The scalable precision medicine open knowledge engine (SPOKE): a massive knowledge graph of biomedical information.

MOTIVATION: Knowledge graphs (KGs) are being adopted in industry, commerce and academia. Biomedical KG presents a challenge due to the complexity, size and heterogeneity of the underlying information. RESULTS: In this work, we present the Scalable Precision Medicine Open Knowledge Engine (SPOKE), a biomedical KG connecting millions of concepts via semantically meaningful relationships. SPOKE contains 27 million nodes of 21 different types and 53 million edges of 55 types downloaded from 41 databases. The graph is built on the framework of 11 ontologies that maintain its structure, enable mappings and facilitate navigation. SPOKE is built weekly by python scripts which download each resource, check for integrity and completeness, and then create a 'parent table' of nodes and edges. Graph queries are translated by a REST API and users can submit searches directly via an API or a graphical user interface. Conclusions/Significance: SPOKE enables the integration of seemingly disparate information to support precision medicine efforts. AVAILABILITY AND IMPLEMENTATION: The SPOKE neighborhood explorer is available at https://spoke.rbvi.ucsf.edu. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Specimen, biological structure, and spatial ontologies in support of a Human Reference Atlas.

The Human Reference Atlas (HRA) is defined as a comprehensive, three-dimensional (3D) atlas of all the cells in the healthy human body. It is compiled by an international team of experts who develop standard terminologies that they link to 3D reference objects, describing anatomical structures. The third HRA release (v1.2) covers spatial reference data and ontology annotations for 26 organs. Experts access the HRA annotations via spreadsheets and view reference object models in 3D editing tools. This paper introduces the Common Coordinate Framework (CCF) Ontology v2.0.1 that interlinks specimen, biological structure, and spatial data, together with the CCF API that makes the HRA programmatically accessible and interoperable with Linked Open Data (LOD). We detail how real-world user needs and experimental data guide CCF Ontology design and implementation, present CCF Ontology classes and properties together with exemplary usage, and report on validation methods. The CCF Ontology graph database and API are used in the HuBMAP portal, HRA Organ Gallery, and other applications that support data queries across multiple, heterogeneous sources.

Modeling community standards for metadata as templates makes data FAIR.

It is challenging to determine whether datasets are findable, accessible, interoperable, and reusable (FAIR) because the FAIR Guiding Principles refer to highly idiosyncratic criteria regarding the metadata used to annotate datasets. Specifically, the FAIR principles require metadata to be "rich" and to adhere to "domain-relevant" community standards. Scientific communities should be able to define their own machine-actionable templates for metadata that encode these "rich," discipline-specific elements. We have explored this template-based approach in the context of two software systems. One system is the CEDAR Workbench, which investigators use to author new metadata. The other is the FAIRware Workbench, which evaluates the metadata of archived datasets for their adherence to community standards. Benefits accrue when templates for metadata become central elements in an ecosystem of tools to manage online datasets-both because the templates serve as a community reference for what constitutes FAIR data, and because they embody that perspective in a form that can be distributed among a variety of software applications to assist with data stewardship and data sharing.

Using association rule mining and ontologies to generate metadata recommendations from multiple biomedical databases.

Metadata-the machine-readable descriptions of the data-are increasingly seen as crucial for describing the vast array of biomedical datasets that are currently being deposited in public repositories. While most public repositories have firm requirements that metadata must accompany submitted datasets, the quality of those metadata is generally very poor. A key problem is that the typical metadata acquisition process is onerous and time consuming, with little interactive guidance or assistance provided to users. Secondary problems include the lack of validation and sparse use of standardized terms or ontologies when authoring metadata. There is a pressing need for improvements to the metadata acquisition process that will help users to enter metadata quickly and accurately. In this paper, we outline a recommendation system for metadata that aims to address this challenge. Our approach uses association rule mining to uncover hidden associations among metadata values and to represent them in the form of association rules. These rules are then used to present users with real-time recommendations when authoring metadata. The novelties of our method are that it is able to combine analyses of metadata from multiple repositories when generating recommendations and can enhance those recommendations by aligning them with ontology terms. We implemented our approach as a service integrated into the CEDAR Workbench metadata authoring platform, and evaluated it using metadata from two public biomedical repositories: US-based National Center for Biotechnology Information BioSample and European Bioinformatics Institute BioSamples. The results show that our approach is able to use analyses of previously entered metadata coupled with ontology-based mappings to present users with accurate recommendations when authoring metadata.

Unleashing the value of Common Data Elements through the CEDAR Workbench.

Developing promising treatments in biomedicine often requires aggregation and analysis of data from disparate sources across the healthcare and research spectrum. To facilitate these approaches, there is a growing focus on supporting interoperation of datasets by standardizing data-capture and reporting requirements. Common Data Elements (CDEs)-precise specifications of questions and the set of allowable answers to each question-are increasingly being adopted to help meet these standardization goals. While CDEs can provide a strong conceptual foundation for interoperation, there are no widely recognized serialization or interchange formats to describe and exchange their definitions. As a result, CDEs defined in one system cannot be easily be reused by other systems. An additional problem is that current CDE-based systems tend to be rather heavyweight and cannot be easily adopted and used by third-parties. To address these problems, we developed extensions to a metadata management system called the CEDAR Workbench to provide a platform to simplify the creation, exchange, and use of CDEs. We show how the resulting system allows users to quickly define and share CDEs and to immediately use these CDEs to build and deploy Web-based forms to acquire conforming metadata. We also show how we incorporated a large CDE library from the National Cancer Institute's caDSR system and made these CDEs publicly available for general use.