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Review
. 2021 Jun 6;17(6):55.
doi: 10.1007/s11306-021-01802-6.

Interpreting the lipidome: bioinformatic approaches to embrace the complexity

Jennifer E Kyle  1 , Lucila Aimo  2 , Alan J Bridge  2 , Geremy Clair  1 , Maria Fedorova  3 , J Bernd Helms  4 , Martijn R Molenaar  5 , Zhixu Ni  3 , Matej Orešič  6   7 , Denise Slenter  8 , Egon Willighagen  8 , Bobbie-Jo M Webb-Robertson  9
Affiliations
Review

Interpreting the lipidome: bioinformatic approaches to embrace the complexity

Jennifer E Kyle et al. Metabolomics. .

Abstract

Background: Improvements in mass spectrometry (MS) technologies coupled with bioinformatics developments have allowed considerable advancement in the measurement and interpretation of lipidomics data in recent years. Since research areas employing lipidomics are rapidly increasing, there is a great need for bioinformatic tools that capture and utilize the complexity of the data. Currently, the diversity and complexity within the lipidome is often concealed by summing over or averaging individual lipids up to (sub)class-based descriptors, losing valuable information about biological function and interactions with other distinct lipids molecules, proteins and/or metabolites.

Aim of review: To address this gap in knowledge, novel bioinformatics methods are needed to improve identification, quantification, integration and interpretation of lipidomics data. The purpose of this mini-review is to summarize exemplary methods to explore the complexity of the lipidome.

Key scientific concepts of review: Here we describe six approaches that capture three core focus areas for lipidomics: (1) lipidome annotation including a resolvable database identifier, (2) interpretation via pathway- and enrichment-based methods, and (3) understanding complex interactions to emphasize specific steps in the analytical process and highlight challenges in analyses associated with the complexity of lipidome data.

Keywords: Bioinformatics; Data integration; Lipid Identification; Lipidomics; Ontologies; Pathway enrichment.

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References

    1. Aimo, L., Liechti, R., Hyka-Nouspikel, N., Niknejad, A., Gleizes, A., Götz, L., Kuznetsov, D., David, F. P. A., van der Goot, F. G., Riezman, H., Bougueleret, L., Xenarios, I., & Bridge, A. (2015). The SwissLipids knowledgebase for lipid biology. Bioinformatics, 31, 2860–2866. https://doi.org/10.1093/bioinformatics/btv285 .
    1. Alves, M. A., Lamichhane, S., Dickens, A., McGlinchey, A., Ribeiro, H. C., Sen, P., Wei, F., Hyötyläinen, T., & Orešič, M. (2021). Systems biology approaches to study lipidomes in health and disease. Biochimica et Biophysica Acta Molecular and Cell Biology of Lipids, 1866, 158857. https://doi.org/10.1016/j.bbalip.2020.158857 . - DOI - PubMed
    1. Bowden, J. A., Ulmer, C. Z., Jones, C. M., Koelmel, J. P., & Yost, R. A. (2018). NIST lipidomics workflow questionnaire: An assessment of community-wide methodologies and perspectives. Metabolomics, 14, 53. https://doi.org/10.1007/s11306-018-1340-1 . - DOI - PubMed
    1. Brunk, E., Sahoo, S., Zielinski, D. C., Altunkaya, A., Dräger, A., Mih, N., Gatto, F., Nilsson, A., Gonzalez, G. A. P., Aurich, M. K., Prlić, A., Sastry, A., Danielsdottir, A. D., Heinken, A., Noronha, A., Rose, P. W., Burley, S. K., Fleming, R. M. T., Nielsen, J., Thiele, I., et al. (2018). Recon3D enables a three-dimensional view of gene variation in human metabolism. Nature Biotechnology, 36, 272–281. https://doi.org/10.1038/nbt.4072 . - DOI - PubMed - PMC
    1. Clair, G., Reehl, S., Stratton, K. G., Monroe, M. E., Tfaily, M. M., Ansong, C., & Kyle, J. E. (2019). Lipid Mini-On: Mining and ontology tool for enrichment analysis of lipidomic data. Bioinformatics, 35, 4507–4508. https://doi.org/10.1093/bioinformatics/btz250 . - DOI - PubMed - PMC

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