OBIB-a novel ontology for biobanking

Update of the Minimum Information About BIobank Data Sharing (MIABIS) Core Terminology to the 3rd Version

Introduction: The Minimum Information About BIobank Data Sharing (MIABIS) is a biobank-specific terminology enabling the sharing of biobank-related data for different purposes across a wide range of database implementations. After 4 years in use and with the first version of the individual-level MIABIS component Sample, Sample donor, and Event, it was necessary to revise the terminology, especially to include biobanks that work more in the data domain than with samples. Materials & Methods: Nine use-cases representing different types of biobanks, studies, and networks participated in the development work. They represent types of data, specific sample types, or levels of organization that were not included earlier in MIABIS. To support our revision of the Biobank entity, we conducted a survey of European biobanks to chart the services they provide. An important stakeholder group for biobanks include researchers as the main users of biobanks. To be able to render MIABIS more researcher-friendly, we collected different sample/data requests to analyze the terminology adjustment needs in detail. During the update process, the Core terminology was iteratively reviewed by a large group of experts until a consensus was reached. Results: With this update, MIABIS was adjusted to encompass data-driven biobanks and to include data collections, while also describing the services and capabilities biobanks offer to their users, besides the retrospective samples. The terminology was also extended to accommodate sample and data collections of nonhuman origin. Additionally, a set of organizational attributes was compiled to describe networks. Discussion: The usability of MIABIS Core v3 was increased by extending it to cover more topics of the biobanking domain. Additionally, the focus was on a more general terminology and harmonization of attributes with the individual-level entities Sample, Sample donor, and Event to keep the overall terminology minimal. With this work, the internal semantics of the MIABIS terminology was improved.

The use of foundational ontologies in biomedical research

BACKGROUND

The FAIR principles recommend the use of controlled vocabularies, such as ontologies, to define data and metadata concepts. Ontologies are currently modelled following different approaches, sometimes describing conflicting definitions of the same concepts, which can affect interoperability. To cope with that, prior literature suggests organising ontologies in levels, where domain specific (low-level) ontologies are grounded in domain independent high-level ontologies (i.e., foundational ontologies). In this level-based organisation, foundational ontologies work as translators of intended meaning, thus improving interoperability. Despite their considerable acceptance in biomedical research, there are very few studies testing foundational ontologies. This paper describes a systematic literature mapping that was conducted to understand how foundational ontologies are used in biomedical research and to find empirical evidence supporting their claimed (dis)advantages.

RESULTS

From a set of 79 selected papers, we identified that foundational ontologies are used for several purposes: ontology construction, repair, mapping, and ontology-based data analysis. Foundational ontologies are claimed to improve interoperability, enhance reasoning, speed up ontology development and facilitate maintainability. The complexity of using foundational ontologies is the most commonly cited downside. Despite being used for several purposes, there were hardly any experiments (1 paper) testing the claims for or against the use of foundational ontologies. In the subset of 49 papers that describe the development of an ontology, it was observed a low adherence to ontology construction (16 papers) and ontology evaluation formal methods (4 papers).

CONCLUSION

Our findings have two main implications. First, the lack of empirical evidence about the use of foundational ontologies indicates a need for evaluating the use of such artefacts in biomedical research. Second, the low adherence to formal methods illustrates how the field could benefit from a more systematic approach when dealing with the development and evaluation of ontologies. The understanding of how foundational ontologies are used in the biomedical field can drive future research towards the improvement of ontologies and, consequently, data FAIRness. The adoption of formal methods can impact the quality and sustainability of ontologies, and reusing these methods from other fields is encouraged.

Assessing the Need for Semantic Data Integration for Surgical Biobanks-A Knowledge Representation Perspective

To improve patient outcomes after trauma, the need to decrypt the post-traumatic immune response has been identified. One prerequisite to drive advancement in understanding that domain is the implementation of surgical biobanks. This paper focuses on the outcomes of patients with one of two diagnoses: post-traumatic arthritis and osteomyelitis. In creating surgical biobanks, currently, many obstacles must be overcome. Roadblocks exist around scoping of data that is to be collected, and the semantic integration of these data. In this paper, the generic component model and the Semantic Web technology stack are used to solve issues related to data integration. The results are twofold: (a) a scoping analysis of data and the ontologies required to harmonize and integrate it, and (b) resolution of common data integration issues in integrating data relevant to trauma surgery.

Evaluating and Extending the Informed Consent Ontology for Representing Permissions from the Clinical Domain

The purpose of this study was to evaluate, revise, and extend the Informed Consent Ontology (ICO) for expressing clinical permissions, including reuse of residual clinical biospecimens and health data. This study followed a formative evaluation design and used a bottom-up modeling approach. Data were collected from the literature on US federal regulations and a study of clinical consent forms. Eleven federal regulations and fifteen permission-sentences from clinical consent forms were iteratively modeled to identify entities and their relationships, followed by community reflection and negotiation based on a series of predetermined evaluation questions. ICO included fifty-two classes and twelve object properties necessary when modeling, demonstrating appropriateness of extending ICO for the clinical domain. Twenty-six additional classes were imported into ICO from other ontologies, and twelve new classes were recommended for development. This work addresses a critical gap in formally representing permissions clinical permissions, including reuse of residual clinical biospecimens and health data. It makes missing content available to the OBO Foundry, enabling use alongside other widely-adopted biomedical ontologies. ICO serves as a machine-interpretable and interoperable tool for responsible reuse of residual clinical biospecimens and health data at scale.

A multidisciplinary approach to the Biobank concept: integrative review

Biobanks are multidisciplinary infrastructures and, accordingly, this integrative research seeks to bring out the concept of biobank in the various sciences that construct and interpret it, so as to arrive at a holistic understanding of its essential components. This integrative review - guided by PRISMA and with quality assessment following CASPe - resulted in a selection of 30 articles. Data were analysed by Aristotelian categories and the results were interpreted on the complexity paradigm of Edgar Morin. The biobank concept was clarified by considering it to be the representation of a biological, social and cultural phenomenon in which knowledge and practices from diverse scientific fields enter into complementary, antagonistic and ambiguous types of relationship. This network of signification, analysed here using categories from Aristotelian philosophy, has impacts on the construction of subjectivity and forms of socialisation.

Extending the Minimum Information About BIobank Data Sharing Terminology to Describe Samples, Sample Donors, and Events

Introduction: The Minimum Information About BIobank data Sharing (MIABIS) was initiated in 2012. MIABIS aims to create a common biobank terminology to facilitate data sharing in biobanks and sample collections. The MIABIS Core terminology consists of three components describing biobanks, sample collections, and studies, in which information on samples and sample donors is provided at aggregated form. However, there is also a need to describe samples and sample donors at an individual level to allow more elaborate queries on available biobank samples and data. Therefore the MIABIS terminology has now been extended with components describing samples and sample donors at an individual level. Materials and Methods: The components were defined according to specific scope and use cases by a large group of experts, and through several cycles of reviews, according to the new MIABIS governance model of BBMRI-ERIC (Biobanking and Biomolecular Resources Research Infrastructure-European Research Infrastructure Consortium). The guiding principles applied in developing these components included the following terms: model should consider only samples of human origin, model should be applicable to all types of samples and all sample donors, and model should describe the current status of samples stored in a given biobank. Results: A minimal set of standard attributes for defining samples and sample donors is presented here. We added an "event" component to describe attributes that are not directly describing samples or sample donors but are tightly related to them. To better utilize the generic data model, we suggest a procedure by which interoperability can be promoted, using specific MIABIS profiles. Discussion: The MIABIS sample and donor component extensions and the new generic data model complement the existing MIABIS Core 2.0 components, and substantially increase the potential usability of this terminology for better describing biobank samples and sample donors. They also support the use of individual level data about samples and sample donors to obtain accurate and detailed biobank availability queries.

Liquid materials for biomedical research: a highly IT-integrated and automated biobanking solution

Various information technology (IT) infrastructures for biobanking, networks of biobanks and biomaterial management are described in the literature. As pre-analytical variables play a major role in the downstream interpretation of clinical as well as research results, their documentation is essential. A description for mainly automated documentation of the complete life-cycle of each biospecimen is lacking so far. Here, the example taken is from the University Medical Center Göttingen (UMG), where the workflow of liquid biomaterials is standardized between the central laboratory and the central biobank. The workflow of liquid biomaterials from sample withdrawal to long-term storage in a biobank was analyzed. Essential data such as time and temperature for processing and freezing can be automatically collected. The proposed solution involves only one major interface between the main IT systems of the laboratory and the biobank. It is key to talk to all the involved stakeholders to ensure a functional and accepted solution. Although IT components differ widely between clinics, the proposed way of documenting the complete life-cycle of each biospecimen can be transferred to other university medical centers. The complete documentation of the life-cycle of each biospecimen ensures a good interpretability of downstream routine as well as research results.

Enhancing Clinical Data and Clinical Research Data with Biomedical Ontologies - Insights from the Knowledge Representation Perspective

OBJECTIVES

There exists a communication gap between the biomedical informatics community on one side and the computer science/artificial intelligence community on the other side regarding the meaning of the terms "semantic integration" and "knowledge representation". This gap leads to approaches that attempt to provide one-to-one mappings between data elements and biomedical ontologies. Our aim is to clarify the representational differences between traditional data management and semantic-web-based data management by providing use cases of clinical data and clinical research data re-representation. We discuss how and why one-to-one mappings limit the advantages of using Semantic Web Technologies (SWTs).

METHODS

We employ commonly used SWTs, such as Resource Description Framework (RDF) and Ontology Web Language (OWL). We reuse pre-existing ontologies and ensure shared ontological commitment by selecting ontologies from a framework that fosters community-driven collaborative ontology development for biomedicine following the same set of principles.

RESULTS

We demonstrate the results of providing SWT-compliant re-representation of data elements from two independent projects managing clinical data and clinical research data. Our results show how one-to-one mappings would hinder the exploitation of the advantages provided by using SWT.

CONCLUSIONS

We conclude that SWT-compliant re-representation is an indispensable step, if using the full potential of SWT is the goal. Rather than providing one-to-one mappings, developers should provide documentation that links data elements to graph structures to specify the re-representation.

BioSCOOP - Biobank Sample Communication Protocol. New approach for the transfer of information between biobanks

Dynamic development of biobanking industry (both business and science) resulted in an increased number of IT systems for samples and data management. The most difficult and complicated case for the biobanking community was cooperation between institutions, equipped with different IT systems, in the field of scientific research, mainly data interchange and information flow. Tools available on the market relate mainly to the biobank or collection level. Efficient and universal protocols including the detailed information about the donor and the sample are still very limited. Here, we have developed BioSCOOP, a communication protocol in the form of a well documented JSON API. The main aim of this study was to harmonize and standardize the rules of communication between biobanks on the level of information about the donor together with information about the sample. The purpose was to create a communication protocol for two applications: to transfer the information between different biobanks and to allow the searching and presentation of the sample and data sets.

DMTO: a realistic ontology for standard diabetes mellitus treatment

BACKGROUND

Treatment of type 2 diabetes mellitus (T2DM) is a complex problem. A clinical decision support system (CDSS) based on massive and distributed electronic health record data can facilitate the automation of this process and enhance its accuracy. The most important component of any CDSS is its knowledge base. This knowledge base can be formulated using ontologies. The formal description logic of ontology supports the inference of hidden knowledge. Building a complete, coherent, consistent, interoperable, and sharable ontology is a challenge.

RESULTS

This paper introduces the first version of the newly constructed Diabetes Mellitus Treatment Ontology (DMTO) as a basis for shared-semantics, domain-specific, standard, machine-readable, and interoperable knowledge relevant to T2DM treatment. It is a comprehensive ontology and provides the highest coverage and the most complete picture of coded knowledge about T2DM patients' current conditions, previous profiles, and T2DM-related aspects, including complications, symptoms, lab tests, interactions, treatment plan (TP) frameworks, and glucose-related diseases and medications. It adheres to the design principles recommended by the Open Biomedical Ontologies Foundry and is based on ontological realism that follows the principles of the Basic Formal Ontology and the Ontology for General Medical Science. DMTO is implemented under Protégé 5.0 in Web Ontology Language (OWL) 2 format and is publicly available through the National Center for Biomedical Ontology's BioPortal at http://bioportal.bioontology.org/ontologies/DMTO . The current version of DMTO includes more than 10,700 classes, 277 relations, 39,425 annotations, 214 semantic rules, and 62,974 axioms. We provide proof of concept for this approach to modeling TPs.

CONCLUSION

The ontology is able to collect and analyze most features of T2DM as well as customize chronic TPs with the most appropriate drugs, foods, and physical exercises. DMTO is ready to be used as a knowledge base for semantically intelligent and distributed CDSS systems.

[A cryobank as an attribute of omics technologies]

Biobanks are systematic and annotated collections of biological samples based on the system of standard operating procedures (SOP) and corresponding to the recommendations of the International Society for Biological and Environmental Repositories (ISBER). Standardization of conditions of obtaining, processing, storage of samples and providing to an end user are crucial in the activities of the biobank. The attributes of biobanks include common principles of labeling and annotation of biological samples using specialized software, an automated monitoring system of storage conditions, and registration of biosamples. Cryobanks are the biobanks maintained at the storage conditions from -196°C to -150°C that provide better cell viability and the highest preservation of biological molecules. Cryobanking is the most essential part of the infrastructure of population and personalized medicine, pharmaceuticals and biopharmacology, conservation of rare and endangered species, as well as biotechnology in general. Next Generation Biobanking, a concept especially designed for omics technologies, involves annotating biological samples on many biomarkers based on Next Generation Sequencing techniques, as well as collecting biological material from the same patient at different time points (for example, at different stages of the disease, before and after the operation, at different periods of therapy) with a detailed annotation of physiological, biochemical and clinical data. Epigenetic studies (DNA methylation, microRNA, etc.), as well as bioinformatic data analysis are of great importance in the activity of Next Generation Biobanking. Such biobanks should function based on the new ethical principles of the post-genomic era.

Consensus-Driven Development of a Terminology for Biobanking, the Duke Experience

Biobanking at Duke University has existed for decades and has grown over time in silos and based on specialized needs, as is true with most biomedical research centers. These silos developed informatics systems to support their own individual requirements, with no regard for semantic or syntactic interoperability. Duke undertook an initiative to implement an enterprise-wide biobanking information system to serve its many diverse biobanking entities. A significant part of this initiative was the development of a common terminology for use in the commercial software platform. Common terminology provides the foundation for interoperability across biobanks for data and information sharing. We engaged experts in research, informatics, and biobanking through a consensus-driven process to agree on 361 terms and their definitions that encompass the lifecycle of a biospecimen. Existing standards, common terms, and data elements from published articles provided a foundation on which to build the biobanking terminology; a broader set of stakeholders then provided additional input and feedback in a secondary vetting process. The resulting standardized biobanking terminology is now available for sharing with the biobanking community to serve as a foundation for other institutions who are considering a similar initiative.

Preparing Data at the Source to Foster Interoperability across Rare Disease Resources

The ability to combine heterogeneous data distributed across the globe is critically important to boost research on rare diseases, but it presents a number of methodological, representational and automation challenges. In this scenario, biomedical ontologies are of critical importance for enabling computers to aid in information retrieval and analysis across data collections.This chapter presents an approach to preparing rare disease data for integration through the application of a global standard for computer-readable data and knowledge. This includes the use of common data elements, ontological codes and computer-readable data. This approach was developed under a number of domain-relevant requirements, such as controlled access to data, independence of the original sources, and the desire to combining the data sources with other computational workflows and data platforms.