MoonlightR

MoonlightR identifies candidate cancer driver genes by inferring gene regulatory networks from The Cancer Genome Atlas (TCGA) expression data and integrating functional enrichment scoring to prioritize genes per cancer type.

Key Features:

• Implementation (R/Bioconductor): Implemented as an R/Bioconductor package for analysis of gene expression data.
• Input data (TCGA expression data): Uses The Cancer Genome Atlas (TCGA) expression datasets as primary input for analyses.
• Gene Regulatory Network Inference: Infers gene regulatory networks from TCGA expression data to map interactions among genes within specific cancer types.
• Functional Enrichment Analysis: Performs functional enrichment analysis to evaluate the significance of biological processes and pathways associated with the inferred networks.
• Enrichment Scoring: Assigns scores to biological processes and pathways based on their relevance and significance in the cancer context.
• Candidate Driver Gene Identification: Integrates inferred networks with enrichment results to generate and prioritize lists of candidate driver genes per cancer type.

Scientific Applications:

• Oncogenic driver discovery: Identification and prioritization of candidate cancer driver genes across different cancer types.
• Target selection for experimental validation: Prioritizes genes for downstream experimental validation based on network context and enrichment scores.
• Support for diagnostic, prognostic, and therapeutic research: Provides gene-level hypotheses that can inform diagnostic, prognostic, and therapeutic studies in oncology.

Methodology:

MoonlightR infers gene regulatory networks from TCGA expression data, performs functional enrichment analysis on the inferred networks to compute enrichment scores for biological processes and pathways, and integrates network and enrichment results to identify candidate driver genes.