MOLGENIS/connect: a system for semi-automatic integration of heterogeneous phenotype data with applications in biobanks

• DOI: 10.1093/bioinformatics/btw155
• Publication Date: 2016-03-22T02:05:11Z
• Authors: Chao Pang; David van Enckevort; Mark de Haan; Fleur Kelpin; Jonathan Jetten; Dennis Hendriksen; Tommy de Boer; Bart Charbon; Erwin Winder; K. Joeri van der ...; Dany Doiron; Isabel Fortier; Hans Hillege; Morris A. Swertz
• Keywords: 03 medical and health sciences; Phenotype; 0302 clinical medicine; Biological Ontologies; Computational Biology; Humans; Original Papers; Algorithms; Software; Biological Specimen Banks; 3. Good health

Abstract Motivation: While the size and number of biobanks, patient registries and other data collections are increasing, biomedical researchers still often need to pool data for statistical power, a task that requires time-intensive retrospective integration. Results: To address this challenge, we developed MOLGENIS/connect, a semi-automatic system to find, match and pool data from different sources. The system shortlists relevant source attributes from thousands of candidates using ontology-based query expansion to overcome variations in terminology. Then it generates algorithms that transform source attributes to a common target DataSchema. These include unit conversion, categorical value matching and complex conversion patterns (e.g. calculation of BMI). In comparison to human-experts, MOLGENIS/connect was able to auto-generate 27% of the algorithms perfectly, with an additional 46% needing only minor editing, representing a reduction in the human effort and expertise needed to pool data. Availability and Implementation: Source code, binaries and documentation are available as open-source under LGPLv3 from http://github.com/molgenis/molgenis and www.molgenis.org/connect. Contact: [e-mail] Supplementary information: Supplementary data are available at Bioinformatics online.