Biomedical Data Repositories Research Articles

Privacy-Enhancing Technologies in Biomedical Data Science.

The rapidly growing scale and variety of biomedical data repositories raise important privacy concerns. Conventional frameworks for collecting and sharing human subject data offer limited privacy protection, often necessitating the creation of data silos. Privacy-enhancing technologies (PETs) promise to safeguard these data and broaden their usage by providing means to share and analyze sensitive data while protecting privacy. Here, we review prominent PETs and illustrate their role in advancing biomedicine. We describe key use cases of PETs and their latest technical advances and highlight recent applications of PETs in a range of biomedical domains. We conclude by discussing outstanding challenges and social considerations that need to be addressed to facilitate a broader adoption of PETs in biomedical data science.

Developing a standardized but extendable framework to increase the findability of infectious disease datasets

Biomedical datasets are increasing in size, stored in many repositories, and face challenges in FAIRness (findability, accessibility, interoperability, reusability). As a Consortium of infectious disease researchers from 15 Centers, we aim to adopt open science practices to promote transparency, encourage reproducibility, and accelerate research advances through data reuse. To improve FAIRness of our datasets and computational tools, we evaluated metadata standards across established biomedical data repositories. The vast majority do not adhere to a single standard, such as Schema.org, which is widely-adopted by generalist repositories. Consequently, datasets in these repositories are not findable in aggregation projects like Google Dataset Search. We alleviated this gap by creating a reusable metadata schema based on Schema.org and catalogued nearly 400 datasets and computational tools we collected. The approach is easily reusable to create schemas interoperable with community standards, but customized to a particular context. Our approach enabled data discovery, increased the reusability of datasets from a large research consortium, and accelerated research. Lastly, we discuss ongoing challenges with FAIRness beyond discoverability.

Exploring how biobanks communicate the possibility of commercial access and its associated benefits and risks in participant documents

BackgroundBiobanks and biomedical research data repositories collect their samples and associated data from volunteer participants. Their aims are to facilitate biomedical research and improve health, and they are framed in terms of contributing to the public good. Biobank resources may be accessible to researchers with commercial motivations, for example, researchers in pharmaceutical companies who may utilise the data to develop new clinical therapeutics and pharmaceutical drugs. Studies exploring citizen perceptions of public/private interactions associated with large health data repositories/biobanks indicate that there are sensitivities around public/private and/or non-profit/profit relationships and international sample and data sharing. Less work has explored how biobanks communicate their public/private partnerships to the public or to their potential research participants.MethodsWe explored how a biobank’s aims, benefits and risks, and private/public relationships have been framed in public facing recruitment documents (consent forms and participant information sheets).ResultsBiobank documents often communicate their commercial access arrangements but not the detail about what these interactions would entail, and how risks and benefits would be distributed to the public.ConclusionWe argue that this leads to a polarised discourse between public and private entities and/or activities, and fails to attend to the blurred lines between them. This results in a lack of attention to more important issues such as how risks and benefits in general are distributed to the public. We call for a nuanced approach that can contribute to the much-needed dialogue in this space.

Dug: a semantic search engine leveraging peer-reviewed knowledge to query biomedical data repositories.

As the number of public data resources continues to proliferate, identifying relevant datasets across heterogenous repositories is becoming critical to answering scientific questions. To help researchers navigate this data landscape, we developed Dug: a semantic search tool for biomedical datasets utilizing evidence-based relationships from curated knowledge graphs to find relevant datasets and explain why those results are returned. Developed through the National Heart, Lung and Blood Institute's (NHLBI) BioData Catalyst ecosystem, Dug has indexed more than 15 911 study variables from public datasets. On a manually curated search dataset, Dug's total recall (total relevant results/total results) of 0.79 outperformed default Elasticsearch's total recall of 0.76. When using synonyms or related concepts as search queries, Dug (0.36) far outperformed Elasticsearch (0.14) in terms of total recall with no significant loss in the precision of its top results. Dug is freely available at https://github.com/helxplatform/dug. An example Dug deployment is also available for use at https://search.biodatacatalyst.renci.org/. Supplementary data are available at Bioinformatics online.

African Genomic Medicine Portal: A Web Portal for Biomedical Applications.

Genomics data are currently being produced at unprecedented rates, resulting in increased knowledge discovery and submission to public data repositories. Despite these advances, genomic information on African-ancestry populations remains significantly low compared with European- and Asian-ancestry populations. This information is typically segmented across several different biomedical data repositories, which often lack sufficient fine-grained structure and annotation to account for the diversity of African populations, leading to many challenges related to the retrieval, representation and findability of such information. To overcome these challenges, we developed the African Genomic Medicine Portal (AGMP), a database that contains metadata on genomic medicine studies conducted on African-ancestry populations. The metadata is curated from two public databases related to genomic medicine, PharmGKB and DisGeNET. The metadata retrieved from these source databases were limited to genomic variants that were associated with disease aetiology or treatment in the context of African-ancestry populations. Over 2000 variants relevant to populations of African ancestry were retrieved. Subsequently, domain experts curated and annotated additional information associated with the studies that reported the variants, including geographical origin, ethnolinguistic group, level of association significance and other relevant study information, such as study design and sample size, where available. The AGMP functions as a dedicated resource through which to access African-specific information on genomics as applied to health research, through querying variants, genes, diseases and drugs. The portal and its corresponding technical documentation, implementation code and content are publicly available.

Making Biomedical Sciences publications more accessible for machines.

With the rapidly expanding catalogue of scientific publications, especially within the Biomedical Sciences field, it is becoming increasingly difficult for researchers to search for, read or even interpret emerging scientific findings. PubMed, just one of the current biomedical data repositories, comprises over 33 million citations for biomedical research, and over 2500 publications are added each day. To further strengthen the impact biomedical research, we suggest that there should be more synergy between publications and machines. By bringing machines into the realm of research and publication, we can greatly augment the assessment, investigation and cataloging of the biomedical literary corpus. The effective application of machine-based manuscript assessment and interpretation is now crucial, and potentially stands as the most effective way for researchers to comprehend and process the tsunami of biomedical data and literature. Many biomedical manuscripts are currently published online in poorly searchable document types, with figures and data presented in formats that are partially inaccessible to machine-based approaches. The structure and format of biomedical manuscripts should be adapted to facilitate machine-assisted interrogation of this important literary corpus. In this context, it is important to embrace the concept that biomedical scientists should also write manuscripts that can be read by machines. It is likely that an enhanced human-machine synergy in reading biomedical publications will greatly enhance biomedical data retrieval and reveal novel insights into complex datasets.

A tool for assessing alignment of biomedical data repositories with open, FAIR, citation and trustworthy principles.

Increasing attention is being paid to the operation of biomedical data repositories in light of efforts to improve how scientific data is handled and made available for the long term. Multiple groups have produced recommendations for functions that biomedical repositories should support, with many using requirements of the FAIR data principles as guidelines. However, FAIR is but one set of principles that has arisen out of the open science community. They are joined by principles governing open science, data citation and trustworthiness, all of which are important aspects for biomedical data repositories to support. Together, these define a framework for data repositories that we call OFCT: Open, FAIR, Citable and Trustworthy. Here we developed an instrument using the open source PolicyModels toolkit that attempts to operationalize key aspects of OFCT principles and piloted the instrument by evaluating eight biomedical community repositories listed by the NIDDK Information Network (dkNET.org). Repositories included both specialist repositories that focused on a particular data type or domain, in this case diabetes and metabolomics, and generalist repositories that accept all data types and domains. The goal of this work was both to obtain a sense of how much the design of current biomedical data repositories align with these principles and to augment the dkNET listing with additional information that may be important to investigators trying to choose a repository, e.g., does the repository fully support data citation? The evaluation was performed from March to November 2020 through inspection of documentation and interaction with the sites by the authors. Overall, although there was little explicit acknowledgement of any of the OFCT principles in our sample, the majority of repositories provided at least some support for their tenets.

Data Appendix for "Alignment of data sharing practices in biomedical data repositories with open, FAIR, citable and trustworthy principles"

Data Appendix for "Alignment of data sharing practices in biomedical data repositories with open, FAIR, citable and trustworthy principles"

Data Appendix for "Alignment of data sharing practices in biomedical data repositories with open, FAIR, citable and trustworthy principles"

Data Appendix for "Alignment of data sharing practices in biomedical data repositories with open, FAIR, citable and trustworthy principles"

Kinematics of Big Biomedical Data to characterize temporal variability and seasonality of data repositories: Functional Data Analysis of data temporal evolution over non-parametric statistical manifolds

AimThe increasing availability of Big Biomedical Data is leading to large research data samples collected over long periods of time. We propose the analysis of the kinematics of data probability distributions over time towards the characterization of data temporal variability. MethodsFirst, we propose a kinematic model based on the estimation of a continuous data temporal trajectory, using Functional Data Analysis over the embedding of a non-parametric statistical manifold which points represent data temporal batches, the Information Geometric Temporal (IGT) plot. This model allows measuring the velocity and acceleration of data changes. Next, we propose a coordinate-free method to characterize the oriented seasonality of data based on the parallelism of lagged velocity vectors of the data trajectory throughout the IGT space, the Auto-Parallelism of Velocity Vectors (APVV) and APVVmap. Finally, we automatically explain the maximum variance components of the IGT space coordinates by means of correlating data points with known temporal factors from the domain application. MaterialsMethods are evaluated on the US National Hospital Discharge Survey open dataset, consisting of 3,25M hospital discharges between 2000 and 2010. ResultsSeasonal and abrupt behaviours were present on the estimated multivariate and univariate data trajectories. The kinematic analysis revealed seasonal effects and punctual increments in data celerity, the latter mainly related to abrupt changes in coding. The APVV and APVVmap revealed oriented seasonal changes on data trajectories. For most variables, their distributions tended to change to the same direction at a 12-month period, with a peak of change of directionality at mid and end of the year. Diagnosis and Procedure codes also included a 9-month periodic component. Kinematics and APVV methods were able to detect seasonal effects on extreme temporal subgrouped data, such as in Procedure code, where Fourier and autocorrelation methods were not able to. The automated explanation of IGT space coordinates was consistent with the results provided by the kinematic and seasonal analysis. Coordinates received different meanings according to the trajectory trend, seasonality and abrupt changes. DiscussionTreating data as a particle moving over time through a multidimensional probabilistic space and studying the kinematics of its trajectory has turned out to a new temporal variability methodology. Its results on the NHDS were aligned with the dataset and population descriptions found in the literature, contributing with a novel temporal variability characterization. We have demonstrated that the APVV and APVVmat are an appropriate tool for the coordinate-free and oriented analysis of trajectories or complex multivariate signals. ConclusionThe proposed methods comprise an exploratory methodology for the characterization of data temporal variability, what may be useful for a reliable reuse of Big Biomedical Data repositories acquired over long periods of time.

CEDAR OnDemand: a browser extension to generate ontology-based scientific metadata

BackgroundPublic biomedical data repositories often provide web-based interfaces to collect experimental metadata. However, these interfaces typically reflect the ad hoc metadata specification practices of the associated repositories, leading to a lack of standardization in the collected metadata. This lack of standardization limits the ability of the source datasets to be broadly discovered, reused, and integrated with other datasets. To increase reuse, discoverability, and reproducibility of the described experiments, datasets should be appropriately annotated by using agreed-upon terms, ideally from ontologies or other controlled term sources.ResultsThis work presents “CEDAR OnDemand”, a browser extension powered by the NCBO (National Center for Biomedical Ontology) BioPortal that enables users to seamlessly enter ontology-based metadata through existing web forms native to individual repositories. CEDAR OnDemand analyzes the web page contents to identify the text input fields and associate them with relevant ontologies which are recommended automatically based upon input fields’ labels (using the NCBO ontology recommender) and a pre-defined list of ontologies. These field-specific ontologies are used for controlling metadata entry. CEDAR OnDemand works for any web form designed in the HTML format. We demonstrate how CEDAR OnDemand works through the NCBI (National Center for Biotechnology Information) BioSample web-based metadata entry.ConclusionCEDAR OnDemand helps lower the barrier of incorporating ontologies into standardized metadata entry for public data repositories. CEDAR OnDemand is available freely on the Google Chrome store https://chrome.google.com/webstore/search/CEDAROnDemand

Uniform resolution of compact identifiers for biomedical data

Most biomedical data repositories issue locally-unique accessions numbers, but do not provide globally unique, machine-resolvable, persistent identifiers for their datasets, as required by publishers wishing to implement data citation in accordance with widely accepted principles. Local accessions may however be prefixed with a namespace identifier, providing global uniqueness. Such “compact identifiers” have been widely used in biomedical informatics to support global resource identification with local identifier assignment. We report here on our project to provide robust support for machine-resolvable, persistent compact identifiers in biomedical data citation, by harmonizing the Identifiers.org and N2T.net (Name-To-Thing) meta-resolvers and extending their capabilities. Identifiers.org services hosted at the European Molecular Biology Laboratory - European Bioinformatics Institute (EMBL-EBI), and N2T.net services hosted at the California Digital Library (CDL), can now resolve any given identifier from over 600 source databases to its original source on the Web, using a common registry of prefix-based redirection rules. We believe these services will be of significant help to publishers and others implementing persistent, machine-resolvable citation of research data.

Datasets2Tools, repository and search engine for bioinformatics datasets, tools and canned analyses

Biomedical data repositories such as the Gene Expression Omnibus (GEO) enable the search and discovery of relevant biomedical digital data objects. Similarly, resources such as OMICtools, index bioinformatics tools that can extract knowledge from these digital data objects. However, systematic access to pre-generated ‘canned’ analyses applied by bioinformatics tools to biomedical digital data objects is currently not available. Datasets2Tools is a repository indexing 31,473 canned bioinformatics analyses applied to 6,431 datasets. The Datasets2Tools repository also contains the indexing of 4,901 published bioinformatics software tools, and all the analyzed datasets. Datasets2Tools enables users to rapidly find datasets, tools, and canned analyses through an intuitive web interface, a Google Chrome extension, and an API. Furthermore, Datasets2Tools provides a platform for contributing canned analyses, datasets, and tools, as well as evaluating these digital objects according to their compliance with the findable, accessible, interoperable, and reusable (FAIR) principles. By incorporating community engagement, Datasets2Tools promotes sharing of digital resources to stimulate the extraction of knowledge from biomedical research data. Datasets2Tools is freely available from: http://amp.pharm.mssm.edu/datasets2tools.

SATORI: a system for ontology-guided visual exploration of biomedical data repositories

MotivationThe ever-increasing number of biomedical datasets provides tremendous opportunities for re-use but current data repositories provide limited means of exploration apart from text-based search. Ontological metadata annotations provide context by semantically relating datasets. Visualizing this rich network of relationships can improve the explorability of large data repositories and help researchers find datasets of interest.ResultsWe developed SATORI—an integrative search and visual exploration interface for the exploration of biomedical data repositories. The design is informed by a requirements analysis through a series of semi-structured interviews. We evaluated the implementation of SATORI in a field study on a real-world data collection. SATORI enables researchers to seamlessly search, browse and semantically query data repositories via two visualizations that are highly interconnected with a powerful search interface.Availability and implementationSATORI is an open-source web application, which is freely available at http://satori.refinery-platform.org and integrated into the Refinery Platform.Supplementary information Supplementary data are available at Bioinformatics online.

Orchestrating differential data access for translational research: a pilot implementation

BackgroundTranslational researchers need robust IT solutions to access a range of data types, varying from public data sets to pseudonymised patient information with restricted access, provided on a case by case basis. The reason for this complication is that managing access policies to sensitive human data must consider issues of data confidentiality, identifiability, extent of consent, and data usage agreements. All these ethical, social and legal aspects must be incorporated into a differential management of restricted access to sensitive data.MethodsIn this paper we present a pilot system that uses several common open source software components in a novel combination to coordinate access to heterogeneous biomedical data repositories containing open data (open access) as well as sensitive data (restricted access) in the domain of biobanking and biosample research. Our approach is based on a digital identity federation and software to manage resource access entitlements.ResultsOpen source software components were assembled and configured in such a way that they allow for different ways of restricted access according to the protection needs of the data. We have tested the resulting pilot infrastructure and assessed its performance, feasibility and reproducibility.ConclusionsCommon open source software components are sufficient to allow for the creation of a secure system for differential access to sensitive data. The implementation of this system is exemplary for researchers facing similar requirements for restricted access data. Here we report experience and lessons learnt of our pilot implementation, which may be useful for similar use cases. Furthermore, we discuss possible extensions for more complex scenarios.

SATORI: a system for ontology-guided visual exploration of biomedical data repositories

Motivation The ever-increasing number of biomedical datasets provides tremendous opportunities for re-use but current data repositories provide limited means of exploration apart from text-based search. Ontological metadata annotations provide context by semantically relating datasets. Visualizing this rich network of relationships can improve the explorability of large data repositories and help researchers find datasets of interest. Results We developed SATORI-an integrative search and visual exploration interface for the exploration of biomedical data repositories. The design is informed by a requirements analysis through a series of semi-structured interviews. We evaluated the implementation of SATORI in a field study on a real-world data collection. SATORI enables researchers to seamlessly search, browse and semantically query data repositories via two visualizations that are highly interconnected with a powerful search interface. Availability and implementation SATORI is an open-source web application, which is freely available at http://satori.refinery-platform.org and integrated into the Refinery Platform. Contact nils@hms.harvard.edu. Supplementary information Supplementary data are available at Bioinformatics online.

Interest propagation for knowledge extraction and representation.

Due to the increasing number of available biomedical data repositories, providing a comprehensive and intuitive access to information is still a demanding task for Information Retrieval systems. In this work we present an interactive data exploration system that retrieves relevant information by propagating the user's interest within a network. The developed techniques have been applied to two different retrieval tasks useful for biomedical research: the prioritization of proteins related to a disease of interest and the search of publications in the literature. The method relies on a network of biomedical entities, scoring of entities of interest by the user, and score propagation. The assessment of the relevance of the retrieved information confirmed a high accuracy of the presented algorithms for both the domains considered.

PhenUMA: a tool for integrating the biomedical relationships among genes and diseases.

BackgroundSeveral types of genetic interactions in humans can be directly or indirectly associated with the causal effects of mutations. These interactions are usually based on their co-associations to biological processes, coexistence in cellular locations, coexpression in cell lines, physical interactions and so on. In addition, pathological processes can present similar phenotypes that have mutations either in the same genomic location or in different genomic regions. Therefore, integrative resources for all of these complex interactions can help us prioritize the relationships between genes and diseases that are most deserving to be studied by researchers and physicians.ResultsPhenUMA is a web application that displays biological networks using information from biomedical and biomolecular data repositories. One of its most innovative features is to combine the benefits of semantic similarity methods with the information taken from databases of genetic diseases and biological interactions. More specifically, this tool is useful in studying novel pathological relationships between functionally related genes, merging diseases into clusters that share specific phenotypes or finding diseases related to reported phenotypes.ConclusionsThis framework builds, analyzes and visualizes networks based on both functional and phenotypic relationships. The integration of this information helps in the discovery of alternative pathological roles of genes, biological functions and diseases. PhenUMA represents an advancement toward the use of new technologies for genomics and personalized medicine.Electronic supplementary materialThe online version of this article (doi:10.1186/s12859-014-0375-1) contains supplementary material, which is available to authorized users.

Harvest: an open platform for developing web-based biomedical data discovery and reporting applications

Biomedical researchers share a common challenge of making complex data understandable and accessible as they seek inherent relationships between attributes in disparate data types. Data discovery in this context is limited by a lack of query systems that efficiently show relationships between individual variables, but without the need to navigate underlying data models. We have addressed this need by developing Harvest, an open-source framework of modular components, and using it for the rapid development and deployment of custom data discovery software applications. Harvest incorporates visualizations of highly dimensional data in a web-based interface that promotes rapid exploration and export of any type of biomedical information, without exposing researchers to underlying data models. We evaluated Harvest with two cases: clinical data from pediatric cardiology and demonstration data from the OpenMRS project. Harvest's architecture and public open-source code offer a set of rapid application development tools to build data discovery applications for domain-specific biomedical data repositories. All resources, including the OpenMRS demonstration, can be found at http://harvest.research.chop.edu

A set of novel mining tools for efficient biological knowledge discovery

In last decades, Bioinformatics has become an emerging field of science with a wide variety of applications in many research areas. The primary goal of bioinformatics is to detect useful biological knowledge hidden under the large volumes of DNA/RNA sequences and structures, literature and other biological and biomedical data, to gain a greater insight into their relationships and, therefore, to enhance the discovery and the comprehension of biological processes. In order to fully exploit the new opportunities that emerge, novel data and text mining techniques have to be developed to effectively address the fundamental biological issue of managing and uncovering meaningful patterns and correlations from these large biological and biomedical data repositories. In this work, we propose an effective data mining technique for analysing biological and biomedical data. The proposed mining process is efficient enough to be applied to various types of biological and biomedical data. To prove the concept, we experiment with applying the data mining technique into two distinct areas, including biomedical text documents and data. In addition, based on the proposed approach, we develop two mining tools, namely the Bio Search Engine and the Genome-Based Population Clustering.