# Isotopic Peak Removal

> Use when after loading raw FT-ICR MS peak lists with assigned molecular formulas when you have detected peaks across multiple m/z values that correspond to isotopic variants of the same parent compound.

- Skill: `holobiomicslab/isotopic-peak-removal` (Agent Skill)
- Install (CLI): `npx skillmds@latest add holobiomicslab/isotopic-peak-removal`
- Raw SKILL.md: https://api.skillmd.com/api/skills/holobiomicslab/isotopic-peak-removal/raw
- Safety review: PASS (external: skill-scanner PASS, skillspector PASS)
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: Coding & Dev Tools
- License: CC-BY-4.0
- Author: HolobiomicsLab (https://skillmd.com/u/holobiomicslab)
- Updated: 2026-09-17
- Page: https://skillmd.com/skills/holobiomicslab/isotopic-peak-removal

---


# isotopic-peak-removal

## Summary

Remove isotopic peaks (primarily ¹³C) from FT-ICR MS peak-abundance matrices to eliminate redundant molecular signals and reduce noise before downstream analysis. This filtering step prevents overrepresentation of naturally occurring isotopic variants of the same compound.

## When to use

Apply this skill after loading raw FT-ICR MS peak lists with assigned molecular formulas when you have detected peaks across multiple m/z values that correspond to isotopic variants of the same parent compound. This is especially critical when the downstream analysis goal is compound-level (rather than isotope-level) interpretation, such as chemodiversity assessment, biochemical transformation networks, or statistical comparison of molecular compositions across samples.

## When NOT to use

- The goal is isotope-ratio analysis or tracing (e.g., ¹³C labeling experiments); isotope peaks are the primary signal of interest.
- Input data are already de-isotoped or derived from tandem MS with neutral loss annotation; redundant isotope removal may have already occurred upstream.
- Raw m/z values are provided without assigned molecular formulas; isotopic peak identification requires formula assignment.

## Inputs

- CSV file containing detected peaks with columns: m/z values, molecular formulas, peak intensities, isotopic annotations or isotopic carbon labels

## Outputs

- Filtered peak-abundance matrix (CSV) with isotopic peaks removed, retaining only monoisotopic peaks for each unique molecular formula

## How to apply

Iterate through the detected peaks and identify isotopic variants by checking the peak annotations for isotopic carbon labeling (¹³C) or equivalent isotopic markers in the assigned molecular formula field. Remove all peaks flagged as isotopic variants, retaining only the monoisotopic (M) peak for each compound. The rationale is that ¹³C peaks are naturally occurring and predictable from elemental composition; including them inflates apparent molecular diversity and can bias subsequent normalization and statistical analyses. This filtering must occur before m/z-based and error-threshold filtering to avoid propagating redundant signals through the pipeline.

## Related tools

- **pandas** (Load, filter, and manipulate the peak CSV file to identify and remove rows with isotopic peak annotations)
- **Formularity** (Assign and annotate molecular formulas to m/z peaks, providing the isotopic carbon and isotopic labeling information needed to flag isotopic variants)

## Evaluation signals

- Peak count decreases by a predictable fraction (typically 1–5% for natural samples, reflecting the expected ¹³C abundance); large deletions suggest over-filtering.
- Remaining peaks are all monoisotopic forms; verify no peaks with ¹³C, ²H, ¹⁸O, or other isotopic labels remain in the output matrix.
- Elemental composition (e.g., number of carbons) of retained peaks is stable across filtering; no systematic bias toward lighter or heavier formulas.
- Downstream diversity metrics (e.g., NOSC, DBE distributions) and Van Krevelen diagrams show expected molecular composition without artificial clustering at +1.003 m/z intervals.
- Comparison with external reference datasets (e.g., known metabolite databases) confirms that retained peaks match expected monoisotopic masses within measurement tolerance (0.5 ppm).

## Limitations

- Isotopic peak detection depends on accurate molecular formula assignment; if formula errors are high (>0.5 ppm), isotopic variants may be misidentified or missed.
- Natural samples with high carbon content will have higher natural ¹³C abundance; filtering may remove a larger proportion of peaks than in low-carbon samples, potentially introducing composition bias.
- The skill assumes that isotopic annotations are unambiguous in the input CSV; if multiple isotopic labels are present or mixed with adducts/fragments, manual curation or post-filtering validation is required.
- Does not address isotopic interference from multiply charged ions or unresolved doublets; requires sufficient mass resolution from the FT-ICR instrument.

## Evidence

- [other] Filter peaks by m/z range (user-defined) and remove isotopic peaks (13C).: "Filter peaks by m/z range (user-defined) and remove isotopic peaks (13C)."
- [methods] detected peaks are filtered by their m/z values (based on the user's input): "detected peaks are filtered by their m/z values (based on the user's input)"
- [methods] isotopic presence (13C peaks): "isotopic presence (13C peaks)"
- [other] MetaboDirect applies multiple sequential filters (isotopic carbon removal, m/z-based filtering, formula assignment error thresholds): "applies multiple sequential filters (isotopic carbon removal, m/z-based filtering, formula assignment error thresholds)"