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 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 tra

SUBMITTER: Chitpin JG 

PROVIDER: S-EPMC8896618 | biostudies-literature | 2022 Mar

REPOSITORIES: biostudies-literature