Related Experiment Video
Updated: Oct 8, 2025
Shotgun Lipidomics of Rodent Tissues
Published on: November 18, 2022
BATL: Bayesian annotations for targeted lipidomics
Justin G Chitpin1,2,3,4,5, Anuradha Surendra6, Thao T Nguyen3,4,5,7
1Regenerative Medicine Program, Ottawa, ON K1H 8L6, Canada.
Motivation:
Bioinformatic tools capable of annotating, rapidly and reproducibly, large, targeted lipidomic datasets are limited. Specifically, few programs enable high-throughput peak assessment of liquid chromatography-electrospray ionization tandem mass spectrometry data acquired in either selected or multiple reaction monitoring modes.
Results:
We present here Bayesian Annotations for Targeted Lipidomics, a Gaussian naïve Bayes classifier for targeted lipidomics that annotates peak identities according to eight features related to retention time, intensity, and peak shape. Lipid identification is achieved by modeling distributions of these eight input features across biological conditions and maximizing the joint posterior probabilities of all peak identities at a given transition. When applied to sphingolipid and glycerophosphocholine selected reaction monitoring datasets, we demonstrate over 95% of all peaks are rapidly and correctly identified.
Availability And Implementation:
BATL software is freely accessible online at https://complimet.ca/batl/ and is compatible with Safari, Firefox, Chrome and Edge.
Supplementary Information:
Supplementary data are available at Bioinformatics online.
