Interdisciplinary approach towards a systems medicine toolbox using the example of inflammatory diseases. (25th March 2016)

• Record Type: Journal Article
• Authors: Bauer, Christian R; Knecht, Carolin; Fretter, Christoph; Baum, Benjamin; Jendrossek, Sandra; Rühlemann, Malte; Heinsen, Femke-Anouska; Umbach, Nadine; Grimbacher, Bodo; Franke, Andre; Lieb, Wolfgang; Krawczak, Michael; Hütt, Marc-Thorsten; Sax, Ulrich
• Is Part Of: Briefings in bioinformatics. Volume 18:Number 3(2017:May)
• Page Start: 479
• Page End: 487
• Publication Date: 2016-03-25
• Subjects: genomics -- systems medicine -- data analysis -- data integration -- microbiome -- inflammation; Genetics -- Data processing -- Periodicals; Molecular biology -- Data processing -- Periodicals; Genomes -- Data processing -- Periodicals; 572.80285
• DOI: 10.1093/bib/bbw024
• Languages: English
• ISSNs: 1467-5463

Abstract

Abstract: 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.