# Lipid Library Format Schema > Use when you have identified lipid species unique to your experimental system (e. - Skill: `holobiomicslab/lipid-library-format-schema` (Agent Skill) - Install (CLI): `npx skillmds@latest add holobiomicslab/lipid-library-format-schema` - Raw SKILL.md: https://api.skillmd.com/api/skills/holobiomicslab/lipid-library-format-schema/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/lipid-library-format-schema --- # lipid-library-format-schema ## Summary Define and validate the structure of user-generated lipid libraries in .csv format for integration into LipidMatch workflows. This skill ensures that custom lipid entries conform to LipidMatch schema specifications so they can be combined with built-in libraries for specialized lipidomics applications. ## When to use You have identified lipid species unique to your experimental system (e.g., synthetic lipids, rare natural variants, or lipids from non-model organisms) that are absent from the built-in LipidMatch library, and you want to augment LipidMatch identifications by adding these species with known or computationally predicted fragmentation patterns. ## When NOT to use - You are working exclusively with common, well-characterized lipid species already present in the built-in LipidMatch library (500,000+ lipid species across 60+ types); the custom library adds no discriminatory value. - Your instrument produces Waters vendor files; LipidMatch does not currently support Waters file formats, so validation against experimental data will not be possible. - You lack reliable fragmentation rules or empirical MS/MS data to populate the fragment m/z column; unpredictable or missing fragmentation data will cause false negatives or misidentifications. ## Inputs - Tab-separated or comma-separated values (.csv) file with lipid metadata - Lipid nomenclature list (standardized names) - Molecular formulas for candidate lipid species - Adduct type specifications - In-silico fragment m/z values (simulated or empirically derived) ## Outputs - Validated .csv lipid library conforming to LipidMatch schema - Extended LipidMatch library combining built-in and user-generated entries - LipidMatch identification results from combined library applied to UHPLC-HRMS/MS data ## How to apply Create a .csv file following LipidMatch schema specifications, including required columns: lipid names (standardized nomenclature), molecular formulas, adduct types (e.g., [M+H]+, [M+Na]+), and in-silico fragment m/z values computed by gas-phase fragmentation rules or empirically validated. Validate the .csv structure and content against LipidMatch format requirements (correct data types, no missing mandatory fields, m/z values within instrument resolution). Integrate the user library into LipidMatch using the documented integration mechanism provided by the software. Test the extended workflow by running LipidMatch identifications against UHPLC-HRMS/MS data (peak-picked by MZmine, XCMS, MS-DIAL, or Compound Discoverer) using the combined library (built-in + user-generated), and verify that custom lipids are correctly matched to experimental fragment m/z values with expected mass accuracy. ## Related tools - **LipidMatch** (Host software that performs fragment m/z matching; accepts integrated user-generated .csv libraries and combines them with built-in library for lipid identification) — https://github.com/GarrettLab-UF/LipidMatch - **MZmine** (Peak picking and feature detection for UHPLC-HRMS/MS data; output used with LipidMatch for validation of user library identifications) - **XCMS** (Peak picking and feature detection for UHPLC-HRMS/MS data; output used with LipidMatch for validation of user library identifications) - **MS-DIAL** (Peak picking and feature detection for UHPLC-HRMS/MS data; output used with LipidMatch for validation of user library identifications) - **Compound Discoverer** (Peak picking and feature detection for UHPLC-HRMS/MS data; output used with LipidMatch for validation of user library identifications) ## Evaluation signals - The .csv file passes schema validation: all required columns present, correct data types (lipid name = string, molecular formula = string, adduct type = string, m/z values = numeric), no missing mandatory fields. - User-generated lipid entries match experimental UHPLC-HRMS/MS peaks with mass accuracy consistent with instrument resolution (Q-Exactive orbitrap ~5 ppm; Q-TOF ~10 ppm). - Fragment m/z values in the custom library align with observed MS/MS spectra; cosine similarity or spectral contrast angle between experimental and simulated fragmentation patterns is above instrument-specific threshold. - Combined library (built-in + user) produces no false-positive identifications on negative control samples; custom lipid identifications are rank-ordered with confidence scores consistent with match quality. - User library entries are successfully combined with built-in library in LipidMatch workflow without runtime errors; output file lists both standard and custom lipid identifications in annotated feature table. ## Limitations - LipidMatch does not currently support Waters vendor file formats, limiting validation to Thermo Q-Exactive, Agilent, Bruker, and SCIEX Q-TOF platforms. - User-generated fragment m/z values rely on in-silico fragmentation rules or empirical data; if fragmentation patterns are misspecified or absent, custom lipids will not be identified or will be ranked incorrectly. - The .csv schema specifications are not formally documented in the provided README; users must infer requirements from examples or contact developers. - Large custom libraries may increase computational burden and collision risk with similar-mass lipids; no filtering or library compression is described. ## Evidence - [readme] LipidMatch identifications are obtained by matching experimental fragment m/z values with simulated library m/z values: "LipidMatch identifications are obtained by matching experimental fragment m/z values with simulated library m/z values" - [readme] User library format and integration mechanism: "LipidMatch allows for facile integration of user generated libraries for unique applications" - [readme] Built-in library comprehensiveness and composition: "in-silico fragmentation libraries of over 500,000 lipid species across over 60 lipid types" - [readme] Validation instruments and acquisition modes: "LipidMatch has been tested and validated using Q-Exactive orbitrap UHPLC-HRMS/MS data obtained from multiple sample types using targeted, data-dependent top-N (ddMS2-topN), and all ion fragmentation" - [readme] Workflow modularity and peak picking compatibility: "LipidMatch can be used with various peak picking software (for example MZmine, XCMS, MS-DIAL, and Compound Discoverer)" - [other] Workflow steps for user library integration (from task card): "Create a user-generated lipid library in .csv format following LipidMatch schema specifications (lipid names, molecular formulas, adduct types, and in-silico fragment m/z values)" - [other] Testing and validation approach: "Test the extended workflow by running LipidMatch identifications against experimental UHPLC-HRMS/MS data using the combined library (built-in library + user-generated library)" - [readme] Known limitation: Waters files not supported: "The software does not currently support Waters files"