mass-difference-pattern-matching
Summary
Grouping of detected LC-MS features into isotopologue and adduct clusters by identifying features that share the same molecular ion but differ by characteristic mass shifts (e.g., C13, N15, D isotopes or [M+H]+, [M+Na]+, [M+NH4]+ adducts). This skill consolidates redundant ion representations into interpretable molecular entities for downstream analysis.
When to use
Apply this skill after peak picking and sample alignment when you have an aligned feature table containing m/z and retention time coordinates. Use it when your untargeted LC-MS workflow needs to reduce feature redundancy caused by naturally occurring stable isotope patterns and common adduct formation. This is especially important for large-scale studies where thousands of features need to be grouped to improve feature interpretability and reduce false positives in annotation.
When NOT to use
- Input is already a consolidated feature matrix with isotopologues and adducts pre-merged or manually curated.
- DIA (data-independent acquisition) experiments without DDA-MS2 data, which the SLAW workflow does not support.
- Profile-mode (non-centroided) mzML data, which must be centroided prior to peak picking and grouping.
Inputs
- aligned feature table (m/z and retention time coordinates from sample alignment step)
- centroided mzML files (optional, for reference detection)
Outputs
- grouped feature table with isotopologue/adduct cluster identifiers
- feature-to-cluster membership annotations
How to apply
Load the aligned feature table (output from sample alignment) containing detected m/z and retention time coordinates. Apply the isotopologue and adduct grouping algorithm to cluster features that share the same molecular ion with mass differences corresponding to isotopic shifts (C13 ≈ +1.00335 Da, N15 ≈ +0.99703 Da, D ≈ +1.00628 Da) or common adduct transformations ([M+H]+ vs [M+Na]+ ≈ +21.98 Da, [M+H]+ vs [M+NH4]+ ≈ +18.03 Da). The algorithm uses retention time co-elution as a constraint to ensure grouped features originate from the same molecule. Assign cluster identifiers to each feature and output the grouped feature table with isotopologue/adduct group membership annotations, which can then be propagated to downstream gap-filling and annotation steps.
Related tools
Evaluation signals
- All features within a cluster share retention time within the expected co-elution window (typically ±5–10 seconds in LC-MS).
- Mass differences between cluster members match known isotope shifts (C13, N15, D) or adduct mass differences within measurement tolerance (typically <5 ppm).
- No feature is assigned to multiple clusters; cluster membership is mutually exclusive.
- Cluster size distribution is reasonable (singletons acceptable, but spurious large clusters suggest incorrect grouping parameters).
- Downstream gap-filling and feature annotation steps show improved consistency when using grouped versus ungrouped feature tables (e.g., fewer conflicting annotations per molecular entity).
Limitations
- Grouping accuracy depends on retention time alignment quality; poor sample alignment will propagate errors into isotopologue/adduct assignment.
- Mass tolerance parameters must be tuned based on instrument resolution; too loose tolerance may merge unrelated features, too tight may fail to group true isotopologues.
- Complex metabolite mixtures with high feature density may result in ambiguous assignments when multiple candidate clusters are equally plausible within tolerance windows.
- High-resolution instruments (e.g., Orbitrap) perform better than low-resolution instruments; algorithm assumes sufficient mass accuracy to distinguish isotope patterns from noise.
Evidence
- [other] SLAW includes a processing step for grouping of isotopologues and adducts as part of its complete untargeted LC-MS workflow, which operates after peak picking and sample alignment.: "SLAW includes a processing step for grouping of isotopologues and adducts as part of its complete untargeted LC-MS workflow, which operates after peak picking and sample alignment."
- [other] Apply isotopologue and adduct grouping algorithm to cluster features sharing the same molecular ion with mass differences corresponding to isotopic shifts (e.g., C13, N15, D) or common adduct transformations (e.g., [M+H]+, [M+Na]+, [M+NH4]+).: "Apply isotopologue and adduct grouping algorithm to cluster features sharing the same molecular ion with mass differences corresponding to isotopic shifts (e.g., C13, N15, D) or common adduct"
- [readme] Complete processing including peak picking, sample alignment, pick picking, grouping of isotopologues and adducts, gap-filling by data recursion: "Complete processing including peak picking, sample alignment, pick picking, grouping of isotopologues and adducts, gap-filling by data recursion"
- [readme] All data must be centroided and of unique polarity. Centroided mzML can be obtained with ProteoWizard.: "All data must be centroided and of unique polarity. Centroided mzML can be obtained with ProteoWizard."
1---2name: mass-difference-pattern-matching3description: Use when after peak picking and sample alignment when you have an aligned feature table containing m/z and retention time coordinates. Use it when your untargeted LC-MS workflow needs to reduce feature redundancy caused by naturally occurring stable isotope patterns and common adduct formation.4license: CC-BY-4.05---67# mass-difference-pattern-matching89## Summary1011Grouping of detected LC-MS features into isotopologue and adduct clusters by identifying features that share the same molecular ion but differ by characteristic mass shifts (e.g., C13, N15, D isotopes or [M+H]+, [M+Na]+, [M+NH4]+ adducts). This skill consolidates redundant ion representations into interpretable molecular entities for downstream analysis.1213## When to use1415Apply this skill after peak picking and sample alignment when you have an aligned feature table containing m/z and retention time coordinates. Use it when your untargeted LC-MS workflow needs to reduce feature redundancy caused by naturally occurring stable isotope patterns and common adduct formation. This is especially important for large-scale studies where thousands of features need to be grouped to improve feature interpretability and reduce false positives in annotation.1617## When NOT to use1819- Input is already a consolidated feature matrix with isotopologues and adducts pre-merged or manually curated.20- DIA (data-independent acquisition) experiments without DDA-MS2 data, which the SLAW workflow does not support.21- Profile-mode (non-centroided) mzML data, which must be centroided prior to peak picking and grouping.2223## Inputs2425- aligned feature table (m/z and retention time coordinates from sample alignment step)26- centroided mzML files (optional, for reference detection)2728## Outputs2930- grouped feature table with isotopologue/adduct cluster identifiers31- feature-to-cluster membership annotations3233## How to apply3435Load the aligned feature table (output from sample alignment) containing detected m/z and retention time coordinates. Apply the isotopologue and adduct grouping algorithm to cluster features that share the same molecular ion with mass differences corresponding to isotopic shifts (C13 ≈ +1.00335 Da, N15 ≈ +0.99703 Da, D ≈ +1.00628 Da) or common adduct transformations ([M+H]+ vs [M+Na]+ ≈ +21.98 Da, [M+H]+ vs [M+NH4]+ ≈ +18.03 Da). The algorithm uses retention time co-elution as a constraint to ensure grouped features originate from the same molecule. Assign cluster identifiers to each feature and output the grouped feature table with isotopologue/adduct group membership annotations, which can then be propagated to downstream gap-filling and annotation steps.3637## Related tools3839- **SLAW grouping module** (Performs isotopologue and adduct clustering on aligned features as part of the complete untargeted LC-MS workflow) — https://github.com/zamboni-lab/SLAW40- **Centwave** (Peak picking algorithm that precedes the grouping step; provides initial feature detection) — https://github.com/zamboni-lab/SLAW4142## Evaluation signals4344- All features within a cluster share retention time within the expected co-elution window (typically ±5–10 seconds in LC-MS).45- Mass differences between cluster members match known isotope shifts (C13, N15, D) or adduct mass differences within measurement tolerance (typically <5 ppm).46- No feature is assigned to multiple clusters; cluster membership is mutually exclusive.47- Cluster size distribution is reasonable (singletons acceptable, but spurious large clusters suggest incorrect grouping parameters).48- Downstream gap-filling and feature annotation steps show improved consistency when using grouped versus ungrouped feature tables (e.g., fewer conflicting annotations per molecular entity).4950## Limitations5152- Grouping accuracy depends on retention time alignment quality; poor sample alignment will propagate errors into isotopologue/adduct assignment.53- Mass tolerance parameters must be tuned based on instrument resolution; too loose tolerance may merge unrelated features, too tight may fail to group true isotopologues.54- Complex metabolite mixtures with high feature density may result in ambiguous assignments when multiple candidate clusters are equally plausible within tolerance windows.55- High-resolution instruments (e.g., Orbitrap) perform better than low-resolution instruments; algorithm assumes sufficient mass accuracy to distinguish isotope patterns from noise.5657## Evidence5859- [other] SLAW includes a processing step for grouping of isotopologues and adducts as part of its complete untargeted LC-MS workflow, which operates after peak picking and sample alignment.: "SLAW includes a processing step for grouping of isotopologues and adducts as part of its complete untargeted LC-MS workflow, which operates after peak picking and sample alignment."60- [other] Apply isotopologue and adduct grouping algorithm to cluster features sharing the same molecular ion with mass differences corresponding to isotopic shifts (e.g., C13, N15, D) or common adduct transformations (e.g., [M+H]+, [M+Na]+, [M+NH4]+).: "Apply isotopologue and adduct grouping algorithm to cluster features sharing the same molecular ion with mass differences corresponding to isotopic shifts (e.g., C13, N15, D) or common adduct"61- [readme] Complete processing including peak picking, sample alignment, pick picking, grouping of isotopologues and adducts, gap-filling by data recursion: "Complete processing including peak picking, sample alignment, pick picking, grouping of isotopologues and adducts, gap-filling by data recursion"62- [readme] All data must be centroided and of unique polarity. Centroided mzML can be obtained with ProteoWizard.: "All data must be centroided and of unique polarity. Centroided mzML can be obtained with ProteoWizard."
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