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Proc Natl Acad Sci U S A. 2016 Nov 29;113(48):13738-13743. Epub 2016 Nov 16.

Topic modeling for untargeted substructure exploration in metabolomics.

Author information

1
Glasgow Polyomics, University of Glasgow, Glasgow G61 1QH, United Kingdom.
2
Institute of Infection, Immunity, and Inflammation, College of Medical, Veterinary, and Life Sciences, University of Glasgow, Glasgow G12 8TA, United Kingdom.
3
School of Computing Science, University of Glasgow, Glasgow G12 8RZ, United Kingdom.
4
Wellcome Trust Centre for Molecular Parasitology, Institute of Infection, Immunity and Inflammation, University of Glasgow, Glasgow G12 8TA, United Kingdom.
5
Glasgow Polyomics, University of Glasgow, Glasgow G61 1QH, United Kingdom; Simon.Rogers@glasgow.ac.uk.

Abstract

The potential of untargeted metabolomics to answer important questions across the life sciences is hindered because of a paucity of computational tools that enable extraction of key biochemically relevant information. Available tools focus on using mass spectrometry fragmentation spectra to identify molecules whose behavior suggests they are relevant to the system under study. Unfortunately, fragmentation spectra cannot identify molecules in isolation but require authentic standards or databases of known fragmented molecules. Fragmentation spectra are, however, replete with information pertaining to the biochemical processes present, much of which is currently neglected. Here, we present an analytical workflow that exploits all fragmentation data from a given experiment to extract biochemically relevant features in an unsupervised manner. We demonstrate that an algorithm originally used for text mining, latent Dirichlet allocation, can be adapted to handle metabolomics datasets. Our approach extracts biochemically relevant molecular substructures ("Mass2Motifs") from spectra as sets of co-occurring molecular fragments and neutral losses. The analysis allows us to isolate molecular substructures, whose presence allows molecules to be grouped based on shared substructures regardless of classical spectral similarity. These substructures, in turn, support putative de novo structural annotation of molecules. Combining this spectral connectivity to orthogonal correlations (e.g., common abundance changes under system perturbation) significantly enhances our ability to provide mechanistic explanations for biological behavior.

KEYWORDS:

bioinformatics; fragmentation; mass spectrometry; metabolomics; topic modeling

PMID:
27856765
PMCID:
PMC5137707
DOI:
10.1073/pnas.1608041113
[Indexed for MEDLINE]
Free PMC Article

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