goProfiles

goProfiles constructs and compares Gene Ontology (GO)-based functional profiles from high-throughput genomic or proteomic experiments to quantify and statistically test differences in GO annotation distributions among gene lists.

Key Features:

• R/Bioconductor implementation: Implemented as an R/Bioconductor package for integration with Bioconductor workflows.
• Functional profile construction: Converts annotation data into empirical distributions of GO terms across a target gene list.
• GO ontologies supported: Handles annotations from Molecular Function, Biological Process, and Cellular Component ontologies.
• Statistical modeling: Implements a statistical modeling framework for comparing functional profiles.
• Hypothesis testing: Provides hypothesis testing approaches to assess whether two functional profiles differ beyond chance.
• Annotation-frequency approach: Operates independently of the hierarchical GO graph and relies on annotation frequencies.
• DAG avoidance for scale: Avoids DAG-based propagation to enable analysis of large gene lists without graph-based computation.
• Profile generation, comparison, and visualization: Provides methods to generate functional profiles from arbitrary gene lists, compare profiles across conditions, and visualize GO terms that disproportionately contribute to differences.
• Input from high-throughput assays: Designed to summarize lists such as differentially expressed genes from gene expression microarrays or protein arrays.

Scientific Applications:

• Functional summarization of differential expression: Summarizes functional characteristics of gene lists produced by microarrays, protein arrays, or other high-throughput assays.
• Comparative analysis of conditions: Quantitatively compares functional profiles between biological conditions or experimental contrasts.
• Meta-analysis and clustering support: Compares profiles across multiple studies or gene clusters to identify consistent functional signatures.
• Complementary downstream analysis: Supports hypothesis generation and complements clustering, enrichment testing, and integrative multi-omics workflows.

Methodology:

Convert annotation data into empirical GO-term distributions (functional profiles), model profile differences with a statistical framework and hypothesis tests, and use annotation frequencies rather than GO graph propagation to generate, compare, and visualize profiles.