Cargando…
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...
Autores principales: | , , , , , , , |
---|---|
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 |
_version_ | 1784858373917769728 |
---|---|
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. |
format | Online Article Text |
id | pubmed-9786801 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
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 |
work_keys_str_mv | AT quinlanzacharya conciseconsensusannotationpropagationofionfeaturesinuntargetedtandemmassspectrometrycombiningmolecularnetworkingandinsilicometabolitestructureprediction AT koesteririna conciseconsensusannotationpropagationofionfeaturesinuntargetedtandemmassspectrometrycombiningmolecularnetworkingandinsilicometabolitestructureprediction AT aronallegrat conciseconsensusannotationpropagationofionfeaturesinuntargetedtandemmassspectrometrycombiningmolecularnetworkingandinsilicometabolitestructureprediction AT petrasdaniel conciseconsensusannotationpropagationofionfeaturesinuntargetedtandemmassspectrometrycombiningmolecularnetworkingandinsilicometabolitestructureprediction AT aluwiharelihinii conciseconsensusannotationpropagationofionfeaturesinuntargetedtandemmassspectrometrycombiningmolecularnetworkingandinsilicometabolitestructureprediction AT dorresteinpieterc conciseconsensusannotationpropagationofionfeaturesinuntargetedtandemmassspectrometrycombiningmolecularnetworkingandinsilicometabolitestructureprediction AT nelsoncraige conciseconsensusannotationpropagationofionfeaturesinuntargetedtandemmassspectrometrycombiningmolecularnetworkingandinsilicometabolitestructureprediction AT wegleykellylinda conciseconsensusannotationpropagationofionfeaturesinuntargetedtandemmassspectrometrycombiningmolecularnetworkingandinsilicometabolitestructureprediction |