Description: Purpose of All Tools

State of the art technology in instrumentation and data-acquisition provide a wealth of information in metabolomics and lipidomics studies. Unlike in proteomics and genomics, lipidomics and metabolomics are emerging fields, with software and methodologies in identification and statistical analysis only recently emerging. Open source tools which can be improved by the larger community can serve to provide the information researchers need through novel and user friendly data processing and data analysis strategies. On this page you will find free open source software tools that can aid in lipidomics and metabolomics research. These tools can be edited to meet your needs, and video tutorials and manuals are included. We ask that you please cite the tools appropriately and maintain the authors on additional edits. Descriptions and links to download software tools are provided below.



LipidMatch identifications are obtained by matching experimental fragment m/z values with simulated library m/z values. LipidMatch has been tested using Q-Exactive orbitrap data obtained from multiple sample types using targeted, data-dependent top-N (ddMS2-topN), and all ion fragmentation (AIF) approaches. For AIF data, LipidMatch uses correlation coefficients to reconstruct precursor-fragment relationships. LipidMatch fragmentation libraries contain over 200,000 species from 45 lipid types, including oxidized lipids, and include multiple adducts. 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. Submitted (2016)



LipidPioneer, an interactive template, can be used to generate exact masses and molecular formulas of lipid species that may be encountered in the mass spectrometric analysis of lipid profiles. Over 60 lipid classes are present in the LipidPioneer template, and include several unique lipid species, such as ether-linked lipids and lipid oxidation products. In the template, users can add any fatty acyl constituents without limitation in the number of carbons or degrees of unsaturation. LipidPioneer accepts naming using the lipid class level (sum composition) and the LIPID MAPS notation for fatty acyl structure level. In addition to lipid identification, user generated lipid m/z values can be used to develop inclusion lists for targeted fragmentation experiments. Resulting lipid names and m/z values can be imported into software such as MZmine or Compound Discoverer to automate exact mass searching and isotopic pattern matching across experimental data.

Candice Z. Ulmer, Jeremy P. Koelmel, Jared M. Ragland, Timothy J. Garrett, John A. Bowden: LipidPioneer: A Comprehensive User-Generated Exact Mass Template for Lipidomics. Journal of the American Society for Mass Spectrometry. (2017. 28: 562. doi:10.1007/s13361-016-1579-6.


IE-Omics is a R script that automates “iterative exclusion” (IE) experiments for Thermo instruments. This script can easily be modified for use with other instruments. IE-Omics applies Iterative exclusion to traditional data-dependent tandem mass spectrometry top N experiments (ddMS2-topN), where the N most intense ions detected in a full scan are selected for fragmentation. In IE, previously selected ions for fragmentation using ddMS2-topN are excluded from selection in sequential injections. Theoretically fragmentation of all ions above a certain intensity threshold can be acquired. Applying this technique in our laboratory to Red Cross plasma and substantia nigra, we were able to identify up to 69 % more lipids than using traditional data-dependent acquisition.

Jeremy P. Koelmel, Nicholas M. Kroeger, Emily L. Gill, Candice Z. Ulmer, John A. Bowden, Rainey E. Patterson, Richard A. Yost, Timothy J. Garrett: Expanding lipidome coverage using LC-MS/MS data-dependent acquisition with automated exclusion list generation. Journal of the American Society for Mass Spectrometry. (2017). 28: 908. doi:10.1007/s13361-017-1608-0


biocyccollection-121014-600x300 is an extensive and user-friendly genome informatics portal containing 7,600 microbial genomes and associated metabolic pathways. This four hour tutorial will cover many aspects of BioCyc, with a focus on metabolomics data analysis.

Future Tools

Future tools include automation of univariate and multivariate statistical analysis of lipidomics and metabolomics datasets using excel and R; also quality control and statistical tool boxes in Galaxy will be added.