BATL: Bayesian annotations for targeted lipidomics

Chitpin, J. G.; Taylor, G. P.; Xu, H.; Nguyen, T. T.; Ortega, R.; Tomlinson, J. J.; Schlossmacher, M. G.; Saunders-Pullman, R.; Bennett, S. A. L.; Perkins, T. J.

doi:10.1101/2021.03.18.435788

bioRxiv · 10.1101/2021.03.18.435788

BATL: Bayesian annotations for targeted lipidomics

Chitpin, J. G.·Taylor, G. P.·Xu, H.·Nguyen, T. T.·Ortega, R.·Tomlinson, J. J.·Schlossmacher, M. G.·Saunders-Pullman, R.·Bennett, S. A. L.·Perkins, T. J.

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Abstract

MotivationBioinformatic 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 (LC-ESI-MS/MS) data acquired in either selected or multiple reaction monitoring (SRM and MRM) modes. ResultsWe present here Bayesian Annotations for Targeted Lipidomics (BATL), a Gaussian naive 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 modelling 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 SRM datasets, we demonstrate over 95% of all peaks are rapidly and correctly identified. Availability and implementationBATL software is freely accessible online at https://complimet.ca/batl/ and is compatible with Safari, Firefox, Chrome and Edge. Supplementary informationSupplementary data are available at Bioinformatics online.

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Chitpin, J. G., Taylor, G. P., Xu, H., Nguyen, T. T., Ortega, R., Tomlinson, J. J., Schlossmacher, M. G., Saunders-Pullman, R., Bennett, S. A. L., Perkins, T. J.. 2021-03-19. BATL: Bayesian annotations for targeted lipidomics. https://doi.org/10.1101/2021.03.18.435788

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