--- name: metabolite-to-gene-mapping description: Use when you have metabolomic data (e.g., from LC-MS or GC-MS comparing patient to controls) showing differential abundant metabolites (DAMs), candidate genes from exome sequencing or variant calling, and access to a protein–protein or gene–gene interaction network (e.g., STRING). license: CC-BY-4.0 metadata: edam_operation: http://edamontology.org/operation_3439 edam_topics: - http://edamontology.org/topic_0625 - http://edamontology.org/topic_3172 - http://edamontology.org/topic_0602 tools: - metPropagate - XCMS - STRING Database v11 - HMDB - Exomiser techniques: - LC-MS - GC-MS derived_from: - doi: 10.1038/s41525-020-0132-5 title: metPropagate evidence_spans: [] claims: [] provenance: collection: https://w3id.org/holobiomicslab/asb-skill/collection/metabolomics/v2 assembled_by: scripts/collect_metabolomics_collection.py sources: - build: coll_metpropagate_cq doi: 10.1038/s41525-020-0132-5 title: metPropagate dedup_kept_from: coll_metpropagate_cq schema_version: 0.2.0 attribution: generator: AgenticScienceBuilder original_doi: 10.1038/s41525-020-0132-5 all_source_dois: - 10.1038/s41525-020-0132-5 zenodo_doi: 10.5281/zenodo.20794027 curators: [] promoter: Louis-Félix Nothias sponsor: CNRS & Université Côte d'Azur --- # Reconstruct the label-propagation algorithm for prioritizing metabolic disease genes ## Summary Map differential abundant metabolites (DAMs) to genes via enrichment scoring, then propagate metabolomic seed labels across a gene/protein interaction network to generate ranked scores for metabolic disease gene prioritization. This skill bridges metabolomic discovery (dysregulated metabolites) to genomic interpretation (candidate disease genes) by network diffusion. ## When to use You have metabolomic data (e.g., from LC-MS or GC-MS comparing patient to controls) showing differential abundant metabolites (DAMs), candidate genes from exome sequencing or variant calling, and access to a protein–protein or gene–gene interaction network (e.g., STRING). Use this skill when you need to rank candidate genes by their proximity to metabolomic signals in the network, especially in rare metabolic disease diagnosis where metabolomic hits provide orthogonal evidence beyond genomic burden alone. ## When NOT to use - Input is already a pre-ranked gene list without underlying metabolomic data — label propagation adds value only when seeded with strong metabolomic signals. - Metabolomic data quality is poor (e.g., >50% missing intensity values, low metabolite identification rate <20% of features matched to HMDB) — propagation will amplify noise. - No interaction network is available or the network is extremely sparse (<<1% edge density) — label propagation requires sufficient network connectivity to diffuse metabolomic information. ## Inputs - ESI+/ESI− mode metabolomics intensity matrices (CSV: sample_ID × m/z feature) - Peak tables from XCMS or equivalent preprocessing (m/z, retention time, p-value) - HMDB metabolite database (CSV: exact mass, HMDB ID, gene associations) - STRING protein–protein interaction network (node_id, gene_name, edge_score) - Candidate gene list from exome sequencing or variant prioritization (e.g., Exomiser output CSV) ## Outputs - Per-gene metabolomic enrichment scores (CSV: gene, DAM_count, MSEA_pvalue, enrichment_score) - Binary seed label file for label propagation (per-gene: gene_id, label ∈ {0, 1}) - Propagated gene scores after label diffusion (CSV: gene_id, original_score, propagated_score, rank) - Network propagation statistics (iteration count, convergence threshold met, label distribution) ## How to apply First, convert raw metabolomic intensity matrices (ESI+/ESI− mode) through log or linear scaling and exact mass matching against HMDB to identify significant features and map them to metabolites. Next, perform metabolite set enrichment analysis (MSEA) to compute per-gene enrichment scores reflecting the concentration of DAMs associated with each gene. Then, construct binary seed labels: genes with significant metabolomic enrichment receive label 1 (metabolomic hits), others receive 0. Initialize a label-propagation algorithm on the STRING network graph (or equivalent PPI) seeded with these labels. Execute iterative propagation (typically 100 iterations or until convergence) where each node's label is updated as a weighted average of its neighbors' labels, weighted by network edge confidence. Finally, compute final ranked gene scores reflecting both the original metabolomic signal strength and network-guided diffusion, ranking candidates by propagated score. This approach ensures that genes not directly hit by metabolomic data but closely connected to metabolomic hits are elevated in ranking. ## Related tools - **metPropagate** (Complete implementation of metabolomic processing, label propagation, and gene ranking; orchestrates MSEA, label construction, and network diffusion) — https://github.com/emmagraham/metPropagate - **XCMS** (Untargeted LC-MS/GC-MS peak detection and feature alignment to produce intensity matrices and peak tables input to metabolomic processing pipeline) - **STRING Database v11** (Provides gene/protein interaction network (nodes: genes, edges: functional associations with confidence scores) used as substrate for label propagation) - **HMDB** (Reference metabolite database for exact mass matching of m/z features to metabolite IDs and retrieval of gene–metabolite associations for enrichment scoring) - **Exomiser** (Produces candidate gene lists from variant calls; output format (CSV) directly compatible as input to metPropagate integration pipeline) ## Examples ``` bash integration/run_label_prop_cluster.sh; outputs will be in integration/results/ containing propagated and original gene scores ``` ## Evaluation signals - Metabolomic feature-to-metabolite matching rate: ≥20% of input m/z values successfully mapped to HMDB entries (indicative of data quality and reference database coverage). - Seed label distribution: ≥5 genes with label=1 (metabolomic hits) to provide meaningful network propagation; ≤100% sparsity (all genes labeled 0) suggests weak metabolomic signal. - Label propagation convergence: achieved within maximum iteration limit and label update magnitude