Interpreting the lipidome: bioinformatic approaches to embrace the complexity

J.E. Kyle, L. Aimo, A.J. Bridge, G. Clair, M. Fedorova, J.B. Helms, M.R. Molenaar, Z.X. Ni, M. Oresic, D. Slenter, E. Willighagen, B.J.M. Webb-Robertson*

*Corresponding author for this work

Research output: Contribution to journal(Systematic) Review article peer-review

3 Citations (Web of Science)

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.
Original languageEnglish
Article number55
Number of pages10
JournalMetabolomics
Volume17
Issue number6
DOIs
Publication statusPublished - 1 Jun 2021

Keywords

  • Lipidomics
  • Bioinformatics
  • Lipid Identification
  • Ontologies
  • Pathway enrichment
  • Data integration
  • COMPUTATIONAL LIPIDOMICS
  • GENOME-SCALE
  • LC-MS
  • SOFTWARE
  • WORKFLOW
  • GENE
  • IDENTIFICATION
  • PHOSPHOLIPIDS
  • SPECTROMETRY
  • WIKIPATHWAYS

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