molecular-network-construction-for-metabolomics
Summary
Construct global molecular networks from untargeted metabolomics data to link structurally related compounds and enable propagation of molecular structure annotations. This skill bridges suspect discovery with biotransformation-based annotation by organizing metabolites into networks where anchor compounds with known structures seed annotation of unknown suspects.
When to use
You have untargeted metabolomics data (e.g., LC-MS/MS spectra) and need to organize compounds by structural relatedness to enable structure discovery for unknown metabolites. Specifically, use this skill when you have anchor compounds with known SMILES/InChI and want to infer structures of suspects by finding those suspects in a molecular network derived from the same sample cohort or biological condition.
When NOT to use
- Input is already a curated, single-compound reference spectral library rather than a discovery dataset—molecular networks require many interconnected spectra to build meaningful pathways.
- You have only targeted metabolomics data (focused on a few known compounds)—the strength of networks comes from global scope; targeted data will yield sparse, uninformative networks.
- Your suspects and anchors are not in the same sample cohort or experiment—network edges require spectral similarity; suspects and anchors from unrelated datasets will not appear as neighbors.
Inputs
- Untargeted metabolomics MS/MS spectra (mzML, mzXML, or vendor format)
- Anchor compound reference library (with SMILES/InChI and identifiers)
- Sample metadata and grouping (optional, for network stratification)
- Spectral similarity scoring parameters (cosine function, m/z tolerance)
Outputs
- Global molecular network (node and edge tables)
- Anchor-suspect pair CSV (identifier, mass, anchor ID, anchor SMILES)
- Network visualization (GraphML, GexF, or adjacency matrix)
- Network statistics (degree distribution, connected components, path lengths)
How to apply
Construct a global molecular network by computing spectral similarity (e.g., cosine similarity) between all MS/MS spectra in your metabolomics dataset and connecting spectra above a similarity threshold. Retain both edges (spectral pairs) and node attributes (m/z, retention time, spectral data). Identify known anchor compounds in the network and mark them with their reference SMILES. For each suspect in the molecular network, record its identifier, precursor m/z, and network neighbors (anchors or suspect-neighbor paths). Export anchor-suspect pairs as a CSV with columns: identifier, mass, anchor (compound ID with known structure), and SMILES (the anchor's structure). This organized network becomes the input for biotransformation-based annotation methods, which will rank candidate structures for suspects based on biotransformation rules connecting them to their anchor neighbors.
Related tools
- GNN-SOM (Ranks candidate molecular structures and site-of-metabolism predictions for suspects identified in the molecular network, leveraging graph neural networks trained on biotransformation patterns) — https://github.com/HassounLab/GNN-SOM
- PROXIMAL2 (Generates biotransformation operators that define chemical transformations between anchor and suspect pairs, constraining the chemical feasibility of structure candidates) — https://github.com/HassounLab/PROXIMAL2
- BAM (Orchestrates end-to-end molecular structure discovery using the constructed network of anchor-suspect pairs combined with biotransformation rules and GNN-SOM ranking) — https://github.com/HassounLab/BAM
Evaluation signals
- Network connectivity: molecular network contains ≥1 connected component linking anchors to suspects; no isolated anchor or suspect nodes (degree ≥1 expected for annotatable suspects)
- Anchor-suspect pair CSV completeness: every row has non-null identifier, mass, anchor ID, and valid SMILES; anchor compounds match reference library entries
- Network topology validation: edge-weighting (spectral similarity scores) are within plausible range (e.g., 0–1 for cosine); suspect-to-anchor paths exist and have biologically plausible lengths (≤5–10 edges typical for connected metabolome regions)
- Reproducibility: re-running the network construction with identical parameters on the same spectra produces identical node and edge lists (deterministic spectral similarity scoring)
- Downstream annotation agreement: candidates for suspects rank known correct structures higher than false positives when biotransformation rules are applied to the network-derived anchor-suspect pairs
Limitations
- Network quality depends critically on spectral similarity threshold; too-low thresholds create spurious edges, too-high thresholds fragment the network and isolate suspects from anchors.
- Suspects present in low abundance or with poor-quality MS/MS spectra may have weak or missing spectral similarity edges, becoming unannotatable even if structural neighbors exist.
- Global molecular networks scale computationally with sample size (spectral similarity is O(n²) in the number of spectra); very large untargeted experiments may require sub-sampling or parallelization.
- Network topology alone does not guarantee correct annotation; suspects may be network-connected to anchors via off-target spectral similarity or noise, requiring validation against reference standards or orthogonal evidence.
- The approach requires that suspects and anchors co-occur in the same sample cohort for network linkage; cross-sample or cross-species network merging can introduce artifactual connections.
Evidence
- [other] Open access repository-scale propagated nearest neighbor suspect spectral library for untargeted metabolomics: "Open access repository-scale propagated nearest neighbor suspect spectral library for untargeted metabolomics"
- [readme] the anchor-suspect pairs of interest need to be specified. We have used a set of pairs derived from the molecular network used to create the suspect library: "the anchor-suspect pairs of interest need to be specified. We have used a set of pairs derived from the molecular network used to create the suspect library"
- [readme] molecules_of_interest = csv file of list of queries. It must have an identifier and mass for the suspect as well as anchor and a SMILES that represents the anchor.: "molecules_of_interest = csv file of list of queries. It must have an identifier and mass for the suspect as well as anchor and a SMILES that represents the anchor"
- [other] Molecular structure discovery from untargeted metabolomics data using biotransformation rules and global molecular networking: "Molecular structure discovery from untargeted metabolomics data using biotransformation rules and global molecular networking"
- [readme] BAM checks if the suspect molecule is known by checking whether the SMILES or InChI is specified in the molecules_of_interest csv file: "BAM checks if the suspect molecule is known by checking whether the SMILES or InChI is specified in the molecules_of_interest csv file"
1---2name: molecular-network-construction-for-metabolomics3description: Use when you have untargeted metabolomics data (e.g., LC-MS/MS spectra) and need to organize compounds by structural relatedness to enable structure discovery for unknown metabolites.4license: CC-BY-4.05---67# molecular-network-construction-for-metabolomics89## Summary1011Construct global molecular networks from untargeted metabolomics data to link structurally related compounds and enable propagation of molecular structure annotations. This skill bridges suspect discovery with biotransformation-based annotation by organizing metabolites into networks where anchor compounds with known structures seed annotation of unknown suspects.1213## When to use1415You have untargeted metabolomics data (e.g., LC-MS/MS spectra) and need to organize compounds by structural relatedness to enable structure discovery for unknown metabolites. Specifically, use this skill when you have anchor compounds with known SMILES/InChI and want to infer structures of suspects by finding those suspects in a molecular network derived from the same sample cohort or biological condition.1617## When NOT to use1819- Input is already a curated, single-compound reference spectral library rather than a discovery dataset—molecular networks require many interconnected spectra to build meaningful pathways.20- You have only targeted metabolomics data (focused on a few known compounds)—the strength of networks comes from global scope; targeted data will yield sparse, uninformative networks.21- Your suspects and anchors are not in the same sample cohort or experiment—network edges require spectral similarity; suspects and anchors from unrelated datasets will not appear as neighbors.2223## Inputs2425- Untargeted metabolomics MS/MS spectra (mzML, mzXML, or vendor format)26- Anchor compound reference library (with SMILES/InChI and identifiers)27- Sample metadata and grouping (optional, for network stratification)28- Spectral similarity scoring parameters (cosine function, m/z tolerance)2930## Outputs3132- Global molecular network (node and edge tables)33- Anchor-suspect pair CSV (identifier, mass, anchor ID, anchor SMILES)34- Network visualization (GraphML, GexF, or adjacency matrix)35- Network statistics (degree distribution, connected components, path lengths)3637## How to apply3839Construct a global molecular network by computing spectral similarity (e.g., cosine similarity) between all MS/MS spectra in your metabolomics dataset and connecting spectra above a similarity threshold. Retain both edges (spectral pairs) and node attributes (m/z, retention time, spectral data). Identify known anchor compounds in the network and mark them with their reference SMILES. For each suspect in the molecular network, record its identifier, precursor m/z, and network neighbors (anchors or suspect-neighbor paths). Export anchor-suspect pairs as a CSV with columns: identifier, mass, anchor (compound ID with known structure), and SMILES (the anchor's structure). This organized network becomes the input for biotransformation-based annotation methods, which will rank candidate structures for suspects based on biotransformation rules connecting them to their anchor neighbors.4041## Related tools4243- **GNN-SOM** (Ranks candidate molecular structures and site-of-metabolism predictions for suspects identified in the molecular network, leveraging graph neural networks trained on biotransformation patterns) — https://github.com/HassounLab/GNN-SOM44- **PROXIMAL2** (Generates biotransformation operators that define chemical transformations between anchor and suspect pairs, constraining the chemical feasibility of structure candidates) — https://github.com/HassounLab/PROXIMAL245- **BAM** (Orchestrates end-to-end molecular structure discovery using the constructed network of anchor-suspect pairs combined with biotransformation rules and GNN-SOM ranking) — https://github.com/HassounLab/BAM4647## Evaluation signals4849- Network connectivity: molecular network contains ≥1 connected component linking anchors to suspects; no isolated anchor or suspect nodes (degree ≥1 expected for annotatable suspects)50- Anchor-suspect pair CSV completeness: every row has non-null identifier, mass, anchor ID, and valid SMILES; anchor compounds match reference library entries51- Network topology validation: edge-weighting (spectral similarity scores) are within plausible range (e.g., 0–1 for cosine); suspect-to-anchor paths exist and have biologically plausible lengths (≤5–10 edges typical for connected metabolome regions)52- Reproducibility: re-running the network construction with identical parameters on the same spectra produces identical node and edge lists (deterministic spectral similarity scoring)53- Downstream annotation agreement: candidates for suspects rank known correct structures higher than false positives when biotransformation rules are applied to the network-derived anchor-suspect pairs5455## Limitations5657- Network quality depends critically on spectral similarity threshold; too-low thresholds create spurious edges, too-high thresholds fragment the network and isolate suspects from anchors.58- Suspects present in low abundance or with poor-quality MS/MS spectra may have weak or missing spectral similarity edges, becoming unannotatable even if structural neighbors exist.59- Global molecular networks scale computationally with sample size (spectral similarity is O(n²) in the number of spectra); very large untargeted experiments may require sub-sampling or parallelization.60- Network topology alone does not guarantee correct annotation; suspects may be network-connected to anchors via off-target spectral similarity or noise, requiring validation against reference standards or orthogonal evidence.61- The approach requires that suspects and anchors co-occur in the same sample cohort for network linkage; cross-sample or cross-species network merging can introduce artifactual connections.6263## Evidence6465- [other] Open access repository-scale propagated nearest neighbor suspect spectral library for untargeted metabolomics: "Open access repository-scale propagated nearest neighbor suspect spectral library for untargeted metabolomics"66- [readme] the anchor-suspect pairs of interest need to be specified. We have used a set of pairs derived from the molecular network used to create the suspect library: "the anchor-suspect pairs of interest need to be specified. We have used a set of pairs derived from the molecular network used to create the suspect library"67- [readme] molecules_of_interest = csv file of list of queries. It must have an identifier and mass for the suspect as well as anchor and a SMILES that represents the anchor.: "molecules_of_interest = csv file of list of queries. It must have an identifier and mass for the suspect as well as anchor and a SMILES that represents the anchor"68- [other] Molecular structure discovery from untargeted metabolomics data using biotransformation rules and global molecular networking: "Molecular structure discovery from untargeted metabolomics data using biotransformation rules and global molecular networking"69- [readme] BAM checks if the suspect molecule is known by checking whether the SMILES or InChI is specified in the molecules_of_interest csv file: "BAM checks if the suspect molecule is known by checking whether the SMILES or InChI is specified in the molecules_of_interest csv file"
HolobiomicsLab