bio-metabolomics-metabolite-annotation
Metabolite identification from m/z and retention time. Covers database matching, MS/MS spectral matching, and confidence level assignment. Use when assigning compound identities to detected features in untargeted metabolomics.
npx skills add majiayu000/claude-skill-registry --skill metabolite-annotation-gptomics-bioskills --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# Metabolite Annotation ## Database Matching by m/z ```r library(MetaboAnalystR) # Load feature table features <- read.csv('feature_table.csv') # Search HMDB by exact mass search_hmdb <- function(mz, adduct = '[M+H]+', ppm = 10) { # Calculate neutral mass from m/z adduct_masses <- list( '[M+H]+' = 1.007276, '[M+Na]+' = 22.989218, '[M-H]-' = -1.007276, '[M+Cl]-' = 34.969402 ) neutral_mass <- mz - adduct_masses[[adduct]] # Query HMDB (or local database) # Returns putative matches matches <- QueryHMDB(neutral_mass, ppm) return(matches) } # Apply to all features annotations <- lapply(features$mz, function(m) search_hmdb(m, '[M+H]+', 10)) ``` ## MS/MS Spectral Matching ```python from matchms import calculate_scores from matchms.importing import load_from_mgf from matchms.similarity import CosineGreedy # Load query spectra queries = list(load_from_mgf('sample_msms.mgf')) # Load reference library (e.g., GNPS, MassBank) references = list(load_from_mgf('reference_library.mgf')) # Calculate similarity scores similarity = CosineGreedy(tolerance=0.01) scores = calculate_scores(references, queries, similarity) # Get best matches for query_idx, query in enumerate(queries): best_match_idx = score
- Database Matching by m/z
- MS/MS Spectral Matching
- SIRIUS + CSI:FingerID
- MetFrag In Silico Fragmentation
- RT Prediction for Validation
- Confidence Levels (MSI)
- CAMERA Adduct Annotation
- Batch Annotation Pipeline
- Export Annotated Results
- Related Skills
Molecular formula and structure prediction sirius \ formula \ fingerid Output structure: sirius_results/ compound_1/ formula_candidates.tsv fingerid_candidates.tsv
What does the bio-metabolomics-metabolite-annotation skill do?
Metabolite identification from m/z and retention time. Covers database matching, MS/MS spectral matching, and confidence level assignment. Use when assigning compound identities to detected features in untargeted metabolomics.
How do I install it?
Run `npx skills add majiayu000/claude-skill-registry --skill metabolite-annotation-gptomics-bioskills --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From majiayu000/claude-skill-registry, a repository with 534 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.