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. 2016 Aug 9;34(8):828-837.
doi: 10.1038/nbt.3597.

Sharing and Community Curation of Mass Spectrometry Data With Global Natural Products Social Molecular Networking

Mingxun Wang #  1   2 Jeremy J Carver #  1   2 Vanessa V Phelan #  3 Laura M Sanchez #  3 Neha Garg #  3 Yao Peng #  4 Don Duy Nguyen  4 Jeramie Watrous  3 Clifford A Kapono  4 Tal Luzzatto-Knaan  3 Carla Porto  3 Amina Bouslimani  3 Alexey V Melnik  3 Michael J Meehan  3 Wei-Ting Liu  5 Max Crüsemann  6 Paul D Boudreau  6 Eduardo Esquenazi  7 Mario Sandoval-Calderón  8 Roland D Kersten  9 Laura A Pace  3 Robert A Quinn  10 Katherine R Duncan  11   6 Cheng-Chih Hsu  4 Dimitrios J Floros  4 Ronnie G Gavilan  12 Karin Kleigrewe  6 Trent Northen  13 Rachel J Dutton  14 Delphine Parrot  15 Erin E Carlson  16 Bertrand Aigle  17 Charlotte F Michelsen  18 Lars Jelsbak  18 Christian Sohlenkamp  8 Pavel Pevzner  2   1 Anna Edlund  19   20 Jeffrey McLean  21   20 Jörn Piel  22 Brian T Murphy  23 Lena Gerwick  6 Chih-Chuang Liaw  24 Yu-Liang Yang  25 Hans-Ulrich Humpf  26 Maria Maansson  18 Robert A Keyzers  27 Amy C Sims  28 Andrew R Johnson  29 Ashley M Sidebottom  29 Brian E Sedio  30   12 Andreas Klitgaard  18 Charles B Larson  6   31 Cristopher A Boya P  12 Daniel Torres-Mendoza  12 David J Gonzalez  31   3 Denise B Silva  32   33 Lucas M Marques  32 Daniel P Demarque  32 Egle Pociute  7 Ellis C O'Neill  6 Enora Briand  6   34 Eric J N Helfrich  22 Eve A Granatosky  35 Evgenia Glukhov  6 Florian Ryffel  22 Hailey Houson  7 Hosein Mohimani  2 Jenan J Kharbush  6 Yi Zeng  4 Julia A Vorholt  22 Kenji L Kurita  36 Pep Charusanti  37 Kerry L McPhail  38 Kristian Fog Nielsen  18 Lisa Vuong  7 Maryam Elfeki  23 Matthew F Traxler  39 Niclas Engene  40 Nobuhiro Koyama  3 Oliver B Vining  38 Ralph Baric  28 Ricardo R Silva  32 Samantha J Mascuch  6 Sophie Tomasi  15 Stefan Jenkins  13 Venkat Macherla  7 Thomas Hoffman  41 Vinayak Agarwal  42 Philip G Williams  43 Jingqui Dai  43 Ram Neupane  43 Joshua Gurr  43 Andrés M C Rodríguez  32 Anne Lamsa  44 Chen Zhang  45 Kathleen Dorrestein  3 Brendan M Duggan  3 Jehad Almaliti  3 Pierre-Marie Allard  46 Prasad Phapale  47 Louis-Felix Nothias  48 Theodore Alexandrov  47 Marc Litaudon  48 Jean-Luc Wolfender  46 Jennifer E Kyle  49 Thomas O Metz  49 Tyler Peryea  50 Dac-Trung Nguyen  50 Danielle VanLeer  50 Paul Shinn  50 Ajit Jadhav  50 Rolf Müller  41 Katrina M Waters  49 Wenyuan Shi  20 Xueting Liu  51 Lixin Zhang  51 Rob Knight  52 Paul R Jensen  6 Bernhard O Palsson  37 Kit Pogliano  44 Roger G Linington  36 Marcelino Gutiérrez  12 Norberto P Lopes  32 William H Gerwick  3   6 Bradley S Moore  3   6   42 Pieter C Dorrestein  3   6   31 Nuno Bandeira  2   3   31
Free PMC article

Sharing and Community Curation of Mass Spectrometry Data With Global Natural Products Social Molecular Networking

Mingxun Wang et al. Nat Biotechnol. .
Free PMC article


The potential of the diverse chemistries present in natural products (NP) for biotechnology and medicine remains untapped because NP databases are not searchable with raw data and the NP community has no way to share data other than in published papers. Although mass spectrometry (MS) techniques are well-suited to high-throughput characterization of NP, there is a pressing need for an infrastructure to enable sharing and curation of data. We present Global Natural Products Social Molecular Networking (GNPS;, an open-access knowledge base for community-wide organization and sharing of raw, processed or identified tandem mass (MS/MS) spectrometry data. In GNPS, crowdsourced curation of freely available community-wide reference MS libraries will underpin improved annotations. Data-driven social-networking should facilitate identification of spectra and foster collaborations. We also introduce the concept of 'living data' through continuous reanalysis of deposited data.


Figure 1
Figure 1. Overview of GNPS
(a) Representation of interactions between the natural product community, GNPS spectral libraries, and GNPS datasets. At present 221,083 MS/MS spectra from 18,163 unique compounds are used for the search in the GNPS. These include both 3rd party libraries such as MassBank, ReSpect, and NIST, as well as spectral libraries created for GNPS (GNPS-Collections) and spectra from the natural product community (GNPS-Community). GNPS spectral libraries grow through user contributions of new identifications of MS/MS spectra. To date, 55 community members have contributed 8,853 MS/MS spectra from 5,568 unique compounds (30.5% of the unique compounds available). In addition, on-going curation efforts have already yielded 563 annotation updates for library spectra. The utility of these libraries is to dereplicate compounds (recognition previously characterized and studied known compounds), in both public and private data. This dereplication process is performed on all public datasets and results are automatically reported, thus enabling users to query all datasets/organisms/conditions. Automatic reanalysis of all public data creates a virtuous cycle in which contributions to libraries can be matched to all public data. Combined with molecular networking (Fig. 3), this automatic reanalysis empowers community members to identify analogs that can then be added to GNPS spectral libraries. (b) The GNPS platform has grown to serve a global user base of 9,200+ users from 100 countries.
Figure 2
Figure 2. GNPS spectral libraries
(a) The computational resources of the metabolomics and natural products community fall into two main categories: i) Reference collections (red dots) of MS/MS spectral libraries and ii) Data Repositories (blue dots)designed to publicly share raw mass spectrometry data associated with research projects. Reference collection resources are contributors and aggregators of reference MS/MS spectra, some of which also include data analysis tools, e.g. online multi-spectrum MS/MS search (magnifying glass icon). Several resources have aggregated MS/MS spectra from various reference collections so that the analysis tools at a respective resource can leverage more of the community efforts to annotate data (red and blue arrows). GNPS has imported all freely available reference collections (>221,000 MS/MS spectra) and makes them available for online analyses. GNPS and several other resources provide both reference MS/MS spectra and data in an open and free manner to the public (pink caps). (b) Comparison of spectral library sizes of available libraries (MassBank, ReSpect, and NIST) and GNPS libraries; GNPS-Collections includes newly acquired spectra from synthetic or purified compounds and GNPS-Community includes all community-contributed spectra. (c) Searching all public GNPS datasets revealed that Massbank/ReSpect/NIST libraries matched to 1,217 unique compounds, with GNPS libraries increasing unique compound matches by 41% (corresponding to 29% of total unique matches) with an accompanying 4% increase in spectral library size. Overall, GNPS libraries increase the total number of spectra matched in public datasets by 144% (59% of total public MS/MS matches) and spectra matches across all GNPS public and private data by 767% (88% of all MS/MS matches). (d) The distribution of precursor masses in all GNPS public datasets is shown in gray and compared to the precursor mass distributions of Massbank, ReSpect, NIST, and GNPS libraries. Though GNPS libraries have a combined size that is smaller than MassBank/ReSpect/NIST, GNPS libraries have a higher proportion of molecules in the higher m/z range and therefore complement the proportionately lower precursor mass molecules in other libraries. (e) The quality of spectrum matches obtained by searching against the available spectral libraries is assessed by user ratings (1 to 4 stars see Supplementary Table 6) of continuous identification results. User ratings of 2.5+ stars for 98%+ of GNPS library matches compares favorably with the 90% mark for NIST matches, whose high marks demonstrate how important these 3rd party libraries still are to the GNPS platform. We note that the lower mark for NIST matches does not suggest lower quality spectra. It is more likely explained by its higher emphasis on lower precursor mass molecules with spectra that have fewer peaks and are generally harder to match.
Figure 3
Figure 3. Molecular Network Creation and Visualization
(a) Molecular networks are constructed from the alignment of MS/MS spectra to one another. Edges connecting nodes (MS/MS spectra) are defined by a modified cosine scoring scheme determines the similarity of two MS/MS spectra with scores ranging from 0 (totally dissimilar) to 1 (completely identical). MS/MS spectra are also searched against GNPS Spectral Libraries, seeding putative nodes matches in the molecular networks. Networks are visualized online in-browser or exported for third party visualization software such as Cytoscape. (b) An example alignment between three MS/MS spectra of compounds with structural modifications that are captured by modification tolerant spectral matching utilized in variable dereplication and molecular networking. (c) In-browser molecular network visualization enables users to interactively explore molecular networks without requiring any external software. To date, more than 11,000 molecular networks have been analyzed using this feature. Within this interface, (i) users are able to define cohorts of input data and correspondingly, nodes within the network are represented as pie charts to visualize spectral count differences for each molecule across cohorts. (ii) Node labels indicate matches made to GNPS spectral libraries, with additional information displayed with mouseovers. These matches provide users a starting point to annotate unidentified MS/MS spectra within the network. (iii) To facilitate identification of unknowns, users can display MS/MS spectra in the right panels by clicking on the nodes in the network, giving direct interactive access to the underlying MS/MS peak data. Furthermore, alignments between spectra are visualized between spectra in the top right and bottom right panels in order to gain insight as to what underlying characteristics of the molecule could elicit fragmentation perturbations.
Figure 4
Figure 4. “Living data” in GNPS by crowdsourcing molecular annotations
(a) A global snapshot of the state of MS/MS matching of public natural product datasets available in GNPS using molecular networking and library search tools. Identified molecules (1.9% of the data) are MS/MS spectrum matches to library spectra with a cosine greater than 0.7. Putative Analog Molecules (another 1.9% of the data) are MS/MS spectra that are not identified by library search but rather are immediate neighbors of identified MS/MS spectra in molecular networks. Identified Networks (9.9% of the data) are connected components within a molecular network that have at least one spectrum match to library spectra. Unidentified Networks (25.2% of the data) are molecular networks where none of the spectra match to library spectra; these networks potentially represent compound classes that have not yet been characterized. Exploratory Networks (an additional 20.1% of the data) are unidentified connected components in molecular networks with more relaxed parameters (Supplementary Table 8). Thus, 55.3% of the MS/MS spectra at least have one related MS/MS spectrum in spectral networks, with 44.7% having none. In this 44.7% of the data, each MS/MS spectrum has been observed in two separate instances and should not constitute noise. Altogether, this analysis indicates that most of the chemical space captured by mass spectrometry remains unexplored. (b) In the past year, there has been significant growth in the GNPS spectral libraries, driving growth in the match rates of all public data. The number of unique compounds matched in the public data has increased 10x; the number of total spectra matched has increased 22x; and the average match rate has increased 3x. It is expected that identification rates will continue to grow with further contributions from the community to the GNPS-Community spectral library.
Figure 5
Figure 5. GNPS enabled discovery of stenothricin
a) The stenothricin molecular family was identified during analysis of a molecular network between chemical extracts of S. roseosporus NRRL 15998 (Green) and Streptomyces sp. DSM5940 (Blue). This analysis indicates that Streptomyces sp. DSM5940 produces a structurally similar compound to stenothricin with a −41 Da m/z difference. An enlarged version of the network can be found in Supplementary. b) Based on preliminary structural analysis, stenothricin-GNPS (41 Da) may contain a Lys to Ser substitution. c) Comparison of the MS/MS of stenothricin D with stenothricin-GNPS 2. d) Although structurally related, stenothricin and stenothricin-GNPS have different effects on E. coli as visualized using fluorescence microscopy. Red is the membrane stain FM4-64, blue is the membrane permeable DNA stain DAPI, green is the membrane impermeable DNA stain SYTOX green. SYTOX green only stains DNA when the cell membrane is damaged. The scale bar represents 2 μm.

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