BATL: Bayesian annotations for targeted lipidomics

Justin G. Chitpin, Anuradha Surendra, Thao T. Nguyen, Graeme P. Taylor, Hongbin Xu, Irina Alecu, Roberto Ortega, Julianna J. Tomlinson, Angela M. Crawley, Michaeline McGuinty, Michael G. Schlossmacher, Rachel Saunders-Pullman, Miroslava Cuperlovic-Culf, Steffany A.L. Bennett, Theodore J. Perkins

Research output: Contribution to journalArticlepeer-review

2 Scopus citations

Abstract

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.

Original languageEnglish
Pages (from-to)1593-1599
Number of pages7
JournalBioinformatics
Volume38
Issue number6
DOIs
StatePublished - 15 Mar 2022

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