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Allows annotation of lipids across a wide range of high resolution tandem mass spectrometry (MS/MS) studies. LipidMatch is based on an accurate assumption of multiple co-eluting lipids sharing m/z values. It was evaluated using data acquired from a Q-Exactive orbitrap mass spectrometer and an Agilent 6540 Q-TOF. The tool contains a large diversity in lipid types of any current open-source software platform and a rule-based strategy for identification and summed fragment intensity based strategy for ranking top hits.


A total solution to deal with not only data dependent MS/MS but also data independent MS/MS experiments for metabolomics and lipidomics. Its feature is 1) implementing de-convolution method for data independent MS/MS 2) using unified criteria for peak identification 3) supporting all data processing step from raw data import to statistical analysis 4) user-friendly graphic user interface. MS-DIAL deals with data independent acquisition MS/MS data (ex. SWATH) by means of two step algorithms (peak spotting and MS2Dec) for spectral deconvolution. Also, it supports compound identification, peak alignment, and principal component analysis on the graphical user interface. The spectrum information is outputted by MassBank, NIST, and Mascot formats. And the organized data matrix (sample vs metabolite) is exported as tab delimited text file.

LOBSTAHS / Lipid and Oxylipin Biomarker Screening Through Adduct Hierarchy Sequences

A multifunction package for screening, annotation, and putative identification of mass spectral features in large, HPLC-MS lipid datasets. In silico databases for a wide range of lipids, oxidized lipids, and oxylipins can be generated from user-supplied structural criteria with a database generation function. LOBSTAHS then applies these databases to assign putative compound identities to features in any high-mass accuracy dataset that has been processed using xcms and CAMERA. Users can then apply a series of orthogonal screening criteria based on adduct ion formation patterns, chromatographic retention time, and other properties, to evaluate and assign confidence scores to this list of preliminary assignments. During the screening routine, LOBSTAHS rejects assignments that do not meet the specified criteria, identifies potential isomers and isobars, and assigns a variety of annotation codes to assist the user in evaluating the accuracy of each assignment.


A data processing tool for the molecular characterization and quantification of lipid species from electrospray mass spectrometry data. LipidView™ Software enables lipid profiling by searching parent- and fragment-ion masses against a lipid fragment database containing over 25,000 entries and reports a numerical and graphical output for various lipid molecular species, lipid classes, fatty acids, and long chain bases. LipidView™ Software streamlines key steps such as automated data processing from template methods, method editing and selection, lipid species identification, comprehensive isotope contribution removal, multiple internal standards-based quantification, visualization and result reporting. Combined with the SCIEX QTRAP or TripleTOF Systems, this complete hardware and software package offers unique data acquisition strategies coupled with automated data processing for lipid profiling.


Helps to discover new lipid molecular species. LipidFinder is a computational workflow which searches three independent online databases to obtain putative identification of lipids, and assigns them to a class based on the LIPID MAPS system. The software quickly distinguishes and quantifies lipid-like features from contaminants, adducts and noise in high resolution liquid chromatography/mass spectrometry (LC/MS) datasets that have been pre-aligned using SIEVE (ThermoFisher) or XCMS.


Determines lipid peroxidation products (LPPs) from Liquid chromatography coupled to tandem mass spectrometry liquid chromatography coupled tandem mass spectrometry (LC-MS/MS) data dependent acquisition (DDA) datasets. LPPtiger is an open-source software that uses three algorithms coupled to a sample-specific native lipidome to predict phospholipids (PL)-bound LPPs and simulates a tandem mass spectra library for LPP identification. Moreover, the software can be customized to be fitted to users’ goals.

LIQUID / Lipid Quantification and Identification

Identifies lipids in Liquide Chromatography-Mass Spectometry (LC-MS)/MS-based lipidomics data. LIQUID provides users with the capability to process high throughput data and contains a customizable target library and scoring model per project needs. It provides visualization of multiple lines of spectral evidence for each lipid identification. This allows rapid examination of data for making confident identifications of lipid molecular species.


A software tool that supports the identification of lipids by interpreting large datasets generated by liquid chromatography-tandem mass spectrometry (LC-MS/MS) using the advanced data-independent acquisition mode MS(E). In the MS(E) mode, the instrument fragments all molecular ions generated from a sample and records time-resolved molecular ion data as well as fragment ion data for every detectable molecular ion. Lipid-Pro matches the retention time-aligned mass-to-charge ratio data of molecular- and fragment ions with a lipid database and generates a report on all identified lipid species.

LMQ / LipidMatch Quant

Allows users to quantify lipidomics using class specific lipid standards and liquid chromatography mass spectrometry (LC-MS) data. LMQ is a standalone software that first picks up internal standards for representing similar lipid structure to the target lipids and then considers signal suppression effects in LCMS while prioritizing a match of lipid class, adduct, and retention time. The application can be incorporated with various lipid identification and feature finding software.


Reduces the number of highly correlated lipid species in a lipidomic dataset without the need for sample class information. LICRE is an unsupervised feature reduction approach that allows the reduced dataset to be analyzed for various outcomes. The algorithm was implemented as a function that can be applied in the initial pre-processing stage of classification modelling. It may be useful to identifying concise markers for risk prediction, diagnostic, prognostic models of disease and for monitoring therapeutic response.