Functions, Disease Phenotypes, Enrichment Scoring and Networks

Q-omics identifies functional and phenotypic characteristics of genes using curated ontologies and calculates their enrichment and connectivity across datasets.

* Ontologies: Gene Functions, Phenotypes and More

Gene functions are derived from the Gene Ontology (GO) biological process terms.

Disease phenotypes are defined by the Human Phenotype Ontology (HPO).

Additionally, Q-omics integrates gene sets from the MSigDB hallmark and oncogenic signature collections to enhance the biological context.

* Enrichment Scoring for Data Associations

Q-omics applies either Fisher exact test (for overrepresentation analysis) or a rank-based GSEA approach (for full ranked lists) to calculate ontology enrichment scores.

These scores quantify the overrepresentation of specific biological functions or phenotypes within ranked gene lists, derived from RNA expression, protein abundance, or CRISPR/shRNA screening data.

The results are used to calculate asscociation scores between datasets and to prioritize gene lists based on biological relevance.

* Network Analysis with NetCrafter

Gene-gene functional relationships are analyzed using ontology-based networks.

In NetCrafter, gene pairs are connected based on weighted Tanimoto scores, which reflect the overlap of shared GO, HPO, MSigDB hallmark, or oncogenic gene sets.

This network enables users to explore gene modules, predict functional neighbors, and identify key regulators or therapeutic targets.

Ontology term search