FELLA

FELLA performs network-based pathway enrichment of metabolomics data to identify affected compounds, reactions, enzymes, modules, and pathways and to provide multi-level biological context for observed metabolite changes.

Key Features:

• Sub-network Analysis: Applies sub-network analysis on a graph representation of reference databases such as KEGG using statistical measures over a null diffusive process to extract relevant entries.
• Comprehensive Biological Context: Reports enriched results as a sub-pathway network that includes pathways, modules, enzymes, reactions, and compound candidates for multi-level interpretation.
• Visualization and Export: Produces an enriched subnetwork that can be exported and visualized to explore interconnected metabolic processes.

Scientific Applications:

• Summary metabolomics interpretation: Interprets lists of altered compounds from metabolomics studies to suggest affected reactions, enzymes, modules, and pathways.
• Experimental dataset analysis: Has been applied to GC-MS and LC-MS datasets, including analysis of cells depleted for an uncharacterized mitochondrial gene.
• Isotope-labeling validation: Supports partial validation via NMR-based tracking of 13C glucose labeling.

Methodology:

FELLA constructs a graph-based knowledge model (e.g., KEGG) and applies label propagation together with statistical evaluation over a null diffusive process that accounts for network topology and pathway crosstalk to rank and extract enriched sub-networks.