Propagating annotations of molecular networks using in silico fragmentation

Silva, Ricardo R. da; Wang, Mingxun; Nothias, Louis Félix; Hooft, Justin J. J. van der; Caraballo‐Rodríguez, Andrés Mauricio; Fox, Evan; Balunas, Marcy J.; Klassen, Jonathan L.; Lopes, Norberto Peporine; Dorrestein, Pieter C.

doi:10.1371/journal.pcbi.1006089

Cited by 291 publications

(311 citation statements)

References 63 publications

(73 reference statements)

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“…Among the advantages of molecular networking of ddMS 2 visualisation, there is the possibility of structure elucidation of closely related structures due to similar fragmentograms . The concept of propagation of compound identification was recently proved . This hypothesis was tested using example files from LipidXplorer tutorial.…”

Section: Resultsmentioning

confidence: 99%

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Extending compound identification for molecular network using the LipidXplorer database independent method: A proof of concept using glycoalkaloids from Solanum pseudoquina A. St.‐Hil.

Soares

Taujale

Garrett

et al. 2018

Phytochemical Analysis

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Introduction Molecular networks are now established as the method of choice for tandem mass spectrometry dereplication and similarity‐based structure elucidation. Node identification can be used to start the propagation of the structure elucidation of unknown compounds progressively. Objective To demonstrate the capabilities of using the LipidXplorer data results along with molecular networking to identify nodes and aid sequential structure elucidation of unknown compounds. Material and Methods Molecular fragmentation query language (MFQL) files were written to identify glycoalkaloids based on known structures described for Solanum species. A dataset generated from liquid chromatography‐high resolution mass spectrometry (LC‐HRMS) analysis of Solanum pseudoquina sample were submitted to dereplication on both LipidXplorer software and Global Natural Products Social Molecular Network (GNPS) online system. The resulting attribute table from GNPS calculations was merged with the LipidXplorer results and this merged file was used for network visualisation in Cytoscape. Nodes in the molecular network were labelled using the LipidXplorer identifiers, thus assisting the structure elucidation of unidentified compounds. Results The combination of the LipidXplorer glycoalkaloids list and GNPS analysis was used in Cytoscape to label nodes in the molecular network. The analysis of the network using these labelled starting points triggered the structure elucidation of closely related nodes leading to the identification of 30 compounds using the LipidXplorer output and four purified and structure elucidated compounds, including a new glycoalkaloids identified as 3‐O‐(β‐d‐xylopyranosyl)‐(20R,25S)‐22,26‐epimino‐16‐acetyl‐cholesta‐5,22(N)‐diene. Conclusion A significant compound identification completely based on molecular formula and fragmentation queries was achieved. This new and effective approach could help researches to expand the identification rate of compounds in dereplication studies using molecular networks.

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Section: Resultsmentioning

confidence: 99%

“…5,9 The concept of propagation of compound identification was recently proved. 11 This hypothesis was tested using example files from LipidXplorer tutorial. These files were submitted to both LipidXplorer using lipid based MFQL files and the GNPS web system for MNs and library match for identification.…”

Section: Proof Of Conceptmentioning

confidence: 99%

Extending compound identification for molecular network using the LipidXplorer database independent method: A proof of concept using glycoalkaloids from Solanum pseudoquina A. St.‐Hil.

Soares

Taujale

Garrett

et al. 2018

Phytochemical Analysis

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“…Network Annotation Propagation (NAP) is a web tool, which is accessible through the Global Natural Product Social Molecular Networking (GNPS) web platform (https://gnps.ucsd.edu/ProteoSAFe/static/gnps-theoretical.jsp) developed for spectral annotations, where molecular networking is used to improve the accuracy of in silico predictions through propagation of structural annotations, even when there is no match to a MS/MS spectrum in spectral libraries .…”

Section: Tools For Analytical Platformsmentioning

confidence: 99%

Tools and resources for metabolomics research community: A 2017–2018 update

Misra¹,

Mohapatra

2018

Electrophoresis

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The scale at which MS‐ and NMR‐based platforms generate metabolomics datasets for both research, core, and clinical facilities to address challenges in the various sciences—ranging from biomedical to agricultural—is underappreciated. Thus, metabolomics efforts spanning microbe, environment, plant, animal, and human systems have led to continual and concomitant growth of in silico resources for analysis and interpretation of these datasets. These software tools, resources, and databases drive the field forward to help keep pace with the amount of data being generated and the sophisticated and diverse analytical platforms that are being used to generate these metabolomics datasets. To address challenges in data preprocessing, metabolite annotation, statistical interrogation, visualization, interpretation, and integration, the metabolomics and informatics research community comes up with hundreds of tools every year. The purpose of the present review is to provide a brief and useful summary of more than 95 metabolomics tools, software, and databases that were either developed or significantly improved during 2017–2018. We hope to see this review help readers, developers, and researchers to obtain informed access to these thorough lists of resources for further improvisation, implementation, and application in due course of time.

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“…In biological samples, many metabolites share molecular substructures and form structurally related molecular families (MFs) of various chemical classes, which has inspired metabolome mining tools exploiting these biochemical relationships. Indeed, since the molecular networking approach was proposed in 2012 [10], numerous complementary molecular mining workflows as well as annotation and classification tools have been introduced including SIRIUS [3], CSI:FingerID [4], MetFusion [11], MetFamily [12], and many others [1,2,7,8,[13][14][15][16][17][18][19][20][21][22] and their combined use for natural product discovery was very recently reviewed [23]. Where tandem mass spectral molecular networking efficiently can group molecular features in molecular families [10], MS2LDA can discover substructures that aid in further annotation of subfamilies and shared modifications [14].…”

Section: Introductionmentioning

confidence: 99%

“…Where tandem mass spectral molecular networking efficiently can group molecular features in molecular families [10], MS2LDA can discover substructures that aid in further annotation of subfamilies and shared modifications [14]. Furthermore, recently introduced tools such as Network Annotation Propagation (NAP) [8], DEREPLICATOR [1], VarQuest [2], or SIRIUS+CSI:FingerID [4] allow for effective searching in chemical databases for candidate structures. These candidate structures can now be automatically chemically classified using the ClassyFire tool [16] which takes molecular descriptors as SMILES or InchiKeys as input and outputs hierarchical chemical ontology terms.…”

Section: Introductionmentioning

confidence: 99%

MolNetEnhancer: enhanced molecular networks by integrating metabolome mining and annotation tools

Ernst

Kang

Caraballo‐Rodríguez

et al. 2019

Preprint

Self Cite

104

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Metabolomics has started to embrace computational approaches for chemical interpretation of large data sets. Yet, metabolite annotation remains a key challenge. Recently, molecular networking and MS2LDA emerged as molecular mining tools that find molecular families and substructures in mass spectrometry fragmentation data. Moreover, in silico annotation tools obtain and rank candidate molecules for fragmentation spectra. Ideally, all structural information obtained and inferred from these computational tools could be combined to increase the resulting chemical insight one can obtain from a data set. However, integration is currently hampered as each tool has its own output format and efficient matching of data across these tools is lacking. Here, we introduce MolNetEnhancer, a workflow that combines the outputs from molecular networking, MS2LDA, in silico annotation tools (such as Network Annotation Propagation or DEREPLICATOR) and the automated chemical classification through ClassyFire to provide a more comprehensive chemical overview of metabolomics data whilst at the same time illuminating structural details for each fragmentation spectrum. We present examples from four plant and bacterial case studies and show how MolNetEnhancer enables the chemical annotation, visualization, and discovery of the subtle substructural diversity within molecular families. We conclude that MolNetEnhancer is a useful tool that greatly assists the metabolomics researcher in deciphering the metabolome through combination of multiple independent in silico pipelines.

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Propagating annotations of molecular networks using in silico fragmentation

Cited by 291 publications

References 63 publications

Extending compound identification for molecular network using the LipidXplorer database independent method: A proof of concept using glycoalkaloids from Solanum pseudoquina A. St.‐Hil.

Extending compound identification for molecular network using the LipidXplorer database independent method: A proof of concept using glycoalkaloids from Solanum pseudoquina A. St.‐Hil.

Tools and resources for metabolomics research community: A 2017–2018 update

MolNetEnhancer: enhanced molecular networks by integrating metabolome mining and annotation tools

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