HolobiomicsLab 
Lipidomics LC-MS/MS Annotation
Summary
End-to-end lipidomics annotation: raw LC-MS/MS in, a confidence-graded lipid table out, with lipid-class-aware identification, normalization, and group-wise statistics.
When to use
Use when you have untargeted lipidomics LC-MS/MS data (mzML) and want a class- and species-level annotated lipid feature table — preprocessing, normalization, lipid identification by MS/MS, retention/adduct rule validation, differential analysis, and a fused master table.
When NOT to use
- The data is not LC-MS.
- You need a single atomic step, not the full pipeline (use the leaf skill directly via the router).
Stages
Stage 1 — preprocess
Goal: raw lipidomics mzML -> aligned feature table + MS/MS export
EDAM operation: operation_3215
Inputs: mzML · Outputs: feature-table, mgf
Candidate leaf skills: lcms-peak-detection-and-alignment (primary), mass-spectrometry-metadata-extraction, file-format-conversion-peak-picking-to-lipidmatch, feature-table-normalization, mass-spectrometry-data-column-mapping
Tools (primary): ISFrag, R, XCMS, CAMERA
Other candidate tools: MZmine, MS-DIAL, Compound Discoverer, LipidMatch
Grounding: 2 KB(s); DOIs: 10.1021/acs.analchem.1c01644, 10.1186/s12859-017-1744-3
Stage 2 — normalize
Goal: normalize + batch-correct the lipid feature table
EDAM operation: operation_3434
Inputs: feature-table · Outputs: feature-table
Candidate leaf skills: batch-aware-normalization-workflows (primary), batch-correction-quality-assessment, batch-effect-correction-in-metabolomics, batch-corrected-feature-table-validation, batch-effect-correction-workflow
Tools (primary): Python, pycombat, Asari
Other candidate tools: ThermoRawFileParser, pcpfm, ADViSELipidomics, limma, edgeR, ComBat, R, Jupyter Notebook, Google Colab, FBMN-STATS
Grounding: 3 KB(s); DOIs: 10.1038/s41596-024-01046-3, 10.1093/bioinformatics/btac706, 10.1371/journal.pcbi.1011912
Stage 3 — lipid_identification
Goal: identify lipids (class + species) from MS/MS fragmentation
EDAM operation: operation_3803
Inputs: mgf · Outputs: tsv
Candidate leaf skills: lipid-identification-scoring (primary), fragment-ion-library-matching, multi-species-lipid-prediction, uhplc-hrms-ms-data-matching, lipid-structure-specification
Tools (primary): LipidMatch, MZmine, XCMS, MS-DIAL, Compound Discoverer
Other candidate tools: Q-Exactive, CAMERA, LipidIN EQ module, LipidIN LCI module, Q-Exactive orbitrap, Agilent Q-TOF, Bruker Q-TOF, SCIEX Q-TOF
Grounding: 2 KB(s); DOIs: 10.1038/s41467-025-59683-5, 10.1186/s12859-017-1744-3
Stage 4 — rule_validation
Goal: validate lipid annotations by adduct / retention-time / class rules
EDAM operation: operation_3695
Inputs: tsv · Outputs: tsv
Candidate leaf skills: false-positive-annotation-filtering (primary), lipid-identification-quality-filtering, lipid-retention-time-rule-application, lipid-species-annotation-assessment
Tools (primary): XCMS, CAMERA, LipidIN LCI module
Other candidate tools: LipoCLEAN, MS-DIAL, LipidIN (LCI Module), RaMS, MetaboAnnotatoR, R
Grounding: 3 KB(s); DOIs: 10.1021/acs.analchem.1c03032, 10.1021/acs.analchem.4c04040, 10.1038/s41467-025-59683-5
Stage 5 — statistics
Goal: differential lipid analysis between sample groups
EDAM operation: operation_3659
Inputs: feature-table, tsv · Outputs: tsv
Candidate leaf skills: multicontrast-statistical-testing-lipidomics (primary), fold-change-calculation, lipid-abundance-differential-analysis, differential-lipid-expression-analysis, metabolite-feature-anova-analysis
Tools (primary): lipidr, limma, R
Other candidate tools: Python (pandas, NumPy, SciPy), R (base stats, tidyverse, or similar), pandas, NumPy, SciPy, edgeR.R, ADViSELipidomics, edgeR, ComBat, LIPID MAPS, Metabolomics Workbench API, margheRita, MS-DIAL
Grounding: 5 KB(s); DOIs: 10.1021/acs.analchem.4c05039, 10.1021/acs.jproteome.0c00082, 10.1093/bioinformatics/btac706, 10.1101/2024.06.20.599545 …
Stage 6 — fusion
Goal: consolidate lipid annotations + stats into one master table
EDAM operation: operation_3434
Inputs: feature-table, tsv · Outputs: tsv
Candidate leaf skills: structured-data-matrix-construction (primary), lipid-class-feature-annotation, lipid-class-annotation-and-parsing, lipid-species-classification-mapping
Tools (primary): ADViSELipidomics, LipidSearch, LIQUID, LIPID MAPS
Other candidate tools: lipidr, R, Skyline, SummarizedExperiment, limma, edgeR, ComBat
Grounding: 2 KB(s); DOIs: 10.1021/acs.jproteome.0c00082, 10.1093/bioinformatics/btac706
Grounding
Each stage carries the kb_slugs/dois of the leaves it draws on. Ground any stage against its source paper with the collection's /ground command or bin/perspicacite_kb_bind.py (Perspicacité KB; serverless local-clone fallback).
Verification contract
workflow.yaml is gradable by asb solve-workflow (checkpoint mode). Each stage declares typed outputs; the final stage emits the master deliverable.
Provenance
Generated by compose_workflows.py (semantic binding + EDAM-aware primary selection). derived_from_workflows lists ASB per-paper workflows whose structure corroborated this pipeline — the eval-ablation set (SPEC §8). Staging only; promote via release_gate.py.