Lipid candidate matching

Skill HolobiomicsLab/asb-skill-collections/collections/metabolomics/v1/skills/lipid-candidate-matching

Curated, evidence-grounded skill and software-tool collections for scientific AI agents, generated by the AgenticScienceBuilder

Install

npx -y skills add HolobiomicsLab/asb-skill-collections --skill lipid-candidate-matching

Assembled from the repository path, not quoted from the project. Check it against their README if it does not work.

One thing to look at

14 stars14 stars. Stars are a popularity signal and not a quality one, but at this level it is likely that nobody has read this closely except its author, and you would be relying on your own review.

What its author says it does

Copied from the file, not written here

Use when you have peak-picked MS/MS data (e.g., from MZmine, XCMS, MS-DIAL, or Compound Discoverer) and need to identify lipid species present in your sample.

The file declares its own license as CC-BY-4.0. That is the author’s claim about this one file, and it is not the same thing as the license GitHub reports for the repository, which is listed with the other numbers below.

SKILL.md

5.6 KB, as published. Nobody here has run it

lipid-candidate-matching

Summary

Match experimental fragment m/z values from high-resolution tandem MS/MS data against in-silico fragmentation libraries to identify and rank lipid candidates. This skill enables annotation of lipidomics datasets by comparing observed ion patterns to simulated spectra from comprehensive lipid databases.

When to use

Apply this skill when you have peak-picked MS/MS data (e.g., from MZmine, XCMS, MS-DIAL, or Compound Discoverer) and need to identify lipid species present in your sample. Use when working with Q-Exactive orbitrap, Agilent, Bruker, or SCIEX Q-TOF UHPLC-HRMS/MS instruments, or when performing direct infusion or imaging experiments. Do NOT use if your data originates from Waters instruments, which are not currently supported.

When NOT to use

Input data originates from Waters instruments (currently unsupported by LipidMatch)
Raw, unprocessed MS data without prior peak picking step
Analysis of non-lipid small molecules or metabolites (tool is lipid-specific)

Inputs

Peak-picked MS/MS dataset or experimental fragment m/z list (from MZmine, XCMS, MS-DIAL, or Compound Discoverer)
In-silico fragmentation lipid library (.csv format or built-in LipidMatch library)
Sample ionization mode and acquisition method parameters (targeted, ddMS2-topN, or AIF)

Outputs

Ranked lipid candidate list with matched m/z fragments and scoring metrics
Annotated MS/MS dataset with assigned lipid identities
Lipidomics feature table with lipid species annotations

How to apply

First, obtain the experimental fragment m/z list from your peak-picked MS/MS dataset and prepare it in the format expected by LipidMatch (typically a tabular format with experimental m/z and intensity values). Load the LipidMatch software with the active in-silico fragmentation library (comprising over 500,000 lipid species across 60+ lipid types), or integrate a custom user-authored .csv lipid library if analyzing specialized lipid classes. Run the matching algorithm to compare each experimental m/z fragmentation pattern against the library's simulated m/z values. Inspect the ranked output candidate list, which prioritizes matches based on agreement between observed and predicted fragment patterns. Filter candidates using domain knowledge (e.g., retention time, ionization mode, expected lipid class) to narrow the final annotation set.

Related tools

MZmine (Peak picking and feature detection prior to lipid matching)
XCMS (Peak picking and feature detection prior to lipid matching)
MS-DIAL (Peak picking and feature detection prior to lipid matching)
Compound Discoverer (Peak picking and feature detection prior to lipid matching)
LipidMatch (Executes fragment m/z matching against in-silico lipid fragmentation libraries) — https://github.com/GarrettLab-UF/LipidMatch

Evaluation signals

At least one candidate from the built-in or custom library appears in the ranked output list with a matching score above background noise threshold
Experimental fragment m/z values show ≤5 ppm mass accuracy when matched to predicted library fragments
Top-ranked candidate lipid species is consistent with expected lipid class, retention time, and ionization polarity for the sample
Custom library entries (if used) successfully appear as matching candidates after integration and library loading workflow
Output candidate list is non-empty and ranked by match quality metric (e.g., fragment pattern similarity, mass error)

Limitations

LipidMatch does not currently support Waters instrument files or data formats
Matching accuracy depends on the completeness and quality of the in-silico fragmentation library; novel or uncommon lipid species may not be represented
Requires properly formatted, pre-processed peak-picked input; raw MS data cannot be used directly
No changelog or version history documented, making reproducibility tracking difficult

Evidence

[readme] Fragment m/z matching methodology: "LipidMatch identifications are obtained by matching experimental fragment m/z values with simulated library m/z values using in-silico fragmentation libraries of over 500,000 lipid species across"
[readme] Supported instrument platforms: "LipidMatch has been tested and validated using Q-Exactive orbitrap UHPLC-HRMS/MS data obtained from multiple sample types using targeted, data-dependent top-N (ddMS2-topN), and all ion fragmentation"
[readme] Workflow modularity and peak picking integration: "LipidMatch can be used with various peak picking software (for example MZmine, XCMS, MS-DIAL, and Compound Discoverer)"
[readme] Custom library integration capability: "LipidMatch allows for facile integration of user generated libraries for unique applications"
[readme] Waters instrument limitation: "The software does not currently support Waters files"