HolobiomicsLab SIRIUS De Novo Structure Elucidation
Summary
End-to-end de novo elucidation with SIRIUS 6: formula, structure and class prediction for novel/unannotated chemistry, with confidence-based filtering. No spectral library.
When to use
Use when you have MS/MS for unknown features (a SIRIUS-flavour mgf / .ms) and want de novo annotation without a spectral match — molecular formula (SIRIUS+ZODIAC), structure (CSI:FingerID + COSMIC), compound class (CANOPUS), optionally against a custom database, filtered by confidence. Library-FREE by design.
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 — formula
Goal: molecular formula determination (SIRIUS + ZODIAC)
EDAM operation: operation_3860
Inputs: mgf/sirius · Outputs: tsv
Candidate leaf skills: energy-based-formula-scoring (primary), molecular-formula-prediction-from-fragmentation, molecular-formula-assignment, fragment-peak-subformula-enumeration, neural-network-based-molecular-formula-inference
Tools (primary): SIRIUS, MIST-CF, ZODIAC
Other candidate tools: msfiddle, FIDDLE, BUDDY, MIST, SCARF
Grounding: 2 KB(s); DOIs: 10.1021/acs.jcim.3c01082, 10.1038/s41467-025-66060-9
Stage 2 — custom_db [OPTIONAL]
Goal: (optional) build a custom structure database for the search space
EDAM operation: operation_3434
Inputs: smiles · Outputs: tsv
Candidate leaf skills: compound-structure-processing (primary), chemical-structure-validation, molecular-structure-input-format-handling, structure-standardization-validation, chemical-structure-serialization
Tools (primary): CFM-ID
Other candidate tools: RDKit, PubChemPy, Python, SIRIUS, MetFrag, biosynfoni, pip, PubChem standardization, rcdk, PHP, Symfony, MySQL 8, MariaDB 10, CycloBranch
Grounding: 5 KB(s); DOIs: 10.1038/s41592-023-02143-z, 10.1186/s13321-021-00530-2, 10.1186/s13321-023-00695-y, 10.26434/chemrxiv-2025-cwq74 …
Stage 3 — structure
Goal: structure prediction (CSI:FingerID + COSMIC)
EDAM operation: operation_3801
Inputs: tsv · Outputs: tsv
Candidate leaf skills: de-novo-structure-candidate-ranking (primary), molecular-fingerprint-prediction, molecular-fingerprint-parsing, web-service-api-integration, spectrum-query-formatting
Tools (primary): MSNovelist, SIRIUS, CSI:FingerID, CANOPUS
Other candidate tools: PyTorch, MIST, MIST-CF, SIRIUS decomp
Grounding: 2 KB(s); DOIs: 10.1038/s41587-021-01045-9, 10.1038/s42256-023-00708-3
Stage 4 — compound_class
Goal: compound class prediction (CANOPUS / NPClassifier)
EDAM operation: operation_3803
Inputs: tsv · Outputs: tsv
Candidate leaf skills: compound-class-annotation-parsing (primary), natural-product-classifier-substitution, classification-workflow-parameter-toggling, chemical-ontology-mapping, consensus-classification-reconciliation, chemical-class-assignment-classyfire
Tools (primary): CANOPUS, SIRIUS
Other candidate tools: NPClassifier, GNPS, ClassyFire, ConCISE, matchms, MS2DeepScore, scikit-learn, Python, RDKit
Grounding: 3 KB(s); DOIs: 10.1038/s41587-021-01045-9, 10.1186/s13321-021-00558-4, 10.3390/metabo12121275
Stage 5 — confidence_filter
Goal: filter annotations by ZODIAC / COSMIC confidence
EDAM operation: operation_3695
Inputs: tsv, tsv · Outputs: tsv
Candidate leaf skills: sirius-zodiac-score-filtering (primary), annotation-table-quality-control, metabolite-annotation-validation, structural-annotation-integration, compound-candidate-ranking
Tools (primary): SIRIUS, INVENTA, CANOPUS
Other candidate tools: GNPS, ISDB, timaR, NPClassifier, ClassyFire, ConCISE, RDKit, PubChemPy, Python, MetFrag
Grounding: 4 KB(s); DOIs: 10.1038/s41467-021-23953-9, 10.1186/s13321-023-00695-y, 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.