MINE 2.0: enhanced biochemical coverage for peak identification in untargeted metabolomics

Although advances in untargeted metabolomics have made it possible to gather data on thousands of cellular metabolites in parallel, identification of novel metabolites from these datasets remains challenging. To address this need, Metabolic in silico Network Expansions (MINEs) were developed. A MINE...

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Bibliographic Details
Published in:Bioinformatics (Oxford, England) England), 2022-05, Vol.38 (13)
Main Authors: Strutz, Jonathan, Shebek, Kevin M., Broadbelt, Linda J., Tyo, Keith J.
Format: Article
Language:English
Subjects:
BASIC BIOLOGICAL SCIENCES
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Summary:Although advances in untargeted metabolomics have made it possible to gather data on thousands of cellular metabolites in parallel, identification of novel metabolites from these datasets remains challenging. To address this need, Metabolic in silico Network Expansions (MINEs) were developed. A MINE is an expansion of known biochemistry which can be used as a list of potential structures for unannotated metabolomics peaks. Here, we present MINE 2.0, which utilizes a new set of biochemical transformation rules that covers 93% of MetaCyc reactions (compared to 25% in MINE 1.0). This results in a 17-fold increase in database size and a 40% increase in MINE database compounds matching unannotated peaks from an untargeted metabolomics dataset. MINE 2.0 is thus a significant improvement to this community resource.
ISSN:1367-4803