Interpreting the lipidome: bioinformatic approaches to embrace the complexity

Interpreting the lipidome : bioinformatic approaches to embrace the complexity

Kyle, Jennifer E. (author): Biological Sciences Division, Pacific Northwest National Laboratory, Richland, WA, USA

Aimo, Lucila (author): Swiss-Prot Group, SIB Swiss Institute of Bioinformatics, Geneva, Switzerland

Bridge, Alan J. (author): Swiss-Prot Group, SIB Swiss Institute of Bioinformatics, Geneva, Switzerland

Clair, Geremy (author): Biological Sciences Division, Pacific Northwest National Laboratory, Richland, WA, USA

Fedorova, Maria (author): Institute of Bioanalytical Chemistry, Faculty of Chemistry and Mineralogy, Center for Biotechnology and Biomedicine, Universität Leipzig, Leipzig, Germany

Helms, J. Bernd (author): Department of Biomolecular Health Sciences, Faculty of Veterinary Medicine, Utrecht University, Utrecht, The Netherlands

Molenaar, Martijn R. (author): Structural and Computational Biology Unit, European Molecular Biology Laboratory, Heidelberg, Germany

Ni, Zhixu (author): Institute of Bioanalytical Chemistry, Faculty of Chemistry and Mineralogy, Center for Biotechnology and Biomedicine, Universität Leipzig, Leipzig, Germany

Oresic, Matej, 1967- (author): Örebro universitet,Institutionen för medicinska vetenskaper,Turku Bioscience Centre, University of Turku and Åbo Akademi University, Turku, Finland

Slenter, Denise (author): Department of Bioinformatics-BiGCaT, NUTRIM, Maastricht University, Maastricht, The Netherlands

Willighagen, Egon (author): Department of Bioinformatics-BiGCaT, NUTRIM, Maastricht University, Maastricht, The Netherlands

Webb-Robertson, Bobbie-Jo M. (author): Biological Sciences Division, Pacific Northwest National Laboratory, Richland, WA, USA

(creator_code:org_t)

2021-06-06
2021
English.
In: Metabolomics. - : Springer-Verlag New York. - 1573-3882 .- 1573-3890. ; 17:6

Related links:: https://urn.kb.se/re...; show more...; https://doi.org/10.1...; show less...

Abstract Subject headings

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.

By the author/editor: Kyle, Jennifer E ...; Aimo, Lucila; Bridge, Alan J.; Clair, Geremy; Fedorova, Maria; Helms, J. Bernd; show more...; Molenaar, Martij ...; Ni, Zhixu; Oresic, Matej, 1 ...; Slenter, Denise; Willighagen, Ego ...; Webb-Robertson, ...; show less...

About the subject

NATURAL SCIENCES: NATURAL SCIENCES; and Biological Scien ...; and Bioinformatics a ...

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