HolobiomicsLab chemical-formula-manipulation
Summary
Systematically enumerate candidate metabolite chemical formulas from a parent drug's raw formula by applying phase I and phase II biotransformation rules. This skill enables high-throughput metabolite prediction for mass spectrometry-based drug metabolism studies.
When to use
When you have a parent drug's raw chemical formula and need to predict its potential metabolites in mass spectrometry data (mzML format) to match against observed peaks. Apply this skill when performing untargeted metabolite detection or when validating metabolite hypotheses against high-resolution MS data.
When NOT to use
- When the parent drug formula is unavailable or ambiguous; MetIDfyR requires a precise starting formula.
- When working with low-resolution MS data that cannot resolve mass differences between predicted metabolites (requires accurate mass for filtering candidate formulas).
- When mzML files have not been peak-picked; the tool expects preprocessed peak-picked mzML input.
Inputs
- Parent drug raw chemical formula (string or from TSV file)
- Configuration file (R script with parameters: TEMPLATE_config.R)
- mzML file containing mass spectrometry sample data
- TSV file with parent drug information (TEMPLATE_start_mlc.tsv)
Outputs
- Structured table (CSV or TSV) of predicted metabolite formulas
- Associated biotransformation types for each predicted metabolite
- Annotated mass spectrometry peaks matched to predicted formulas
How to apply
Load the drug's raw chemical formula (e.g., from a TSV file with parent drug information) into MetIDfyR's prediction module in R. The module systematically applies phase I (oxidation, reduction, hydrolysis) and phase II (conjugation) biotransformation rules to enumerate all plausible metabolite formulas. Execute the prediction with a configuration file specifying parameters, then collect predicted metabolite formulas and their associated transformation types into a structured output table (CSV or TSV). Validate predictions by matching generated formulas against mass peaks extracted from the mzML file, using mass accuracy tolerance (typically in ppm range) as a filter criterion.
Related tools
- R (Execution environment and scripting language for running MetIDfyR formula enumeration and biotransformation rule application) — https://www.r-project.org/
- MSnbase (R package dependency for parsing and processing mzML mass spectrometry data files)
- Rdisop (R package dependency for precise isotope distribution and formula calculation)
- MetApp (Shiny application for visualization and interactive exploration of predicted and detected metabolites) — https://github.com/GIELCH/MetApp
Examples
Rscript MetIDfyR.R -i input/lgd_DIA_peak-picked.tsv -o LGD4033_results -c input/config_LGD.R
Evaluation signals
- Output TSV contains all predicted metabolite formulas with corresponding parent formula, transformation type, and mass delta.
- Number of predicted metabolites matches expected enumeration given the biotransformation rule set (phase I + phase II rules applied).
- Predicted metabolite masses match observed peaks in mzML data within the configured mass accuracy tolerance (verify via mass error in ppm).
- Transformation type annotations correctly map each predicted formula to its corresponding phase I or phase II reaction (e.g., +16 for oxidation, +162 for glucuronidation).
- Output can be successfully imported into MetApp visualization without schema errors or missing required columns.
Limitations
- MetIDfyR has been developed and tested on R versions 3.6.1 to 4.0; compatibility with newer R versions is not explicitly documented.
- The tool enumerates formulas but does not validate stereochemistry or confirm that predicted metabolites are biologically feasible or observed in vivo.
- Prediction accuracy depends on the completeness and accuracy of the configured biotransformation rules; phase I and phase II rules are user-configurable and may require domain expertise to customize.
- No changelog is available, making it difficult to track bug fixes or method refinements across versions.
Evidence
- [intro] MetIDfyR predicts and detects metabolites in mass spectrometry data based on the raw formula of the drug of interest: "predict and detect metabolites in mass spectrometry data (mzML) based on the raw formula of the drug of interest"
- [readme] Systematic enumeration of candidate metabolite formulas using biotransformation rules: "enumerate candidate metabolite formulas based on systematic phase I and phase II biotransformation rules"
- [readme] Output is a structured table of predicted metabolites with transformation types: "Collect and format predicted metabolite formulas and associated transformation types into a structured output table (CSV or TSV)"
- [readme] MetIDfyR is an open-source R script: "MetIDfyR is an open-source, cross-platform and versatile R script to predict and detect metabolites in mass spectrometry data"
- [readme] Requires configuration file, mzML file, and parent drug information file as inputs: "Need - a configuration file with the parameter (see TEMPLATE_config.R). - a mzML file containing the sample informations - a tsv file containing the parent drug informations (see"
- [readme] Command-line invocation syntax for running the tool: "Rscript MetIDfyR.R -i path-to-input-file -o output-directory -c config-file"