Reproducible Molecular Networking Of Untargeted Mass Spectrometry Data Using GNPS.

Aron, AllegraT.; Gentry, Emily C.; McPhail, Kerry L.; Nothias, Louis Félix; Nothias-Esposito, Mélissa; Bouslimani, Amina; Petras, Daniel; Gauglitz, JuliaM.; Sikora, Nicole; Vargas, Fernando; Hooft, JustinJ. J. van der; Ernst, Madeleine; Kang, Kyo Bin; Aceves, Christine M.; Caraballo‐Rodríguez, Andrés Mauricio; Koester, Irina; Weldon, Kelly C.; Bertrand, Samuel; Roullier, Catherine; Sun, Kunyang; Tehan, Richard M.; Boya, Cristopher A.; Martin, H Christian; Gutiérrez, Marcelino; Ulloa, Aldo Moreno; Mora, Javier Andres Tejeda; Mojica-Flores, Randy; Lakey-Beitia, Johant; Vásquez-Chaves, Víctor; Calderón, Ángela I.; Tayler, Nicole; Keyzers, Robert A.; Tugizimana, Fidele; Ndlovu, Nombuso; Aksenov, AlexanderA.; Jarmusch, Alan K.; Schmid, Robin; Truman, Andrew W.; Bandeira, Nuno; Wang, Mingxun; Dorrestein, Pieter C.

doi:10.26434/chemrxiv.9333212.v1

Cited by 26 publications

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“…ReDU enables reanalysis based on metadata-selected files for molecular networking. 5,9,10 Re-analyzing human blood plasma and serum, urine, and fecal samples, networked 5,053,666 MS/MS spectra (~5.6% annotated) and included annotations to clindamycin. Clindamycin’s ( 1 ) molecular family matched multiple datasets and sample types (Fig 1e).…”

Section: Resultsmentioning

confidence: 99%

Repository-scale Co- and Re-analysis of Tandem Mass Spectrometry Data

Wang

Aceves

et al. 2019

Preprint

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68Metabolomics data are difficult to find and reuse, even in public repositories. We, therefore, developed the 69Reanalysis of Data User (ReDU) interface (https://redu.ucsd.edu/), a community-and data-driven approach that 70 solves this problem at the repository scale. ReDU enables public data discovery and co-or re-analysis via 71 uniformly formatted, publicly available MS/MS data and metadata in the Global Natural Product Social Molecular 72Networking Platform (GNPS), consistent with findable, accessible, interoperable, and reusable (FAIR) 73principles. 1 74 75 76 Many simple but important questions can be asked using repository-scale public data. For example, what 77 human biospecimen or sampling location is best for detecting a given drug? Or what molecules are found in 78 humans <2 years old? Current metabolomics repositories typically require manual navigation and conversion of 79 thousands of different vendor-formatted files with inconsistent metadata formats, and developing data integration 80 algorithms, greatly complicating analyses. 81 Results and DiscussionReDU addresses FAIR principles by enabling users to find and choose files (Fig 1a). This is possible 82because ReDU formats sample information consistently via a template and drag-and-drop validator backed by 83 standard controlled vocabularies and ontologies (e.g. NCBI taxonomy, 2 UBERON 3, Disease Ontology 4 and MS 84 ontology), and includes geographical location (important for natural products and environmental samples). ReDU 85 automatically uses all public data in the GNPS/MassIVE repository that has the corresponding ReDU-compliant 86 sample information. 34,087 files in GNPS are ReDU-compatible including natural and human-built environments, 87human and animal tissues, biofluids, food, and other data from around the world (Fig 1f), analyzed using different 88 instruments, ionization methods, sample preparation methods, etc. From the 103,230,404 million MS/MS spectra 89 included in ReDU, 4,528,624 spectra were annotated (rate of 4.39% with settings yielding ~1% FDR) as one of 90 13,217 unique chemicals (Table S1). 5,6,7 91 Uniformity of data and sample information in ReDU enables metadata-based and repository-scale 92 analyses ( Fig. 1b-g). Chemical explorer enables selection of a molecule and retrieval of its associations with the 93 metadata, i.e. sample information association. For instance, selecting 12-ketodeoxycholic acid (filtering to 94 include human feces) revealed it was observed after infancy (Fig 1c), whereas cholic acid displayed the opposite 95 trend, coupled to the developing microbiome. Similarly, rosuvastatin was found in adults matching prescription 96 demographics. Another approach enabled is chemical enrichment analysis. For example, human blood, feces, 97 and urine differed by bilirubin, urobilin, and stercobilin (Fig 1d). Bilirubin was more frequently annotated in blood, 98and urobilin and stercobilin were most often annotated in feces. 8 Similarly, comparison of bacterial cultures 99 revealed differences in annotati...

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Section: Resultsmentioning

confidence: 99%

Repository-scale Co- and Re-analysis of Tandem Mass Spectrometry Data

Wang

Aceves

et al. 2019

Preprint

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“…Using this metal-infusion native ESI method to analyze a complex biological sample when one (or multiple) metals are infused post-LC presents a complex combinatorial problem of possible metal-small molecule binding interactions. A computational workflow was required to solve this; toward this end, we used ion identity molecular networking (IIN, Figure 1b ) 38 within the software tools MZmine 2 39,40 linked with Global Natural Products Social Molecular Networking (GNPS) 41,42 . In IIN, LC-MS features, defined here as chromatographic peaks with a specific m/z , are grouped based on their retention time and chromatographic feature shape correlation and identified as specific ion types of the same analyte molecule akin to the way it is accomplished by CAMERA 43 or RAMClust 44 .…”

Section: Resultsmentioning

confidence: 99%

“…Using the native metal metabolomics method developed here, both apo-desferrioxamine E (DFE) and the Fe 3+ -bound ferrioxamine E were observed from culture extracts and were connected by an Fe 3+ -binding IIN edge. DFE was annotated as a spectral match provided via molecular networking in GNPS 41,42 . Strikingly, DFE was the only Fe 3+ -binding connection observed using IIN ( Figure 4a ); this important observation illustrates the specificity of Fe 3+ -binding during the post-LC infusion as Fe 3+ binds only one specific molecule and none of the other molecules detected in this complex biological sample ( Figure 4b-e ).…”

Section: Resultsmentioning

confidence: 99%

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Native Electrospray-based Metabolomics Enables the Detection of Metal-binding Compounds

Aron

Petras

Schmid

et al. 2019

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38Metals are essential for the molecular machineries of life, and microbes have evolved a 39 variety of small molecules to acquire, compete for, and utilize metals. Systematic methods 40for the discovery of metal-small molecule complexes from biological samples are limited. 41Here we describe a two-step native electrospray ionization mass spectrometry method, in 42 which double-barrel post-column metal-infusion and pH adjustment is combined with ion 43 identity molecular networking, a rule-based informatics workflow. This method can be used 44to identify metal-binding compounds in complex samples based on defined mass (m/z) 45 offsets of ion features with the same chromatographic profiles. As this native metal 46 metabolomics approach can be easily implemented on any liquid chromatography-based 47

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“…Several tools have been developed to assist with MS/MS pattern recognition. Molecular networking-based visualization is becoming increasingly popular in metabolomics and is used by tools such as Global Natural Products Social Molecular Networking (GNPS) [2][3][4] . Whilst use of such tools is becoming more prevalent, GNPS is web-based requiring upload of data to a server and is limited in parameter customization of workflow and little in exportable, easy to interrogate results.…”

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confidence: 99%

Hierarchical clustering of MS/MS spectra from the firefly metabolome identifies new lucibufagin compounds

Rawlinson

Jones

Rakshit

et al. 2020

Sci Rep

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Metabolite identification is the greatest challenge when analysing metabolomics data, as only a small proportion of metabolite reference standards exist. clustering MS/MS spectra is a common method to identify similar compounds, however interrogation of underlying signature fragmentation patterns within clusters can be problematic. Previously published high-resolution LC-MS/MS data from the bioluminescent beetle (Photinus pyralis) provided an opportunity to mine new specialized metabolites in the lucibufagin class, compounds important for defense against predation. We aimed to 1) provide a workflow for hierarchically clustering MS/MS spectra for metabolomics data enabling users to cluster, visualise and easily interrogate the identification of underlying cluster ion profiles, and 2) use the workflow to identify key fragmentation patterns for lucibufagins in the hemolymph of P. pyralis. Features were aligned to their respective MS/MS spectra, then product ions were dynamically binned and resulting spectra were hierarchically clustered and grouped based on a cutoff distance threshold. Using the simplified visualization and the interrogation of cluster ion tables the number of lucibufagins was expanded from 17 to a total of 29.

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Reproducible Molecular Networking Of Untargeted Mass Spectrometry Data Using GNPS.

Cited by 26 publications

References 0 publications

Repository-scale Co- and Re-analysis of Tandem Mass Spectrometry Data

Repository-scale Co- and Re-analysis of Tandem Mass Spectrometry Data

Native Electrospray-based Metabolomics Enables the Detection of Metal-binding Compounds

Hierarchical clustering of MS/MS spectra from the firefly metabolome identifies new lucibufagin compounds

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