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. Links to download software tools are provided below.
- BioCyc.org 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.
- IE-Omics is a R script that automates “iterative exclusion” (IE) experiments. In IE, previously selected ions for fragmentation using ddMS2-topN are excluded from selection in sequential injections. IE significantly increases the number of lipid identifications obtained in data-dependent analysis.
- LipidMatch can be used to annotate lipids detected using LC-HRMS/MS targeted, data-dependent and data-independent experiments using various vendor formats. LipidMatch can also be employed for direct infusion HRMS/MS experiments. LipidMatch in-silico fragmentation libraries contain over 250,000 lipid species across more than 50 lipid types.
- A user-friendly software covering the entire lipidomics data-processing workflow for LC-HRMS/MS data. Simply drag the correctly named vendor files onto the interface, select an output directory, and click Run. Behind the scenes, LipidMatch Flow incorporates MSConvert for file conversion, MZmine for peak picking optimized for each vendor, LipidMatch for identification, and combines adducts and polarities into a single file which can be uploaded into metaboanalyst for statistics. Feature finding uses unique algorithms along with MZmine to increase reproducibility of peak picking across samples, as was as increase data-processing speeds. Lipid identifications are obtained using the most comprehensive open-source in-silico MS/MS libraries to date. Currently works for Agilent Q-TOF, Thermo orbitrap, and SCIEX Q-TOF files. Future updates will include: normalization to lipid internal standards, acceptance of Bruker files, statistics, and pathway analysis. This is the first release, please report bugs, feedback, or just let me know the software worked well at: email@example.com
- LipidMatch Normalizer (LMN) can be used to perform relative quantification for lipidomics using class specific lipid standards and LC-MS data. The user provides a feature table containing all the annotated lipids and their respective m/z values, retention time, and intensities across samples. The feature table format is flexible, and hence LMN can be integrated with any lipid identification and feature finding software. LMN automatically selects internal standards that represent similar lipid structure to the target lipids and takes into account signal suppression effects in liquid chromatography mass spectrometry, prioritizing a match of lipid class, adduct, and retention time, respectively. If multiple internal standards are provided for a particular lipid class, internal standards with the closest retention time to the target analyte will be chosen.
- LipidPioneer, an interactive excel template, can be used to generate exact masses and molecular formulas of various lipid adducts. 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.
- Users can quickly compare experimental concentrations (nmol/mL) of lipid species quantified for SRM 1950 and the SRM 2378:1–3 series against the consensus estimates and corresponding uncertainties obtained from the NIST Lipidomics Interlaboratory Comparison Exercise.
- SECIMTools project aims to develop a suite of tools for processing of metabolomics data, which can be run in a standalone mode or via Galaxy Genomics Framework.
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.