菜单
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]+).
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 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.
Use when when you have derivatized metabolite structures (SMILES or mol format) and need to predict their ionization products in MS imaging, particularly when the derivatizing matrix produces non-standard adducts (e.
| name | adduct-annotation-against-reference-library |
| description | 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]+). |
| license | CC-BY-4.0 |
| metadata | {"edam_operation":"http://edamontology.org/operation_3632","edam_topics":["http://edamontology.org/topic_0121","http://edamontology.org/topic_3520"],"tools":["mass2adduct","R"],"techniques":["MS-imaging"],"license_tier":"open"} |
| derived_from | [{"doi":"10.1021/acs.analchem.0c04720","title":"mass2adduct"}] |
| evidence_spans | ["This package presents tools for counting and identifying possible adducts in MS data","library(mass2adduct)"] |
| claims | [] |
| provenance | {"collection":"https://w3id.org/holobiomicslab/asb-skill/collection/metabolomics/v2","assembled_by":"scripts/collect_metabolomics_collection.py","sources":[{"build":"coll_mass2adduct","doi":"10.1021/acs.analchem.0c04720","title":"mass2adduct"}],"dedup_kept_from":"coll_mass2adduct"} |
| schema_version | 0.2.0 |
| attribution | {"generator":"AgenticScienceBuilder","original_doi":"10.1021/acs.analchem.0c04720","all_source_dois":["10.1021/acs.analchem.0c04720"],"zenodo_doi":"10.5281/zenodo.20794027","curators":[],"promoter":"Louis-Félix Nothias","sponsor":"CNRS & Université Côte d'Azur"} |
Match observed mass differences from MS imaging data against a curated reference library of known chemical adducts (e.g., matrix or salt ion complexes) to identify and annotate probable molecular adduct ion pairs. This skill bridges untargeted mass difference detection with chemical interpretation by assigning biological or instrumental significance to specific pairwise mass shifts.
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]+). Apply it to reduce the chemical ambiguity of mass shifts and to select ion pairs for downstream spatial correlation testing or intensity covariance analysis.
Load a mass difference histogram (output from binning pairwise mass differences with user-specified bin width appropriate to your instrument's mass precision) and the reference adduct library (built-in adducts or adducts2 datasets, or a custom data.frame with columns name, formula, mass). Use the adductMatch() function to find the closest-matching bin in the histogram for each reference adduct mass, reporting counts and quantiles. Optionally use topAdducts() to rank observed mass differences by frequency and report any matches to known adducts in descending order of prevalence. The matched adducts become annotated massdiff objects that can be used to filter ion pairs for spatial correlation testing (via corrPairsMSI()) or peak annotation on the mass spectrum (via pointsAdducts()).
adductMatch(d.diff.hist); topAdducts(d.diff.hist, n=10)
adductMatch) only looks for known adducts. What about peaks in the histogram which may not have a good match with the reference list? The function topAdducts performs the complementary function: Rank mass differences by the number of times they are observed, and report any matches to known adducts.: "topAdducts performs the complementary function: Rank mass differences by the number of times they are observed, and report any matches to known adducts"adducts and adducts2 (shorter), which list biologically-relevant chemical species that might occur in biological samples.: "two built-in data sets adducts and adducts2 (shorter), which list biologically-relevant chemical species"