What does this strategy enable?
Combine molecular-network edges with abundance changes across ordered samples to prioritize possible chemical transformations.
Transferable workflow
Workflow
- 1
Prepare matched network and abundance data
Connect quantified features to fragmentation-based network edges and ordered sample metadata.
- 2
Score proportional changes
Apply the appropriate ChemProp scoring mode to connected feature pairs.
- 3
Investigate candidate transformations
Inspect abundance trends, mass differences and spectra before follow-up validation.
Connected resources
Tools used in this strategy
Evidence of reuse
Published implementations and adaptations
These papers applied, adapted, extended, benchmarked or formalized the Strategy after its originating publication. They are examples of precedent, not a ranking of software choices.
A Functional Metabolomics Framework to Track Microbiome Drug Metabolism
ChemProp2 extends scoring to time-series correlations with a decoy-based empirical FDR procedure. Demonstrated with a synthetic gut community and drugs; microbial association does not establish a producing organism.
Navigator tools used: Chemical Proportionality (ChemProp), GNPS2 Feature-Based Molecular Networking workflow
Strategy profile
Components
Molecular networking, Metadata analysis, Statistical analysis
Scope and considerations
Designed for
- Prioritizing possible transformations among network-connected features.
Important requirements
- MS/MS network edges linked to quantified features.
- Consistent sample identifiers and an ordered temporal or spatial variable.
- Choose the scoring mode for the available sampling design.
Considerations
- Direction and proportionality are hypotheses, not proof of a reaction or its mechanism.
- Co-variation, normalization and missing signals affect interpretation.
- This does not estimate metabolic flux.
Reusable resources
Publication
Petras D, et al. Chemical Proportionality within Molecular Networks. Analytical Chemistry 93, 12833-12839 (2021).