Biology subjects

Willighagen, E.

Publications and source records attributed to Willighagen, E..

9 recordsLinked to original sources

bioRxiv2026

Ontology-Driven Annotation of Key Events Enables Transcriptomics-Based Assessment of Adverse Outcome Pathway Networks

Chemical safety assessment is moving towards new approach methodologies (NAMs) to improve human safety and reduce animal testing. The Adverse Outcome Pathway (AOP) is a NAM framework describing toxicity as a chain of Key Events (KEs). Many AOP-Wiki KEs describe molecular, cellular, or tissue-level processes and can therefore be assessed with gene expression data, making transcriptomics of human in vitro models a NAM data generator. In AOP-Wiki, KEs are described by Key Event Components (KECs). A gene set can be derived when the biological process (BP) component carries a Gene Ontology (GO) term. Most KEs carry no such term, so their perturbation cannot be assessed using omics data. We assigned GO BP terms to the unmapped BP component across the brain, lung, liver and kidney AOP networks. We also replaced obsolete and direction-carrying terms in the pre-existing mapping with current, direction-neutral ones. For liver, we reviewed all pre-existing terms and replaced overly broad terms with better-matched alternatives. We then applied this approach to pesticide-induced hepatotoxicity, using Open TG-GATEs transcriptomics data from primary human hepatocytes exposed to cycloheximide, 2,4-dinitrophenol, hexachlorobenzene, and rotenone. KE perturbation was scored using single-sample Gene Set Enrichment Analysis (ssGSEA). Annotation increased the proportion of transcriptomics-linkable KEs in the liver network from 53% to 77%, enhancing identification of perturbed KEs after exposure. The KEs perturbed by all four compounds fell into five functional categories associated with hepatotoxicity. Our approach resulted in a more relevant AOP network for transcriptomics-based assessment of KE perturbation. These findings show molecular perturbation rather than direct proof of KE activation or causal progression through an AOP. When validated on larger and independent datasets, such networks will support mechanistic interpretation of transcriptomics data in chemical safety assessment.

bioinformatics↗

bioRxiv2023

Creating, curating and querying computer models of biological pathways for Inherited Metabolic Disorders

Collecting knowledge about Inherited Metabolic Disorders (IMDs) has the potential to support early diagnosis as well as foster research into treatment options. Biological pathway databases can be used to create an overview of relevant articles and books describing IMDs and collect, curate, and summarize knowledge about these disorders. Reuse of the information captured in these databases in research requires the knowledge to be accurate but also machine-readable. WikiPathways is a community-driven project to establish a machinereadable knowledge base of biological processes. We here describe how pathways models from WikiPathways were used to represent the underlying biological mechanisms of many IMDs collected by domain experts over a period of six years. This paper describes a standardized approach to depict IMDs in WikiPathways, shows the current limitations in creating machine-readable disease information, and introduces an approach to support data curation based on these machine-readable biological pathways. Furthermore, several SPARQL-queries were developed to analyze the biological content created in these models. Using this approach, 47 pathways were collected about 345 diseases, involving 877 metabolites, 421 annotated metabolic interactions, 262 genes, and 587 proteins related to these disorders.

bioinformatics↗

bioRxiv2022

The Bioregistry: Unifying the Identification of Biomedical Entities through an Integrative, Open, Community-driven Metaregistry

The standardized identification of biomedical entities is a cornerstone of interoperability, reuse, and data integration in the life sciences. Several registries have been developed to catalog resources maintaining identifiers for biomedical entities such as small molecules, proteins, cell lines, and clinical trials. However, existing registries have struggled to provide sufficient coverage and metadata standards that meet the evolving needs of modern life sciences researchers. Here, we introduce the Bioregistry, an integrative, open, community-driven metaregistry that synthesizes and substantially expands upon 23 existing registries. The Bioregistry addresses the need for a sustainable registry by leveraging public infrastructure and automation, and employing a progressive governance model centered around open code and open data to foster community contribution. The Bioregistry can be used to support the standardized annotation of data, models, ontologies, and scientific literature, thereby promoting their interoperability and reuse. The Bioregistry can be accessed through https://bioregistry.io and its source code and data are available under the MIT and CC0 Licenses at https://github.com/biopragmatics/bioregistry.

bioinformatics↗

bioRxiv2021

Open Natural Products Research: Curation and Dissemination of Biological Occurrences of Chemical Structures through Wikidata

Contemporary bioinformatic and chemoinformatic capabilities hold promise to reshape knowledge management, analysis and interpretation of data in natural products research. Currently, reliance on a disparate set of non-standardized, insular, and specialized databases presents a series of challenges for data access, both within the discipline and for integration and interoperability between related fields. The fundamental elements of exchange are referenced structure-organism pairs that establish relationships between distinct molecular structures and the living organisms from which they were identified. Consolidating and sharing such information via an open platform has strong transformative potential for natural products research and beyond. This is the ultimate goal of the newly established LOTUS initiative, which has now completed the first steps toward the harmonization, curation, validation and open dissemination of 750,000+ referenced structure-organism pairs. LOTUS data is hosted on Wikidata and regularly mirrored on https://lotus.naturalproducts.net. Data sharing within the Wikidata framework broadens data access and interoperability, opening new possibilities for community curation and evolving publication models. Furthermore, embedding LOTUS data into the vast Wikidata knowledge graph will facilitate new biological and chemical insights. The LOTUS initiative represents an important advancement in the design and deployment of a comprehensive and collaborative natural products knowledge base. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=61 SRC="FIGDIR/small/433265v3_ufig1.gif" ALT="Figure 1"> View larger version (14K): org.highwire.dtl.DTLVardef@184136borg.highwire.dtl.DTLVardef@16e0d6org.highwire.dtl.DTLVardef@30ae1org.highwire.dtl.DTLVardef@1bf4825_HPS_FORMAT_FIGEXP M_FIG C_FIG

bioinformatics↗

bioRxiv2020

Understanding signaling and metabolic paths using semantified and harmonized information about biological interactions

BackgroundTo grasp the complexity of biological processes, the biological knowledge is often translated into schematic diagrams of biological pathways, such as signalling and metabolic pathways. These pathway diagrams describe relevant connections between biological entities and incorporate domain knowledge in a visual format that is easier for humans to interpret. It has already been established that these diagrams can be represented in machine readable formats, as done in KEGG, Reactome, and WikiPathways. However, while humans are good at interpreting the message of the creator of such a diagram, algorithms struggle when the diversity in drawing approaches increases. WikiPathways supports multiple drawing styles, and therefore needs to harmonize this to offer semantically enriched access via the Resource Description Framework format. Particularly challenging in the normalization of diagrams are the interactions between the biological entities, so that we can glean information about the connectivity of the entities represented. These interactions include information about the type of interaction (metabolic conversion, inhibition, etc.), the direction, and the participants. Availability of the interactions in a semantic and harmonized format enables searching the full network of biological interactions and integration with the linked data cloud. ResultsWe here study how the graphically modelled biological knowledge in diagrams can be semantified and harmonized efficiently, and exemplify how the resulting data can be used to programmatically answer biological questions. We find that we can translate graphically modelled biological knowledge to a sufficient degree into a semantic model of biological knowledge and discuss some of the current limitations. Furthermore, we show how this interaction knowledge base can be used to answer specific biological questions. ConclusionThis paper demonstrates that most of the graphical biological knowledge from WikiPathways is modelled in the semantic layer of WikiPathways with the semantic information intact and connectivity information preserved. The usability of the WikiPathways pathway and connectivity information has shown to be useful and has been integrated into other platforms. Being able to evaluate how biological elements affect each other is useful and allows, for example, the identification of up or downstream targets that will have a similar effect when modified.

bioinformatics↗

bioRxiv2019

Wikidata as a FAIR knowledge graph for the life sciences

Wikidata is a community-maintained knowledge base that epitomizes the FAIR principles of Findability, Accessibility, Interoperability, and Reusability. Here, we describe the breadth and depth of biomedical knowledge contained within Wikidata, assembled from primary knowledge repositories on genomics, proteomics, genetic variants, pathways, chemical compounds, and diseases. We built a collection of open-source tools that simplify the addition and synchronization of Wikidata with source databases. We furthermore demonstrate several use cases of how the continuously updated, crowd-contributed knowledge in Wikidata can be mined. These use cases cover a diverse cross section of biomedical analyses, from crowdsourced curation of biomedical ontologies, to phenotype-based diagnosis of disease, to drug repurposing.

bioinformatics↗

bioRxiv2019

Two real use cases of FAIR maturity indicators in the life sciences

Data sharing and reuse are crucial to enhance scientific progress and maximize return of investments in science. Although attitudes are increasingly favorable, data reuse remains difficult for lack of infrastructures, standards, and policies. The FAIR (findable, accessible, interoperable, reusable) principles aim to provide recommendations to increase data reuse. Because of the broad interpretation of the FAIR principles, maturity indicators are necessary to determine FAIRness of a dataset. In this work, we propose a reproducible computational workflow to assess data FAIRness in the life sciences. Our implementation follows principles and guidelines recommended by the maturity indicator authoring group and integrates concepts from the literature. In addition, we propose a FAIR balloon plot to summarize and compare dataset FAIRness. We evaluated our method on two real use cases where researchers looked for datasets to answer their scientific questions. We retrieved information from repositories (ArrayExpress and Gene Expression Omnibus), a registry of repositories (re3data.org), and a searchable resource (Google Dataset Search) via application program interface (API) wherever possible. With our analysis, we found that the two datasets met the majority of the criteria defined by the maturity indicators, and we showed areas where improvements can easily be reached. We suggest that use of standard schema for metadata and presence of specific attributes in registries of repositories could increase FAIRness of datasets.

bioinformatics↗

bioRxiv2019

History of rare diseases and their genetic causes - a data driven approach

This dataset provides information about monogenic, rare diseases with a known genetic cause supplemented with manually extracted provenance of both the disease and the discovery of the underlying genetic cause of the disease.\n\nWe collected 4166 rare monogenic diseases according to their OMIM identifier, linked them to 3163 causative genes which are annotated with Ensembl identifiers and HGNC symbols. The PubMed identifier of the scientific publication, which for the first time describes the rare disease, and the publication which found the gene causing this disease were added using information from OMIM, Wikipedia, Google Scholar, Whonamedit, and PubMed. The data is available as a spreadsheet and as RDF in a semantic model modified from DisGeNET.\n\nThis dataset relies on publicly available data and publications with a PubMed IDs but this is to our knowledge the first time this data has been linked and made available for further study under a liberal license. Analysis of this data reveals the timeline of rare disease and causative genes discovery and links them to developments in methods and databases.

systems biology↗

bioRxiv2017

Automatic OpenAPI to Bio.tools Conversion

Computation has become a central component of life sciences research. Making computational services FAIR has had a strong interest from the life sciences community in the past 15 years. Admittedly, uptake of any of the developed solutions has been limited, and the existence of multiple approaches will not have helped. Interoperability of solution may be essential. This work introduces an interoperability layer between two approaches for FAIR annotation of web services: OpenAPI and bio. tools.

bioinformatics↗