step in functional genomic analysis. In this approach, all significant genes contribute equally to the final GO classification regardless of their actual expression levels. However, the original level of gene expression can significantly affect protein production and consequently GO term enrichment. Furthermore, even genes with low expression levels can participate in the final GO enrichment through cumulative effects.GO terms have regulatory relationships allowing the construction of a regulatory directed network combined with gene expression levels to study biological mechanisms and select important genes for functional studies. ResultsIn this report, we have used gene expression levels in bacteria to determine GO term enrichments. This approach provided the opportunity to enrich GO terms in across the entire transcriptome (instead of a subset of differentially expressed genes) and enabled us to compare transcriptomes across multiple biological conditions. As a case study for whole transcriptome GO analysis, we have shown that during the infection course of different host tissues by streptococcus pneumonia, Biological Process and Molecular Functions' GO term protein enrichment proportions changed significantly as opposed to those for Cellular Components. In the second case study, we compared Salmonella Enteritidis transcriptomes between low and high pathogenic strains and showed that GO protein enrichment proportions remained unchanged in contrast to a previous case study.In the second part of this study we show for the first time a dynamically developed enriched interaction network between Biological Process GO terms for any gene samples. This type of network presents regulatory relationships between GO terms and their genes. Furthermore, the network topology highlights the centrally located genes in the network which can be used for network based gene selection. As a case study, GO regulatory networks of streptococcus pneumonia and Salmonella enteritidis were constructed and studied. ConclusionsIn both Streptococcus pneumonia and Salmonella enteritidis, the pathways related to GO terms "Environmental Information Processing", "Signal transduction" and "two-component system" were associated with increasing pathogenicity, breaching host barriers and the generation of new strains.. This study demonstrates a comprehensive GO enrichment based on whole transcriptome data, along with a novel method for developing a GO regulatory network showing overview of central and marginal GOs that can contribute to efficient gene selection.

Despite frequent co-occurrence of drought and heat stress, the molecular mechanisms governing plant responses to these stresses in combination have not often been studied. This is particularly evident in non-model, perennial plants. We conducted large scale physiological and transcriptome analyses to identify genes and pathways associated with grapevine response to drought and/or heat stress during stress progression and recovery. We identified gene clusters with expression correlated to leaf temperature and water stress and five hub genes for the combined stress co-expression network. Several differentially expressed genes were common to the individual and combined stresses, but the majority were unique to the individual or combined stress treatments. These included heat-shock proteins, mitogen-activated kinases, sugar metabolizing enzymes, and transcription factors, while phenylpropanoid biosynthesis and histone modifying genes were unique to the combined stress treatment. Following physiological recovery, differentially expressed genes were found only in plants under heat stress, both alone and combined with drought. Taken collectively, our results suggest that the effect of the combined stress on physiology and gene expression is more severe than that of individual stresses, but not simply additive, and that epigenetic chromatin modifications may play an important role in grapevine responses to combined drought and heat stress.

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.