Lecture
Seamless integration and in-depth analysis of lipidomics data enabled by computational lipidomics
- at -
- ICM Saal 4b
- Type: Lecture
Lecture description
Lipidomics has experienced consistent growth in research activity over the past decade, and the rise of high-throughput omics platforms built on chromatography and high-resolution mass spectrometry has intensified the demand for a centralized, wellintegrated, and comprehensive resource to support both experimental and computational researchers.
To satisfy these demands, the LIFS (Lipidomics Informatics for Life Sciences) tools provide a comprehensive and integrated platform for lipidomics research that supports the entire workflow from experimental design to biological interpretation [1-6]. This
platform combines specialized software and web-based applications for designing lipid assays, processing and analyzing mass-spectrometry data in both targeted and untargeted workflows, identifying, quantifying, and structurally annotating lipids, comparing lipidomes across samples, conditions, or studies, and harmonizing lipid nomenclature and reporting formats. Furthermore, it also offers capabilities for quality control, validation of standardized data files, interactive visualization, and exploration of lipidomic patterns, thereby enabling researchers to produce reproducible, comparable, and biologically meaningful results from complex lipidomics datasets and to share, interpret, and integrate lipid data more effectively across the life sciences. It also promotes best practices in lipidomics by embedding community standards and transparent reporting into the analysis pipeline, which improves data quality and interoperability between laboratories and databases.
Together, these tools help to bridge the gap between raw mass-spectrometry measurements and higher-level biological insights, making lipidomics more accessible, reliable, and impactful.
[1] B Peng, D Kopczynski, BS Pratt, et al. LipidCreator workbench to probe the lipidomic landscape. Nature Communications 11 (1), 2057, 2020.
[2] D Kopczynski, N Hoffmann, B Peng, R Ahrends. Goslin: A Grammar of Succinct Lipid Nomenclature, Analytical Chemistry 92 (16), 10957–10960, 2020.
[3] N Hoffmann, J Rein, et al. mzTab-M: a data standard for sharing quantitative results in mass spectrometry metabolomics. Analytical chemistry 91 (5), 3302-3310, 2019.
[4] R Herzog, K Schuhmann, et al. LipidXplorer: a software for consensual crossplatform lipidomics. PloS one 7 (1), e29851, 2012.
[5] D Kopczynski, N Hoffmann, et al. LipidSpace: simple exploration, reanalysis, and quality control of large-scale lipidomics studies. Analytical Chemistry 95 (41), 15236-15244, 2023.
[6] JG McDonald, CS Ejsing, D Kopczynski, et al. Introducing the lipidomics minimal reporting checklist. Nature Metabolism 4 (9), 1086-1088, 2022.
To satisfy these demands, the LIFS (Lipidomics Informatics for Life Sciences) tools provide a comprehensive and integrated platform for lipidomics research that supports the entire workflow from experimental design to biological interpretation [1-6]. This
platform combines specialized software and web-based applications for designing lipid assays, processing and analyzing mass-spectrometry data in both targeted and untargeted workflows, identifying, quantifying, and structurally annotating lipids, comparing lipidomes across samples, conditions, or studies, and harmonizing lipid nomenclature and reporting formats. Furthermore, it also offers capabilities for quality control, validation of standardized data files, interactive visualization, and exploration of lipidomic patterns, thereby enabling researchers to produce reproducible, comparable, and biologically meaningful results from complex lipidomics datasets and to share, interpret, and integrate lipid data more effectively across the life sciences. It also promotes best practices in lipidomics by embedding community standards and transparent reporting into the analysis pipeline, which improves data quality and interoperability between laboratories and databases.
Together, these tools help to bridge the gap between raw mass-spectrometry measurements and higher-level biological insights, making lipidomics more accessible, reliable, and impactful.
[1] B Peng, D Kopczynski, BS Pratt, et al. LipidCreator workbench to probe the lipidomic landscape. Nature Communications 11 (1), 2057, 2020.
[2] D Kopczynski, N Hoffmann, B Peng, R Ahrends. Goslin: A Grammar of Succinct Lipid Nomenclature, Analytical Chemistry 92 (16), 10957–10960, 2020.
[3] N Hoffmann, J Rein, et al. mzTab-M: a data standard for sharing quantitative results in mass spectrometry metabolomics. Analytical chemistry 91 (5), 3302-3310, 2019.
[4] R Herzog, K Schuhmann, et al. LipidXplorer: a software for consensual crossplatform lipidomics. PloS one 7 (1), e29851, 2012.
[5] D Kopczynski, N Hoffmann, et al. LipidSpace: simple exploration, reanalysis, and quality control of large-scale lipidomics studies. Analytical Chemistry 95 (41), 15236-15244, 2023.
[6] JG McDonald, CS Ejsing, D Kopczynski, et al. Introducing the lipidomics minimal reporting checklist. Nature Metabolism 4 (9), 1086-1088, 2022.
