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

doi:10.1093/bioinformatics/btab854

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 journal › Article › peer-review

4 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 language	English
Pages (from-to)	1593-1599
Number of pages	7
Journal	Bioinformatics
Volume	38
Issue number	6
DOIs	https://doi.org/10.1093/bioinformatics/btab854
State	Published - 15 Mar 2022

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Chitpin, J. G., Surendra, A., Nguyen, T. T., Taylor, G. P., Xu, H., Alecu, I., Ortega, R., Tomlinson, J. J., Crawley, A. M., McGuinty, M., Schlossmacher, M. G., Saunders-Pullman, R., Cuperlovic-Culf, M., Bennett, S. A. L., & Perkins, T. J. (2022). BATL: Bayesian annotations for targeted lipidomics. Bioinformatics, 38(6), 1593-1599. https://doi.org/10.1093/bioinformatics/btab854

@article{24dd2c174afd4b0eb44f1b1c3b90dd37,

title = "BATL: Bayesian annotations for targeted lipidomics",

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 na{\"i}ve 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.",

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

note = "Publisher Copyright: {\textcopyright} 2022 The Author(s) 2021. Published by Oxford University Press.",

year = "2022",

month = mar,

day = "15",

doi = "10.1093/bioinformatics/btab854",

language = "English",

volume = "38",

pages = "1593--1599",

journal = "Bioinformatics",

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T2 - Bayesian annotations for targeted lipidomics

AU - Chitpin, Justin G.

AU - Surendra, Anuradha

AU - Nguyen, Thao T.

AU - Taylor, Graeme P.

AU - Xu, Hongbin

AU - Alecu, Irina

AU - Ortega, Roberto

AU - Tomlinson, Julianna J.

AU - Crawley, Angela M.

AU - McGuinty, Michaeline

AU - Schlossmacher, Michael G.

AU - Saunders-Pullman, Rachel

AU - Cuperlovic-Culf, Miroslava

AU - Bennett, Steffany A.L.

AU - Perkins, Theodore J.

PY - 2022/3/15

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N2 - 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.

AB - 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.

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DO - 10.1093/bioinformatics/btab854

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VL - 38

SP - 1593

EP - 1599

JO - Bioinformatics

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BATL: Bayesian annotations for targeted lipidomics

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