KG-Hub-building and exchanging biological knowledge graphs

MOTIVATION

Knowledge graphs (KGs) are a powerful approach for integrating heterogeneous data and making inferences in biology and many other domains, but a coherent solution for constructing, exchanging, and facilitating the downstream use of KGs is lacking.

RESULTS

Here we present KG-Hub, a platform that enables standardized construction, exchange, and reuse of KGs. Features include a simple, modular extract-transform-load pattern for producing graphs compliant with Biolink Model (a high-level data model for standardizing biological data), easy integration of any OBO (Open Biological and Biomedical Ontologies) ontology, cached downloads of upstream data sources, versioned and automatically updated builds with stable URLs, web-browsable storage of KG artifacts on cloud infrastructure, and easy reuse of transformed subgraphs across projects. Current KG-Hub projects span use cases including COVID-19 research, drug repurposing, microbial-environmental interactions, and rare disease research. KG-Hub is equipped with tooling to easily analyze and manipulate KGs. KG-Hub is also tightly integrated with graph machine learning (ML) tools which allow automated graph ML, including node embeddings and training of models for link prediction and node classification.

AVAILABILITY AND IMPLEMENTATION

https://kghub.org.

Biolink Model: A universal schema for knowledge graphs in clinical, biomedical, and translational science

Within clinical, biomedical, and translational science, an increasing number of projects are adopting graphs for knowledge representation. Graph‐based data models elucidate the interconnectedness among core biomedical concepts, enable data structures to be easily updated, and support intuitive queries, visualizations, and inference algorithms. However, knowledge discovery across these “knowledge graphs” (KGs) has remained difficult. Data set heterogeneity and complexity; the proliferation of ad hoc data formats; poor compliance with guidelines on findability, accessibility, interoperability, and reusability; and, in particular, the lack of a universally accepted, open‐access model for standardization across biomedical KGs has left the task of reconciling data sources to downstream consumers. Biolink Model is an open‐source data model that can be used to formalize the relationships between data structures in translational science. It incorporates object‐oriented classification and graph‐oriented features. The core of the model is a set of hierarchical, interconnected classes (or categories) and relationships between them (or predicates) representing biomedical entities such as gene, disease, chemical, anatomic structure, and phenotype. The model provides class and edge attributes and associations that guide how entities should relate to one another. Here, we highlight the need for a standardized data model for KGs, describe Biolink Model, and compare it with other models. We demonstrate the utility of Biolink Model in various initiatives, including the Biomedical Data Translator Consortium and the Monarch Initiative, and show how it has supported easier integration and interoperability of biomedical KGs, bringing together knowledge from multiple sources and helping to realize the goals of translational science.

Model organism data evolving in support of translational medicine

Model organism databases (MODs) have been collecting and integrating biomedical research data for 30 years and were designed to meet specific needs of each model organism research community. The contributions of model organism research to understanding biological systems would be hard to overstate. Modern molecular biology methods and cost reductions in nucleotide sequencing have opened avenues for direct application of model organism research to elucidating mechanisms of human diseases. Thus, the mandate for model organism research and databases has now grown to include facilitating use of these data in translational applications. Challenges in meeting this opportunity include the distribution of research data across many databases and websites, a lack of data format standards for some data types, and sustainability of scale and cost for genomic database resources like MODs. The issues of widely distributed data and application of data standards are some of the challenges addressed by FAIR (Findable, Accessible, Interoperable, and Re-usable) data principles. The Alliance of Genome Resources is now moving to address these challenges by bringing together expertly curated research data from fly, mouse, rat, worm, yeast, zebrafish, and the Gene Ontology consortium. Centralized multi-species data access, integration, and format standardization will lower the data utilization barrier in comparative genomics and translational applications and will provide a framework in which sustainable scale and cost can be addressed. This article presents a brief historical perspective on how the Alliance model organisms are complementary and how they have already contributed to understanding the etiology of human diseases. In addition, we discuss four challenges for using data from MODs in translational applications and how the Alliance is working to address them, in part by applying FAIR data principles. Ultimately, combined data from these animal models are more powerful than the sum of the parts.

ZFIN, The zebrafish model organism database: Updates and new directions

The Zebrafish Model Organism Database (ZFIN; http://zfin.org) is the central resource for genetic and genomic data from zebrafish (Danio rerio) research. ZFIN staff curate detailed information about genes, mutants, genotypes, reporter lines, sequences, constructs, antibodies, knockdown reagents, expression patterns, phenotypes, gene product function, and orthology from publications. Researchers can submit mutant, transgenic, expression, and phenotype data directly to ZFIN and use the ZFIN Community Wiki to share antibody and protocol information. Data can be accessed through topic-specific searches, a new site-wide search, and the data-mining resource ZebrafishMine (http://zebrafishmine.org). Data download and web service options are also available. ZFIN collaborates with major bioinformatics organizations to verify and integrate genomic sequence data, provide nomenclature support, establish reciprocal links, and participate in the development of standardized structured vocabularies (ontologies) used for data annotation and searching. ZFIN-curated gene, function, expression, and phenotype data are available for comparative exploration at several multi-species resources. The use of zebrafish as a model for human disease is increasing. ZFIN is supporting this growing area with three major projects: adding easy access to computed orthology data from gene pages, curating details of the gene expression pattern changes in mutant fish, and curating zebrafish models of human diseases.

Cross-organism analysis using InterMine

InterMine is a data integration warehouse and analysis software system developed for large and complex biological data sets. Designed for integrative analysis, it can be accessed through a user-friendly web interface. For bioinformaticians, extensive web services as well as programming interfaces for most common scripting languages support access to all features. The web interface includes a useful identifier look-up system, and both simple and sophisticated search options. Interactive results tables enable exploration, and data can be filtered, summarized, and browsed. A set of graphical analysis tools provide a rich environment for data exploration including statistical enrichment of sets of genes or other entities. InterMine databases have been developed for the major model organisms, budding yeast, nematode worm, fruit fly, zebrafish, mouse, and rat together with a newly developed human database. Here, we describe how this has facilitated interoperation and development of cross-organism analysis tools and reports. InterMine as a data exploration and analysis tool is also described. All the InterMine-based systems described in this article are resources freely available to the scientific community.

ZFIN: enhancements and updates to the Zebrafish Model Organism Database

ZFIN, the Zebrafish Model Organism Database, http://zfin.org, serves as the central repository and web-based resource for zebrafish genetic, genomic, phenotypic and developmental data. ZFIN manually curates comprehensive data for zebrafish genes, phenotypes, genotypes, gene expression, antibodies, anatomical structures and publications. A wide-ranging collection of web-based search forms and tools facilitates access to integrated views of these data promoting analysis and scientific discovery. Data represented in ZFIN are derived from three primary sources: curation of zebrafish publications, individual research laboratories and collaborations with bioinformatics organizations. Data formats include text, images and graphical representations. ZFIN is a dynamic resource with data added daily as part of our ongoing curation process. Software updates are frequent. Here, we describe recent additions to ZFIN including (i) enhanced access to images, (ii) genomic features, (iii) genome browser, (iv) transcripts, (v) antibodies and (vi) a community wiki for protocols and antibodies.

The Zebrafish Information Network: the zebrafish model organism database provides expanded support for genotypes and phenotypes

The Zebrafish Information Network (ZFIN, http://zfin.org), the model organism database for zebrafish, provides the central location for curated zebrafish genetic, genomic and developmental data. Extensive data integration of mutant phenotypes, genes, expression patterns, sequences, genetic markers, morpholinos, map positions, publications and community resources facilitates the use of the zebrafish as a model for studying gene function, development, behavior and disease. Access to ZFIN data is provided via web-based query forms and through bulk data files. ZFIN is the definitive source for zebrafish gene and allele nomenclature, the zebrafish anatomical ontology (AO) and for zebrafish gene ontology (GO) annotations. ZFIN plays an active role in the development of cross-species ontologies such as the phenotypic quality ontology (PATO) and the gene ontology (GO). Recent enhancements to ZFIN include (i) a new home page and navigation bar, (ii) expanded support for genotypes and phenotypes, (iii) comprehensive phenotype annotations based on anatomical, phenotypic quality and gene ontologies, (iv) a BLAST server tightly integrated with the ZFIN database via ZFIN-specific datasets, (v) a global site search and (vi) help with hands-on resources.