Abstract
MotivationIdentification 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).ResultsWe 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 inference for scoring and ranking potential molecular structures. Our experiments show improved identification accuracy by combining MS2 data and retention orders using our approach, thereby outperforming state-of-the-art methods. Furthermore, we demonstrate the benefit of our model when only a subset of LC-MS features has MS2 measurements available besides MS1.Availability and implementationSoftware and data are freely available at https://github.com/aalto-ics-kepaco/msms_rt_score_integration.Supplementary information Supplementary data are available at Bioinformatics online.
Highlights
The identification of small molecules, such as metabolites or drugs, in biological samples is a challenging task posing a bottleneck in various research fields, such as biomedicine, biotechnology, environmental chemistry and drug discovery
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)
We demonstrate the benefit of our model when only a subset of LC-MS features has MS2 measurements available besides MS1
Summary
The identification of small molecules, such as metabolites or drugs, in biological samples is a challenging task posing a bottleneck in various research fields, such as biomedicine, biotechnology, environmental chemistry and drug discovery. The samples typically contain thousands of different molecules, the vast majority of which remain unidentified (Aksenov et al, 2017; da Silva et al, 2015). LC separates molecules by their differential physicochemical interaction between the stationary and mobile phase, which results in retention time (RT) differences and MS separates molecular ions by their mass per charge (MS1). In an untargeted metabolomics experiment, large sets of MS features (MS1 and RT, plus optionally MS2), are observed, corresponding to the different molecules in the sample. Metabolite identification concerns the structural annotation of the observed MS features
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