agentsclimarketplace

Feature group spectral mapping

Skill HolobiomicsLab/asb-skill-collections/collections/metabolomics/v1/skills/feature-group-spectral-mapping

Use when after sample alignment and isotopologue/adduct grouping are complete, when you need to associate MS2 spectral data (DDA-acquired) with the consolidated feature groups to enable MS/MS-based compound annotation or to bundle MS1 quantification with MS2 evidence.From its SKILL.md

Install
npx -y skills add HolobiomicsLab/asb-skill-collections --skill feature-group-spectral-mapping

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

  • 15 stars15 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 file declares

Copied from the file, not written here

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

6.9 KB, ~1.3k tokens by cl100k_base, as published. Nobody here has run it

Reconstruct the Isotopologue and Adduct Grouping Component

Summary

Map consolidated MS2 spectra and isotopic metadata to feature groups (isotopologues and adducts) that have already been aligned and clustered across samples. This skill extracts representative tandem mass spectra for each feature group and annotates their isotopic relationships and adduct identities.

When to use

After sample alignment and isotopologue/adduct grouping are complete, when you need to associate MS2 spectral data (DDA-acquired) with the consolidated feature groups to enable MS/MS-based compound annotation or to bundle MS1 quantification with MS2 evidence. Use this step before exporting final peaktables and spectral libraries.

When NOT to use

  • Input LC-MS data are DIA (data-independent acquisition): SLAW will skip DIA-MS2 spectra and only process MS1 features
  • Feature grouping has not yet been performed: spectral mapping requires consolidated feature group assignments as input
  • No MS2 data are present in the raw files (MS1-only experiments): no spectra can be consolidated or mapped

Inputs

  • Feature-grouped peaktable (with isotopologue and adduct assignments)
  • Raw LC-MS mzML files (centroided, DDA-acquired)
  • Sample metadata and polarity information

Outputs

  • Consolidated MS2 spectrum per feature group (e.g., MGF or mzTab with MS/MS spectra)
  • Annotated isotopic and adduct metadata linked to each feature group
  • Complete peaktable enriched with MS2 spectrum references

How to apply

Load the feature-grouped data from the preceding alignment and grouping steps (isotopologues and adducts already consolidated by feature group ID). For each feature group, retrieve all MS2 scans associated with its member ions from the raw LC-MS files or a processed spectral index (matching by retention time and m/z tolerance). Consolidate multiple MS2 scans per feature group by selecting a representative spectrum (e.g., highest-intensity scan or merged consensus spectrum). Annotate each consolidated spectrum entry with isotopic relationships (e.g., M, M+1, M+2) and adduct type (e.g., [M+H]+, [M+Na]+) inferred from the grouping step. Export the consolidated spectra and their metadata to a structured output format (e.g., mzTab, JSON, or MGF) alongside the quantitative peaktable.

Related tools

  • SLAW (Complete untargeted LC-MS workflow orchestrator that performs feature grouping, gap-filling, and then spectral consolidation and export as integrated pipeline step) — https://github.com/zamboni-lab/SLAW
  • Centwave (Peak picking algorithm (one of three options) that detects LC-MS features before alignment and grouping)

Examples

docker run --rm -v /path/to/input:/input -v /path/to/output:/output zambonilab/slaw:latest

Evaluation signals

  • All feature groups in the input peaktable have an associated consolidated MS2 spectrum entry in the output (completeness check)
  • Isotopic annotations (M, M+1, M+2) and adduct identities match the grouping assignments from the prior step (consistency check)
  • Retention time and m/z of each consolidated spectrum's precursor ion fall within the tolerance window of the feature group (validation against alignment)
  • Output file format (MGF, mzTab, or JSON) is valid and can be parsed by downstream annotation tools (schema/format check)
  • No duplicate spectra across feature groups; each MS2 scan is assigned to exactly one feature group (uniqueness check)

Limitations

  • DIA-MS2 spectra are skipped and not consolidated; only DDA-acquired MS2 data are processed
  • Spectral consolidation assumes that multiple MS2 scans assigned to a single feature group are chemically identical; co-eluting isomers or isobaric species will be merged and may compromise annotation specificity
  • Requires that raw mzML files remain accessible during the consolidation step; file paths must be correctly resolved
  • Quality of consolidated spectra depends on prior alignment and grouping accuracy; errors in isotopologue/adduct assignment propagate to spectrum metadata

Evidence

  • [other] SLAW performs extraction of consolidated MS2 spectra and isotopic data as part of its complete processing pipeline, which follows peak picking, sample alignment, grouping of isotopologues and adducts, and gap-filling by data recursion.: "SLAW performs extraction of consolidated MS2 spectra and isotopic data as part of its complete processing pipeline, which follows peak picking, sample alignment, grouping of isotopologues and"
  • [other] 1. Load feature-grouped data from preceding alignment and grouping steps (isotopologues and adducts already consolidated by feature group). 2. Retrieve MS2 spectra associated with each feature group from raw LC-MS files or processed spectral index. 3. Consolidate multiple MS2 scans per feature group by merging or selecting representative spectrum(a). 4. Annotate isotopic relationships and adduct information within each consolidated spectrum entry. 5. Export consolidated spectra and metadata to structured output file (e.g., JSON, mzTab, or proprietary format).: "1. Load feature-grouped data from preceding alignment and grouping steps (isotopologues and adducts already consolidated by feature group). 2. Retrieve MS2 spectra associated with each feature group"
  • [readme] Complete processing including peak picking, sample alignment, pick picking, grouping of isotopologues and adducts, gap-filling by data recursion, extraction of consolidated MS2 spectra (only for DDA experiments! DIA-MS2 spectra will be skipped) and isotopic data: "extraction of consolidated MS2 spectra (only for DDA experiments! DIA-MS2 spectra will be skipped) and isotopic data"
  • [readme] Raw MS data in mzML format. Files can include MS1 and DDA-MS2 scans. DIA-MS is not supported. All data must be centroided and of unique polarity.: "Raw MS data in mzML format. Files can include MS1 and DDA-MS2 scans. DIA-MS is not supported."

What ships with it

Read from the repository

Just SKILL.md. No reference files, no scripts.

Keep looking

Skills are one crate of 325,949. Ordering is by how many stacks a row turns up in, so the top of any crate is what has actually been picked rather than what has the most stars.