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Concise: Consensus Annotation Propagation Of Ion Features In Untargeted Tandem Mass Spectrometry Combining Molecular Networking And In Silico Metabolite Structure Prediction.
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2022
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Source: Metabolites 2022, 12(12), 1275
[PDF-1.78 MB]
Details:
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Journal Title:Metabolites
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NOAA Program & Office:
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Description:Recent developments in molecular networking have expanded our ability to characterize the metabolome of diverse samples that contain a significant proportion of ion features with no mass spectral match to known compounds. Manual and tool-assisted natural annotation propagation is readily used to classify molecular networks; however, currently no annotation propagation tools leverage consensus confidence strategies enabled by hierarchical chemical ontologies or enable the use of new in silico tools without significant modification. Herein we present ConCISE (Consensus Classifications of In Silico Elucidations) which is the first tool to fuse molecular networking, spectral library matching and in silico class predictions to establish accurate putative classifications for entire subnetworks. By limiting annotation propagation to only structural classes which are identical for the majority of ion features within a subnetwork, ConCISE maintains a true positive rate greater than 95% across all levels of the ChemOnt hierarchical ontology used by the ClassyFire annotation software (superclass, class, subclass). The ConCISE framework expanded the proportion of reliable and consistent ion feature annotation up to 76%, allowing for improved assessment of the chemo-diversity of dissolved organic matter pools from three complex marine metabolomics datasets comprising dominant reef primary producers, five species of the diatom genus Pseudo-nitzchia, and stromatolite sediment samples.
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Content Notes:The source code and all data from the case studies used are available on github (github.com/zquinlan/concise) and have been permanently cached at the doi: 10.5281/zenodo.7044828. The original spectra from the case studies are available from massive.ucsd.edu under the ascession numbers: MSV000082083, MSV000081731, MSV000083729.
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Source:Metabolites 2022, 12(12), 1275
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Rights Information:CC BY
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Compliance:Submitted
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