Interdisciplinary approach towards a systems medicine toolbox using the example of inflammatory diseases.

Electronic access to multiple data types, from generic information on biological systems at different functional and cellular levels to high-throughput molecular data from human patients, is a prerequisite of successful systems medicine research. However, scientists often encounter technical and conceptual difficulties that forestall the efficient and effective use of these resources. We summarize and discuss some of these obstacles, and suggest ways to avoid or evade them.The methodological gap between data capturing and data analysis is huge in human medical research. Primary data producers often do not fully apprehend the scientific value of their data, whereas data analysts maybe ignorant of the circumstances under which the data were collected. Therefore, the provision of easy-to-use data access tools not only helps to improve data quality on the part of the data producers but also is likely to foster an informed dialogue with the data analysts.We propose a means to integrate phenotypic data, questionnaire data and microbiome data with a user-friendly Systems Medicine toolbox embedded into i2b2/tranSMART. Our approach is exemplified by the integration of a basic outlier detection tool and a more advanced microbiome analysis (alpha diversity) script. Continuous discussion with clinicians, data managers, biostatisticians and systems medicine experts should serve to enrich even further the functionality of toolboxes like ours, being geared to be used by 'informed non-experts' but at the same time attuned to existing, more sophisticated analysis tools.

Clinical application of genomic high-throughput data: Infrastructural, ethical, legal and psychosocial aspects.

Genomic high-throughput technologies (GHTT) such as next-generation sequencing represent a fast and cost-effective tool toward a more comprehensive understanding of the molecular background of complex diseases. However, technological advances contrast with insufficient application in clinical practice. Thus, patients, physicians, and other professionals are faced with tough challenges that forestall the efficient and effective implementation. With the increasing application of genetic testing, it is of paramount importance that physicians and other professionals in healthcare recognize the restrictions and potential of GHTT, in order to understand and interpret the complex data in the context of health and disease. At the same time, the growing volume and complexity of data is forever increasing the need for sustainable infrastructure and state-of-the-art tools for efficient data management, including their analysis and integration. The large pool of sensitive information remains difficult to interpret and fundamental questions spanning from billing to legal, social, and ethical issues have still not been resolved. Here we summarize and discuss these obstacles in an interdisciplinary context and suggest ways to overcome them. Continuous discussion with clinicians, data managers, biostatisticians, systems medicine experts, ethicists, legal scholars, and patients illuminates the strengths, weakness, and current practices in the pipeline from biomaterial to sequencing and data management. This discussion also highlights the new, cross-disciplinary working collaborations to realize the wide-ranging challenges in clinical genomics including the exceptional demands placed on the staff preparing and presenting the data, as well as the question as to how to report the data and results to patients.

Cardiac Tissue Engineering as Use Case to Connect Biomedical Research Laboratories to an Emerging Global Data Infrastructure.

Methods for cardiac tissue engineering and application in experiments are core technologies developed at the Institute of Pharmacology and Toxicology in Göttingen. As is the case in many academic research laboratories data capture and documentation may be improved to latest methods of digital research. A comprehensive information system infrastructure is the foundation of further advances toward automation of lab processes. A data management system concept is proposed and prototypically deployed that enables traceability of assets within the lab and reproducibility of published assays and results. The prototype integrates existing electronic lab notebook, experiment result database, and a newly introduced research data management system by means of a custom developed portal and integration component. The architecture concept and developed integration tools explore connection of routine experimental work in a biomedical research lab to a universal infrastructure of data.

Architecture of a Biomedical Informatics Research Data Management Pipeline.

In University Medical Centers, heterogeneous data are generated that cannot always be clearly attributed to patient care or biomedical research. Each data set has to adhere to distinct intrinsic and operational quality standards. However, only if high-quality data, tools to work with the data, and most importantly guidelines and rules of how to work with the data are addressed adequately, an infrastructure can be sustainable. Here, we present the IT Research Architecture of the University Medical Center Göttingen and describe our ten years' experience and lessons learned with infrastructures in networked medical research.

Managing OMICS-Data: Considerations for the Design of a Clinical Research IT-Infrastructure.

Biomarker-based translational research enables deep insight into cellular processes and human diseases. As a result, high-throughput technologies promulgating a fast and cost-effective generation of data are widely used to advance our understanding in the molecular background of individuals. However, the increasing volume and complexity of data increases the need for sustainable infrastructures and state-of-the-art tools allowing management, analysis, and integration of OMICS data. To address these challenges, we have performed site visits of core facilities with a focus on high-throughput technologies to explore their (IT) infrastructure, organizational aspects, and data management strategies. Different stakeholders were interviewed regarding requirements and needs for dealing with high-throughput data. We have identified four different fields of action: (1) the interface from biorepositories to service providers of high-throughput technologies, (2) aspects within services providers, (3) the interface from service providers to bioinformatical analysis, and (4) organizational and other aspects. For each field, recommendations and strategies were developed for implementation of a seamless pipeline from biorepositories to highly specialized high-throughput laboratories including the sustainable management and integration of OMICS data.

Improving IT-Support for Biobank Users.

To improve IT-support for biobank users by the Department of Medical Informatics (MI) a questionnaire was designed addressing topics regarding the provided software and the IT-support. Different stakeholders of supported projects were identified and the interviews were performed with five users. The developed questionnaire consisted of five sections and 50 questions. The results of the interviews showed an overall satisfaction with the software and the support. Differences were identified between "long-term" and "short-term" users. Most improvements should be made in the usability of the software. Furthermore, the interviewees showed a great interest in a user-group to discuss and compare the functionalities and workflows of the different research groups. The results of the interviews will be used to make a prioritized list of improvements for the software. After validating the questionnaire users of the other IT-Systems provided by the MI will be interviewed.