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Use when you have pseudo-MS/MS spectra from LC-MS all-ion fragmentation (AIF) data that have been matched against one or more ion fragment databases (e.g., LipidPos, MassBank), generating multiple candidate annotations per feature.
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| name | annotation-candidate-comparative-scoring |
| description | Use when you have pseudo-MS/MS spectra from LC-MS all-ion fragmentation (AIF) data that have been matched against one or more ion fragment databases (e.g., LipidPos, MassBank), generating multiple candidate annotations per feature. |
| license | CC-BY-4.0 |
| metadata | {"edam_operation":"http://edamontology.org/operation_3632","edam_topics":["http://edamontology.org/topic_3370","http://edamontology.org/topic_0157"],"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","To install this package, 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","doi":"10.1021/acs.analchem.1c03032","title":"metaboannotator"}],"dedup_kept_from":"coll_metaboannotator"} |
| 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"} |
Rank and compare metabolite annotation candidates for LC-MS features by matching fragment ion patterns against libraries and scoring candidate matches to identify the most plausible metabolite identity. This skill is essential when a single LC-MS feature generates multiple candidate annotations and you need to determine which is most likely correct based on fragment ion agreement.
Apply this skill when you have pseudo-MS/MS spectra from LC-MS all-ion fragmentation (AIF) data that have been matched against one or more ion fragment databases (e.g., LipidPos, MassBank), generating multiple candidate annotations per feature. Use it to prioritize candidates when a feature yields 2+ hits with different metabolite identities, or when you need to validate whether the top-ranked candidate is sufficiently confident for reporting.
Execute the annotateRC function on xcms and RAMClustR objects with your chosen fragment library (e.g., LipidPos for lipids) to generate candidate annotations ranked by matching score. The function compares observed fragment ions in each feature's pseudo-MS/MS spectrum against the fragment patterns in the library, assigning a score that reflects the quality of the ion match. Inspect the ranked candidate list for each feature; the rank-1 candidate is the highest-scoring match. Cross-check multiple candidates for the same feature to assess confidence: if the rank-1 match has a substantially higher score than rank-2, the annotation is more reliable. Verify that observed features contain at least a threshold number of matched ions (the paper demonstrated annotation success with three of six features returning lipid matches). Save and visualize the spectra with matched ions overlaid to confirm visual plausibility of the top candidate.
annotateRC(RC = RC_object, xset = xcms_object, targetTable = targetTable_csv, libraries = 'LipidPos')