GOModeler- A tool for hypothesis-testing of functional genomics datasets

Department of Computer Science and Engineering, Mississippi State University, MS, USA.
BMC Bioinformatics (Impact Factor: 2.58). 10/2010; 11 Suppl 6(Suppl 6):S29. DOI: 10.1186/1471-2105-11-S6-S29
Source: PubMed

ABSTRACT Functional genomics technologies that measure genome expression at a global scale are accelerating biological knowledge discovery. Generating these high throughput datasets is relatively easy compared to the downstream functional modelling necessary for elucidating the molecular mechanisms that govern the biology under investigation. A number of publicly available 'discovery-based' computational tools use the computationally amenable Gene Ontology (GO) for hypothesis generation. However, there are few tools that support hypothesis-based testing using the GO and none that support testing with user defined hypothesis terms.Here, we present GOModeler, a tool that enables researchers to conduct hypothesis-based testing of high throughput datasets using the GO. GOModeler summarizes the overall effect of a user defined gene/protein differential expression dataset on specific GO hypothesis terms selected by the user to describe a biological experiment. The design of the tool allows the user to complement the functional information in the GO with his/her domain specific expertise for comprehensive hypothesis testing.
GOModeler tests the relevance of the hypothesis terms chosen by the user for the input gene dataset by providing the individual effects of the genes on the hypothesis terms and the overall effect of the entire dataset on each of the hypothesis terms. It matches the GO identifiers (ids) of the genes with the GO ids of the hypothesis terms and parses the names of those ids that match to assign effects. We demonstrate the capabilities of GOModeler with a dataset of nine differentially expressed cytokine genes and compare the results to those obtained through manual analysis of the dataset by an immunologist. The direction of overall effects on all hypothesis terms except one was consistent with the results obtained by manual analysis. The tool's editing capability enables the user to augment the information extracted. GOModeler is available as a part of the AgBase tool suite (
GOModeler allows hypothesis driven analysis of high throughput datasets using the GO. Using this tool, researchers can quickly evaluate the overall effect of quantitative expression changes of gene set on specific biological processes of interest. The results are provided in both tabular and graphical formats.

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Available from: Susan Bridges, Sep 28, 2015
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    • "The top 5 functions and canonical pathways represented by DE genes unique to early and late time points identified by Ingenuity pathway analysis (IPA) are shown in Table 1. The overall effect of the increased/decreased gene expression on the significant molecular functions (Table 1) at 2 h and 18 h was assessed by GOModeler [35] workflow. GOModeler enables Gene Ontology based hypothesis-driven interrogation of high throughput data. "
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    ABSTRACT: Background The events leading to sepsis start with an invasive infection of a primary organ of the body followed by an overwhelming systemic response. Intra-abdominal infections are the second most common cause of sepsis. Peritoneal fluid is the primary site of infection in these cases. A microarray-based approach was used to study the temporal changes in cells from the peritoneal cavity of septic mice and to identify potential biomarkers and therapeutic targets for this subset of sepsis patients. Results We conducted microarray analysis of the peritoneal cells of mice infected with a non-pathogenic strain of Escherichia coli. Differentially expressed genes were identified at two early (1 h, 2 h) and one late time point (18 h). A multiplexed bead array analysis was used to confirm protein expression for several cytokines which showed differential expression at different time points based on the microarray data. Gene Ontology based hypothesis testing identified a positive bias of differentially expressed genes associated with cellular development and cell death at 2 h and 18 h respectively. Most differentially expressed genes common to all 3 time points had an immune response related function, consistent with the observation that a few bacteria are still present at 18 h. Conclusions Transcriptional regulators like PLAGL2, EBF1, TCF7, KLF10 and SBNO2, previously not described in sepsis, are differentially expressed at early and late time points. Expression pattern for key biomarkers in this study is similar to that reported in human sepsis, indicating the suitability of this model for future studies of sepsis, and the observed differences in gene expression suggest species differences or differences in the response of blood leukocytes and peritoneal leukocytes.
    BMC Genomics 09/2012; 13(1):509. DOI:10.1186/1471-2164-13-509 · 3.99 Impact Factor
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    • "Very many GO enrichment analysis tools exist and several are expanding their capacity to support new species (including agricultural species) or are specifically designed to support functional modeling of agricultural data (33). Our novel approach to using the GO for functional modeling is GOModeler, which enables hypothesis testing of gene expression data (34). GOModeler enables the researcher to ‘translate’ hypothesis statements (or expected phenotypes) into equivalent GO terms which are then scored for their effect on each gene in an expression data set (pro, anti, no effect). "
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    ABSTRACT: AgBase ( provides resources to facilitate modeling of functional genomics data and structural and functional annotation of agriculturally important animal, plant, microbe and parasite genomes. The website is redesigned to improve accessibility and ease of use, including improved search capabilities. Expanded capabilities include new dedicated pages for horse, cat, dog, cotton, rice and soybean. We currently provide 590 240 Gene Ontology (GO) annotations to 105 454 gene products in 64 different species, including GO annotations linked to transcripts represented on agricultural microarrays. For many of these arrays, this provides the only functional annotation available. GO annotations are available for download and we provide comprehensive, species-specific GO annotation files for 18 different organisms. The tools available at AgBase have been expanded and several existing tools improved based upon user feedback. One of seven new tools available at AgBase, GOModeler, supports hypothesis testing from functional genomics data. We host several associated databases and provide genome browsers for three agricultural pathogens. Moreover, we provide comprehensive training resources (including worked examples and tutorials) via links to Educational Resources at the AgBase website.
    Nucleic Acids Research 11/2010; 39(Database issue):D497-506. DOI:10.1093/nar/gkq1115 · 9.11 Impact Factor
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    Pharmacology Biochemistry and Behavior 11/1991; 40(2):279-81. DOI:10.1016/0091-3057(91)90552-D · 2.78 Impact Factor
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