A digital repository with an extensible data model for biobanking and genomic analysis management

Standardizing digital biobanks: integrating imaging, genomic, and clinical data for precision medicine

Advancements in data acquisition and computational methods are generating a large amount of heterogeneous biomedical data from diagnostic domains such as clinical imaging, pathology, and next-generation sequencing (NGS), which help characterize individual differences in patients. However, this information needs to be available and suitable to promote and support scientific research and technological development, supporting the effective adoption of the precision medicine approach in clinical practice. Digital biobanks can catalyze this process, facilitating the sharing of curated and standardized imaging data, clinical, pathological and molecular data, crucial to enable the development of a comprehensive and personalized data-driven diagnostic approach in disease management and fostering the development of computational predictive models. This work aims to frame this perspective, first by evaluating the state of standardization of individual diagnostic domains and then by identifying challenges and proposing a possible solution towards an integrative approach that can guarantee the suitability of information that can be shared through a digital biobank. Our analysis of the state of the art shows the presence and use of reference standards in biobanks and, generally, digital repositories for each specific domain. Despite this, standardization to guarantee the integration and reproducibility of the numerical descriptors generated by each domain, e.g. radiomic, pathomic and -omic features, is still an open challenge. Based on specific use cases and scenarios, an integration model, based on the JSON format, is proposed that can help address this problem. Ultimately, this work shows how, with specific standardization and promotion efforts, the digital biobank model can become an enabling technology for the comprehensive study of diseases and the effective development of data-driven technologies at the service of precision medicine.

Spontaneous movements in the newborns: a tool of quantitative video analysis of preterm babies

BACKGROUND AND OBJECTIVES

The number of preterm babies is steadily growing world-wide and these neonates are at risk of neuro-motor-cognitive deficits. The observation of spontaneous movements in the first three months of age is known to predict such risk. However, the analysis by specifically trained physiotherapists is not suited for the clinical routine, motivating the development of simple computerized video analysis systems, integrated with a well-structured Biobank to make available for preterm babies a growing service with diagnostic, prognostic and epidemiological purposes.

METHODS

MIMAS (Markerless Infant Movement Analysis System) is a simple, low-cost system of video analysis of spontaneous movements of newborns in their natural environment, based on a single standard RGB camera, without markers attached to the body. The original videos are transformed into binarized sequences highlighting the silhouette of the baby, in order to minimize the illumination effects and increase the robustness of the analysis; such sequences are then coded by a large set of parameters (39) related to the spatial and spectral changes of the silhouette. The parameter vectors of each baby were stored in the Biobank together with related clinical information.

RESULTS

The preliminary test of the system was carried out at the Gaslini Pediatric Hospital in Genoa, where 46 preterm (PT) and 21 full-term (FT) babies (as controls) were recorded at birth (T0) and 8-12 weeks thereafter (T1). A simple statistical analysis of the data showed that the coded parameters are sensitive to the degree of maturation of the newborns (comparing T0 with T1, for both PT and FT babies), and to the conditions at birth (PT vs. FT at T0), whereas this difference tends to vanish at T1. Moreover, the coding method seems also able to detect the few 'abnormal' preterm babies in the PT populations that were analyzed as specific case studies.

CONCLUSIONS

Preliminary results motivate the adoption of this tool in clinical practice allowing for a systematic accumulation of cases in the Biobank, thus for improving the accuracy of data analysis performed by MIMAS and ultimately allowing the adoption of data mining techniques.

Aspects of Modern Biobank Activity - Comprehensive Review

Biobanks play an increasing role in contemporary research projects. These units meet all requirements to regard them as a one of the most innovative and up-to-date in the field of biomedical research. They enable conducting wide-scale research by the professional collection of biological specimens and correlated clinical data. Pathology units may be perceived roots of biobanking. The review aims at describing the concept of biobanks, their model of function and scientific potential. It comprises the division of biobanks, sample preservation methods and IT solutions as well as guidelines and recommendations for management of a vast number of biological samples and clinical data. Therefore, appropriate standard operating procedures and protocols are outlined. Constant individualization of diagnostic process and treatment procedures creates the niche for translational units. Thus, the role of biobanks in personalized medicine was also specified. The exceptionality of biobanks poses some new ethical-legal issues which have various solutions, in each legal system, amongst the world. Finally, distribution and activity of European biobanks are mentioned.

What incentives increase data sharing in health and medical research? A systematic review

BACKGROUND

The foundation of health and medical research is data. Data sharing facilitates the progress of research and strengthens science. Data sharing in research is widely discussed in the literature; however, there are seemingly no evidence-based incentives that promote data sharing.

METHODS

A systematic review (registration: 10.17605/OSF.IO/6PZ5E) of the health and medical research literature was used to uncover any evidence-based incentives, with pre- and post-empirical data that examined data sharing rates. We were also interested in quantifying and classifying the number of opinion pieces on the importance of incentives, the number observational studies that analysed data sharing rates and practices, and strategies aimed at increasing data sharing rates.

RESULTS

Only one incentive (using open data badges) has been tested in health and medical research that examined data sharing rates. The number of opinion pieces (n = 85) out-weighed the number of article-testing strategies (n = 76), and the number of observational studies exceeded them both (n = 106).

CONCLUSIONS

Given that data is the foundation of evidence-based health and medical research, it is paradoxical that there is only one evidence-based incentive to promote data sharing. More well-designed studies are needed in order to increase the currently low rates of data sharing.

OBIB-a novel ontology for biobanking

BACKGROUND

Biobanking necessitates extensive integration of data to allow data analysis and specimen sharing. Ontologies have been demonstrated to be a promising approach in fostering better semantic integration of biobank-related data. Hitherto no ontology provided the coverage needed to capture a broad spectrum of biobank user scenarios.

METHODS

Based in the principles laid out by the Open Biological and Biomedical Ontologies Foundry two biobanking ontologies have been developed. These two ontologies were merged using a modular approach consistent with the initial development principles. The merging was facilitated by the fact that both ontologies use the same Upper Ontology and re-use classes from a similar set of pre-existing ontologies.

RESULTS

Based on the two previous ontologies the Ontology for Biobanking (http://purl.obolibrary.org/obo/obib.owl) was created. Due to the fact that there was no overlap between the two source ontologies the coverage of the resulting ontology is significantly larger than of the two source ontologies. The ontology is successfully used in managing biobank information of the Penn Medicine BioBank.

CONCLUSIONS

Sharing development principles and Upper Ontologies facilitates subsequent merging of ontologies to achieve a broader coverage.

Next-Generation Sequencing Informatics: Challenges and Strategies for Implementation in a Clinical Environment

CONTEXT

-Next-generation sequencing (NGS) is revolutionizing the discipline of laboratory medicine, with a deep and direct impact on patient care. Although it empowers clinical laboratories with unprecedented genomic sequencing capability, NGS has brought along obvious and obtrusive informatics challenges. Bioinformatics and clinical informatics are separate disciplines with typically a small degree of overlap, but they have been brought together by the enthusiastic adoption of NGS in clinical laboratories. The result has been a collaborative environment for the development of novel informatics solutions. Sustaining NGS-based testing in a regulated clinical environment requires institutional support to build and maintain a practical, robust, scalable, secure, and cost-effective informatics infrastructure.

OBJECTIVE

-To discuss the novel NGS informatics challenges facing pathology laboratories today and offer solutions and future developments to address these obstacles.

DATA SOURCES

-The published literature pertaining to NGS informatics was reviewed. The coauthors, experts in the fields of molecular pathology, precision medicine, and pathology informatics, also contributed their experiences.

CONCLUSIONS

-The boundary between bioinformatics and clinical informatics has significantly blurred with the introduction of NGS into clinical molecular laboratories. Next-generation sequencing technology and the data derived from these tests, if managed well in the clinical laboratory, will redefine the practice of medicine. In order to sustain this progress, adoption of smart computing technology will be essential. Computational pathologists will be expected to play a major role in rendering diagnostic and theranostic services by leveraging "Big Data" and modern computing tools.