HolobiomicsLab Compound-Class Annotation (SIRIUS formula -> CANOPUS / NPClassifier class)
Summary
MS2 in, a chemical-class-annotated table out: SIRIUS molecular formula, molecular fingerprint, and CANOPUS / NPClassifier compound-class prediction per feature.
When to use
Use when you want chemical-class-level annotations for untargeted LC-MS/MS features rather than exact structures — determine molecular formulas with SIRIUS, compute CSI:FingerID fingerprints, and predict compound classes with CANOPUS and NPClassifier (superclass / class / pathway), producing a class-annotated feature table for chemical-inventory and enrichment analysis.
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 -> feature table + SIRIUS-flavour MS2 export
EDAM operation: operation_3215
Inputs: mzML · Outputs: feature-table, mgf/sirius
Candidate leaf skills: peak-detection-and-mass-alignment (primary), mass-spectrometry-feature-table-construction, cross-sample-feature-alignment, lcms-feature-table-construction, mass-spectrometry-feature-annotation
Tools (primary): MZmine2, Optimus, OpenMS
Other candidate tools: Python, pyOpenMS, MSConvert, PFΔScreen, Centwave, FeatureFinderMetabo, ADAP, ProteoWizard, q2-qemistree, SIRIUS, GNPS FBMN, Classyfire
Grounding: 4 KB(s); DOIs: 10.1007/s00216-023-05070-2, 10.1021/acs.analchem.1c02687, 10.1021/acs.jnatprod.7b00737, 10.1038/s41589-020-00677-3
Stage 2 — formula
Goal: MS2 spectra -> molecular formula (SIRIUS + ZODIAC re-ranking)
EDAM operation: operation_3860
Inputs: mgf/sirius · Outputs: tsv
Candidate leaf skills: molecular-formula-prediction-from-fragmentation (primary), energy-based-formula-scoring, neural-network-based-molecular-formula-inference, molecular-formula-assignment, fragment-peak-subformula-enumeration
Tools (primary): msfiddle, FIDDLE, BUDDY, SIRIUS
Other candidate tools: MIST-CF, MIST, SCARF
Grounding: 2 KB(s); DOIs: 10.1021/acs.jcim.3c01082, 10.1038/s41467-025-66060-9
Stage 3 — fingerprint
Goal: formula + MS2 -> molecular fingerprint (CSI:FingerID)
EDAM operation: operation_3801
Inputs: tsv · Outputs: tsv
Candidate leaf skills: molecular-fingerprint-parsing (primary), spectrum-query-formatting, spectrum-fingerprint-contrastive-learning, molecular-fingerprint-generation, molecular-fingerprint-representation-learning
Tools (primary): CSI:FingerID, SIRIUS, CANOPUS
Other candidate tools: MIST, MIST-CF, RDKit, matchms, Python, MS2DeepScore, Spec2Vec, scikit-learn, pubchempy, TensorFlow, PyTorch, PyFingerprint, Open Babel
Grounding: 4 KB(s); DOIs: 10.1007/s11306-020-01726-7, 10.1038/s41587-021-01045-9, 10.1038/s42256-023-00708-3, 10.1186/s13321-021-00558-4
Stage 4 — classify
Goal: fingerprint -> compound class (CANOPUS / NPClassifier: superclass/class/pathway)
EDAM operation: operation_0224
Inputs: tsv · Outputs: tsv
Candidate leaf skills: natural-product-classification-prediction (primary), chemical-classification-scheme-validation, chemical-ontology-mapping, classyfire-taxonomy-assignment, chemical-class-metadata-integration
Tools (primary): Python, Docker, docker-compose, TensorFlow 2.3.0, Keras, TensorFlow Serving, NP Classifier Repository
Other candidate tools: NPClassifier, SIRIUS, GNPS, ClassyFire, ConCISE, Fiehn Labs ClassyFire Batch, CANOPUS, PubChem standardization, rcdk, pyMolNetEnhancer, RMolNetEnhancer, Cytoscape
Grounding: 4 KB(s); DOIs: 10.1021/acs.jnatprod.1c00399, 10.1038/s41592-023-02143-z, 10.3390/metabo12121275, 10.3390/metabo9070144
Stage 5 — consolidate
Goal: consolidate formula + fingerprint + class into a class-annotated feature table
EDAM operation: operation_3434
Inputs: feature-table, tsv · Outputs: tsv
Candidate leaf skills: consensus-classification-reconciliation (primary), consensus-taxonomy-generation, annotation-table-quality-control, sample-centric-metabolite-annotation, taxonomic-classification-merging
Tools (primary): SIRIUS, NPClassifier, GNPS, ClassyFire, ConCISE
Other candidate tools: CANOPUS, Fiehn Labs ClassyFire Batch, Inventa, ENPKG, MZmine, enpkg_mn_isdb_taxo, enpkg_sirius_canopus, enpkg_meta_analysis, Open Tree of Life, Wikidata, ChEMBL, pandas
Grounding: 4 KB(s); DOIs: 10.1021/acscentsci.3c00800, 10.1038/s41467-021-23953-9, 10.3389/fmolb.2022.1028334, 10.3390/metabo12121275
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.