GOModeler

GOModeler enables hypothesis-driven interrogation of high-throughput functional genomics datasets by matching gene identifiers to Gene Ontology (GO) terms to test user-defined biological hypotheses.

Key Features:

• Hypothesis-Based Testing: Performs hypothesis-driven analysis by evaluating user-selected GO terms against high-throughput datasets.
• User-Defined Hypothesis Terms: Accepts user-selected Gene Ontology (GO) identifiers to explicitly define experimental hypotheses.
• Comprehensive Analysis: Evaluates both individual gene effects and the collective impact of a dataset on selected hypothesis GO terms by matching gene GO identifiers to those terms.
• Data Integration and Augmentation: Parses and matches relevant GO terms and permits augmentation of the extracted GO annotations through user editing.
• Result Presentation: Produces outputs in tabular and graphical formats summarizing matches between expression data and hypothesis GO terms.

Scientific Applications:

• Hypothesis-driven functional genomics: Enables focused testing of specific biological processes or molecular functions in genome-wide expression studies.
• Immunology validation: Applied to a dataset of differentially expressed cytokine genes with results largely consistent with manual analyses performed by an immunologist.

Methodology:

Integrates user-defined GO hypothesis terms with high-throughput gene or protein expression data by parsing gene GO identifiers, matching them to selected GO terms, and quantifying the individual and collective effects of differential expression on specified biological processes.