菜单
Use when you have LC–MS all-ion fragmentation chromatograms already processed by xcms and clustered by RamClustR, a feature table (targetTable.csv format) listing features to annotate, and you need rank-1 metabolite or lipid identifications with confidence metrics.
用 Codex 或 Claude 帮你安装 复制这段 Prompt，粘贴到 Codex、Claude 或其他助手里，让它检查 Skill 页面并帮你完成安装。
基于 SOC 职业分类
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 | annotaterc-function-parameterization |
| description | Use when you have LC–MS all-ion fragmentation chromatograms already processed by xcms and clustered by RamClustR, a feature table (targetTable.csv format) listing features to annotate, and you need rank-1 metabolite or lipid identifications with confidence metrics. |
| license | CC-BY-4.0 |
| metadata | {"edam_operation":"http://edamontology.org/operation_3627","edam_topics":["http://edamontology.org/topic_0153","http://edamontology.org/topic_3172","http://edamontology.org/topic_0091"],"tools":["MetaboAnnotatoR","R","xcms","RamClustR"],"techniques":["LC-MS"],"license_tier":"open"} |
| derived_from | [{"doi":"10.1021/acs.analchem.1c03032","title":"metaboannotator"}] |
| evidence_spans | ["MetaboAnnotatoR is designed to perform metabolite annotation of features from LC-MS All-ion fragmentation (AIF) datasets","start R (version \"4.5.0\" or higher)"] |
| claims | [] |
| provenance | {"collection":"https://w3id.org/holobiomicslab/asb-skill/collection/metabolomics/v2","assembled_by":"scripts/collect_metabolomics_collection.py","sources":[{"build":"coll_metaboannotator_cq","doi":"10.1021/acs.analchem.1c03032","title":"metaboannotator"}],"dedup_kept_from":"coll_metaboannotator_cq"} |
| schema_version | 0.2.0 |
| attribution | {"generator":"AgenticScienceBuilder","original_doi":"10.1021/acs.analchem.1c03032","all_source_dois":["10.1021/acs.analchem.1c03032"],"zenodo_doi":"10.5281/zenodo.20794027","curators":[],"promoter":"Louis-Félix Nothias","sponsor":"CNRS & Université Côte d'Azur"} |
Configure and execute the annotateRC function to match experimental LC–MS all-ion fragmentation spectra against fragment ion databases (e.g., LipidPos) and rank candidate metabolite annotations. This skill bridges processed xcms/RamClustR objects and feature tables to produce ranked lipid or metabolite identifications.
You have LC–MS all-ion fragmentation chromatograms already processed by xcms and clustered by RamClustR, a feature table (targetTable.csv format) listing features to annotate, and you need rank-1 metabolite or lipid identifications with confidence metrics. Use this skill when annotation libraries (e.g., LipidPos for positive-mode lipids) match your ionization mode and metabolite class of interest.
Load the xcms xset object and RamClustR RC object containing processed all-ion fragmentation data; load the target feature table (e.g., targetTable.csv) specifying which features to annotate. Call the annotateRC function, supplying the xset, RC object, and the appropriate fragment library (LipidPos for lipids in positive mode, or other MAMP/MSMS databases as applicable). The function will match experimental spectra against database entries and return a ranked annotations object. Extract the rankedResult object to inspect matched ions, rank scores, and candidate identities. Visualize matched spectra using plotResultSpec to validate ion matches before reporting results; save annotation outputs to a user-specified directory. Success is indicated by rank-1 annotations (highest confidence match) for a subset of features; expect that not all features will receive valid annotations depending on database coverage and spectral quality.
annotateRC(xset = xset, RC = RC, targets = targetTable, libname = 'LipidPos', ionmode = 'pos'); rankedResult <- annotations$rankedResult