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

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, Tomá?, Shevchenko, Andrej, Slenter, Denise, Siuzdak, Gary, Kutmon, Martina, Tsugawa, Hiroshi, Willighagen, Egon L., Xia, Jianguo, O'Donnell, Valerie B. ORCID: https://orcid.org/0000-0003-4089-8460 and Fedorova, Maria 2022. Guiding the choice of informatics software and tools for lipidomics research applications. Nature Methods 20 (2) , pp. 193-204. 10.1038/s41592-022-01710-0

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Abstract

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.

Item Type:	Article
Date Type:	Published Online
Status:	Published
Schools:	Medicine
Publisher:	Nature Research
ISSN:	1548-7091
Date of First Compliant Deposit:	17 February 2023
Date of Acceptance:	2 November 2022
Last Modified:	15 Dec 2023 19:55
URI:	https://orca.cardiff.ac.uk/id/eprint/157134

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