oncoEnrichR

oncoEnrichR provides R-based interpretation of human gene sets in cancer research by leveraging prior molecular knowledge to annotate, rank, and predict cancer-relevant properties of candidate genes.

Key Features:

• R package implementation: Implements analyses and annotations as an R package for programmatic use.
• Exploratory analysis and prioritization: Supports exploratory analysis and prioritization of gene lists from high-throughput cancer experiments including siRNA/CRISPR screens, protein proximity labeling, and transcriptomics (differential expression).
• Data resource integration: Queries numerous high-quality external data resources to compile comprehensive gene annotations and analytical results with provenance logging.
• Ranked gene–tumor type associations: Computes and reports ranked associations between genes and tumor types.
• Literature-supported oncogene/tumor suppressor annotations: Provides annotations for proto-oncogenes and tumor suppressor genes based on literature evidence.
• Target druggability data: Annotates gene targets with druggability information relevant to therapeutic development.
• Regulatory interaction analysis: Analyzes regulatory interactions that may influence cancer biology.
• Synthetic lethality prediction: Identifies potential synthetic lethality relationships among genes.
• Prognostic associations and gene aberration evaluation: Evaluates prognostic associations and gene aberrations across tumor types.
• Co-expression pattern analysis: Examines co-expression patterns to characterize gene interactions within tumors.

Scientific Applications:

• Interpretation of candidate gene lists: Interprets extensive gene lists generated from genome-scale cancer screening experiments to derive biological insights.
• Prioritization from genetic and proteomic screens: Prioritizes candidate genes from siRNA/CRISPR and proteomic screens for follow-up studies.
• Transcriptomic analysis contextualization: Contextualizes differential expression results from transcriptomics within cancer-relevant annotations.
• Therapeutic target discovery: Supports identification of therapeutic targets through druggability annotation and synthetic lethality predictions.

Methodology:

Queries external high-quality data resources to compile gene annotations and analytical inputs; computes ranked gene–tumor type associations; derives literature-based proto-oncogene and tumor suppressor annotations; annotates target druggability; analyzes regulatory interactions; predicts synthetic lethality relationships; evaluates prognostic associations and gene aberrations; examines co-expression patterns; and outputs a structured analysis report with logging of underlying data resources.