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Lipids as Anchors

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...

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Updated: Oct 8, 2025

Shotgun Lipidomics of Rodent Tissues

Shotgun Lipidomics of Rodent Tissues

Published on: November 18, 2022

BATL: Bayesian annotations for targeted lipidomics.

Justin G Chitpin^1,2,3,4,5, Anuradha Surendra⁶, Thao T Nguyen^3,4,5,7

¹Regenerative Medicine Program, Ottawa, ON K1H 8L6, Canada.

Bioinformatics (Oxford, England)

|December 24, 2021

Summary

This summary is machine-generated.

Limited bioinformatic tools hinder rapid lipidomic data analysis. Bayesian Annotations for Targeted Lipidomics (BATL) software accurately identifies over 95% of peaks in targeted lipidomics datasets, improving high-throughput assessment.

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Area of Science:

Computational biology
Lipidomics
Mass spectrometry

Background:

Existing bioinformatic tools struggle with rapid and reproducible annotation of large targeted lipidomic datasets.
High-throughput peak assessment of liquid chromatography-electrospray ionization tandem mass spectrometry data, especially in selected or multiple reaction monitoring modes, is challenging.

Purpose of the Study:

To develop and present a novel Gaussian naïve Bayes classifier for targeted lipidomics.
To enable rapid and reproducible annotation of large targeted lipidomic datasets.

Main Methods:

Developed Bayesian Annotations for Targeted Lipidomics (BATL), a Gaussian naïve Bayes classifier.
Annotates lipid peaks using eight features: retention time, intensity, and peak shape.
Models feature distributions across biological conditions to maximize joint posterior probabilities for peak identification.

Main Results:

Demonstrated over 95% accurate and rapid identification of peaks in sphingolipid and glycerophosphocholine selected reaction monitoring datasets.
BATL effectively annotates targeted lipidomics data, addressing limitations in current software.

Conclusions:

BATL software provides a robust solution for high-throughput peak annotation in targeted lipidomics.
The tool enhances the speed and accuracy of lipid identification from mass spectrometry data.