OpenXGR

OpenXGR performs interpretation and analysis of human genomic summary data to identify enriched ontology terms, discover gene subnetworks, and map single nucleotide polymorphisms (SNPs) or genomic regions to candidate genes using integrated ontologies, networks, and functional genomic datasets such as promoter capture Hi-C, expression/protein quantitative trait loci (e/pQTL), and enhancer-gene maps.

Key Features:

• Supported inputs: Accepts lists of genes, single nucleotide polymorphisms (SNPs), or genomic regions as input for analysis.
• Data integration: Integrates ontologies, gene networks, promoter capture Hi-C, e/pQTL, and enhancer-gene maps to inform analyses.
• Analytical modes: Performs near real-time enrichment analyses and subnetwork analyses on input data.
• Six analyzers: Implements three enrichment analyzers and three subnetwork analyzers for distinct interpretation modes.
• Enrichment analyzers: Identify ontology terms enriched in input genes and determine genes linked from input SNPs or genomic regions.
• Subnetwork analyzers: Identify gene subnetworks derived from input at the gene, SNP, or genomic region level.
• SNP/region-to-gene linking: Links SNPs and genomic regions to candidate genes using promoter capture Hi-C, e/pQTL, and enhancer-gene maps.

Scientific Applications:

• Genomic summary data interpretation: Convert lists of genes, SNPs, or genomic regions into biologically interpretable results.
• Ontology enrichment analysis: Identify biological processes, molecular functions, and cellular components enriched in gene lists or linked genes.
• Noncoding variant interpretation: Map noncoding SNPs and genomic regions to candidate genes using promoter capture Hi-C, e/pQTL, and enhancer-gene maps.
• Gene subnetwork discovery: Detect gene subnetworks to explore molecular interactions and pathway-level relationships.

Methodology:

Performs near real-time enrichment and subnetwork analyses; integrates ontologies, networks, promoter capture Hi-C, e/pQTL, and enhancer-gene maps to link SNPs or genomic regions to candidate genes; employs six analyzers (three enrichment analyzers that identify enriched ontology terms and linked genes, and three subnetwork analyzers that identify gene subnetworks from gene, SNP, or genomic region inputs).