Sumer

Sumer summarizes and consolidates gene set enrichment results across multiple omics experiments to reduce annotation redundancy and highlight representative pathways for biological interpretation.

Key Features:

• Reduction of Annotation Redundancy: Uses a weighted set cover algorithm to minimize redundancy among gene sets identified within individual experiments, reducing the number of gene sets by 52–77%.
• Consolidation of Gene Sets: Employs affinity propagation to cluster similar gene sets across experiments and selects the most representative gene set per cluster.
• Focus on Relevant Genes: Prioritizes overlapping genes between input lists and enriched gene sets in over-representation analysis and leading-edge genes in gene set enrichment analysis to refine candidate gene sets.
• Multi-Omics Integration: Integrates results from different omics platforms such as RNA-Seq and proteomics and, in a use case comparing basal versus luminal A breast cancer, highlighted differences in proliferation and DNA damage response.
• Pan-Cancer Survival Analysis: Has been applied to pan-cancer survival analysis to identify prognosis-related pathways common to multiple cancer types, cancer-specific pathways, and pathways with divergent prognostic implications across cancers.
• Visualization and Reporting: Generates comprehensive tables and both static and interactive plots for exploration and reporting of summarized enrichment results.

Scientific Applications:

• Functional Interpretation of Omics Data: Integrates gene set analysis results across experiments to improve identification and interpretation of relevant biological processes and pathways.
• Research on Cancer Prognosis: Identifies common and cancer-specific prognosis-related pathways to support investigation of cancer biology and potential therapeutic targets.

Methodology:

Applies a weighted set cover algorithm to reduce redundancy, uses affinity propagation to cluster gene sets, and prioritizes overlapping genes in over-representation analysis and leading-edge genes in gene set enrichment analysis.