Ms Ms Spectrum Peak Annotation

MS/MS Spectrum Peak Annotation

Summary

Annotate fragment peaks in an MS/MS spectrum by applying noise and marker peak thresholds, then scoring matched ions against a metabolite library using occurrence-based scoring. This skill converts experimental spectra into library entries with scored fragment annotations suitable for downstream feature annotation in untargeted metabolomics workflows.

When to use

You have an experimental MS/MS spectrum (e.g., from MassBank or local data) and need to identify significant fragment ions above noise, assign occurrence scores to peaks, and generate a CSV-formatted library entry for use in metabolite feature annotation pipelines. Apply this skill when building custom fragment libraries or validating fragment picks for a known metabolite with accurate m/z and adduct notation.

When NOT to use

• Input is already a feature table or annotated library; use this skill only on raw or lightly processed MS/MS spectra.
• Spectrum is in profile (non-centroided) mode; genFragEntry requires centroid data.
• Metabolite identity, adduct type, or accurate precursor m/z are unknown or uncertain; the function requires explicit, accurate definitions to assign correct fragment annotations.

Inputs

• Centroid-mode MS/MS spectrum (numeric intensity array indexed by m/z)
• Metabolite name (string identifier)
• Adduct notation (string, e.g., '[M+H]+')
• Accurate adduct m/z (float, monoisotopic mass)
• Output filename (string, .csv format)

Outputs

• CSV-formatted metabolite library entry
• Annotated fragment table with columns: fragment m/z, intensity, occurrence score, match metric

How to apply

Load the centroid-mode MS/MS spectrum and apply two cascading peak-picking filters: (1) remove signals below the noise threshold (default 0.005 relative intensity) to eliminate background noise; (2) retain only marker peaks above the mpeaksThres threshold (default 0.1) to identify significant fragments. Execute the genFragEntry function with parameters mpeaksScore=0.9 and mzTol=0.01 m/z tolerance to annotate fragment ions, compute occurrence-based match scores, and associate each peak with its mass, intensity, and scoring metric. Output the result as a CSV-formatted library entry containing the metabolite name, adduct notation (e.g., [M+H]+), accurate adduct m/z, and all scored fragments. The rationale is that dual thresholding reduces false-positive fragments (noise/minor isotopes) while preserving biologically relevant marker ions, and occurrence scoring weights peaks by their prevalence across reference spectra.

Related tools

• MetaboAnnotatoR (Provides the genFragEntry function for MS/MS spectrum peak annotation and occurrence scoring; part of the broader annotation workflow for AIF LC–MS data) — https://github.com/gggraca/MetaboAnnotatoR
• R (Runtime environment (version 4.5.0 or higher) required to execute genFragEntry and related functions)
• xcms (Upstream tool for peak-picking and feature detection from raw LC-MS chromatograms in centroid mode prior to spectrum assembly)
• RamClustR (Companion tool for pseudo-MS/MS spectrum generation from AIF data; produces the spectral input for genFragEntry)

Examples

genFragEntry(spectrum = ms2_spectrum, metabolite = 'D-Pantothenic Acid', adduct = '[M+H]+', mz_adduct = 220.1168, mpeaksThres = 0.1, mpeaksScore = 0.9, mzTol = 0.01, outfile = 'dpantothenic_acid_lib.csv')

Evaluation signals

• Output CSV file is well-formed and contains columns for m/z, intensity, and occurrence score with no missing values for retained peaks.
• All retained peaks have intensity ≥ mpeaksThres (0.1) and pass the noise threshold (≥ 0.005); verify by inspecting the intensity distribution.
• Match scores are in the expected range (0–1 or 0–0.9 depending on mpeaksScore) and reflect peak occurrence frequency in reference spectra.
• Fragment annotations are chemically reasonable for the specified adduct (e.g., neutral losses, common rearrangements) and do not include peaks below noise level.
• Library entry metadata (metabolite name, adduct notation, accurate m/z) are correctly preserved and match the input specification.

Limitations

• No changelog or version history is documented, limiting reproducibility across MetaboAnnotatoR releases.
• Threshold defaults (noise=0.005, mpeaksThres=0.1) may require optimization for different MS platforms, ionization modes, or metabolite classes; the article does not provide guidance on threshold tuning.
• Occurrence scoring depends on availability of reference spectra in the underlying database; novel metabolites or those underrepresented in public databases may receive poor scores.
• The function requires explicit, accurate metabolite name and adduct notation; errors in these inputs will propagate into library entries and cause downstream annotation failures.

Evidence

• [intro] noise=0.005 and mpeaksThres=0.1: "Peak-picking above noise level threshold (default: 0.005) ... Peak-picking above marker peak threshold (default: 0.1)"
• [other] genFragEntry parameters and output format: "genFragEntry converts an MS/MS spectrum into a library entry by identifying marker peaks above the mpeaksThres threshold (0.1 default) and noise level (0.005 default), attributing occurrence scores"
• [intro] Centroid mode requirement: "It requires raw LC-MS AIF chromatograms acquired/transformed in centroid mode"
• [other] CSV output format: "Output results as a CSV-formatted library entry containing annotated fragments with mass, intensity, and match scores."
• [readme] MetaboAnnotatoR purpose: "This R package is designed to perform metabolite annotation of features from LC-MS All-ion fragmentation (AIF) datasets, using ion fragment databases."