Publications by authors named "Fleur Kelpin"

5 Publications

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MOLGENIS research: advanced bioinformatics data software for non-bioinformaticians.

Bioinformatics 2019 03;35(6):1076-1078

Genomics Coordination Center, University of Groningen and University Medical Center Groningen, Groningen, The Netherlands.

Motivation: The volume and complexity of biological data increases rapidly. Many clinical professionals and biomedical researchers without a bioinformatics background are generating big '-omics' data, but do not always have the tools to manage, process or publicly share these data.

Results: Here we present MOLGENIS Research, an open-source web-application to collect, manage, analyze, visualize and share large and complex biomedical datasets, without the need for advanced bioinformatics skills.

Availability And Implementation: MOLGENIS Research is freely available (open source software). It can be installed from source code (see http://github.com/molgenis), downloaded as a precompiled WAR file (for your own server), setup inside a Docker container (see http://molgenis.github.io), or requested as a Software-as-a-Service subscription. For a public demo instance and complete installation instructions see http://molgenis.org/research.
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http://dx.doi.org/10.1093/bioinformatics/bty742DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6419911PMC
March 2019

BiobankUniverse: automatic matchmaking between datasets for biobank data discovery and integration.

Bioinformatics 2017 Nov;33(22):3627-3634

Department of Genetics, Genomics Coordination Center, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands.

Motivation: Biobanks are indispensable for large-scale genetic/epidemiological studies, yet it remains difficult for researchers to determine which biobanks contain data matching their research questions.

Results: To overcome this, we developed a new matching algorithm that identifies pairs of related data elements between biobanks and research variables with high precision and recall. It integrates lexical comparison, Unified Medical Language System ontology tagging and semantic query expansion. The result is BiobankUniverse, a fast matchmaking service for biobanks and researchers. Biobankers upload their data elements and researchers their desired study variables, BiobankUniverse automatically shortlists matching attributes between them. Users can quickly explore matching potential and search for biobanks/data elements matching their research. They can also curate matches and define personalized data-universes.

Availability And Implementation: BiobankUniverse is available at http://biobankuniverse.com or can be downloaded as part of the open source MOLGENIS suite at http://github.com/molgenis/molgenis.

Contact: m.a.swertz@rug.nl.

Supplementary Information: Supplementary data are available at Bioinformatics online.
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http://dx.doi.org/10.1093/bioinformatics/btx478DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5870622PMC
November 2017

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

Bioinformatics 2016 07 21;32(14):2176-83. Epub 2016 Mar 21.

Department of Genetics, University Medical Center Groningen, Genomics Coordination Center, University of Groningen, Groningen, The Netherlands Department of Epidemiology, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands.

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: : m.a.swertz@rug.nl

Supplementary Information: Supplementary data are available at Bioinformatics online.
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http://dx.doi.org/10.1093/bioinformatics/btw155DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4937195PMC
July 2016

SORTA: a system for ontology-based re-coding and technical annotation of biomedical phenotype data.

Database (Oxford) 2015 18;2015. Epub 2015 Sep 18.

University of Groningen, University Medical Centre Groningen, Genomics Coordination Centre, Department of Genetics, Groningen, The Netherlands, University of Groningen, University Medical Center Groningen, Department of Epidemiology, Groningen, The Netherlands and LifeLines Cohort Study and Biobank, Groningen, The Netherlands

There is an urgent need to standardize the semantics of biomedical data values, such as phenotypes, to enable comparative and integrative analyses. However, it is unlikely that all studies will use the same data collection protocols. As a result, retrospective standardization is often required, which involves matching of original (unstructured or locally coded) data to widely used coding or ontology systems such as SNOMED CT (clinical terms), ICD-10 (International Classification of Disease) and HPO (Human Phenotype Ontology). This data curation process is usually a time-consuming process performed by a human expert. To help mechanize this process, we have developed SORTA, a computer-aided system for rapidly encoding free text or locally coded values to a formal coding system or ontology. SORTA matches original data values (uploaded in semicolon delimited format) to a target coding system (uploaded in Excel spreadsheet, OWL ontology web language or OBO open biomedical ontologies format). It then semi- automatically shortlists candidate codes for each data value using Lucene and n-gram based matching algorithms, and can also learn from matches chosen by human experts. We evaluated SORTA's applicability in two use cases. For the LifeLines biobank, we used SORTA to recode 90‚ÄČ000 free text values (including 5211 unique values) about physical exercise to MET (Metabolic Equivalent of Task) codes. For the CINEAS clinical symptom coding system, we used SORTA to map to HPO, enriching HPO when necessary (315 terms matched so far). Out of the shortlists at rank 1, we found a precision/recall of 0.97/0.98 in LifeLines and of 0.58/0.45 in CINEAS. More importantly, users found the tool both a major time saver and a quality improvement because SORTA reduced the chances of human mistakes. Thus, SORTA can dramatically ease data (re)coding tasks and we believe it will prove useful for many more projects. Database URL: http://molgenis.org/sorta or as an open source download from http://www.molgenis.org/wiki/SORTA.
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http://dx.doi.org/10.1093/database/bav089DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4574036PMC
May 2016

Modelling microbial adaptation to changing availability of substrates.

Water Res 2004 Feb;38(4):1003-13

Department of Theoretical Biology, Faculty of Earth and Life Sciences, Vrije Universiteit, De Boelelaan 1085, 1081 HV Amsterdam, The Netherlands.

In their natural environment microorganisms encounter changes in substrate availability, involving either nutrient concentrations or nutrient types. They have to adapt to the new conditions in order to survive. We present a model for slow microbial adaptation, involving the synthesis of new enzymes, in response to changes in the availability of substitutable substrates. The model is based on reciprocal (or mutual) inhibition of expression of both the substrate-specific carriers and the associated assimilatory machinery. The inhibition kinetics is derived from the kinetics of synthesizing units. An interesting property of the adaptation model is that the presence of a single limiting resource results in a constant maximum specific substrate consumption rate for fully adapted microorganisms. Because the maximum specific consumption rate is not a function of substrate concentration, for growth on one substrate, the Monod and Pirt models for instance are still valid. Other adaptation models known to us do not fulfil this property. The simplest version of our model describes adaptation during diauxic growth, using only one preference parameter and one initial condition. The applicability of the model is exemplified by fitting it to published data from diauxic growth experiments.
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http://dx.doi.org/10.1016/j.watres.2003.09.037DOI Listing
February 2004