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Use when you have raw or processed TWIM-MS data with arrival time and m/z dimensions, and you need to label each experimental feature by biomolecular class before feature identification or peak detection steps are complete.
Codex または Claude でインストール この Prompt をコピーして Codex、Claude、または他のアシスタントに貼り付けると、Skill ページを確認してインストールできます。
SOC 職業分類に基づく
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| name | arrival-time-based-class-assignment |
| description | Use when you have raw or processed TWIM-MS data with arrival time and m/z dimensions, and you need to label each experimental feature by biomolecular class before feature identification or peak detection steps are complete. |
| license | CC-BY-4.0 |
| metadata | {"edam_operation":"http://edamontology.org/operation_3629","edam_topics":["http://edamontology.org/topic_0121","http://edamontology.org/topic_3520"],"tools":["Python","MOCCal","DEIMoS"],"techniques":["ion-mobility-MS"],"license_tier":"open"} |
| derived_from | [{"doi":"10.1021/acs.analchem.3c04290","title":"moccal"}] |
| evidence_spans | ["MOCCal, or Multi-Omic CCS Calibrator, is a Python application","Functionality includes collision cross section (CCS) calibration, experimental data biomolecular class assignment, and experimental class-specific CCS calculations"] |
| claims | [] |
| provenance | {"collection":"https://w3id.org/holobiomicslab/asb-skill/collection/metabolomics/v2","assembled_by":"scripts/collect_metabolomics_collection.py","sources":[{"build":"coll_moccal_cq","doi":"10.1021/acs.analchem.3c04290","title":"moccal"}],"dedup_kept_from":"coll_moccal_cq"} |
| schema_version | 0.2.0 |
| attribution | {"generator":"AgenticScienceBuilder","original_doi":"10.1021/acs.analchem.3c04290","all_source_dois":["10.1021/acs.analchem.3c04290"],"zenodo_doi":"10.5281/zenodo.20794027","curators":[],"promoter":"Louis-Félix Nothias","sponsor":"CNRS & Université Côte d'Azur"} |
Assign biomolecular class labels (lipid, peptide, carbohydrate, etc.) to experimental features directly from TWIM-MS arrival time and m/z data without requiring prior feature identification. This enables immediate class-specific CCS calculations on raw multi-omic ion mobility data.
You have raw or processed TWIM-MS data with arrival time and m/z dimensions, and you need to label each experimental feature by biomolecular class before feature identification or peak detection steps are complete. Use this when you want to bypass traditional feature detection workflows and assign class labels directly to the high-dimensional data for downstream class-specific CCS calibration.
Load your TWIM-MS data (containing arrival time and m/z dimensions) into MOCCal. Apply MOCCal's biomolecular class assignment algorithm to label each experimental feature by class directly from the high-dimensional data without pre-identifying feature peaks. The algorithm operates on arrival time (the time at which ions reach the detector, not drift time within the TWIM cell) and m/z to infer biomolecular class. Once class labels are assigned, MOCCal generates class-specific CCS calculations. Output the class-labeled feature table as a structured file for downstream analysis or validation.
python MOCCal_UserDT.py --input experimental_data.csv --output class_labeled_features.csv