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

Zhixu Ni¹, Michele Wölk¹, Geoff Jukes², Karla Mendivelso Espinosa², Robert Ahrends³, Lucila Aimo⁴, Jorge Alvarez-Jarreta^{2

5}, Simon Andrews⁶, Robert Andrews², Alan Bridge⁴, Geremy C Clair⁷, Matthew J Conroy², Eoin Fahy⁸, Caroline Gaud⁶, Laura Goracci⁹, Jürgen Hartler^{10

11}, Nils Hoffmann¹², Dominik Kopczyinki³, Ansgar Korf¹³, Andrea F Lopez-Clavijo⁶, Adnan Malik⁵, Jacobo Miranda Ackerman¹⁴, Martijn R Molenaar¹⁵, Claire O'Donovan⁵, Tomáš Pluskal¹⁶, Andrej Shevchenko¹⁴, Denise Slenter¹⁷, Gary Siuzdak¹⁸, Martina Kutmon^{17

19}, Hiroshi Tsugawa^{20

21

22

23}, Egon L Willighagen¹⁷, Jianguo Xia²⁴, Valerie B O'Donnell²⁵, Maria Fedorova²⁶

Affiliations

¹ Center of Membrane Biochemistry and Lipid Research, Faculty of Medicine Carl Gustav Carus of TU Dresden, Dresden, Germany.
² Systems Immunity Research Institute, School of Medicine, Cardiff University, Cardiff, UK.
³ Department of Analytical Chemistry, University of Vienna, Vienna, Austria.
⁴ Swiss-Prot group, SIB Swiss Institute of Bioinformatics, Centre Medical Universitaire, Geneva, Switzerland.
⁵ European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, UK.
⁶ Babraham Institute, Babraham Research Campus, Cambridge, UK.
⁷ Biological Science Division, Pacific Northwest National Laboratory, Richland, WA, USA.
⁸ Department of Bioengineering, University of California, San Diego, CA, USA.
⁹ Department of Chemistry, Biology and Biotechnology, University of Perugia, Perugia, Italy.
¹⁰ Institute of Pharmaceutical Sciences, University of Graz, Graz, Austria.
¹¹ Field of Excellence BioHealthe-University of Graz, Graz, Austria.
¹² Center for Biotechnology, University of Bielefeld, Bielefeld, Germany.
¹³ Bruker Daltonics GmbH & Co. KG, Bremen, Germany.
¹⁴ Max Planck Institute of Molecular Cell Biology and Genetics, Dresden, Germany.
¹⁵ Structural and Computational Biology Unit, European Molecular Biology Laboratory, Heidelberg, Germany.
¹⁶ Institute of Organic Chemistry and Biochemistry of the Czech Academy of Sciences, Prague, Czech Republic.
¹⁷ Department of Bioinformatics - BiGCaT, NUTRIM, Maastricht University, Maastricht, The Netherlands.
¹⁸ Scripps Center for Metabolomics and Mass Spectrometry, The Scripps Research Institute, La Jolla, CA, USA.
¹⁹ Maastricht Centre for Systems Biology, Maastricht University, Maastricht, The Netherlands.
²⁰ Department of Biotechnology and Life Science, Tokyo University of Agriculture and Technology, Tokyo, Japan.
²¹ RIKEN Center for Sustainable Resource Science, Yokohama, Japan.
²² RIKEN Center for Integrative Medical Sciences, Yokohama, Japan.
²³ Graduate School of Medical Life Science, Yokohama City University, Yokohama, Japan.
²⁴ Institute of Parasitology, McGill University, Montreal, Canada.
²⁵ Systems Immunity Research Institute, School of Medicine, Cardiff University, Cardiff, UK. O-DonnellVB@cardiff.ac.uk.
²⁶ Center of Membrane Biochemistry and Lipid Research, Faculty of Medicine Carl Gustav Carus of TU Dresden, Dresden, Germany. maria.fedorova@tu-dresden.de.

PMID: 36543939
PMCID: PMC10263382
DOI: 10.1038/s41592-022-01710-0

Review

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

Zhixu Ni et al. Nat Methods. 2023 Feb.

. 2023 Feb;20(2):193-204.

doi: 10.1038/s41592-022-01710-0. Epub 2022 Dec 21.

Authors

Affiliations

¹ Center of Membrane Biochemistry and Lipid Research, Faculty of Medicine Carl Gustav Carus of TU Dresden, Dresden, Germany.
² Systems Immunity Research Institute, School of Medicine, Cardiff University, Cardiff, UK.
³ Department of Analytical Chemistry, University of Vienna, Vienna, Austria.
⁴ Swiss-Prot group, SIB Swiss Institute of Bioinformatics, Centre Medical Universitaire, Geneva, Switzerland.
⁵ European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, UK.
⁶ Babraham Institute, Babraham Research Campus, Cambridge, UK.
⁷ Biological Science Division, Pacific Northwest National Laboratory, Richland, WA, USA.
⁸ Department of Bioengineering, University of California, San Diego, CA, USA.
⁹ Department of Chemistry, Biology and Biotechnology, University of Perugia, Perugia, Italy.
¹⁰ Institute of Pharmaceutical Sciences, University of Graz, Graz, Austria.
¹¹ Field of Excellence BioHealthe-University of Graz, Graz, Austria.
¹² Center for Biotechnology, University of Bielefeld, Bielefeld, Germany.
¹³ Bruker Daltonics GmbH & Co. KG, Bremen, Germany.
¹⁴ Max Planck Institute of Molecular Cell Biology and Genetics, Dresden, Germany.
¹⁵ Structural and Computational Biology Unit, European Molecular Biology Laboratory, Heidelberg, Germany.
¹⁶ Institute of Organic Chemistry and Biochemistry of the Czech Academy of Sciences, Prague, Czech Republic.
¹⁷ Department of Bioinformatics - BiGCaT, NUTRIM, Maastricht University, Maastricht, The Netherlands.
¹⁸ Scripps Center for Metabolomics and Mass Spectrometry, The Scripps Research Institute, La Jolla, CA, USA.
¹⁹ Maastricht Centre for Systems Biology, Maastricht University, Maastricht, The Netherlands.
²⁰ Department of Biotechnology and Life Science, Tokyo University of Agriculture and Technology, Tokyo, Japan.
²¹ RIKEN Center for Sustainable Resource Science, Yokohama, Japan.
²² RIKEN Center for Integrative Medical Sciences, Yokohama, Japan.
²³ Graduate School of Medical Life Science, Yokohama City University, Yokohama, Japan.
²⁴ Institute of Parasitology, McGill University, Montreal, Canada.
²⁵ Systems Immunity Research Institute, School of Medicine, Cardiff University, Cardiff, UK. O-DonnellVB@cardiff.ac.uk.
²⁶ Center of Membrane Biochemistry and Lipid Research, Faculty of Medicine Carl Gustav Carus of TU Dresden, Dresden, Germany. maria.fedorova@tu-dresden.de.

PMID: 36543939
PMCID: PMC10263382
DOI: 10.1038/s41592-022-01710-0

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.

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Figures

**Figure 1.. A screenshot of Lipidomics Tools Guide guiding the choice of the tools organized by the major tasks for lipidomics data processing.**
Each task is marked by a clickable button to redirect the user to the list of corresponding tools and their descriptions.

**Figure 2.. List of tools represented within LIPID MAPS Lipidomics Tools Guide assigned for each of the seven data processing categories**
These include (1) lipid-oriented databases, (2) MS 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.

**Figure 3.. A screenshot example of individual tool details pages from LIPID MAPS Structure Database.**
Each tool has three sections: a “**Description**” section for the brief introduction of the tool, a “**Technical Information**” section listing all essential technical information from links to compatible file formats, and a “***Task specific information***” section containing detailed features tailored to the corresponding task. Hovering on the underlined text field displays further features for the section.

**Figure 4.. A screenshot example of individual tool details pages LipidLynxX.**
Each tool has three sections: a “**Description**” section for the brief introduction of the tool, a “**Technical Information**” section listing all essential technical information from links to compatible file formats, and a “***Task specific information***” section containing detailed features tailored to the corresponding task. Hovering on the underlined text field displays further features for the section.

See this image and copyright information in PMC

References

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1. Yang K & Han X Lipidomics: Techniques, Applications, and Outcomes Related to Biomedical Sciences. Trends in Biochemical Sciences 41, 954–969 (2016). - PMC - PubMed
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1. Yang L et al. Recent advances in lipidomics for disease research. Journal of Separation Science 39, 38–50 (2016). - PubMed

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

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

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