HolobiomicsLab Suspect Screening for Exposomics (suspect lists -> confidence-levelled IDs)
Summary
HRMS in, a confidence-levelled suspect-hit table out: suspect-list matching, in-silico fragmentation, and identification-confidence assignment for exposomics.
When to use
Use when you have untargeted HRMS data and want to screen for a defined suspect list of environmental / exposure-relevant compounds — detect features, match them to suspect-list entries by exact mass/RT/MS2, elucidate structures of hits by in-silico fragmentation, and assign identification confidence levels (Schymanski), producing a confidence-annotated suspect-hit 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 HRMS -> feature table + MS2 export
EDAM operation: operation_3215
Inputs: mzML · Outputs: feature-table, mgf/gnps-fbmn
Candidate leaf skills: peak-detection-and-mass-alignment (primary), mass-spectrometry-feature-table-construction, mass-spectrometry-feature-detection-validation, non-targeted-preprocessing-tool-comparison, non-targeted-feature-detection-and-screening
Tools (primary): MZmine2, Optimus, OpenMS
Other candidate tools: Python, pyOpenMS, MSConvert, PFΔScreen, mzRAPP, MZmine 2, R, XCMS, enviPat, Skyline, R (with mzRAPP library), pymzml, pandas, numpy, scipy, joblib, tqdm, tqdm_joblib, matplotlib
Grounding: 4 KB(s); DOIs: 10.1007/s00216-023-05070-2, 10.1021/acs.analchem.5c00060, 10.1021/acs.jnatprod.7b00737, 10.1093/bioinformatics/btab231/6214530
Stage 2 — suspect_match
Goal: features -> suspect-list matches by exact mass / RT / MS2
EDAM operation: operation_3631
Inputs: feature-table, mgf/gnps-fbmn · Outputs: tsv
Candidate leaf skills: suspect-database-matching (primary), ms1-feature-extraction, mass-spectrometry-screening-workflows, multi-criterion-scoring-integration, feature-annotation-with-chemical-descriptors
Tools (primary): R Shiny, EISA-EXPOSOME, T3DB
Other candidate tools: Scannotation, patRoon, XCMS, OpenMS, BioTransformer, CTS, MetFrag, SIRIUS, CAMERA, RAMClustR, ProteoWizard, Python, pyOpenMS, MSConvert, PFΔScreen
Grounding: 4 KB(s); DOIs: 10.1007/s00216-023-05070-2, 10.1021/acs.analchem.3c02697, 10.1021/acs.est.3c04764, 10.1186/s13321-020-00477-w
Stage 3 — in_silico_fragment
Goal: suspect hits -> in-silico fragmentation structure ranking (MetFrag / SIRIUS)
EDAM operation: operation_3860
Inputs: tsv, mgf/gnps-fbmn · Outputs: tsv
Candidate leaf skills: in-silico-fragmentation-prediction (primary), candidate-structure-ranking, candidate-rank-scoring, fragment-ion-scoring-and-ranking, candidate-structure-ranking-from-spectrum
Tools (primary): MAGMa, PubChem
Other candidate tools: Python, pyrwr, MetFrag, ChemWalker, DiffSpectra, Diffusion Molecule Transformer (DMT), SpecFormer, Spectra, SIRIUS, R, RDKit, PubChemPy, MetaboAnnotatoR, R (version or higher), xcms, RamClustR, ICEBERG WebUI, SCARF
Grounding: 6 KB(s); DOIs: 10.1021/acs.analchem.1c03032, 10.1038/s42256-024-00816-8, 10.1093/bioinformatics/btad078/7067745, 10.1186/s13321-023-00695-y …
Stage 4 — confidence
Goal: assign identification confidence levels (Schymanski 1-5) to hits
EDAM operation: operation_0224
Inputs: tsv · Outputs: tsv
Candidate leaf skills: compound-annotation-confidence-assessment (primary), metabolite-annotation-confidence-assignment, annotation-confidence-assessment, annotation-scoring-and-ranking, bayesian-annotation-probability-inference
Tools (primary): masscube, Python
Other candidate tools: R, XCMS, MS-Dial, GetFeatistics, patRoon, MS-CleanR, MS-FINDER, commons-math3, jfreechart, jopt-simple, trove4j, Passatutto, ipaPy2
Grounding: 5 KB(s); DOIs: 10.1021/acs.analchem.0c01594, 10.1038/s41467-025-60640-5, 10.1093/bioinformatics/btad455, 10.1515/jib-2025-0047 …
Stage 5 — report
Goal: consolidate suspect hits + structures + confidence into an annotated table
EDAM operation: operation_3434
Inputs: tsv, feature-table · Outputs: tsv
Candidate leaf skills: feature-metadata-annotation (primary), chemical-structure-validation, reference-compound-verification
Tools (primary): msFeaST, jupyter-notebook, msFeaST Dashboard bundle
Other candidate tools: RDKit, PubChemPy, Python, SIRIUS, MetFrag, R Shiny, EISA-EXPOSOME
Grounding: 3 KB(s); DOIs: 10.1021/acs.analchem.3c02697, 10.1093/bioinformatics/btae584, 10.1186/s13321-023-00695-y
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.