메뉴
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 또는 Claude로 설치 이 Prompt를 복사해 Codex, Claude 또는 다른 어시스턴트에 붙여 넣으면 Skill 페이지를 검토하고 설치를 진행할 수 있습니다.
SOC 직업 분류 기준
Use when when you have a neural network or machine learning model with multiple tunable hyperparameters (layer size, regularization strength, dropout) or design choices (e.
Use when when building a visualization library that must support multiple plotting backends (e.g., matplotlib, bokeh, plotly) and multiple data types (e.g., chromatograms, spectra, peak maps) without duplicating core logic or configuration handling across backend–plot-type combinations.
Use when after peaks have been assigned to heteroatom classes (e.g., CHO, CHON, CHOS, CHOP) and you need to compare molecular composition across samples, classes, or time series.
Use when after running inference on a trained structure prediction model with one or more input modalities (1H NMR, 13C NMR, or combined), you have generated predicted molecular formulas and connectivity graphs that need to be compared against known ground truth structures.
Use when after computing a histogram of all pairwise mass differences from an MSI dataset, use this skill when you have observed mass difference peaks that may correspond to known adducts (e.g., [M+H]+, [M+Na]+, [M−H2O]+).
Use when you have a trained formula ranking model (such as MIST-CF) and want to measure the specific performance gain from incorporating multiple positive-mode adduct types (e.g., [M+H]+, [M+Na]+, [M+K]+, [M+NH4]+) instead of restricting predictions to [M+H]+ only.
| 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