GENEVA

GENEVA analyzes RNA-seq datasets to identify experimental conditions, drugs, genetic perturbations, and diseases that modulate gene expression, including ACE2 (the cell-entry receptor for SARS-CoV-2), to support discovery of modulators and associated clinical risk factors.

Key Features:

• Semi-Automated Framework: A semi-automated approach for systematic exploration of large RNA-seq collections to identify conditions that modulate gene expression.
• Extensive Dataset Analysis: Applied to over 28,665 publicly accessible RNA-seq samples from the Gene Expression Omnibus (GEO) to survey expression patterns at scale.
• Identification of Modulators: Detection of drugs, genetic perturbations, and diseases that influence gene expression, with specific identification of modulators affecting ACE2.
• Clinical Integration: Integration with electronic health records from 3,936 COVID-19 patients to relate expression findings to clinical outcomes, including an association between pre-existing cardiomyopathy and increased mortality versus other cardiovascular conditions.
• Broad Applicability: Applicable to analysis of any gene or gene signature of interest beyond ACE2 and COVID-19 studies.

Scientific Applications:

• Modulator Discovery: Identification of drugs and genetic perturbations that alter target gene expression for hypothesis generation.
• Disease Mechanism Investigation: Characterization of disease-associated changes in gene expression, exemplified by ACE2 modulation in cardiomyopathy.
• Clinical Risk Association: Linking molecular expression modulators to patient outcomes and risk factors in COVID-19 through electronic health record integration.

Methodology:

Semi-automated analysis of public RNA-seq datasets from GEO (over 28,665 samples), identification of expression modulators (drugs, genetic perturbations, diseases) for genes such as ACE2, and integration with electronic health records from 3,936 COVID-19 patients for clinical association analyses.