Untargeted metabolomics workflow implementation

untargeted-metabolomics-workflow-implementation

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Summary

Implementation of an untargeted LC-MS/MS metabolomics workflow using DNMS2Purifier to detect and remove chimeric MS/MS spectra acquired in Data-Dependent Acquisition (DDA) mode. This skill ensures data quality in untargeted metabolomics studies by purifying MS/MS spectra before downstream spectral library matching and metabolite identification.

When to use

Apply this skill when you have LC-MS/MS data acquired in DDA mode from untargeted metabolomics experiments and need to remove chimeric (co-fragmented) MS/MS spectra that result from multiple precursor ions fragmented simultaneously. Use it as a pre-processing step before spectral matching, annotation, or quantification workflows.

When NOT to use

• Input data acquired in Targeted MS/MS (SRM/MRM) mode rather than DDA — DNMS2Purifier is designed specifically for DDA-mode data.
• MS/MS spectra already pre-processed by other chimeric filtering methods — applying redundant purification may introduce artifacts.
• Single-precursor isolation with no co-fragmentation expected — the purification overhead adds no value.

Inputs

• LC-MS/MS raw data files acquired in DDA mode (mzML, NetCDF, or vendor format)
• MS/MS spectrum table with m/z and intensity pairs
• Pre-trained chimeric spectrum detection model (included in repository)

Outputs

• Purified MS/MS spectra dataset with chimeric signals removed or flagged
• Spectrum metadata indicating purification status per MS/MS scan
• Quality control report of chimeric spectra identified and filtered

How to apply

First, verify R version 4.2.1 is installed and obtain the DNMS2Purifier.r script from the HuanLab GitHub repository. Load the script to confirm all MS/MS purification logic and chimeric spectrum filtering routines are syntactically correct. Execute DNMS2Purifier.r on your DDA-mode LC-MS/MS data (typically in mzML or NetCDF format); the program analyzes MS/MS spectra to identify and flag chimeric signals based on trained statistical models. For customized training on instrument-specific data, optionally use the accompanying DNMS2Purifier_model_generation.r script to retrain the detection model. Validate output by confirming that all spectra are properly formatted and that chimeric MS/MS signals have been successfully flagged or removed.

Related tools

• DNMS2Purifier.r (Main R script that executes MS/MS purification on DDA-mode LC-MS/MS data to identify and remove chimeric spectra) — https://github.com/HuanLab/DNMS2Purifier
• DNMS2Purifier_model_generation.r (Optional R script for retraining the chimeric spectrum detection model on custom LC-MS/MS datasets) — https://github.com/HuanLab/DNMS2Purifier
• R (Runtime environment (version 4.2.1) required to execute DNMS2Purifier scripts)

Examples

Rscript DNMS2Purifier.r --input dda_data.mzML --output purified_spectra.csv --model trained_model.RData

Evaluation signals

• Output spectra conform to expected MS/MS format (m/z, intensity pairs with valid metadata)
• All input spectra are accounted for in output (either passed, flagged, or removed with documented reason)
• Chimeric spectrum detection rate is stable and reproducible across technical replicates
• Peak intensity distribution and fragment patterns in purified spectra match known reference standards for non-chimeric spectra
• False positive rate of chimeric flagging remains <5% when validated against manually curated spectra

Limitations

• No changelog available in repository — version history and bug fixes are not formally documented.
• Model performance depends on DDA acquisition parameters; custom retraining may be required for different MS instrument platforms or acquisition settings.
• Purification accuracy may degrade for low-abundance metabolites or complex mixtures with extensive co-elution.
• Requires R 4.2.1 specifically; compatibility with newer or older R versions is not stated.

Evidence

• [readme] DNMS2Purifier is a bioinformatic solution that purifies chimeric MS/MS spectra from LC-MS/MS-based untargeted metabolomics in Data-Dependent Acquisition (DDA) mode.: "DNMS2Purifier is a bioinformatic solution that purifies chimeric MS/MS spectra from LC-MS/MS-based untargeted metabolomics in Data-Dependent Acquisition (DDA) mode."
• [readme] The R script DNMS2Purifier.r is the main program for MS/MS purification, we also provide the script DNMS2Purifier_model_generation.r for customized model training: "The R script DNMS2Purifier.r is the main program for MS/MS purification, we also provide the script DNMS2Purifier_model_generation.r for customized model training"
• [readme] The program is written in R (ver 4.2.1).: "The program is written in R (ver 4.2.1)."
• [other] Verify R 4.2.1 is installed and required dependencies for the DNMS2Purifier.r script are available.: "Verify R 4.2.1 is installed and required dependencies for the DNMS2Purifier.r script are available."
• [other] Load and parse the DNMS2Purifier.r script to confirm all function definitions, MS/MS purification logic, and chimeric spectrum filtering routines are syntactically correct.: "Load and parse the DNMS2Purifier.r script to confirm all function definitions, MS/MS purification logic, and chimeric spectrum filtering routines are syntactically correct."
• [other] Execute the program on representative DDA-mode LC-MS/MS test data (if available in the repository) to demonstrate chimeric spectrum detection and purification.: "Execute the program on representative DDA-mode LC-MS/MS test data (if available in the repository) to demonstrate chimeric spectrum detection and purification."
• [other] Validate that the output spectra are properly formatted and chimeric MS/MS signals have been successfully removed or flagged.: "Validate that the output spectra are properly formatted and chimeric MS/MS signals have been successfully removed or flagged."