Probabilistic framework for integration of mass spectrum and retention time information in small molecule identification.

ABSTRACT:

Motivation

Identification of small molecules in a biological sample remains a major bottleneck in molecular biology, despite a decade of rapid development of computational approaches for predicting molecular structures using mass spectrometry (MS) data. Recently, there has been increasing interest in utilizing other information sources, such as liquid chromatography (LC) retention time (RT), to improve identifications solely based on MS information, such as precursor mass-per-charge and tandem mass spectrometry (MS2).

Results

We put forward a probabilistic modelling framework to integrate MS and RT data of multiple features in an LC-MS experiment. We model the MS measurements and all pairwise retention order information as a Markov random field and use efficient approximate

SUBMITTER: Bach E 

PROVIDER: S-EPMC8289373 | biostudies-literature | 2021 Jul

REPOSITORIES: biostudies-literature