HolobiomicsLab 
Ion Mobility (4D LC-IMS-MS/MS) Annotation
Summary
End-to-end 4D ion-mobility annotation: extract CCS-resolved features, calibrate CCS, and annotate with collision-cross-section-aware matching.
When to use
Use when you have ion-mobility LC-IMS-MS/MS data (e.g. timsTOF / PASEF) and want CCS-aware annotations — 4D feature extraction with collision cross section, CCS calibration and filtering, CCS-aware library matching, and (optional) networking.
When NOT to use
- The data is not ion-mobility-MS.
- You need a single atomic step, not the full pipeline (use the leaf skill directly via the router).
Stages
Stage 1 — preprocess_4d
Goal: 4D LC-IMS-MS/MS feature extraction (with CCS)
EDAM operation: operation_3215
Inputs: mzML · Outputs: feature-table, mgf
Candidate leaf skills: multidimensional-feature-detection-and-alignment (primary), ion-mobility-heatmap-visualization, ion-mobility-feature-classification, ion-mobility-dimension-detection, multidimensional-coordinate-alignment
Tools (primary): DEIMoS, Python, conda, pip, Snakemake, ProteoWizard msconvert
Other candidate tools: Mirador, IonToolPack, PeakQC, MOCCal, mzmine, JDK 25, JavaFX 24, numpy
Grounding: 4 KB(s); DOIs: 10.1021/acs.analchem.1c05017, 10.1021/acs.analchem.3c04290, 10.1021/jasms.4c00146, 10.1038/s41587-023-01690-2
Stage 2 — ccs_calibration
Goal: collision cross section calibration + filtering
EDAM operation: operation_3695
Inputs: feature-table · Outputs: feature-table
Candidate leaf skills: collision-cross-section-calibration-ccs (primary), collision-cross-section-calibration, collision-cross-section-calculation, collision-cross-section-measurement-quality-control
Tools (primary): DEIMoS, conda, pip, Python, numpy
Other candidate tools: Snakemake, MOCCal, MOCCal (Multi-Omic CCS Calibrator), DEIMoS (Data-Exploratory Ion Mobility MS), R, MobiLipid, ggplot2, data.table
Grounding: 3 KB(s); DOIs: 10.1021/acs.analchem.1c05017, 10.1021/acs.analchem.3c04290, 10.1021/acs.analchem.4c01253
Stage 3 — ccs_library_match
Goal: CCS-aware spectral / library annotation
EDAM operation: operation_3631
Inputs: mgf, feature-table · Outputs: tsv
Candidate leaf skills: collision-cross-section-matching-and-annotation (primary), reference-library-alignment, 4d-lcimmsms-feature-extraction, fragmentation-pattern-spectral-matching
Tools (primary): Python, Jupyter Notebook, scikit-learn
Other candidate tools: R, MobiLipid, R (ggplot2, data.table, DT packages), RDKit
Grounding: 2 KB(s); DOIs: 10.1002/anie.202507483, 10.1021/acs.analchem.4c01253
Stage 4 — networking [OPTIONAL]
Goal: (optional) molecular networking of IM-resolved features
EDAM operation: operation_3432
Inputs: mgf, feature-table, tsv · Outputs: graphml
Candidate leaf skills: feature-based-molecular-network-interpretation (primary), spectral-similarity-network-building, molecular-networking-construction, feature-network-construction-from-mass-spectrometry, spectral-similarity-network-generation
Tools (primary): R, Jupyter Notebook, MZmine3, GNPS FBMN, Google Colab
Other candidate tools: q2-qemistree, SIRIUS, CSI:FingerID, ZODIAC, MZmine2, ClassyFire, ENPKG, MZmine, MEMO, networkx, treelib, mass2chem, metDataModel, Python 3, asari, khipu, Optimus, GNPS, Cytoscape
Grounding: 5 KB(s); DOIs: 10.1021/acs.analchem.2c05810, 10.1021/acs.jnatprod.7b00737, 10.1021/acscentsci.3c00800, 10.1038/s41589-020-00677-3 …
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.