Guiding the choice of informatics software and tools for lipidomics research applications

• Published in: Nature methods Vol. 20; no. 2; pp. 193 - 204
• Main Authors: Ni, Zhixu, Wölk, Michele, Jukes, Geoff, Mendivelso Espinosa, Karla, Ahrends, Robert, Aimo, Lucila, Alvarez-Jarreta, Jorge, Andrews, Simon, Andrews, Robert, Bridge, Alan, Clair, Geremy C., Conroy, Matthew J., Fahy, Eoin, Gaud, Caroline, Goracci, Laura, Hartler, Jürgen, Hoffmann, Nils, Kopczyinki, Dominik, Korf, Ansgar, Lopez-Clavijo, Andrea F., Malik, Adnan, Ackerman, Jacobo Miranda, Molenaar, Martijn R., O’Donovan, Claire, Pluskal, Tomáš, Shevchenko, Andrej, Slenter, Denise, Siuzdak, Gary, Kutmon, Martina, Tsugawa, Hiroshi, Willighagen, Egon L., Xia, Jianguo, O’Donnell, Valerie B., Fedorova, Maria
• Format: Journal Article
• Language: English
• Published: New York Nature Publishing Group US 01.02.2023; Nature Publishing Group
• Subjects: 631/114/2398; 631/114/2416; 631/45/287; 631/45/608; 706/648/697; Bioinformatics; Biological Microscopy; Biological Techniques; Biomedical and Life Sciences; Biomedical Engineering/Biotechnology; Computational Biology - methods; Data analysis; Data integration; Data processing; Datasets; Graphical user interface; Informatics; Life Sciences; Lipidomics; Lipids; Lipids - chemistry; Mass spectrometry; Mass spectroscopy; Perspective; Proteomics; Scientific imaging; Software; Software utilities; Statistical analysis
• ISSN: 1548-7091; 1548-7105
• DOI: 10.1038/s41592-022-01710-0

Progress in mass spectrometry lipidomics has led to a rapid proliferation of studies across biology and biomedicine. These generate extremely large raw datasets requiring sophisticated solutions to support automated data processing. To address this, numerous software tools have been developed and tailored for specific tasks. However, for researchers, deciding which approach best suits their application relies on ad hoc testing, which is inefficient and time consuming. Here we first review the data processing pipeline, summarizing the scope of available tools. Next, to support researchers, LIPID MAPS provides an interactive online portal listing open-access tools with a graphical user interface. This guides users towards appropriate solutions within major areas in data processing, including (1) lipid-oriented databases, (2) mass spectrometry data repositories, (3) analysis of targeted lipidomics datasets, (4) lipid identification and (5) quantification from untargeted lipidomics datasets, (6) statistical analysis and visualization, and (7) data integration solutions. Detailed descriptions of functions and requirements are provided to guide customized data analysis workflows. This Perspective discusses available software tools for lipidomics data analysis and provides a web-based Lipidomics Tools Guide to help guide the choice of these tools, organized by the major tasks for lipidomics research.