gGost

gGost performs functional enrichment and related analyses to interpret gene lists from high-throughput genomic studies by testing associations with biomedical ontologies, pathways, transcription factor regulatory motifs, microRNA interactions, and protein-protein interactions.

Key Features:

• Functional analysis: Interprets gene lists against biomedical ontologies including Gene Ontology (GO), pathways, transcription factor regulatory motifs, microRNA interactions, and protein-protein interactions.
• SNP and polymorphism mapping: Supports functional analysis of single nucleotide polymorphisms (SNPs) and other DNA polymorphisms via chromosomal queries.
• Network analysis: Identifies enriched protein-protein interaction modules within gene lists.
• Disease gene analysis: Extends functional interpretation to human disease genes.
• Statistical methods: Employs enhanced statistical testing and filtering, including a method to estimate the true effect of multiple testing over complex structures such as Gene Ontology.
• Ranked-list analysis: Applies efficient algorithms for interpretation of ranked gene lists.
• Identifier conversion and homology: Provides g:Convert for database identifier conversion and g:Orth for finding orthologous genes across species.
• Gene expression co-expression search: Uses g:Sorter to search large public gene expression datasets for co-expression patterns.
• Data integration and outputs: Integrates data from Ensembl and provides textual output formats for integration into bioinformatics workflows.

Scientific Applications:

• Gene-list characterization: Annotating and interpreting gene sets derived from high-throughput genomic experiments.
• Functional annotation and pathway interpretation: Assigning Gene Ontology and pathway annotations to identify enriched biological processes and pathways.
• Genetic association and disease analysis: Investigating functional implications of SNPs and human disease genes.
• Network and co-expression analysis: Detecting enriched protein-protein interaction modules and co-expression patterns to reveal functional modules.

Methodology:

Performs enrichment testing against ontologies, pathways, transcription factor motifs, microRNA targets, and protein-protein interactions; conducts chromosomal queries for SNPs and polymorphisms; detects enriched PPI modules; applies enhanced statistical testing including a method for estimating multiple-testing effects across Gene Ontology; interprets ranked lists with efficient algorithms; and provides identifier conversion (g:Convert), orthology mapping (g:Orth), and co-expression searches in public gene expression datasets via g:Sorter.