LipidMatch identifications are obtained by matching experimental fragment m/z values with simulated library m/z values using in-silico fragmentation libraries of over 250,000 lipid species. LipidMatch has been tested and validated using Q-Exactive orbitrap data obtained from multiple sample types using targeted, data-dependent top-N (ddMS2-topN), and all ion fragmentation (AIF) approaches with UHPLC-HRMS/MS experiments. For AIF data, LipidMatch uses correlation coefficients to reconstruct precursor-fragment relationships. LipidMatch has also been applied for the annotation of direct infusion and imaging experiments using Q-Exactive data. The software is vendor neutral and has been tested on Agilent data-dependent (auto-MS/MS) files. The software does not currently support Waters files. LipidMatch also allows for facile integration of user generated libraries for unique applications. The link below contains video tutorials, a manual, lipid libraries in .csv format, a batch file for lipidomics with MZmine processing, and LipidMatch software.
Jeremy P. Koelmel, Nicholas M. Kroeger, Candice Z. Ulmer, John A. Bowden, Rainey E. Patterson, Jason A. Cochran, Christopher W. W. Beecher, Timothy J. Garrett, Richard A. Yost: LipidMatch: an automated workflow for rule-based lipid identification using untargeted high-resolution tandem mass spectrometry data. BMC Bioinformatics. Accepted 18:331. doi: 10.1186/s12859-017-1744-3