Enabling Open Science: Wikidata for Research (Wiki4R)

Mietchen, Daniel; Hagedorn, Gregor; Willighagen, Egon; Rico, Mariano; Gómez-Pérez, Asuncíon; Aibar, Eduard; Rafes, Karima; Germain, Cécile; Dunning, Alastair; Pintscher, Lydia; Kinzler, Daniel

doi:10.3897/rio.1.e7573

Cited by 18 publications

(14 citation statements)

References 4 publications

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“…Interactions with at least two annotated interaction participants (gene product, metabolite, complex) are included. Gene products have unified Ensembl (Zerbino et al, 2017) identifiers, metabolites have either Wikidata (Mietchen et al, 2015), ChEBI (Hastings et al, 2015) or HMDB identifiers (Wishart et al, 2017), and complexes have Reactome identifiers. A list of frequently occurring small molecules (Supplementary Table 1), e.g., H + , H 2 0, ATP, were removed from the network to prevent inclusion of paths with no specific biological relevance.…”

Section: Methodsmentioning

confidence: 99%

Beyond Pathway Analysis: Identification of Active Subnetworks in Rett Syndrome

et al. 2019

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Pathway and network approaches are valuable tools in analysis and interpretation of large complex omics data. Even in the field of rare diseases, like Rett syndrome, omics data are available, and the maximum use of such data requires sophisticated tools for comprehensive analysis and visualization of the results. Pathway analysis with differential gene expression data has proven to be extremely successful in identifying affected processes in disease conditions. In this type of analysis, pathways from different databases like WikiPathways and Reactome are used as separate, independent entities. Here, we show for the first time how these pathway models can be used and integrated into one large network using the WikiPathways RDF containing all human WikiPathways and Reactome pathways, to perform network analysis on transcriptomics data. This network was imported into the network analysis tool Cytoscape to perform active submodule analysis. Using a publicly available Rett syndrome gene expression dataset from frontal and temporal cortex, classical enrichment analysis, including pathway and Gene Ontology analysis, revealed mainly immune response, neuron specific and extracellular matrix processes. Our active module analysis provided a valuable extension of the analysis prominently showing the regulatory mechanism of MECP2 , especially on DNA maintenance, cell cycle, transcription, and translation. In conclusion, using pathway models for classical enrichment and more advanced network analysis enables a more comprehensive analysis of gene expression data and provides novel results.

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Section: Methodsmentioning

confidence: 99%

Beyond Pathway Analysis: Identification of Active Subnetworks in Rett Syndrome

et al. 2019

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“…Comparing the last five years, the total number of annotated unique metabolite nodes follow a positive trend, having grown by 158% from 1213 in 2013 to 3133 in 2017. The total number of unique metabolites was estimated by counting the unique number of identifiers: all metabolite annotations were first normalized to Wikidata identifiers ( 18 ), second to ChEBI ( 19 ), third to HMDB ( 20 ), in that order, when the normalization to Wikidata was not possible. If none of these three databases provided an identifier for the listed compound, we refer to the metabolite identifier as unmapped.…”

Section: Updates For Biologists and Chemistsmentioning

confidence: 99%

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

Slenter

Kutmon

Hanspers

et al. 2017

Nucleic Acids Research

812

647

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WikiPathways (wikipathways.org) captures the collective knowledge represented in biological pathways. By providing a database in a curated, machine readable way, omics data analysis and visualization is enabled. WikiPathways and other pathway databases are used to analyze experimental data by research groups in many fields. Due to the open and collaborative nature of the WikiPathways platform, our content keeps growing and is getting more accurate, making WikiPathways a reliable and rich pathway database. Previously, however, the focus was primarily on genes and proteins, leaving many metabolites with only limited annotation. Recent curation efforts focused on improving the annotation of metabolism and metabolic pathways by associating unmapped metabolites with database identifiers and providing more detailed interaction knowledge. Here, we report the outcomes of the continued growth and curation efforts, such as a doubling of the number of annotated metabolite nodes in WikiPathways. Furthermore, we introduce an OpenAPI documentation of our web services and the FAIR (Findable, Accessible, Interoperable and Reusable) annotation of resources to increase the interoperability of the knowledge encoded in these pathways and experimental omics data. New search options, monthly downloads, more links to metabolite databases, and new portals make pathway knowledge more effortlessly accessible to individual researchers and research communities.

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“…However, not all of these CAS numbers are linked to open structure data that is incorporated in the BridgeDb mapping that we performed. It is essential that these CAS numbers are included in public databases, such as WikiData (Mietchen et al, 2015) or that public database identifiers are used, such as from ChEBI or even Wikidata as an outside database for chemical information. Besides chemicals, nanomaterials, which are extensively investigated for toxicity, also require annotations, for example with the eNanoMapper ontology (Hastings et al, 2015).…”

Section: Discussionmentioning

confidence: 99%

Introducing WikiPathways as a Data-Source to Support Adverse Outcome Pathways for Regulatory Risk Assessment of Chemicals and Nanomaterials

et al. 2018

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A paradigm shift is taking place in risk assessment to replace animal models, reduce the number of economic resources, and refine the methodologies to test the growing number of chemicals and nanomaterials. Therefore, approaches such as transcriptomics, proteomics, and metabolomics have become valuable tools in toxicological research, and are finding their way into regulatory toxicity. One promising framework to bridge the gap between the molecular-level measurements and risk assessment is the concept of adverse outcome pathways (AOPs). These pathways comprise mechanistic knowledge and connect biological events from a molecular level toward an adverse effect outcome after exposure to a chemical. However, the implementation of omics-based approaches in the AOPs and their acceptance by the risk assessment community is still a challenge. Because the existing modules in the main repository for AOPs, the AOP Knowledge Base (AOP-KB), do not currently allow the integration of omics technologies, additional tools are required for omics-based data analysis and visualization. Here we show how WikiPathways can serve as a supportive tool to make omics data interoperable with the AOP-Wiki, part of the AOP-KB. Manual matching of key events (KEs) indicated that 67% could be linked with molecular pathways. Automatic connection through linkage of identifiers between the databases showed that only 30% of AOP-Wiki chemicals were found on WikiPathways. More loose linkage through gene names in KE and Key Event Relationships descriptions gave an overlap of 70 and 71%, respectively. This shows many opportunities to create more direct connections, for example with extended ontology annotations, improving its interoperability. This interoperability allows the needed integration of omics data linked to the molecular pathways with AOPs. A new AOP Portal on WikiPathways is presented to allow the community of AOP developers to collaborate and populate the molecular pathways that underlie the KEs of AOP-Wiki. We conclude that the integration of WikiPathways and AOP-Wiki will improve risk assessment because omics data will be linked directly to KEs and therefore allow the comprehensive understanding and description of AOPs. To make this assessment reproducible and valid, major changes are needed in both WikiPathways and AOP-Wiki.

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Enabling Open Science: Wikidata for Research (Wiki4R)

Cited by 18 publications

References 4 publications

Beyond Pathway Analysis: Identification of Active Subnetworks in Rett Syndrome

Beyond Pathway Analysis: Identification of Active Subnetworks in Rett Syndrome

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

Introducing WikiPathways as a Data-Source to Support Adverse Outcome Pathways for Regulatory Risk Assessment of Chemicals and Nanomaterials

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