This section is structured as follows: first, we present an overview of the LOTUS initiative at its current stage of development. The central curation and dissemination elements of the LOTUS initiative are then explained in detail. The third section addresses the interaction modes between end-users and LOTUS, including data retrieval, addition, and editing. Some examples on how LOTUS data can be used to answer research questions or develop hypothesis are given. The final section is dedicated to the interpretation of LOTUS data and illustrates the dimensions and qualities of the current LOTUS dataset from chemical and biological perspectives.

Blueprint of the LOTUS initiative

Building on the standards established by the related WikiProjects on Wikidata (Chemistry, Taxonomy and Source Metadata), a NP chemistry-oriented subproject was created (Chemistry/Natural products). Its central data consists of three minimal sufficient objects:

• A chemical structure object, with associated Simplified Molecular Input Line Entry System (SMILES) (Weininger, 1988), International Chemical Identifier (InChI) (Heller et al., 2013) and InChIKey (a hashed version of the InChI).

• A biological organism object, with associated taxon name, the taxonomic DB where it was described and the taxon ID in the respective DB.

• A reference object describing the structure-organism pair, with the associated article title and a Digital Object Identifier (DOI), a PubMed (PMID), or PubMed Central (PMCID) ID.

As data formats are largely inhomogeneous among existing electronic NP resources, fields related to chemical structure, biological organism and references are variable and essentially not standardized. Therefore, LOTUS implements multiple stages of harmonization, processing, and validation (Figure 1, stages 1–3). LOTUS employs a Single Source of Truth (SSOT, Single_source_of_truth) to ensure data reliability and continuous availability of the latest curated version of LOTUS data in both Wikidata and LNPN (Figure 1, stage 4). The SSOT approach consists of a PostgreSQL DB that structures links and data schemes such that every data element has a single place. The LOTUS processing pipeline is tailored to efficiently include and diffuse novel or curated data directly from new sources or at the Wikidata level. This iterative workflow relies both on data addition and retrieval actions as described in the Data Interaction section. The overall process leading to referenced and curated structure-organisms pairs is illustrated in Figure 1 and detailed hereafter.

Figure 1

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By design, this iterative process fosters community participation, essential to efficiently document NP occurrences. All stages of the workflow are described on the git sites of the LOTUS initiative at https://github.com/lotusnprod and in the methods. At the time of writing, 750,000+ LOTUS entries contained a curated chemical structure, biological organism and reference and were available on both Wikidata and LNPN. As the LOTUS data volume is expected to increase over time, a frozen (as of 2021-12-20), tabular version of this dataset with its associated metadata is made available at https://doi.org/10.5281/zenodo.5794106 (Rutz et al., 2021a).

Data processing and validation

As shown in Figure 1, data curation consisted of three stages: harmonization, processing, and validation. Thereby, after the harmonization stage, each of the three central objects – chemical compounds, biological organisms, and reference – were processed, as described in related methods section. Given the data size (2.5M+ initial entries), manual validation was unfeasible. Curating the references was a particularly challenging part of the process. Whereas organisms are typically reported by at least their vernacular or scientific denomination and chemical structures via their SMILES, InChI, InChIKey or image (not covered in this work), references suffer from largely insufficient reporting standards. Despite poor standardization of the initial reference field, proper referencing remains an indispensable way to establish the validity of structure-organism pairs. Better reporting practices, supported by tools such as Scholia (Blomqvist et al., 2017; Rasberry et al., 2019) and relying on Wikidata, Fatcat, or Semantic Scholar should improve reference-related information retrieval in the future.

In addition to curating the entries during data processing, 420 referenced structure-organism pairs were selected for manual validation. An entry was considered as valid if: (i) the structure (in the form of any structural descriptor that could be linked to the final sanitized InChIKey) was described in the reference (ii) the containing organism (as any organism descriptor that could be linked to the accepted canonical name) was described in the reference and (iii) the reference was describing the occurrence of the chemical structure in the biological organism. More details are available in the related methods section. This process allowed us to establish rules for automatic filtering and validation of the entries. The parameters of the automatic filtering are available as a function (filter_dirty.R) and are further described in the related methods section. The automatic filtering was then applied to all entries. To confirm the efficacy of the filtering process, a new subset of 100 diverse, automatically curated and automatically validated entries was manually checked, yielding a rate of 97% of true positives. The detailed results of the two manual validation steps are reported in Appendix 2. The resulting data is also available in the dataset shared at https://doi.org/10.5281/zenodo.5794106 (Rutz et al., 2021a). Table 1 shows an example of a referenced structure-organism pair before and after curation. This process resolved the structure to an InChIKey, the organism to a valid taxonomic name and the reference to a DOI, thereby completing the establishment of the essential referenced structure-organism pair.

Table 1

	Structure	Organism	Reference
Before curation	Cyathocaline	Stem bark of Cyathocalyx zeylanica CHAMP. ex HOOK. f. & THOMS. (Annonaceae)	Wijeratne E. M. K., de Silva L. B., Kikuchi T., Tezuka Y., Gunatilaka A. A. L., Kingston D. G. I., J. Nat. Prod., 58, 459–462 (1995).
After curation	VFIIVOHWCNHINZ-UHFFFAOYSA-N	Cyathocalyx zeylanicus	10.1021 /NP50117A020

Challenging examples encountered during the development of the curation process were compiled in an edge cases table (tests/tests.tsv) to allow for automated unit testing. These tests allow a continuous revalidation of any change made to the code, ensuring that corrected errors will not reappear. The alluvial plot in Figure 2 illustrates the individual contribution of each source and original subcategory that led to the processed categories: structure, organism, and reference.

Figure 2

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The figure highlights, for example, the essential contribution of the reference DOI category to the final validated entries. A similar pattern can be seen concerning structures, where the validation rate of structural identifiers is higher than chemical names. The combination of the results of the automated curation pipeline and the manually curated entries led to the establishment of four categories (manually validated, manually rejected, automatically validated and automatically rejected) of the referenced structure-organism pairs that formed the processed part of the SSOT. Out of a total of 2.5M+ initial pairs, the manual and automatic validation retained 750,000+ pairs (approximately 30%), which were then selected for dissemination on Wikidata. Among validated entries, multiple ones were redundant among the source databases, thus also explaining the decrease of entries between the initial pairs and validated ones. Moreover, because data quality was favored over quantity, the number of rejected entries is high. Among them, multiple correct entries were certainly falsely rejected, but still not disseminated. All rejected entries were kept aside for later manual inspection and validation. These are publicly available at https://doi.org/10.5281/zenodo.5794597 (Rutz et al., 2021b). In the end, the disseminated data contained 290,000+ unique chemical structures, 40,000+ distinct organisms and 75,000+ references.

Wikidata

The easiest way to search for NP occurrence information in Wikidata is by typing the name of a chemical structure directly into the ‘Search Wikidata’ field, which (for left-to-right languages) can be found in the upper right corner of the Wikidata homepage or any other Wikidata page. For example, by typing ‘erysodine’, the user will land on the page of this compound (Q27265641). Scrolling down to the ‘found in taxon’ statement will allow the user to view the biological organisms reported to contain this NP (Figure 3). Clicking the reference link under each taxon name links to the publication(s) documenting the occurrence.

Figure 3

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The typical approach to more elaborated querying involves writing SPARQL queries using the Wikidata Query Service or another direct connection to a SPARQL endpoint. Table 2 contains some examples from simple to more elaborated queries, demonstrating what can be done using this approach. The full-text queries with explanations are included in Supplementary file 1.

Table 2

Question	Wikidata SPARQL query
What are the compounds present in Mouse-ear cress (Arabidopsis thaliana) or its child taxa?	https://w.wiki/4Vcv
Which organisms are known to contain β-sitosterol?	https://w.wiki/4VFn
Which organisms are known to contain stereoisomers of β-sitosterol?	https://w.wiki/4VFq
Which pigments are found in which taxa, according to which reference?	https://w.wiki/4VFx
What are examples of organisms where compounds were found in an organism sharing the same parent taxon, but not in the organism itself?	https://w.wiki/4Wt3
Which Zephyranthes species lack compounds known from at least two species in the genus?	https://w.wiki/4VG3
How many compounds are structurally similar to compounds labeled as antibiotics? (grouped by the parent taxon of the containing organism)	https://w.wiki/4VG4
Which organisms contain indolic scaffolds? Count occurrences, group and order the results by the parent taxon.	https://w.wiki/4VG9
Which compounds with known bioactivities were isolated from Actinobacteria, between 2014 and 2019, with related organisms and references?	https://w.wiki/4VGC
Which compounds labeled as terpenoids were found in Aspergillus species, between 2010 and 2020, with related references?	https://w.wiki/4VGD
Which are the available referenced structure-organism pairs on Wikidata? (example limited to 1000 results)	https://w.wiki/4VFh

The queries presented in Table 2 are only selected examples, and many other ways of interrogating LOTUS can be formulated. Generic queries can be used, for example, for hypothesis generation when starting a research project. For instance, a generic SPARQL query - listed in Table 2 as “Which are the available referenced structure-organism pairs on Wikidata?” - retrieves all structures, identified by their InChIKey (P235), which contain ‘found in taxon’ (P703) statements that are stated in (P248) a bibliographic reference: https://w.wiki/4VFh. Data can then be exported in various formats, such as classical tabular formats, json, or html tables (see Download tab on the lower right of the query frame). At the time of writing (2021-12-20), this query (without the LIMIT 1000) returned 951,800 entries; a frozen query result is available at https://doi.org/10.5281/zenodo.5668854 (Rutz et al., 2021d).

Targeted queries allowing to interrogate LOTUS data from the perspective of one of the three objects forming the referenced structure-organism pairs can also be built. Users can, for example, retrieve a list of all structures reported from a given organism, such as all structures reported from Arabidopsis thaliana (Q158695) or its child taxa (https://w.wiki/4Vcv). Alternatively, all organisms containing a given chemical can be queried via its structure, such as in the search for all organisms where β-sitosterol (Q121802) was found in (https://w.wiki/4VFn). For programmatic access, the lotus-wikidata-exporter repository also allows data retrieval in RDF format and as TSV tables.

To further showcase the possibilities, two additional queries were established (https://w.wiki/4VGC and https://w.wiki/4VGD). Both queries were inspired by recent literature review works (Jose et al., 2021; Zhao et al., 2022). The first work describes compounds found in Actinobacteria, with a biological focus on compounds with reported bioactivity. The second one describes compounds found in Aspergillus spp., with a chemical focus on terpenoids. In both cases, in seconds, the queries allow retrieving a table similar to the ones in the mentioned literature reviews. While these queries are not a direct substitute for manual literature review, they do allow researchers to quickly begin such a review process with a very strong body of relevant references.

For a convenient expansion or limitation of the results, certain types of queries such as structure or similarity searches usually exist in molecular electronic resources. As these queries are not natively integrated by SPARQL, they are not readily available for Wikidata exploration. To address such limitation, Galgonek et al. developed an in-house SPARQL engine that allows utilization of Sachem, a high-performance chemical DB cartridge for PostgreSQL for fingerprint-guided substructure and similarity search (Kratochvíl et al., 2018). The engine is used by the Integrated Database of Small Molecules (IDSM) that operates, among other things, several dedicated endpoints allowing structural search in selected small-molecule datasets via SPARQL (Kratochvíl et al., 2019). To allow substructure and similarity searches via SPARQL also on compounds from Wikidata, a dedicated IDSM/Sachem endpoint was created for the LOTUS project. The endpoint indexes isomeric (P2017) and canonical (P233) SMILES code available in Wikidata. To ensure that data is kept up-to-date, SMILES codes are automatically downloaded from Wikidata daily. The endpoint allows users to run federated queries and, thereby, proceed to structure-oriented searches on the LOTUS data hosted at Wikidata. For example, the SPARQL query https://w.wiki/4VG9 returns a list of all organisms that produce NP with an indolic scaffold. The output is aggregated at the parent taxa level of the containing organisms and ranked by the number of scaffold occurrences.

Regarding the versioning aspects, some challenges are implied by the dynamic nature of the Wikidata environment. However, tracking of the data evolution can be achieved in multiple ways and at different levels: at the full Wikidata level, dumps are regularly created (https://dumps.wikimedia.org/wikidatawiki/entities) while at the individual entry level the full history of modification can be consulted (see following link for the full edit history of erythromycin for example (https://www.wikidata.org/w/index.php?title=Q213511&action=history)).

We propose to the users a simple approach to document, version and share the output of queries on the LOTUS data at a defined time point. For this, in addition to sharing the short url of a the SPARQL query (which will return results evolving over time), a simple archiving of the returned table to Zenodo or similar platform can be done. In order to gather results of SPARQL queries, we established the LOTUS Initiative Community repository. The following link allows to directly contribute to the community repository https://zenodo.org/deposit/new?c=the-lotus-initiative. For example, the output of this Wikidata SPARQL query https://w.wiki/4N8G realized on the 2021-11-10T16:56 can be easily archived and shared in a publication via its DOI 10.5281/zenodo.5668380.

Distributions of organisms per structure and structures per organism

Readily achievable outcomes from LOTUS show that the depth of exploration of the world of NP is rather limited: as depicted in Figure 4, on average, three organisms are reported per chemical structure and eleven structures per organism. Notably, half of all structures have been reported from a single organism and half of all studied organisms are reported to contain five or fewer structures. Metabolomics studies suggest that these numbers are heavily underrated (Noteborn et al., 2000; Wang et al., 2020) and indicate that a better reporting of the metabolites detected in the course of NP chemistry investigations should greatly improve coverage.

This incomplete coverage may be partially explained by the habit in classical NP journals to accept only new and/or bioactive chemical structures for publication. Another possible explanation is the fact that specific chemical classes have been under heavier scrutiny by the natural products community than others. For example, alkaloids have three specific characteristics which favor their reporting in the literature. First, they are often endowed with potent biological activities making them a target in the frame of pharmacognosy research. Second, their chemical nature makes them readily accessible from complex biological matrices through acido-basic extraction. Third, they ionize greatly in positive MS mode, which makes their detection even at a very low concentration possible, where other compounds present in much higher concentrations are not detected. It is thus a complex task to answer the following question: “Is the currently observed repartition of alkaloids across the tree of life a reflection of their true biological occurrence or is this repartition biased by the aforementioned characteristics of this chemical class?” While the LOTUS initiative does not allow yet disentangling the bias from the true occurrence, it should offer sound and strong foundations for such challenging research problematic.

Another obvious explanation for the limited coverage (see Figure 4) is the fact that most of the chemical structures in LOTUS have been physically isolated and described. This is an extremely time-consuming effort that can obviously not be carried on all metabolites of all biological organisms. Here, the sensitivity of mass spectrometry and the ever-increasing efficiency of computational metabolite annotation solutions could offer a strong take. The documentation of metabolite annotation results obtained on large collections of biological matrices and the associated metadata within knowledge graphs offers exciting perspectives regarding the possibilities to expand both the chemical and biological coverage of the LOTUS data in a feasible manner.

Figure 4

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Contribution of individual electronic NP resources to LOTUS

The added value of the LOTUS initiative to assemble multiple electronic NP resources is illustrated in Figure 5: Panel A shows the contributions of the individual electronic NP resources to the ensemble of chemical structures found in one of the most studied vascular plants, Arabidopsis thaliana (“Mouse-ear cress”; Q147096). Panel B shows the ensemble of taxa reported to contain the planar structure of the widely occurring triterpenoid β-sitosterol (Q121802).

Figure 5

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Figure 5A also shows that according to NPClassifier, the ‘chemical pathway’ category distribution across electronic NP resources is not conserved. Note that NPClassifier and ClassyFire (Djoumbou Feunang et al., 2016) chemical classification results are both available as metadata in the frozen LOTUS export and LNPN. Both classification tools return a chemical taxonomy for individual structures, thus allowing their grouping at higher hierarchical levels, in the same way as it is done for biological taxonomies. The UpSet plot in Figure 5 indicates the poor overlap of preexisting electronic NP resources and the added value of an aggregated dataset. This is particularly well illustrated in Figure 5B., where the number of organisms for which the planar structure of β-sitosterol (KZJWDPNRJALLNS) has been reported is shown for each intersection. NAPRALERT has by far the highest number of entries (2330 in total), while other electronic NP resources complement this well: e.g. UNPD has 573 reported organisms with β-sitosterol that do not overlap with any other resource. Of note, β-sitosterol is documented in only 13 organisms in the DNP, highlighting the importance of a better systematic reporting of ubiquitous metabolites and the interest of multiple data sources agglomeration.

A biologically interpreted chemical tree

The chemical diversity captured in LOTUS is here displayed using TMAP (Figure 6), a visualization library allowing the structural organization of large chemical datasets as a minimum spanning tree (Probst and Reymond, 2020). Using Faerun, an interactive HTML file is generated to display metadata and molecule structures by embedding the SmilesDrawer library (Probst and Reymond, 2018a; Probst and Reymond, 2018b). Planar structures were used for all compounds to generate the TMAP (chemical space tree-map) using MAP4 encoding (Capecchi et al., 2020). As the tree organizes structures according to their molecular fingerprint, an anticipated coherence between the clustering of compounds and the mapped NPClassifier chemical class is observed (Figure 6A.). For clarity, some of the most represented chemical classes of LOTUS plus quassinoids and stigmastane steroids are mapped, with examples of a quassinoid (NXZXPYYKGQCDRO) (light green star) and a stigmastane steroid (KZJWDPNRJALLNS) (dark green diamond) and their corresponding location in the TMAP.

Figure 6

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To explore relationships between chemistry and biology, it is possible to map taxonomical information such as the most reported biological family per chemical compound (Figure 6B.) or the biological specificity of chemical classes (Figure 6C.) on the TMAP. The biological specificity score at a given taxonomic level for a given chemical class is calculated as a Jensen-Shannon divergence. A score of 1 suggests that compounds are highly specific, 0 that they are ubiquitous. For more details, see 3_analyzing/jensen_shannon_divergence.R. This visualization allows to highlight chemical classes specific to a given taxon, such as the quassinoids in the Simaroubaceae family. In this case, it is striking to see how well the compounds of a given chemical class (quassinoids) (Figure 6A.) and the most reported plant family per compound (Simaroubaceae) (Figure 6B.) overlap. This is also evidenced on Figure 6C. with a Jensen-Shannon divergence of 0.99 at the biological family level for quassinoids. In this plot, it is also possible to identify chemical classes that are widely spread among living organisms, such as the stigmastane steroids, which exhibit a Jensen-Shannon divergence of 0.73 at the biological family level. This means that repartition of stigmastane steroids among families is not specific. Figure 7—figure supplement 1 further supports this statement.

A chemically interpreted biological tree

An alternative view of the biological and chemical diversity covered by LOTUS is illustrated in Figure 7. Here, chemical compounds are not organized but biological organisms are placed in their taxonomy. To limit bias due to under-reporting in the literature and keep a reasonable display size, only families with at least 50 reported compounds were included. Organisms were classified according to the OTL taxonomy and structures according to NPClassifier. The tips were labeled according to the biological family and colored according to their biological kingdom. The bars represent structure specificity of the most characteristic chemical class of the given biological family (the higher the more specific). This specificity score was a Jaccard index between the chemical class and the biological family. For more details, see 4_visualizing/plot_magicTree.R.

Figure 7 with 1 supplement see all

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Figure 7 makes it possible to spot highly specific compound classes such as trinervitane terpenoids in the Termitidae, the rhizoxin macrolides in the Rhizopodaceae, or the quassinoids and limonoids typical, respectively, of Simaroubaceae and Meliaceae. Similarly, tendencies of more generic occurrence of NP can be observed. For example, within the fungal kingdom, Basidiomycotina appear to have a higher biosynthetic specificity toward terpenoids than other fungi, which mostly focus on polyketides production. As explained previously, Figure 7 is highly dependent of the data reported in literature. As also illustrated in Figure 4, some compounds can be over-studied among several organisms, and many organisms studied for specific compounds only. This is a direct consequence of the way the NP community reports its data actually. Having this in mind, when observed at a finer scale, down to the structure level, such chemotaxonomic representation can give valuable insights. For example, among all chemical structures, only two were found in all biological kingdoms, namely heptadecanoic acid (KEMQGTRYUADPNZ-UHFFFAOYSA-N) and β-carotene (OENHQHLEOONYIE-JLTXGRSLSA-N). Looking at the distribution of β-sitosterol (KZJWDPNRJALLNS-VJSFXXLFSA-N) within the overall biological tree, Figure 7—figure supplement 1 plots its presence/absence versus those of its superior chemical classifications, namely the stigmastane, steroid and terpenoid derivatives, over the same tree used in Figure 7. The comparison of these five chemically interpreted biological trees clearly highlights the increasing speciation of the β-sitosterol biosynthetic pathway in the Archaeplastida kingdom, while the superior classes are distributed across all kingdoms. Figure 7 is zoomable and vectorized for detailed inspection.

As illustrated, the possibility of data interrogation at multiple precision levels, from fully defined chemical structures to broader chemical classes, is of great interest, for example, for taxonomic and evolution studies. This makes LOTUS a unique resource for the advancement of chemotaxonomy, a discipline pioneered by Augustin Pyramus de Candolle and pursued by other notable researchers (Robert Hegnauer, Otto R. Gottlieb) (Gottlieb, 1982; Hegnauer, 1986a; Candolle, 1816). Six decades after Hegnauer’s publication of ‘Die Chemotaxonomie der Pflanzen’ (Hegnauer, 1986b) much remains to be done for the advancement of this field of study and the LOTUS initiative aims to provide a solid basis for researchers willing to pursue these exciting explorations at the interface of chemistry, biology and evolution.

As shown recently in the context of spectral annotation (Dührkop et al., 2021), lowering the precision level of the annotation allows a broader coverage along with greater confidence. Genetic studies investigating the pathways involved and the organisms carrying the responsible biosynthetic genes would be of interest to confirm the previous observations. These forms of data interpretation exemplify the importance of reporting not only new structures, but also novel occurrences of known structures in organisms as comprehensive chemotaxonomic studies are pivotal for a better understanding of the metabolomes of living organisms.

The integration of multiple knowledge sources, for example genetics for NP producing gene clusters (Kautsar et al., 2020) combined to taxonomies and occurrences DB, also opens new opportunities to understand if an organism is responsible for the biosynthesis of a NP or merely contains it. This understanding is of utmost importance for the chemotaxonomic field and will help to understand to which extent microorganisms (endosymbionts) play a role in host development and its NP expression potential (Saikkonen et al., 2004).

A snapshot of the obtained data at the time of re-submission (2021-12-20) is available at the following Zenodo community: https://zenodo.org/communities/the-lotus-initiative and related records: https://zenodo.org/record/5793224, https://zenodo.org/record/5794107, https://zenodo.org/record/5794597 and https://zenodo.org/record/5801816. The https://lotus.nprod.net website is intended to gather news and features related to the LOTUS initiative in the future.

1. 1. Afendi FM
   2. Okada T
   3. Yamazaki M
   4. Hirai-Morita A
   5. Nakamura Y
   6. Nakamura K
   7. Ikeda S
   8. Takahashi H
   9. Altaf-Ul-Amin M
   10. Darusman LK
   11. Saito K
   12. Kanaya S

   (2012) KNApSAcK family databases: integrated metabolite-plant species databases for multifaceted plant research

   Plant & Cell Physiology 53:e1.

   https://doi.org/10.1093/pcp/pcr165

   • PubMed
   • Google Scholar
2. 1. Agosti D
   2. Johnson NF

   (2002) Taxonomists need better access to published data

   Nature 417:222.

   https://doi.org/10.1038/417222b

   • PubMed
   • Google Scholar
3. 1. All natural

   (2007) All natural

   Nature Chemical Biology 3:351.

   https://doi.org/10.1038/nchembio0707-351

   • PubMed
   • Google Scholar
4. 1. Allard PM
   2. Bisson J
   3. Azzollini A
   4. Pauli GF
   5. Cordell GA
   6. Wolfender JL

   (2018) Pharmacognosy in the digital era: shifting to contextualized metabolomics

   Current Opinion in Biotechnology 54:57–64.

   https://doi.org/10.1016/j.copbio.2018.02.010

   • PubMed
   • Google Scholar
5. Software

   1. Allard PM
   2. Bisson J
   3. Rutz A

   (2021) ISDB: In Silico Spectral Databases of Natural Products

   Zenodo.

   https://doi.org/10.5281/zenodo.5607264
6. 1. Balietti S
   2. Mäs M
   3. Helbing D

   (2015) On disciplinary fragmentation and scientific progress

   PLOS ONE 10:e0118747.

   https://doi.org/10.1371/journal.pone.0118747

   • PubMed
   • Google Scholar
7. 1. Bisson J
   2. Simmler C
   3. Chen SN
   4. Friesen J
   5. Lankin DC
   6. McAlpine JB
   7. Pauli GF

   (2016a) Dissemination of original NMR data enhances reproducibility and integrity in chemical research

   Natural Product Reports 33:1028–1033.

   https://doi.org/10.1039/c6np00022c

   • PubMed
   • Google Scholar
8. 1. Bisson J
   2. McAlpine JB
   3. Friesen JB
   4. Chen SN
   5. Graham J
   6. Pauli GF

   (2016b) Can Invalid Bioactives Undermine Natural Product-Based Drug Discovery?

   Journal of Medicinal Chemistry 59:1671–1690.

   https://doi.org/10.1021/acs.jmedchem.5b01009

   • PubMed
   • Google Scholar
9. Software

   1. Bisson J
   2. Rutz A
   3. Allard P

   (2021) lotusnprod/lotus-wikidata-interact, version v1.0.0

   Zenodo.

   https://doi.org/10.5281/zenodo.5802113
10. Book

    1. Blomqvist E
    2. Hose K
    3. Paulheim H
    4. Ławrynowicz A
    5. Ciravegna F
    6. Hartig O

    (2017)

    The Semantic Web: ESWC 2017 Satellite Events

    Cham: Springer.

    • Google Scholar
11. 1. Boonen J
    2. Bronselaer A
    3. Nielandt J
    4. Veryser L
    5. De Tré G
    6. De Spiegeleer B

    (2012) Alkamid database: Chemistry, occurrence and functionality of plant N-alkylamides

    Journal of Ethnopharmacology 142:563–590.

    https://doi.org/10.1016/j.jep.2012.05.038

    • PubMed
    • Google Scholar
12. 1. Brunson J

    (2020) ggalluvial: Layered Grammar for Alluvial Plots

    Journal of Open Source Software 5:2017.

    https://doi.org/10.21105/joss.02017

    • Google Scholar
13. 1. Campbell AK

    (2003) Save those molecules! Molecular biodiversity and life*

    Journal of Applied Ecology 40:193–203.

    https://doi.org/10.1046/j.1365-2664.2003.00803.x

    • Google Scholar
14. Software

    1. Campitelli E

    (2021) ggnewscale: Multiple fill and colour scales in ’ggplot2

    CRAN.

    https://CRAN.R-project.org/package=ggnewscale
15. Book

    1. Candolle A de

    (1816) Essai Sur Les Propriâetâes Mâedicales Des Plantes, Comparâees Avec Leurs Formes Extâerieures et Leur Classification Naturelle /

    Paris: Biodiversity Heritage Library.

    https://doi.org/10.5962/bhl.title.112422

    • Google Scholar
16. 1. Cao Y
    2. Charisi A
    3. Cheng LC
    4. Jiang T
    5. Girke T

    (2008) ChemmineR: a compound mining framework for R

    Bioinformatics (Oxford, England) 24:1733–1734.

    https://doi.org/10.1093/bioinformatics/btn307

    • PubMed
    • Google Scholar
17. 1. Capecchi A
    2. Probst D
    3. Reymond J-L

    (2020) One molecular fingerprint to rule them all: drugs, biomolecules, and the metabolome

    Journal of Cheminformatics 12:43.

    https://doi.org/10.1186/s13321-020-00445-4

    • PubMed
    • Google Scholar
18. Software

    1. Chamberlain S
    2. Zhu H
    3. Jahn N
    4. Boettiger C
    5. Ram K

    (2020) rcrossref: Client for Various “CrossRef” “APIs.”

    CRAN.

    https://CRAN.R-project.org/package=rcrossref
19. 1. Choi H
    2. Cho SY
    3. Pak HJ
    4. Kim Y
    5. Choi J-Y
    6. Lee YJ
    7. Gong BH
    8. Kang YS
    9. Han T
    10. Choi G
    11. Cho Y
    12. Lee S
    13. Ryoo D
    14. Park H

    (2017) NPCARE: database of natural products and fractional extracts for cancer regulation

    Journal of Cheminformatics 9:2.

    https://doi.org/10.1186/s13321-016-0188-5

    • PubMed
    • Google Scholar
20. 1. Cordell GA

    (2017a) Cognate and cognitive ecopharmacognosy — in an anthropogenic era

    Phytochemistry Letters 20:540–549.

    https://doi.org/10.1016/j.phytol.2016.10.009

    • Google Scholar
21. 1. Cordell GA

    (2017b) Sixty Challenges – A 2030 Perspective on Natural Products and Medicines Security

    Natural Product Communications 12:1934578X1701200.

    https://doi.org/10.1177/1934578X1701200849

    • Google Scholar
22. 1. Cousijn H
    2. Kenall A
    3. Ganley E
    4. Harrison M
    5. Kernohan D
    6. Lemberger T
    7. Murphy F
    8. Polischuk P
    9. Taylor S
    10. Martone M
    11. Clark T

    (2018) A data citation roadmap for scientific publishers

    Scientific Data 5:180259.

    https://doi.org/10.1038/sdata.2018.259

    • PubMed
    • Google Scholar
23. 1. Cousijn H
    2. Feeney P
    3. Lowenberg D
    4. Presani E
    5. Simons N

    (2019) Bringing Citations and Usage Metrics Together to Make Data Count

    Data Science Journal 18:9.

    https://doi.org/10.5334/dsj-2019-009

    • Google Scholar
24. 1. Crameri F
    2. Shephard GE
    3. Heron PJ

    (2020) The misuse of colour in science communication

    Nature Communications 11:5444.

    https://doi.org/10.1038/s41467-020-19160-7

    • PubMed
    • Google Scholar
25. Software

    1. Crameri F

    (2021) Scientific colour map

    Zenodo.

    https://doi.org/10.5281/zenodo.1243862
26. 1. Davis GJ
    2. Vasanthi AR

    (2011) Seaweed metabolite database (SWMD): A database of natural compounds from marine algae

    Bioinformation 5:361–364.

    https://doi.org/10.6026/97320630005361

    • PubMed
    • Google Scholar
27. 1. Defossez E
    2. Pitteloud C
    3. Descombes P
    4. Glauser G
    5. Allard PM
    6. Walker TWN
    7. Fernandez-Conradi P
    8. Wolfender JL
    9. Pellissier L
    10. Rasmann S

    (2021) Spatial and evolutionary predictability of phytochemical diversity

    PNAS 118:e2013344118.

    https://doi.org/10.1073/pnas.2013344118

    • PubMed
    • Google Scholar
28. Report

    1. Derese S
    2. Ndakala A
    3. Rogo M
    4. Maynim C
    5. Oyim J

    (2019) Mitishamba database: a web based in silico database of natural products from Kenya plants

    University of Nairobi.

    http://erepository.uonbi.ac.ke/handle/11295/92273

    • Google Scholar
29. 1. Djoumbou Feunang Y
    2. Eisner R
    3. Knox C
    4. Chepelev L
    5. Hastings J
    6. Owen G
    7. Fahy E
    8. Steinbeck C
    9. Subramanian S
    10. Bolton E
    11. Greiner R
    12. Wishart DS

    (2016) ClassyFire: automated chemical classification with a comprehensive, computable taxonomy

    Journal of Cheminformatics 8:61.

    https://doi.org/10.1186/s13321-016-0174-y

    • PubMed
    • Google Scholar
30. Software

    1. Dowle M
    2. Srinivasan A

    (2020) data.table: Extension of “data.frame.”

    CRAN.

    https://CRAN.R-project.org/package=data.table
31. 1. Ducarme F
    2. Couvet D

    (2020) What does ‘nature’ mean?

    Palgrave Communications 6:14.

    https://doi.org/10.1057/s41599-020-0390-y

    • Google Scholar
32. 1. Dührkop K
    2. Nothias L-F
    3. Fleischauer M
    4. Reher R
    5. Ludwig M
    6. Hoffmann MA
    7. Petras D
    8. Gerwick WH
    9. Rousu J
    10. Dorrestein PC
    11. Böcker S

    (2021) Systematic classification of unknown metabolites using high-resolution fragmentation mass spectra

    Nature Biotechnology 39:462–471.

    https://doi.org/10.1038/s41587-020-0740-8

    • PubMed
    • Google Scholar
33. 1. Finn RD
    2. Gardner PP
    3. Bateman A

    (2012) Making your database available through Wikipedia: the pros and cons

    Nucleic Acids Research 40:D9–D12.

    https://doi.org/10.1093/nar/gkr1195

    • PubMed
    • Google Scholar
34. Software

    1. Flor M

    (2020) chorddiag: Interactive Chord Diagrams

    GitHub.

    http://github.com/mattflor/chorddiag/
35. Software

    1. Gagolewski M

    (2020) stringi: Character String Processing Facilities

    CRAN.

    https://cran.r-project.org/web/packages/stringi/index.html
36. Website

    1. GBIF

    (2020) GBIF

    Accessed December 9, 2021.

    https://www.gbif.org
37. Software

    1. Gehlenborg N

    (2019) UpSetR: A More Scalable Alternative to Venn and Euler Diagrams for Visualizing Intersecting Sets

    CRAN.

    https://CRAN.R-project.org/package=UpSetR
38. Conference

    1. Giacomoni F
    2. Silva A
    3. Bronze M
    4. Gladine C
    5. Peter Hollman RK
    6. Yanwen DL
    7. Micheau P
    8. Nunes dos Santos MC
    9. Pavot B
    10. Schmidt G
    11. Morand C
    12. Sarda MU
    13. Vazquez Manjarrez N
    14. Verny MA
    15. Wiczkowski W
    16. Knox C
    17. Manach C

    (2017) PhytoHub, an online platform to gather expert knowledge on polyphenols and other dietary phytochemicals

    International Conference on Polyphenols and Health (ICPH 2017).

    https://hal.archives-ouvertes.fr/hal-01697081

    • Google Scholar
39. Book

    1. Gottlieb OR

    (1982) Micromolecular Evolution, Systematics and Ecology

    Berlin, Heidelberg: Springer.

    https://doi.org/10.1007/978-3-642-68641-2

    • Google Scholar
40. 1. Graham JG
    2. Farnsworth NR

    (2010) 3.04 - The NAPRALERT Database as an Aid for Discovery of Novel Bioactive Compounds

    Comprehensive Natural Products 3:81–94.

    https://doi.org/10.1016/b978-008045382-8.00060-5

    • Google Scholar
41. 1. Gu J
    2. Gui Y
    3. Chen L
    4. Yuan G
    5. Lu H-Z
    6. Xu X

    (2013) Use of natural products as chemical library for drug discovery and network pharmacology

    PLOS ONE 8:e62839.

    https://doi.org/10.1371/journal.pone.0062839

    • PubMed
    • Google Scholar
42. 1. Günthardt BF
    2. Hollender J
    3. Hungerbühler K
    4. Scheringer M
    5. Bucheli TD

    (2018) Comprehensive Toxic Plants-Phytotoxins Database and Its Application in Assessing Aquatic Micropollution Potential

    Journal of Agricultural and Food Chemistry 66:7577–7588.

    https://doi.org/10.1021/acs.jafc.8b01639

    • PubMed
    • Google Scholar
43. 1. Hatherley R
    2. Brown DK
    3. Musyoka TM
    4. Penkler DL
    5. Faya N
    6. Lobb KA
    7. Tastan Bishop Ö

    (2015) SANCDB: a South African natural compound database

    Journal of Cheminformatics 7:29.

    https://doi.org/10.1186/s13321-015-0080-8

    • PubMed
    • Google Scholar
44. 1. Haug K
    2. Cochrane K
    3. Nainala VC
    4. Williams M
    5. Chang J
    6. Jayaseelan KV
    7. O’Donovan C

    (2020) MetaboLights: a resource evolving in response to the needs of its scientific community

    Nucleic Acids Research 48:D440–D444.

    https://doi.org/10.1093/nar/gkz1019

    • PubMed
    • Google Scholar
45. 1. Hegnauer R

    (1986a) Phytochemistry and plant taxonomy — an essay on the chemotaxonomy of higher plants

    Phytochemistry 25:1519–1535.

    https://doi.org/10.1016/S0031-9422(00)81204-2

    • Google Scholar
46. Book

    1. Hegnauer R

    (1986b) Chemotaxonomie Der Pflanzen

    Basel: springer.

    https://doi.org/10.1007/978-3-0348-9314-5

    • Google Scholar
47. 1. Heller S
    2. McNaught A
    3. Stein S
    4. Tchekhovskoi D
    5. Pletnev I

    (2013) InChI - the worldwide chemical structure identifier standard

    Journal of Cheminformatics 5:7.

    https://doi.org/10.1186/1758-2946-5-7

    • PubMed
    • Google Scholar
48. 1. Helmy M
    2. Crits-Christoph A
    3. Bader GD

    (2016) Ten Simple Rules for Developing Public Biological Databases

    PLOS Computational Biology 12:e1005128.

    https://doi.org/10.1371/journal.pcbi.1005128

    • PubMed
    • Google Scholar
49. 1. Himmelstein DS
    2. Rubinetti V
    3. Slochower DR
    4. Hu D
    5. Malladi VS
    6. Greene CS
    7. Gitter A

    (2019) Open collaborative writing with Manubot

    PLOS Computational Biology 15:e1007128.

    https://doi.org/10.1371/journal.pcbi.1007128

    • PubMed
    • Google Scholar
50. Preprint

    1. Hoffmann MA
    2. Nothias LF
    3. Ludwig M
    4. Fleischauer M
    5. Gentry EC
    6. Witting M
    7. Dorrestein PC
    8. Dührkop K
    9. Böcker S

    (2021) Assigning Confidence to Structural Annotations from Mass Spectra with COSMIC

    bioRxiv.

    https://doi.org/10.1101/2021.03.18.435634

    • Google Scholar
51. 1. Horai H
    2. Arita M
    3. Kanaya S
    4. Nihei Y
    5. Ikeda T
    6. Suwa K
    7. Ojima Y
    8. Tanaka K
    9. Tanaka S
    10. Aoshima K
    11. Oda Y
    12. Kakazu Y
    13. Kusano M
    14. Tohge T
    15. Matsuda F
    16. Sawada Y
    17. Hirai MY
    18. Nakanishi H
    19. Ikeda K
    20. Akimoto N
    21. Maoka T
    22. Takahashi H
    23. Ara T
    24. Sakurai N
    25. Suzuki H
    26. Shibata D
    27. Neumann S
    28. Iida T
    29. Tanaka K
    30. Funatsu K
    31. Matsuura F
    32. Soga T
    33. Taguchi R
    34. Saito K
    35. Nishioka T

    (2010) MassBank: a public repository for sharing mass spectral data for life sciences

    Journal of Mass Spectrometry 45:703–714.

    https://doi.org/10.1002/jms.1777

    • PubMed
    • Google Scholar
52. 1. Huang W
    2. Brewer LK
    3. Jones JW
    4. Nguyen AT
    5. Marcu A
    6. Wishart DS
    7. Oglesby-Sherrouse AG
    8. Kane MA
    9. Wilks A

    (2018) PAMDB: a comprehensive Pseudomonas aeruginosa metabolome database

    Nucleic Acids Research 46:D575–D580.

    https://doi.org/10.1093/nar/gkx1061

    • PubMed
    • Google Scholar
53. 1. Hunter JD

    (2007) Matplotlib: A 2D Graphics Environment

    Computing in Science & Engineering 9:90–95.

    https://doi.org/10.1109/MCSE.2007.55

    • Google Scholar
54. 1. Ibezim A
    2. Debnath B
    3. Ntie-Kang F
    4. Mbah CJ
    5. Nwodo NJ

    (2017) Binding of anti-Trypanosoma natural products from African flora against selected drug targets: a docking study

    Medicinal Chemistry Research 26:562–579.

    https://doi.org/10.1007/s00044-016-1764-y

    • Google Scholar
55. 1. Jarmusch AK
    2. Wang M
    3. Aceves CM
    4. Advani RS
    5. Aguirre S
    6. Aksenov AA
    7. Aleti G
    8. Aron AT
    9. Bauermeister A
    10. Bolleddu S
    11. Bouslimani A
    12. Caraballo Rodriguez AM
    13. Chaar R
    14. Coras R
    15. Elijah EO
    16. Ernst M
    17. Gauglitz JM
    18. Gentry EC
    19. Husband M
    20. Jarmusch SA
    21. Jones KL
    22. Kamenik Z
    23. Le Gouellec A
    24. Lu A
    25. McCall LI
    26. McPhail KL
    27. Meehan MJ
    28. Melnik AV
    29. Menezes RC
    30. Montoya Giraldo YA
    31. Nguyen NH
    32. Nothias LF
    33. Nothias-Esposito M
    34. Panitchpakdi M
    35. Petras D
    36. Quinn RA
    37. Sikora N
    38. van der Hooft JJJ
    39. Vargas F
    40. Vrbanac A
    41. Weldon KC
    42. Knight R
    43. Bandeira N
    44. Dorrestein PC

    (2020) ReDU: a framework to find and reanalyze public mass spectrometry data

    Nature Methods 17:901–904.

    https://doi.org/10.1038/s41592-020-0916-7

    • PubMed
    • Google Scholar
56. 1. Jones MR
    2. Pinto E
    3. Torres MA
    4. Dörr F
    5. Mazur-Marzec H
    6. Szubert K
    7. Tartaglione L
    8. Dell’Aversano C
    9. Miles CO
    10. Beach DG
    11. McCarron P
    12. Sivonen K
    13. Fewer DP
    14. Jokela J
    15. Janssen EM-L

    (2021) CyanoMetDB, a comprehensive public database of secondary metabolites from cyanobacteria

    Water Research 196:117017.

    https://doi.org/10.1016/j.watres.2021.117017

    • PubMed
    • Google Scholar
57. 1. Jose PA
    2. Maharshi A
    3. Jha B

    (2021) Actinobacteria in natural products research: Progress and prospects

    Microbiological Research 246:126708.

    https://doi.org/10.1016/j.micres.2021.126708

    • PubMed
    • Google Scholar
58. 1. Kautsar SA
    2. Blin K
    3. Shaw S
    4. Navarro-Muñoz JC
    5. Terlouw BR
    6. van der Hooft JJJ
    7. van Santen JA
    8. Tracanna V
    9. Suarez Duran HG
    10. Pascal Andreu V
    11. Selem-Mojica N
    12. Alanjary M
    13. Robinson SL
    14. Lund G
    15. Epstein SC
    16. Sisto AC
    17. Charkoudian LK
    18. Collemare J
    19. Linington RG
    20. Weber T
    21. Medema MH

    (2020) MIBiG 2.0: a repository for biosynthetic gene clusters of known function

    Nucleic Acids Research 48:D454–D458.

    https://doi.org/10.1093/nar/gkz882

    • PubMed
    • Google Scholar
59. 1. Kessler A
    2. Kalske A

    (2018) Plant Secondary Metabolite Diversity and Species Interactions

    Annual Review of Ecology, Evolution, and Systematics 49:115–138.

    https://doi.org/10.1146/annurev-ecolsys-110617-062406

    • Google Scholar
60. 1. Kim SK
    2. Nam S
    3. Jang H
    4. Kim A
    5. Lee JJ

    (2015a) TM-MC: a database of medicinal materials and chemical compounds in Northeast Asian traditional medicine

    BMC Complementary and Alternative Medicine 15:218.

    https://doi.org/10.1186/s12906-015-0758-5

    • PubMed
    • Google Scholar
61. 1. Kim S
    2. Thiessen PA
    3. Bolton EE
    4. Bryant SH

    (2015b) PUG-SOAP and PUG-REST: web services for programmatic access to chemical information in PubChem

    Nucleic Acids Research 43:W605–W611.

    https://doi.org/10.1093/nar/gkv396

    • PubMed
    • Google Scholar
62. 1. Kim S
    2. Thiessen PA
    3. Cheng T
    4. Yu B
    5. Bolton EE

    (2018) An update on PUG-REST: RESTful interface for programmatic access to PubChem

    Nucleic Acids Research 46:W563–W570.

    https://doi.org/10.1093/nar/gky294

    • PubMed
    • Google Scholar
63. 1. Kim S
    2. Chen J
    3. Cheng T
    4. Gindulyte A
    5. He J
    6. He S
    7. Li Q
    8. Shoemaker BA
    9. Thiessen PA
    10. Yu B
    11. Zaslavsky L
    12. Zhang J
    13. Bolton EE

    (2019) PubChem 2019 update: improved access to chemical data

    Nucleic Acids Research 47:D1102–D1109.

    https://doi.org/10.1093/nar/gky1033

    • PubMed
    • Google Scholar
64. 1. Kim HW
    2. Wang M
    3. Leber CA
    4. Nothias L-F
    5. Reher R
    6. Kang KB
    7. van der Hooft JJJ
    8. Dorrestein PC
    9. Gerwick WH
    10. Cottrell GW

    (2021) NPClassifier: A Deep Neural Network-Based Structural Classification Tool for Natural Products

    Journal of Natural Products 84:2795–2807.

    https://doi.org/10.1021/acs.jnatprod.1c00399

    • PubMed
    • Google Scholar
65. 1. Klementz D
    2. Döring K
    3. Lucas X
    4. Telukunta KK
    5. Erxleben A
    6. Deubel D
    7. Erber A
    8. Santillana I
    9. Thomas OS
    10. Bechthold A
    11. Günther S

    (2016) StreptomeDB 2.0--an extended resource of natural products produced by streptomycetes

    Nucleic Acids Research 44:D509–D514.

    https://doi.org/10.1093/nar/gkv1319

    • PubMed
    • Google Scholar
66. 1. Kratochvíl M
    2. Vondrášek J
    3. Galgonek J

    (2018) Sachem: a chemical cartridge for high-performance substructure search

    Journal of Cheminformatics 10:27.

    https://doi.org/10.1186/s13321-018-0282-y

    • PubMed
    • Google Scholar
67. 1. Kratochvíl M
    2. Vondrášek J
    3. Galgonek J

    (2019) Interoperable chemical structure search service

    Journal of Cheminformatics 11:45.

    https://doi.org/10.1186/s13321-019-0367-2

    • PubMed
    • Google Scholar
68. Software

    1. Kuang K
    2. Kong Q
    3. Napolitano F

    (2019) pbmcapply: Tracking the Progress of Mc*pply with Progress Bar

    CRAN.

    https://CRAN.R-project.org/package=pbmcapply
69. Software

    1. Lang DT

    (2020) XML: Tools for Parsing and Generating XML Within R and S-Plus

    CRAN.

    https://CRAN.R-project.org/package=XML
70. 1. Lee CJ
    2. Sugimoto CR
    3. Zhang G
    4. Cronin B

    (2013) Bias in peer review

    Journal of the American Society for Information Science and Technology 64:2–17.

    https://doi.org/10.1002/asi.22784

    • Google Scholar
71. 1. Lin D
    2. Crabtree J
    3. Dillo I
    4. Downs RR
    5. Edmunds R
    6. Giaretta D
    7. De Giusti M
    8. L’Hours H
    9. Hugo W
    10. Jenkyns R
    11. Khodiyar V
    12. Martone ME
    13. Mokrane M
    14. Navale V
    15. Petters J
    16. Sierman B
    17. Sokolova DV
    18. Stockhause M
    19. Westbrook J

    (2020) The TRUST Principles for digital repositories

    Scientific Data 7:144.

    https://doi.org/10.1038/s41597-020-0486-7

    • PubMed
    • Google Scholar
72. 1. Loo M

    (2014) The stringdist Package for Approximate String Matching

    The R Journal 6:111.

    https://doi.org/10.32614/RJ-2014-011

    • Google Scholar
73. 1. Lowe DM
    2. Corbett PT
    3. Murray-Rust P
    4. Glen RC

    (2011) Chemical name to structure: OPSIN, an open source solution

    Journal of Chemical Information and Modeling 51:739–753.

    https://doi.org/10.1021/ci100384d

    • PubMed
    • Google Scholar
74. 1. Madariaga-Mazón A
    2. Naveja JJ
    3. Medina-Franco JL
    4. Noriega-Colima KO
    5. Martinez-Mayorga K

    (2021) DiaNat-DB: a molecular database of antidiabetic compounds from medicinal plants

    RSC Advances 11:5172–5178.

    https://doi.org/10.1039/D0RA10453A

    • Google Scholar
75. Software

    1. Mahto A

    (2019) splitstackshape: Stack and Reshape Datasets After Splitting Concatenated Values

    Splitstackshape.

    https://CRAN.R-project.org/package=splitstackshape
76. 1. Martens M
    2. Ammar A
    3. Riutta A
    4. Waagmeester A
    5. Slenter DN
    6. Hanspers K
    7. A Miller R
    8. Digles D
    9. Lopes EN
    10. Ehrhart F
    11. Dupuis LJ
    12. Winckers LA
    13. Coort SL
    14. Willighagen EL
    15. Evelo CT
    16. Pico AR
    17. Kutmon M

    (2021) WikiPathways: connecting communities

    Nucleic Acids Research 49:D613–D621.

    https://doi.org/10.1093/nar/gkaa1024

    • PubMed
    • Google Scholar
77. 1. McAlpine JB
    2. Chen S-N
    3. Kutateladze A
    4. MacMillan JB
    5. Appendino G
    6. Barison A
    7. Beniddir MA
    8. Biavatti MW
    9. Bluml S
    10. Boufridi A
    11. Butler MS
    12. Capon RJ
    13. Choi YH
    14. Coppage D
    15. Crews P
    16. Crimmins MT
    17. Csete M
    18. Dewapriya P
    19. Egan JM
    20. Garson MJ
    21. Genta-Jouve G
    22. Gerwick WH
    23. Gross H
    24. Harper MK
    25. Hermanto P
    26. Hook JM
    27. Hunter L
    28. Jeannerat D
    29. Ji N-Y
    30. Johnson TA
    31. Kingston DGI
    32. Koshino H
    33. Lee H-W
    34. Lewin G
    35. Li J
    36. Linington RG
    37. Liu M
    38. McPhail KL
    39. Molinski TF
    40. Moore BS
    41. Nam J-W
    42. Neupane RP
    43. Niemitz M
    44. Nuzillard J-M
    45. Oberlies NH
    46. Ocampos FMM
    47. Pan G
    48. Quinn RJ
    49. Reddy DS
    50. Renault J-H
    51. Rivera-Chávez J
    52. Robien W
    53. Saunders CM
    54. Schmidt TJ
    55. Seger C
    56. Shen B
    57. Steinbeck C
    58. Stuppner H
    59. Sturm S
    60. Taglialatela-Scafati O
    61. Tantillo DJ
    62. Verpoorte R
    63. Wang B-G
    64. Williams CM
    65. Williams PG
    66. Wist J
    67. Yue J-M
    68. Zhang C
    69. Xu Z
    70. Simmler C
    71. Lankin DC
    72. Bisson J
    73. Pauli GF

    (2019) The value of universally available raw NMR data for transparency, reproducibility, and integrity in natural product research

    Natural Product Reports 36:35–107.

    https://doi.org/10.1039/c7np00064b

    • PubMed
    • Google Scholar
78. 1. Michonneau F
    2. Brown JW
    3. Winter DJ
    4. Fitzjohn R

    (2016) rotl: an R package to interact with the Open Tree of Life data

    Methods in Ecology and Evolution 7:1476–1481.

    https://doi.org/10.1111/2041-210X.12593

    • Google Scholar
79. Software

    1. Mohamed A
    2. Abuoda G
    3. Ghanem A
    4. Kaoudi Z
    5. Aboulnaga A

    (2020) RDFFrames: Knowledge Graph Access for Machine Learning Tools

    RDFFrames.

    https://www.wikidata.org/wiki/Q106204599
80. 1. Mongia M
    2. Mohimani H

    (2021) Repository scale classification and decomposition of tandem mass spectral data

    Scientific Reports 11:8314.

    https://doi.org/10.1038/s41598-021-87796-6

    • PubMed
    • Google Scholar
81. Software

    1. Müller K
    2. Wickham H
    3. James DA
    4. Falcon S

    (2021) RSQLite: “SQLite” interface for r

    RSQLite.

    https://CRAN.R-project.org/package=RSQLite
82. 1. Murray-Rust P

    (2008) Open Data in Science

    Nature Precedings 4:26.

    https://doi.org/10.1038/npre.2008.1526.1

    • Google Scholar
83. 1. Noteborn HP
    2. Lommen A
    3. van der Jagt RC
    4. Weseman JM

    (2000) Chemical fingerprinting for the evaluation of unintended secondary metabolic changes in transgenic food crops

    Journal of Biotechnology 77:103–114.

    https://doi.org/10.1016/s0168-1656(99)00210-2

    • PubMed
    • Google Scholar
84. 1. Ntie-Kang F
    2. Telukunta KK
    3. Döring K
    4. Simoben CV
    5. A Moumbock AF
    6. Malange YI
    7. Njume LE
    8. Yong JN
    9. Sippl W
    10. Günther S

    (2017) NANPDB: A Resource for Natural Products from Northern African Sources

    Journal of Natural Products 80:2067–2076.

    https://doi.org/10.1021/acs.jnatprod.7b00283

    • PubMed
    • Google Scholar
85. 1. Nupur LNU
    2. Vats A
    3. Dhanda SK
    4. Raghava GPS
    5. Pinnaka AK
    6. Kumar A

    (2016) ProCarDB: a database of bacterial carotenoids

    BMC Microbiology 16:96.

    https://doi.org/10.1186/s12866-016-0715-6

    • PubMed
    • Google Scholar
86. Software

    1. Ooms J

    (2014) The jsonlite Package: A Practical and Consistent Mapping Between JSON Data and R Objects

    Wikidata.

    https://www.wikidata.org/wiki/Q106204620
87. Software

    1. Pedersen TL

    (2020) ggraph: An Implementation of Grammar of Graphics for Graphs and Networks

    Ggraph.

    https://CRAN.R-project.org/package=ggraph
88. 1. Pierce HH
    2. Dev A
    3. Statham E
    4. Bierer BE

    (2019) Credit data generators for data reuse

    Nature 570:30–32.

    https://doi.org/10.1038/d41586-019-01715-4

    • PubMed
    • Google Scholar
89. 1. Pilon AC
    2. Valli M
    3. Dametto AC
    4. Pinto MEF
    5. Freire RT
    6. Castro-Gamboa I
    7. Andricopulo AD
    8. Bolzani VS

    (2017) NuBBE_DB: an updated database to uncover chemical and biological information from Brazilian biodiversity

    Scientific Reports 7:7215.

    https://doi.org/10.1038/s41598-017-07451-x

    • PubMed
    • Google Scholar
90. 1. Pilón-Jiménez BA
    2. Saldívar-González FI
    3. Díaz-Eufracio BI
    4. Medina-Franco JL

    (2019) BIOFACQUIM: A Mexican Compound Database of Natural Products

    Biomolecules 9:E31.

    https://doi.org/10.3390/biom9010031

    • PubMed
    • Google Scholar
91. 1. Probst D
    2. Reymond JL

    (2018a) FUn: a framework for interactive visualizations of large, high-dimensional datasets on the web

    Bioinformatics (Oxford, England) 34:1433–1435.

    https://doi.org/10.1093/bioinformatics/btx760

    • PubMed
    • Google Scholar
92. 1. Probst D
    2. Reymond JL

    (2018b) SmilesDrawer: Parsing and Drawing SMILES-Encoded Molecular Structures Using Client-Side JavaScript

    Journal of Chemical Information and Modeling 58:1–7.

    https://doi.org/10.1021/acs.jcim.7b00425

    • PubMed
    • Google Scholar
93. 1. Probst D
    2. Reymond J-L

    (2020) Visualization of very large high-dimensional data sets as minimum spanning trees

    Journal of Cheminformatics 12:12.

    https://doi.org/10.1186/s13321-020-0416-x

    • PubMed
    • Google Scholar
94. 1. Rasberry L
    2. Willighagen E
    3. Nielsen F
    4. Mietchen D

    (2019) Robustifying Scholia: paving the way for knowledge discovery and research assessment through Wikidata

    Research Ideas and Outcomes 5:e35820.

    https://doi.org/10.3897/rio.5.e35820

    • Google Scholar
95. Software

    1. RDKit

    (2021) RDKit: Open-source cheminformatics

    GitHub/SourceForge.

    http://www.rdkit.org
96. Software

    1. Reback J
    2. McKinney W
    3. Jbrockmendel J
    4. Augspurger T
    5. Cloud P
    6. Gfyoung S
    7. Hawkins S
    8. Roeschke M

    (2020) pandas-dev/pandas: Pandas

    Zenodo.

    https://doi.org/10.5281/zenodo.4161697
97. 1. Rees JA
    2. Cranston K

    (2017) Automated assembly of a reference taxonomy for phylogenetic data synthesis

    Biodiversity Data Journal 10:e12581.

    https://doi.org/10.3897/BDJ.5.e12581

    • PubMed
    • Google Scholar
98. 1. Rothwell JA
    2. Perez-Jimenez J
    3. Neveu V
    4. Medina-Remón A
    5. M’hiri N
    6. García-Lobato P
    7. Manach C
    8. Knox C
    9. Eisner R
    10. Wishart DS
    11. Scalbert A

    (2013) Phenol-Explorer 3.0: a major update of the Phenol-Explorer database to incorporate data on the effects of food processing on polyphenol content

    Database 2013:bat070.

    https://doi.org/10.1093/database/bat070

    • PubMed
    • Google Scholar
99. 1. Rutz A
    2. Dounoue-Kubo M
    3. Ollivier S
    4. Bisson J
    5. Bagheri M
    6. Saesong T
    7. Ebrahimi SN
    8. Ingkaninan K
    9. Wolfender J-L
    10. Allard P-M

    (2019) Taxonomically Informed Scoring Enhances Confidence in Natural Products Annotation

    Frontiers in Plant Science 10:1329.

    https://doi.org/10.3389/fpls.2019.01329

    • PubMed
    • Google Scholar
100. Software

     1. Rutz A

     (2021) The LOTUS Initiative for Open Natural Products Research: custom dictionaries

     Zenodo.

     https://doi.org/10.5281/zenodo.5801816
101. Software

     1. Rutz A
     2. Gaudry A

     (2021) The LOTUS Initiative for Open Natural Products Research: TMAP, version 4.0

     Zenodo.

     https://doi.org/10.5281/zenodo.5801807
102. Software

     1. Rutz A
     2. Bisson J
     3. Allard PM

     (2021a) The LOTUS Initiative for Open Natural Products Research: biological and chemical trees

     Zenodo.

     https://doi.org/10.5281/zenodo.5794106
103. Software

     1. Rutz A
     2. Bisson J
     3. Allard PM

     (2021b) The LOTUS Initiative for Open Natural Products Research: waste to recycle

     Zenodo.

     https://doi.org/10.5281/zenodo.5794597
104. Software

     1. Rutz A
     2. Bisson J
     3. Allard PM

     (2021c) The LOTUS Initiative for Open Natural Products Research: frozen dataset union wikidata

     Zenodo.

     https://doi.org/10.5281/zenodo.5794107
105. Software

     1. Rutz A
     2. Bisson J
     3. Allard PM
     4. Community W

     (2021d) The LOTUS Initiative for Open Natural Products Research: wikidata query results

     Zenodo.

     https://doi.org/10.5281/zenodo.5668854
106. Software

     1. Rutz A
     2. Bisson J
     3. Allard PM
     4. Community W

     (2021e) The LOTUS Initiative for Open Natural Products Research: wikidata query results

     Zenodo.

     https://doi.org/10.5281/zenodo.5793224
107. Software

     1. Rutz A
     2. Bisson J
     3. Allard PM
     4. Gaudry W

     (2021f) lotusnprod/lotus-processor, version v1.0.0

     Zenodo.

     https://doi.org/10.5281/zenodo.5802107
108. Software

     1. Rutz A

     (2022a) The LOTUS Initiative, version swh:1:rev:78e6065d8eb9d0b0d11c2ea8de6ac66b445bca0e

     Software Heritage.

     https://archive.softwareheritage.org/swh:1:dir:06f92b6efba0c694b9ff259ee9406c8269a9bc3f;origin=https://github.com/lotusnprod/lotus-processor;visit=swh:1:snp:816d6826154073ce81ea66e18893029abb53a8e9;anchor=swh:1:rev:78e6065d8eb9d0b0d11c2ea8de6ac66b445bca0e
109. Software

     1. Rutz A

     (2022b) LOTUS web, version swh:1:rev:278a5ab82389ebd5df720b1876a1724d15937644

     Software Heritage.

     https://archive.softwareheritage.org/swh:1:dir:b00de761fdb113deab6cad0143190006edd0181f;origin=https://github.com/lotusnprod/lotus-web;visit=swh:1:snp:aa23783a4ecd32578845345e497259a5fdd78a0c;anchor=swh:1:rev:278a5ab82389ebd5df720b1876a1724d15937644
110. Software

     1. Rutz A

     (2022c) Wikidata interactions for the LOTUS Initiative, version swh:1:rev:92d19b8995a69f5bba39f438172ba425fdcc0f28

     Software Heritage.

     https://archive.softwareheritage.org/swh:1:dir:3c6e7a6d7c939a4ae63ef03a039bd843839ac34f;origin=https://github.com/lotusnprod/lotus-wikidata-interact;visit=swh:1:snp:86ac8009d72baef9426fe2d7cc55fe980e4d3b78;anchor=swh:1:rev:92d19b8995a69f5bba39f438172ba425fdcc0f28
111. 1. Saikkonen K
     2. Wäli P
     3. Helander M
     4. Faeth SH

     (2004) Evolution of endophyte-plant symbioses

     Trends in Plant Science 9:275–280.

     https://doi.org/10.1016/j.tplants.2004.04.005

     • PubMed
     • Google Scholar
112. 1. Sander T
     2. Freyss J
     3. von Korff M
     4. Rufener C

     (2015) DataWarrior: an open-source program for chemistry aware data visualization and analysis

     Journal of Chemical Information and Modeling 55:460–473.

     https://doi.org/10.1021/ci500588j

     • PubMed
     • Google Scholar
113. 1. Sawada Y
     2. Nakabayashi R
     3. Yamada Y
     4. Suzuki M
     5. Sato M
     6. Sakata A
     7. Akiyama K
     8. Sakurai T
     9. Matsuda F
     10. Aoki T
     11. Hirai MY
     12. Saito K

     (2012) RIKEN tandem mass spectral database (ReSpect) for phytochemicals: a plant-specific MS/MS-based data resource and database

     Phytochemistry 82:38–45.

     https://doi.org/10.1016/j.phytochem.2012.07.007

     • PubMed
     • Google Scholar
114. 1. Sedio BE

     (2017) Recent breakthroughs in metabolomics promise to reveal the cryptic chemical traits that mediate plant community composition, character evolution and lineage diversification

     The New Phytologist 214:952–958.

     https://doi.org/10.1111/nph.14438

     • PubMed
     • Google Scholar
115. 1. Sharma A
     2. Dutta P
     3. Sharma M
     4. Rajput NK
     5. Dodiya B
     6. Georrge JJ
     7. Kholia T
     8. Bhardwaj A
     9. OSDD Consortium

     (2014) BioPhytMol: a drug discovery community resource on anti-mycobacterial phytomolecules and plant extracts

     Journal of Cheminformatics 6:46.

     https://doi.org/10.1186/s13321-014-0046-2

     • PubMed
     • Google Scholar
116. Book

     1. Shinbo Y
     2. Nakamura Y
     3. Altaf-Ul-Amin M
     4. Asahi H
     5. Kurokawa K
     6. Arita M
     7. Saito K
     8. Ohta D
     9. Shibata D
     10. Kanaya S

     (2006) Plant Metabolomics

     Springer.

     https://doi.org/10.1007/3-540-29782-0_13

     • Google Scholar
117. Book

     1. Sievert C

     (2020) Interactive Web-Based Data Visualization with R, Plotly, and Shiny

     Chapman and Hall/CRC.

     https://doi.org/10.1201/9780429447273

     • Google Scholar
118. 1. Slenter DN
     2. Kutmon M
     3. Hanspers K
     4. Riutta A
     5. Windsor J
     6. Nunes N
     7. Mélius J
     8. Cirillo E
     9. Coort SL
     10. Digles D
     11. Ehrhart F
     12. Giesbertz P
     13. Kalafati M
     14. Martens M
     15. Miller R
     16. Nishida K
     17. Rieswijk L
     18. Waagmeester A
     19. Eijssen LMT
     20. Evelo CT
     21. Pico AR
     22. Willighagen EL

     (2018) WikiPathways: a multifaceted pathway database bridging metabolomics to other omics research

     Nucleic Acids Research 46:D661–D667.

     https://doi.org/10.1093/nar/gkx1064

     • PubMed
     • Google Scholar
119. Software

     1. Sorokina M
     2. Steinbeck C

     (2020a) COCONUT: the COlleCtion of Open NatUral producTs

     Zenodo.

     https://doi.org/10.5281/zenodo.3778405
120. 1. Sorokina M
     2. Steinbeck C

     (2020b) Review on natural products databases: where to find data in 2020

     Journal of Cheminformatics 12:20.

     https://doi.org/10.1186/s13321-020-00424-9

     • PubMed
     • Google Scholar
121. 1. Sorokina M
     2. Merseburger P
     3. Rajan K
     4. Yirik MA
     5. Steinbeck C

     (2021a) COCONUT online: Collection of Open Natural Products database

     Journal of Cheminformatics 13:2.

     https://doi.org/10.1186/s13321-020-00478-9

     • PubMed
     • Google Scholar
122. Software

     1. Sorokina M
     2. Rutz A
     3. Renovate W
     4. Willighagen E

     (2021b) Imgbot. lotusnprod/lotus

     Zenodo.

     https://doi.org/10.5281/zenodo.5802120
123. 1. Szöcs E
     2. Stirling T
     3. Scott ER
     4. Scharmüller A
     5. Schäfer RB

     (2020) webchem: An R Package to Retrieve Chemical Information from the Web

     Journal of Statistical Software 10:i13.

     https://doi.org/10.18637/jss.v093.i13

     • Google Scholar
124. 1. Taylor NG
     2. Dunn AM

     (2018) Predatory impacts of alien decapod Crustacea are predicted by functional responses and explained by differences in metabolic rate

     Biological Invasions 20:2821–2837.

     https://doi.org/10.1007/s10530-018-1735-y

     • Google Scholar
125. Conference

     1. Tomiki T
     2. Saito T
     3. Ueki M
     4. Konno H
     5. Asaoka T
     6. Suzuki R
     7. Uramoto M
     8. Kakeya H
     9. Osada H

     (2006) RIKEN natural products encyclopedia (RIKEN NPEdia) a chemical database of RIKEN natural products depository (RIKEN NPDepo

     Proceedings of the Symposium on Chemoinformatics.

     https://doi.org/10.11545/ciqs.2006.0.jl6.0

     • Google Scholar
126. 1. Tsugawa H

     (2018) Advances in computational metabolomics and databases deepen the understanding of metabolisms

     Current Opinion in Biotechnology 54:10–17.

     https://doi.org/10.1016/j.copbio.2018.01.008

     • PubMed
     • Google Scholar
127. Software

     1. U.S. Department of Agriculture

     (1992) Dr. Duke’s Phytochemical and Ethnobotanical Databases

     Agricultural Research Service.

     https://phytochem.nal.usda.gov/
128. 1. van Santen JA
     2. Jacob G
     3. Singh AL
     4. Aniebok V
     5. Balunas MJ
     6. Bunsko D
     7. Neto FC
     8. Castaño-Espriu L
     9. Chang C
     10. Clark TN
     11. Cleary Little JL
     12. Delgadillo DA
     13. Dorrestein PC
     14. Duncan KR
     15. Egan JM
     16. Galey MM
     17. Haeckl FPJ
     18. Hua A
     19. Hughes AH
     20. Iskakova D
     21. Khadilkar A
     22. Lee J-H
     23. Lee S
     24. LeGrow N
     25. Liu DY
     26. Macho JM
     27. McCaughey CS
     28. Medema MH
     29. Neupane RP
     30. O’Donnell TJ
     31. Paula JS
     32. Sanchez LM
     33. Shaikh AF
     34. Soldatou S
     35. Terlouw BR
     36. Tran TA
     37. Valentine M
     38. van der Hooft JJJ
     39. Vo DA
     40. Wang M
     41. Wilson D
     42. Zink KE
     43. Linington RG

     (2019) The Natural Products Atlas: An Open Access Knowledge Base for Microbial Natural Products Discovery

     ACS Central Science 5:1824–1833.

     https://doi.org/10.1021/acscentsci.9b00806

     • PubMed
     • Google Scholar
129. 1. van Santen JA
     2. Poynton EF
     3. Iskakova D
     4. McMann E
     5. Alsup TA
     6. Clark TN
     7. Fergusson CH
     8. Fewer DP
     9. Hughes AH
     10. McCadden CA
     11. Parra J
     12. Soldatou S
     13. Rudolf JD
     14. Janssen EM-L
     15. Duncan KR
     16. Linington RG

     (2022) The Natural Products Atlas 2.0: a database of microbially-derived natural products

     Nucleic Acids Research 50:D1317–D1323.

     https://doi.org/10.1093/nar/gkab941

     • PubMed
     • Google Scholar
130. 1. Virtanen P
     2. Gommers R
     3. Oliphant TE
     4. Haberland M
     5. Reddy T
     6. Cournapeau D
     7. Burovski E
     8. Peterson P
     9. Weckesser W
     10. Bright J
     11. van der Walt SJ
     12. Brett M
     13. Wilson J
     14. Millman KJ
     15. Mayorov N
     16. Nelson ARJ
     17. Jones E
     18. Kern R
     19. Larson E
     20. Carey CJ
     21. Polat İ
     22. Feng Y
     23. Moore EW
     24. VanderPlas J
     25. Laxalde D
     26. Perktold J
     27. Cimrman R
     28. Henriksen I
     29. Quintero EA
     30. Harris CR
     31. Archibald AM
     32. Ribeiro AH
     33. Pedregosa F
     34. van Mulbregt P
     35. SciPy 1.0 Contributors

     (2020) SciPy 1.0: fundamental algorithms for scientific computing in Python

     Nature Methods 17:261–272.

     https://doi.org/10.1038/s41592-019-0686-2

     • PubMed
     • Google Scholar
131. 1. Waagmeester A
     2. Stupp G
     3. Burgstaller-Muehlbacher S
     4. Good BM
     5. Griffith M
     6. Griffith OL
     7. Hanspers K
     8. Hermjakob H
     9. Hudson TS
     10. Hybiske K
     11. Keating SM
     12. Manske M
     13. Mayers M
     14. Mietchen D
     15. Mitraka E
     16. Pico AR
     17. Putman T
     18. Riutta A
     19. Queralt-Rosinach N
     20. Schriml LM
     21. Shafee T
     22. Slenter D
     23. Stephan R
     24. Thornton K
     25. Tsueng G
     26. Tu R
     27. Ul-Hasan S
     28. Willighagen E
     29. Wu C
     30. Su AI

     (2020) Wikidata as a knowledge graph for the life sciences

     eLife 9:e52614.

     https://doi.org/10.7554/eLife.52614

     • PubMed
     • Google Scholar
132. Website

     1. Wakankenaku

     (2020) WAKANKENSAKU

     Accessed July 2, 2020.

     https://wakankensaku.inm.u-toyama.ac.jp/wiki/Main_Page
133. 1. Wang L-G
     2. Lam TT-Y
     3. Xu S
     4. Dai Z
     5. Zhou L
     6. Feng T
     7. Guo P
     8. Dunn CW
     9. Jones BR
     10. Bradley T
     11. Zhu H
     12. Guan Y
     13. Jiang Y
     14. Yu G

     (2020) Treeio: An R Package for Phylogenetic Tree Input and Output with Richly Annotated and Associated Data

     Molecular Biology and Evolution 37:599–603.

     https://doi.org/10.1093/molbev/msz240

     • PubMed
     • Google Scholar
134. Software

     1. Warnes GR
     2. Bolker B
     3. Gorjanc G
     4. Grothendieck G
     5. Korosec A
     6. Lumley T
     7. MacQueen D
     8. Magnusson A

     (2017) gdata: Various r programming tools for data manipulation

     Gdata.

     https://CRAN.R-project.org/package=gdata
135. 1. Weininger D

     (1988) SMILES, a chemical language and information system. 1. Introduction to methodology and encoding rules

     Journal of Chemical Information and Modeling 28:31–36.

     https://doi.org/10.1021/ci00057a005

     • Google Scholar
136. Software

     1. Wickham H

     (2018) readxl: Read Excel Files

     Readxl.

     https://CRAN.R-project.org/package=readxl
137. 1. Wickham H
     2. Averick M
     3. Bryan J
     4. Chang W
     5. McGowan L
     6. François R
     7. Grolemund G
     8. Hayes A
     9. Henry L
     10. Hester J
     11. Kuhn M
     12. Pedersen T
     13. Miller E
     14. Bache S
     15. Müller K
     16. Ooms J
     17. Robinson D
     18. Seidel D
     19. Spinu V
     20. Takahashi K
     21. Vaughan D
     22. Wilke C
     23. Woo K
     24. Yutani H

     (2019) Welcome to the Tidyverse

     Journal of Open Source Software 4:1686.

     https://doi.org/10.21105/joss.01686

     • Google Scholar
138. Software

     1. Wickham H

     (2020) rvest: Easily Harvest (Scrape) Web Pages

     Rvest.

     https://CRAN.R-project.org/package=rvest
139. Software

     1. Wickham H
     2. Hester J

     (2020) Jeroen Ooms. xml2

     Parse XML.

     https://CRAN.R-project.org/package=xml2
140. Software

     1. Wickham H
     2. Müller K

     (2021) DBI: R database interface R Special Interest Group on Databases

     DBI.

     https://CRAN.R-project.org/package=DBI
141. Software

     1. Wilkins D

     (2020) ggfittext: Fit Text Inside a Box in ’ggplot2

     Ggplot2.

     https://CRAN.R-project.org/package=ggfittext
142. 1. Wilkinson MD
     2. Dumontier M
     3. Aalbersberg IJJ
     4. Appleton G
     5. Axton M
     6. Baak A
     7. Blomberg N
     8. Boiten JW
     9. da Silva Santos LB
     10. Bourne PE
     11. Bouwman J
     12. Brookes AJ
     13. Clark T
     14. Crosas M
     15. Dillo I
     16. Dumon O
     17. Edmunds S
     18. Evelo CT
     19. Finkers R
     20. Gonzalez-Beltran A
     21. Gray AJG
     22. Groth P
     23. Goble C
     24. Grethe JS
     25. Heringa J
     26. ’t Hoen PAC
     27. Hooft R
     28. Kuhn T
     29. Kok R
     30. Kok J
     31. Lusher SJ
     32. Martone ME
     33. Mons A
     34. Packer AL
     35. Persson B
     36. Rocca-Serra P
     37. Roos M
     38. van Schaik R
     39. Sansone SA
     40. Schultes E
     41. Sengstag T
     42. Slater T
     43. Strawn G
     44. Swertz MA
     45. Thompson M
     46. van der Lei J
     47. van Mulligen E
     48. Velterop J
     49. Waagmeester A
     50. Wittenburg P
     51. Wolstencroft K
     52. Zhao J
     53. Mons B

     (2016) The FAIR Guiding Principles for scientific data management and stewardship

     Scientific Data 3:160018.

     https://doi.org/10.1038/sdata.2016.18

     • PubMed
     • Google Scholar
143. 1. Willighagen EL
     2. Mayfield JW
     3. Alvarsson J
     4. Berg A
     5. Carlsson L
     6. Jeliazkova N
     7. Kuhn S
     8. Pluskal T
     9. Rojas-Chertó M
     10. Spjuth O
     11. Torrance G
     12. Evelo CT
     13. Guha R
     14. Steinbeck C

     (2017) The Chemistry Development Kit (CDK) v2.0: atom typing, depiction, molecular formulas, and substructure searching

     Journal of Cheminformatics 9:33.

     https://doi.org/10.1186/s13321-017-0220-4

     • PubMed
     • Google Scholar
144. 1. Winter D

     (2017) rentrez: An R package for the NCBI eUtils API

     The R Journal 9:520.

     https://doi.org/10.32614/RJ-2017-058

     • Google Scholar
145. 1. Wohlgemuth G
     2. Haldiya PK
     3. Willighagen E
     4. Kind T
     5. Fiehn O

     (2010) The Chemical Translation Service--a web-based tool to improve standardization of metabolomic reports

     Bioinformatics (Oxford, England) 26:2647–2648.

     https://doi.org/10.1093/bioinformatics/btq476

     • PubMed
     • Google Scholar
146. Software

     1. Xu S

     (2021) ggstar: Star Layer for ’ggplot2

     CRAN.

     https://CRAN.R-project.org/package=ggstar
147. 1. Xu S
     2. Dai Z
     3. Guo P
     4. Fu X
     5. Liu S
     6. Zhou L
     7. Tang W
     8. Feng T
     9. Chen M
     10. Zhan L
     11. Wu T
     12. Hu E
     13. Jiang Y
     14. Bo X
     15. Yu G

     (2021) ggtreeExtra: Compact Visualization of Richly Annotated Phylogenetic Data

     Molecular Biology and Evolution 38:4039–4042.

     https://doi.org/10.1093/molbev/msab166

     • PubMed
     • Google Scholar
148. 1. Yabuzaki J

     (2017) Carotenoids Database: structures, chemical fingerprints and distribution among organisms

     Database 2017:bax004.

     https://doi.org/10.1093/database/bax004

     • PubMed
     • Google Scholar
149. 1. Yu G

     (2017) ggtree: an r package for visualization and annotation of phylogenetic trees with their covariates and other associated data

     Methods in Ecology and Evolution 8:28–36.

     https://doi.org/10.1111/2041-210x.12628

     • Google Scholar
150. 1. Yue Y
     2. Chu G-X
     3. Liu X-S
     4. Tang X
     5. Wang W
     6. Liu G-J
     7. Yang T
     8. Ling T-J
     9. Wang X-G
     10. Zhang Z-Z
     11. Xia T
     12. Wan X-C
     13. Bao G-H

     (2014) TMDB: A literature-curated database for small molecular compounds found from tea

     BMC Plant Biology 14:243.

     https://doi.org/10.1186/s12870-014-0243-1

     • PubMed
     • Google Scholar
151. 1. Zeng X
     2. Zhang P
     3. He W
     4. Qin C
     5. Chen S
     6. Tao L
     7. Wang Y
     8. Tan Y
     9. Gao D
     10. Wang B
     11. Chen Z
     12. Chen W
     13. Jiang YY
     14. Chen YZ

     (2018) NPASS: natural product activity and species source database for natural product research, discovery and tool development

     Nucleic Acids Research 46:D1217–D1222.

     https://doi.org/10.1093/nar/gkx1026

     • PubMed
     • Google Scholar
152. 1. Zhang R
     2. Lin J
     3. Zou Y
     4. Zhang X-J
     5. Xiao W-L

     (2019) Chemical Space and Biological Target Network of Anti-Inflammatory Natural Products

     Journal of Chemical Information and Modeling 59:66–73.

     https://doi.org/10.1021/acs.jcim.8b00560

     • PubMed
     • Google Scholar
153. 1. Zhao W-Y
     2. Yi J
     3. Chang Y-B
     4. Sun C-P
     5. Ma X-C

     (2022) Recent studies on terpenoids in Aspergillus fungi: Chemical diversity, biosynthesis, and bioactivity

     Phytochemistry 193:113011.

     https://doi.org/10.1016/j.phytochem.2021.113011

     • PubMed
     • Google Scholar

Author details

1. Adriano Rutz

   1. School of Pharmaceutical Sciences, University of Geneva, Geneva, Switzerland
   2. Institute of Pharmaceutical Sciences of Western Switzerland, University of Geneva, Geneva, Switzerland

   Contribution

   Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Project administration, Software, Validation, Visualization, Wikidata, Writing – original draft, Writing – review and editing

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0003-0443-9902

2. Maria Sorokina

   Institute for Inorganic and Analytical Chemistry, Friedrich-Schiller-University Jena, Jena, Germany

   Contribution

   LNPN Website, Software, Writing – review and editing

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0001-9359-7149

3. Jakub Galgonek

   Institute of Organic Chemistry and Biochemistry of the CAS, Prague, Czech Republic

   Contribution

   Sachem, IDSM, Software, Writing – review and editing

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-7038-544X

4. Daniel Mietchen

   1. Ronin Institute, Montclair, United States
   2. Leibniz Institute of Freshwater Ecology and Inland Fisheries, Berlin, Germany
   3. School of Data Science, University of Virginia, Charlottesville, United States

   Contribution

   Wikidata, Writing – review and editing

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0001-9488-1870

5. Egon Willighagen

   Department of Bioinformatics-BiGCaT, Maastricht University, Maastricht, Netherlands

   Contribution

   Wikidata, Writing – review and editing

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0001-7542-0286

6. Arnaud Gaudry

   1. School of Pharmaceutical Sciences, University of Geneva, Geneva, Switzerland
   2. Institute of Pharmaceutical Sciences of Western Switzerland, University of Geneva, Geneva, Switzerland

   Contribution

   Visualization, Writing – review and editing

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-3648-7362

7. James G Graham

   1. Center for Natural Product Technologies and WHO Collaborating Centre for Traditional Medicine (WHO CC/TRM), Pharmacognosy Institute; College of Pharmacy, University of Illinois at Chicago, Chicago, United States
   2. Department of Pharmaceutical Sciences, College of Pharmacy, University of Illinois at Chicago, Chicago, United States

   Contribution

   NAPRALERT, Writing – review and editing

   Competing interests

   No competing interests declared

8. Ralf Stephan

   Ontario Institute for Cancer Research (OICR), University Ave Suite, Toronto, Canada

   Contribution

   Wikidata, Writing – review and editing

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-4650-631X

9. Roderic Page

   University of Glasgow, Glasgow, United Kingdom

   Contribution

   Wikidata

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-7101-9767

10. Jiří Vondrášek

    Institute of Organic Chemistry and Biochemistry of the CAS, Prague, Czech Republic

    Contribution

    Funding acquisition, Resources, Sachem, IDSM

    Competing interests

    No competing interests declared

    "This ORCID iD identifies the author of this article:" 0000-0002-6066-973X

11. Christoph Steinbeck

    Institute for Inorganic and Analytical Chemistry, Friedrich-Schiller-University Jena, Jena, Germany

    Contribution

    Funding acquisition, LNPN Website, Resources, Writing – review and editing

    Competing interests

    No competing interests declared

    "This ORCID iD identifies the author of this article:" 0000-0001-6966-0814

12. Guido F Pauli

    1. Center for Natural Product Technologies and WHO Collaborating Centre for Traditional Medicine (WHO CC/TRM), Pharmacognosy Institute; College of Pharmacy, University of Illinois at Chicago, Chicago, United States
    2. Department of Pharmaceutical Sciences, College of Pharmacy, University of Illinois at Chicago, Chicago, United States

    Contribution

    Funding acquisition, NAPRALERT, Resources, Writing – review and editing

    Competing interests

    No competing interests declared

    "This ORCID iD identifies the author of this article:" 0000-0003-1022-4326

13. Jean-Luc Wolfender

    1. School of Pharmaceutical Sciences, University of Geneva, Geneva, Switzerland
    2. Institute of Pharmaceutical Sciences of Western Switzerland, University of Geneva, Geneva, Switzerland

    Contribution

    Funding acquisition, Resources, Writing – review and editing

    Competing interests

    No competing interests declared

    "This ORCID iD identifies the author of this article:" 0000-0002-0125-952X

14. Jonathan Bisson

    1. Center for Natural Product Technologies and WHO Collaborating Centre for Traditional Medicine (WHO CC/TRM), Pharmacognosy Institute; College of Pharmacy, University of Illinois at Chicago, Chicago, United States
    2. Department of Pharmaceutical Sciences, College of Pharmacy, University of Illinois at Chicago, Chicago, United States

    Contribution

    Conceptualization, Data curation, Formal analysis, Investigation, Methodology, NAPRALERT, Project administration, Resources, Software, Supervision, Validation, Writing – review and editing

    For correspondence

    bjo@uic.edu

    Competing interests

    No competing interests declared

    "This ORCID iD identifies the author of this article:" 0000-0003-1640-9989

15. Pierre-Marie Allard

    1. School of Pharmaceutical Sciences, University of Geneva, Geneva, Switzerland
    2. Institute of Pharmaceutical Sciences of Western Switzerland, University of Geneva, Geneva, Switzerland
    3. Department of Biology, University of Fribourg, Fribourg, Switzerland

    Contribution

    Conceptualization, Data curation, Formal analysis, Funding acquisition, Investigation, Methodology, Project administration, Resources, Software, Supervision, Validation, Wikidata, Writing – original draft, Writing – review and editing

    For correspondence

    pierre-marie.allard@unifr.ch

    Competing interests

    No competing interests declared

    "This ORCID iD identifies the author of this article:" 0000-0003-3389-2191

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