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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

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 cla...

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Autores principales: Quinlan, Zachary A., Koester, Irina, Aron, Allegra T., Petras, Daniel, Aluwihare, Lihini I., Dorrestein, Pieter C., Nelson, Craig E., Wegley Kelly, Linda
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9786801/
https://www.ncbi.nlm.nih.gov/pubmed/36557313
http://dx.doi.org/10.3390/metabo12121275
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author Quinlan, Zachary A.
Koester, Irina
Aron, Allegra T.
Petras, Daniel
Aluwihare, Lihini I.
Dorrestein, Pieter C.
Nelson, Craig E.
Wegley Kelly, Linda
author_facet Quinlan, Zachary A.
Koester, Irina
Aron, Allegra T.
Petras, Daniel
Aluwihare, Lihini I.
Dorrestein, Pieter C.
Nelson, Craig E.
Wegley Kelly, Linda
author_sort Quinlan, Zachary A.
collection PubMed
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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spelling pubmed-97868012022-12-24 ConCISE: Consensus Annotation Propagation of Ion Features in Untargeted Tandem Mass Spectrometry Combining Molecular Networking and In Silico Metabolite Structure Prediction Quinlan, Zachary A. Koester, Irina Aron, Allegra T. Petras, Daniel Aluwihare, Lihini I. Dorrestein, Pieter C. Nelson, Craig E. Wegley Kelly, Linda Metabolites Article 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. MDPI 2022-12-16 /pmc/articles/PMC9786801/ /pubmed/36557313 http://dx.doi.org/10.3390/metabo12121275 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Quinlan, Zachary A.
Koester, Irina
Aron, Allegra T.
Petras, Daniel
Aluwihare, Lihini I.
Dorrestein, Pieter C.
Nelson, Craig E.
Wegley Kelly, Linda
ConCISE: Consensus Annotation Propagation of Ion Features in Untargeted Tandem Mass Spectrometry Combining Molecular Networking and In Silico Metabolite Structure Prediction
title ConCISE: Consensus Annotation Propagation of Ion Features in Untargeted Tandem Mass Spectrometry Combining Molecular Networking and In Silico Metabolite Structure Prediction
title_full ConCISE: Consensus Annotation Propagation of Ion Features in Untargeted Tandem Mass Spectrometry Combining Molecular Networking and In Silico Metabolite Structure Prediction
title_fullStr ConCISE: Consensus Annotation Propagation of Ion Features in Untargeted Tandem Mass Spectrometry Combining Molecular Networking and In Silico Metabolite Structure Prediction
title_full_unstemmed ConCISE: Consensus Annotation Propagation of Ion Features in Untargeted Tandem Mass Spectrometry Combining Molecular Networking and In Silico Metabolite Structure Prediction
title_short ConCISE: Consensus Annotation Propagation of Ion Features in Untargeted Tandem Mass Spectrometry Combining Molecular Networking and In Silico Metabolite Structure Prediction
title_sort concise: consensus annotation propagation of ion features in untargeted tandem mass spectrometry combining molecular networking and in silico metabolite structure prediction
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9786801/
https://www.ncbi.nlm.nih.gov/pubmed/36557313
http://dx.doi.org/10.3390/metabo12121275
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