--- name: lipid-library-annotation-from-mz description: Use when you have experimental peaklist data (CSV or mzML-derived tables) from UHPLC-HRMS/MS instruments (Q-Exactive, Agilent/Bruker/SCIEX Q-TOF) with fragment m/z values and want to annotate them to known lipid identities. license: CC-BY-4.0 metadata: edam_operation: http://edamontology.org/operation_3767 edam_topics: - http://edamontology.org/topic_0153 - http://edamontology.org/topic_3375 tools: - Q-Exactive orbitrap - LipidMatch - MZmine - XCMS - MS-DIAL - Compound Discoverer - Agilent Q-TOF UHPLC-HRMS/MS - Bruker Q-TOF UHPLC-HRMS/MS - SCIEX Q-TOF UHPLC-HRMS/MS derived_from: - doi: 10.1186/s12859-017-1744-3 title: lipidmatch evidence_spans: - tested and validated using Q-Exactive orbitrap UHPLC-HRMS/MS data claims: [] provenance: collection: https://w3id.org/holobiomicslab/asb-skill/collection/metabolomics/v1 assembled_by: scripts/collect_metabolomics_collection.py sources: - build: coll_lipidmatch doi: 10.1186/s12859-017-1744-3 title: lipidmatch dedup_kept_from: coll_lipidmatch schema_version: 0.2.0 --- # lipid-library-annotation-from-mz ## Summary Match experimental fragment m/z values from high-resolution tandem MS data against in-silico fragmentation libraries to identify lipid species. This skill applies LipidMatch's rule-based matching algorithm to annotate lipids across >500,000 species spanning >60 lipid classes using mass tolerance thresholds. ## When to use You have experimental peaklist data (CSV or mzML-derived tables) from UHPLC-HRMS/MS instruments (Q-Exactive, Agilent/Bruker/SCIEX Q-TOF) with fragment m/z values and want to annotate them to known lipid identities. Apply this skill after peak-picking (via MZmine, XCMS, MS-DIAL, or Compound Discoverer) when you need comprehensive lipid class assignments with match quality metrics. ## When NOT to use - Input is from Waters instruments (software does not currently support Waters files) - You have only precursor m/z data without fragment ion information (LipidMatch requires fragment-level m/z matching) - Your lipid species of interest are not represented in the 500,000-species library and you do not have a user-generated library available ## Inputs - Experimental peaklist file (CSV or mzML-derived table format) containing fragment m/z values from UHPLC-HRMS/MS data - LipidMatch in-silico fragmentation library CSV files - Mass tolerance threshold (instrument-dependent, e.g., 5 ppm for Q-Exactive) - Peak-picked intensity/abundance data (optional, for match quality filtering) ## Outputs - Lipid annotation table with columns: lipid name, lipid class, matched m/z, match quality metrics - Candidate lipid matches ranked by fragment ion match count and mass accuracy - Structured identifications compatible with downstream lipidomics analysis and visualization ## How to apply Load your experimental fragment m/z peaklist and the LipidMatch in-silico fragmentation library CSV files covering the 500,000+ lipid species across 60+ lipid types. Apply the LipidMatch m/z matching algorithm by comparing each experimental m/z against library fragments using mass tolerance thresholds appropriate to your instrument's resolution (e.g., 5 ppm for Q-Exactive orbitrap, tighter for higher-resolution data). The algorithm ranks candidate lipid matches based on the number of fragment ions matched and their mass accuracy. Output matched identifications as a structured table with lipid name, class, m/z, and match quality metrics. Use the modularity of LipidMatch to integrate results with other lipidomics software or user-generated libraries for specialized lipid classes. ## Related tools - **LipidMatch** (Core matching engine; performs m/z-based fragment ion annotation against in-silico fragmentation libraries) — https://github.com/GarrettLab-UF/LipidMatch - **MZmine** (Peak picking and peaklist generation upstream of LipidMatch) - **XCMS** (Peak picking and peaklist generation upstream of LipidMatch) - **MS-DIAL** (Peak picking and peaklist generation upstream of LipidMatch) - **Compound Discoverer** (Peak picking and peaklist generation upstream of LipidMatch) - **Q-Exactive orbitrap** (UHPLC-HRMS/MS instrument; validated platform for LipidMatch annotation) - **Agilent Q-TOF UHPLC-HRMS/MS** (UHPLC-HRMS/MS instrument; validated platform for LipidMatch annotation) - **Bruker Q-TOF UHPLC-HRMS/MS** (UHPLC-HRMS/MS instrument; validated platform for LipidMatch annotation) - **SCIEX Q-TOF UHPLC-HRMS/MS** (UHPLC-HRMS/MS instrument; validated platform for LipidMatch annotation) ## Evaluation signals - Output table contains non-empty lipid name and class columns for matched peaks, with one row per unique lipid annotation - Match quality metrics are present and reasonable (e.g., number of matched fragments >0, mass error within defined tolerance) - No lipid identifications are assigned to experimental m/z values with zero matching library fragments - Matched lipid annotations are consistent with lipid class structure rules (e.g., fragmentation patterns match known lipid class behavior) - Mass error distribution of matched fragments is centered near zero and does not exceed the specified m/z tolerance threshold ## Limitations - Software does not currently support Waters file formats - Annotation coverage is limited to the 500,000+ lipid species in the library; novel or rare lipids outside this set cannot be identified without user-generated library extension - Match quality depends critically on appropriate mass tolerance threshold selection; mismatched instrument resolution or calibration will degrade accuracy - Fragment ion matching alone may not distinguish between lipid isomers with identical fragmentation patterns; retention time or additional orthogonal data may be needed for disambiguation ## 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 using in-silico fragmentation libraries of over 500,000 lipid species across" - [other] Compare each experimental m/z against library fragments using mass tolerance thresholds: "Apply LipidMatch m/z matching algorithm: compare each experimental m/z against library fragments using mass tolerance thresholds to identify candidate lipid matches" - [other] Load experimental fragment m/z values from peaklist file and load LipidMatch library CSV files: "Load experimental fragment m/z values from a peaklist file (Q-Exactive or Q-TOF format, e.g., CSV or mzML-derived table). 2. Load LipidMatch in-silico fragmentation library CSV files covering the" - [readme] Tested and validated using Q-Exactive orbitrap and Agilent, Bruker, SCIEX Q-TOF UHPLC-HRMS/MS: "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] LipidMatch can be used with various peak picking software: "LipidMatch can be used with various peak picking software (for example MZmine, XCMS, MS-DIAL, and Compound Discoverer)" - [readme] Software does not currently support Waters files: "The software does not currently support Waters files" - [readme] LipidMatch allows integration of user-generated libraries for unique applications: "LipidMatch allows for facile integration of user generated libraries for unique applications"