REAL-TIME GENOMIC SUMMARY DATA INTERPRETATION
(leveraging ontologies, networks & others)
Enrichment analysis for genes using ontologies
EAG uses gene-centric ontology annotations to perform enrichment analysis.
SNPs linked to genes for enrichment analysis
EAS identifies genes linked from input SNPs (alongside the significance information) and conducts ontology enrichment analysis for the linked genes. Linking SNPs to genes is enabled by genomic proximity or using functional genomic datasets about PCHi-C and e/pQTL.
Regions linked to genes for enrichment analysis
EAR first identifies genes linked from input genomic regions using functional genomic datasets about PCHi-C and enhancer-gene maps and then conducts ontology enrichment analysis based on the linked genes.
Subnetwork analysis for gene-level summary data
SAG takes as input gene-level summary data to identify a subset of the gene network in a manner that the resulting subnetwork contains a desired number of highly scored and interconnected genes.
SNPs linked to genes for subnetwork analysis
SAS identifies a gene subnetwork from input SNP-level summary data. It first uses genomic proximity, e/pQTL or PCHi-C to link SNPs to genes, and then uses information on the linked genes to identify the gene subnetwork.
Regions linked to genes for subnetwork analysis
SAR first identifies genes linked from input genomic regions using PCHi-C datasets or enhancer-gene maps, followed by subnetwork analysis based on the linked genes.