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Related Concept Videos

Overview of Lipid Metabolism01:24

Overview of Lipid Metabolism

Lipid metabolism is a crucial process in the human body that involves the synthesis and degradation of lipids. This process is essential for energy production, cell membrane formation, and hormone production, among other functions.
Lipolysis: The Breakdown of Lipids:
Lipolysis is the process of breaking down lipids, particularly triglycerides, into glycerol and fatty acids. This process typically occurs in the adipose tissue and is triggered by various hormones, including glucagon and...
Lipid-derived Compounds in the Human Body01:31

Lipid-derived Compounds in the Human Body

Fats and lipids are crucial components in the human body. Some lipid-derived compounds, such as fat-soluble vitamins, eicosanoids, lipoproteins, and glycolipids, also play unique roles to support various  biological processes .
Fat-soluble Vitamins
Fat-soluble vitamins, including vitamins A, D, E, and K, are required in minimal quantities, but their deficiencies can lead to severely abnormal physiological conditions. For example, vitamin A deficiency can cause night blindness, dry skin, delayed...
Lipids as Anchors01:32

Lipids as Anchors

In the plasma membrane, the lipids forming the bilayer can also act as an anchor to tether proteins to the membrane. The three main types of lipid anchors found in eukaryotes are – prenyl groups, fatty acyl groups, and glycosylphosphatidylinositol or GPI groups. Prenyl and fatty acyl groups act as anchors on the cytosolic surface of the membrane, whereas GPI anchors proteins on the extracellular side.
The carboxy-terminal of most of the prenylated proteins, such as Ras proteins, contains the...

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Related Experiment Video

Updated: Jun 6, 2026

Lipidomics and Transcriptomics in Neurological Diseases
09:58

Lipidomics and Transcriptomics in Neurological Diseases

Published on: March 18, 2022

Assessing current capabilities for incorporating lipidomics in multiomics data integration.

Dylan H Ross1, Raghav Jain1, Hyeyoon Kim1

  • 1Biological Sciences Division, Pacific Northwest National Laboratory, 902 Batelle Boulevard, Richland, WA 99354,  United States.

Briefings in Bioinformatics
|June 4, 2026
PubMed
Summary

Multiomics data integration, especially for metabolomics and lipidomics, offers deep biological insights. Overcoming challenges in small molecule data analysis, particularly lipidomics, is key to advancing biological understanding.

Keywords:
artificial intelligencelipidomicsmulti-omics integrationpathway analysis

Related Experiment Videos

Last Updated: Jun 6, 2026

Lipidomics and Transcriptomics in Neurological Diseases
09:58

Lipidomics and Transcriptomics in Neurological Diseases

Published on: March 18, 2022

Area of Science:

  • * Systems biology and bioinformatics.
  • * Computational biology and data science.

Background:

  • * Multiomics analyses, integrating data from nucleic acids, proteins, metabolites, and lipids, provide comprehensive biological insights.
  • * Pathway analysis is a direct method for translating molecular data into biological understanding, but faces challenges with small molecule -omics data.

Purpose of the Study:

  • * To review current multiomics integration tools, focusing on metabolomics and lipidomics.
  • * To identify limitations and gaps in existing tools for pathway integration of small molecule data.
  • * To propose improvements for enhanced multiomics data utility, particularly for lipidomics.

Main Methods:

  • * Literature review of multiomics integration tools.
  • * Analysis of capabilities and limitations using real multiomics data examples.
  • * Focus on pathway analysis integration for metabolomics and lipidomics.

Main Results:

  • * Significant barriers hinder the full utilization of metabolomics and lipidomics in pathway integration.
  • * Challenges include metabolite annotation rates, lipid isomerism, and limited lipid representation in pathway databases.
  • * Current tools often lack comprehensive support for small molecule -omics data.

Conclusions:

  • * Improvements in lipid representation within pathway knowledge bases are crucial.
  • * Adoption of artificial intelligence approaches can enhance multiomics pathway integration.
  • * Enhanced tools are needed to fully leverage small molecule -omics data for biological discovery.