# Transformation Classification Biotic Abiotic

> Use when after mass-difference network generation has matched pairwise peak mass differences to a reference biochemical transformation key with mass error ≤1 ppm, and you need to distinguish metabolic transformations driven by microbial activity from those arising from non-biological chemical.

- Skill: `holobiomicslab/transformation-classification-biotic-abiotic` (Agent Skill)
- Install (CLI): `npx skillmds@latest add holobiomicslab/transformation-classification-biotic-abiotic`
- Raw SKILL.md: https://api.skillmd.com/api/skills/holobiomicslab/transformation-classification-biotic-abiotic/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/transformation-classification-biotic-abiotic

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# transformation-classification-biotic-abiotic

## Summary

Classify matched mass-difference transformations detected in FT-ICR MS data as either biotic (microbial metabolic) or abiotic (chemical/environmental) based on prior categorization in a reference biochemical transformation key. This step anchors the interpretation of molecular transformation networks to biological process origin.

## When to use

After mass-difference network generation has matched pairwise peak mass differences to a reference biochemical transformation key with mass error ≤1 ppm, and you need to distinguish metabolic transformations driven by microbial activity from those arising from non-biological chemical reactions or environmental processes. Use this skill when your research question separates microbial metabolism from abiotic chemical weathering or degradation pathways.

## When NOT to use

- Input transformations have not yet been matched to a reference key or mass-matching step was not performed with ≤1 ppm error tolerance.
- Reference biochemical transformation key is unavailable or does not contain biotic/abiotic labels for the detected transformation types.
- Analysis goal is purely structural network analysis (e.g., centrality, modularity) without interpretation of metabolic origin — classification adds no value.

## Inputs

- matched transformations table (source peak m/z, target peak m/z, transformation type, mass error in ppm)
- reference biochemical transformation key (transformation type → reference mass difference and biotic/abiotic label)

## Outputs

- edge CSV file with transformation metadata including biotic/abiotic classification for each edge
- transformation frequency statistics stratified by biotic vs. abiotic class
- summary counts of biotic and abiotic transformations per sample

## How to apply

For each matched transformation (source peak m/z → target peak m/z with assigned transformation type), consult the reference biochemical transformation key to retrieve the prior biotic/abiotic classification assigned to that transformation type. Retain this classification in the edge CSV output alongside transformation metadata (source m/z, target m/z, mass error). The classification is deterministic: each reference transformation entry carries a fixed biotic or abiotic label. No statistical threshold or re-inference is applied; the classification propagates from the reference key. Rationale: biotic transformations (e.g., methylation, acetylation, hydroxylation) are signatures of enzymatic pathways in active microbial communities, while abiotic transformations reflect non-enzymatic reactions. This distinction enables hub-metabolite analysis and network topology interpretation specific to metabolic state.

## Related tools

- **MetaboDirect** (End-to-end pipeline that integrates transformation network generation, biotic/abiotic classification, and Cytoscape export) — https://github.com/Coayala/MetaboDirect
- **Cytoscape** (Network visualization and analysis tool that imports node and edge CSV files with classification attributes for downstream topology and hub analysis)
- **KEGG database** (Reference source for biochemical transformations and reaction classifications that can populate the reference transformation key)

## Evaluation signals

- Edge CSV output contains no null or unassigned biotic/abiotic labels for any matched transformation.
- All matched transformations present in the edge CSV have a corresponding entry in the reference transformation key; no transformation type is orphaned.
- Biotic + abiotic transformation counts per sample sum to the total number of matched transformations in that sample.
- Cytoscape-imported network nodes and edges display biotic/abiotic labels as visual attributes (e.g., edge color or style) without import errors.
- Sample-level transformation statistics (total biotic, total abiotic, ratio) align with manual spot-check of edge CSV rows stratified by classification.

## Limitations

- Classification accuracy depends entirely on the completeness and correctness of the reference biochemical transformation key; unknown or misclassified transformations in the key propagate into results.
- Ambiguous transformations that could arise from either biotic or abiotic pathways are assigned a single fixed label; no confidence score or probabilistic assignment is supported.
- Reference key must be curated and maintained externally; MetaboDirect does not validate or update transformation classifications based on novel literature or experimental evidence.
- Mass-difference matching is sensitive to unassigned peaks and formula assignment errors upstream; mismatches due to poor peak quality or formula error will produce false or missing transformations before classification is applied.

## Evidence

- [other] Classify matched transformations as biotic or abiotic based on prior categorization.: "Classify matched transformations as biotic or abiotic based on prior categorization."
- [abstract] MetaboDirect is uniquely able to automatically generate biochemical transformation networks based on mass differences.: "MetaboDirect is also uniquely able to automatically generate biochemical transformation networks (ab initio) based on mass differences (mass difference network‑based approach)"
- [other] The networks are designed to quantify differences in microbial metabolic pathways and identify hub metabolites involved in many reactions.: "the networks designed to quantify differences in microbial metabolic pathways and identify hub metabolites involved in many reactions"
- [other] Match each mass difference to the reference biochemical transformation key, retaining transformations with mass error ≤1 ppm against reference values.: "Match each mass difference to the reference biochemical transformation key, retaining transformations with mass error ≤1 ppm against reference values."
- [other] Edge CSV files containing putative transformations for each sample.: "Generate edge CSV files containing putative transformations (source peak m/z, target peak m/z, transformation type, error) for each sample."