HolobiomicsLab Untargeted LC-MS/MS Metabolomite Annotation (FBMN + SIRIUS)
Summary
End-to-end untargeted LC-MS/MS annotation: raw mzML in, an evidence-grounded master feature table out, combining molecular networking, library matching and SIRIUS.
When to use
Use when you have untargeted LC-MS/MS data (mzML) and want an annotated feature table — preprocessing, blank/QC filtering, feature-based molecular networking, spectral library matching, SIRIUS de novo annotation, optional taxonomy-aware re-weighting, and a fused master table. This is the canonical metabopipe-style annotation pipeline.
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 mzML -> aligned feature table + MS2 exports (GNPS-FBMN mgf + SIRIUS mgf)
EDAM operation: operation_3215
Inputs: mzML · Outputs: feature-table, mgf/gnps-fbmn, mgf/sirius
Candidate leaf skills: peak-detection-and-mass-alignment (primary), mass-spectrometry-feature-detection-validation, lcms-peak-detection-and-alignment, spectral-feature-table-generation, cross-sample-feature-alignment
Tools (primary): MZmine2, Optimus, OpenMS
Other candidate tools: mzRAPP, MZmine 2, R, XCMS, enviPat, Skyline, R (with mzRAPP library), ISFrag, CAMERA, JPA, MS-Convert
Grounding: 4 KB(s); DOIs: 10.1021/acs.analchem.1c01644, 10.1021/acs.jnatprod.7b00737, 10.1093/bioinformatics/btab231/6214530, 10.3390/metabo12030212
Stage 2 — qc_filter [OPTIONAL]
Goal: (optional) remove blank / background / low-quality features before annotation
EDAM operation: operation_3695
Inputs: feature-table · Outputs: feature-table
Candidate leaf skills: blank-sample-feature-filtering (primary), feature-table-blank-intensity-detection, background-ion-contaminant-removal, feature-table-quality-control, background-ion-blank-comparison
Tools (primary): R, MZmine3, Jupyter Notebook, FBMN-STATS
Other candidate tools: ThermoRawFileParser, Python, PCPFM (Python-Centric Pipeline for Metabolomics), Asari, GetFeatistics, XCMS, MS-Dial, metDataModel
Grounding: 3 KB(s); DOIs: 10.1038/s41596-024-01046-3, 10.1371/journal.pcbi.1011912, 10.1515/jib-2025-0047
Stage 3 — network
Goal: MS2 spectra -> molecular family graph (modified cosine; GNPS-style components)
EDAM operation: operation_3214
Inputs: mgf/gnps-fbmn · Outputs: graphml, tsv
Candidate leaf skills: molecular-networking-construction (primary), molecular-family-graph-construction, spectral-similarity-network-generation, spectral-library-molecular-networking, gnps-molecular-network-integration, graph-based-metabolite-similarity-assessment
Tools (primary): ENPKG, MZmine, MEMO
Other candidate tools: nplinker, Python, pytest, GNPS, MZmine2, Optimus, Cytoscape, MSHub, mineMS2, igraph, R, MSnbase
Grounding: 5 KB(s); DOIs: 10.1021/acs.jnatprod.7b00737, 10.1021/acscentsci.3c00800, 10.1038/s41587-020-0700-3, 10.1186/s13321-025-01051-y …
Stage 4 — library_match
Goal: MS2 spectra -> spectral library annotations (cosine match to reference libraries)
EDAM operation: operation_3631
Inputs: mgf/gnps-fbmn · Outputs: tsv
Candidate leaf skills: spectral-library-matching-annotation (primary), spectral-library-matching, spectral-library-matching-with-cosine-similarity, mass-spectrometry-library-ranking, spectral-library-annotation-matching
Tools (primary): MSThunder, Windows, GNPS, MSConvert
Other candidate tools: microbeMASST, metadataMASST, plantMASST, tissueMASST, microbiomeMASST, foodMASST, GNPS_MASST, GNPS libraries, Fast Search API, MZmine, MASSBANK, DrugBANK, meRgeION2, RChemMass, MS2Compound, CFM-id, mssearchr, R, NIST API, ANN-SoLo, Python, Anaconda, Git, MSBERT, PyTorch, matchms, Spec2Vec, nplinker, pytest
Grounding: 8 KB(s); DOIs: 10.1016/j.enceco.2025.07.022, 10.1021/acs.analchem.2c04343, 10.1021/acs.analchem.4c02426, 10.1021/acs.jproteome.8b00359 …
Stage 5 — sirius
Goal: MS2 spectra -> formula + structure + class (SIRIUS / CSI:FingerID / CANOPUS)
EDAM operation: operation_3860
Inputs: mgf/sirius · Outputs: tsv
Candidate leaf skills: sirius-spectral-request-construction (primary), web-service-api-integration, spectrum-query-formatting, spectral-fingerprint-web-service-query, molecular-fingerprint-parsing
Tools (primary): CSI:FingerID, SIRIUS, CANOPUS
Other candidate tools: MSNovelist, ClassyFire
Grounding: 1 KB(s); DOIs: 10.1038/s41587-021-01045-9
Stage 6 — taxonomy_propagate [OPTIONAL]
Goal: (optional) taxonomy-aware re-weighting / propagation of annotations
EDAM operation: —
Inputs: tsv, tsv, metadata · Outputs: tsv
Candidate leaf skills: taxonomic-weighting-in-annotation (primary), metabolite-annotation-taxonomic-integration, metabolite-annotation-scoring, metabolite-annotation-network-architecture
Tools (primary): R, Docker, tima (Taxonomically Informed Metabolite Annotation), LOTUS, SIRIUS, GNPS-FBMN
Other candidate tools: tima (R package), GNPS, Spectra (R package), MrnAnnoAlgo3 (MetDNA3), MrnAnnoAlgo3, MetDNA3
Grounding: 4 KB(s); DOIs: 10.1038/nbt.3597, 10.1038/s41467-025-63536-6, 10.1038/s41592-019-0344-8, 10.3389/fpls.2019.01329
Stage 7 — fusion
Goal: consolidate networking + library + SIRIUS (+ taxonomy) into one master table
EDAM operation: operation_3434
Inputs: feature-table, graphml, tsv · Outputs: tsv
Candidate leaf skills: feature-metadata-annotation (primary), feature-network-construction-from-mass-spectrometry, sample-centric-metabolite-annotation, feature-table-consensus-aggregation, unannotated-feature-characterization, feature-table-integration-and-normalization
Tools (primary): msFeaST, jupyter-notebook, msFeaST Dashboard bundle
Other candidate tools: networkx, treelib, mass2chem, metDataModel, Python 3, asari, khipu, ENPKG, MZmine, enpkg_mn_isdb_taxo, enpkg_sirius_canopus, enpkg_meta_analysis, SIRIUS, Open Tree of Life, Wikidata, NPClassifier, ChEMBL, Python, DEIMoS, numpy, ProteoWizard msconvert, MZmine2, MZmine3, timaR, Ion Identity, Inventa, ISFrag, R, XCMS
Grounding: 7 KB(s); DOIs: 10.1021/acs.analchem.1c01644, 10.1021/acs.analchem.1c05017, 10.1021/acs.analchem.2c05810, 10.1021/acscentsci.3c00800 …
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.