Pi

Pi prioritizes drug targets using human genetic and genome-wide association study (GWAS) evidence to identify genes, pathways, and networks for early-stage target validation.

Key Features:

• Multi-level prioritization: Prioritizes potential drug targets at gene, pathway, and network levels.
• GWAS evidence integration: Integrates genome-wide association studies (GWAS) disease-association evidence to support target nomination.
• Modulated gene identification: Systematically generates evidence linking genetic association signals to specific modulated genes.
• High-throughput data integration: Performs integration and analysis of high-throughput genomic data.
• Bioconductor and R interoperability: Operates within the Bioconductor ecosystem and leverages the statistical programming language R and Bioconductor's 934 interoperable packages.
• Analytical reproducibility: Leverages Bioconductor's package review process and continuous automated testing to support reproducible analyses.

Scientific Applications:

• Early-stage target validation: Supports early-stage drug target validation using human genetic and GWAS evidence.
• Drug target prioritization: Ranks candidate targets for drug discovery at gene, pathway, and network levels.
• Mapping association signals: Identifies modulated genes responsible for observed GWAS associations.
• Integrative genomics: Enables integrative analysis of high-throughput genomic datasets within the Bioconductor/R environment.

Methodology:

Integration and analysis of high-throughput genomic data using Bioconductor packages in R and systematic generation of evidence linking GWAS disease-association signals to modulated genes, leveraging Bioconductor's package review and continuous automated testing.