Author

Roan Kanninga

University Medical Center Groningen

Listed by name: these papers carry no ORCID for this author, so different people with the same name may be mixed together.

Biology Disciplines

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Bioinformatics· 1 Genetic & Genomic Medicine· 1 Genetics· 1

Signature Assays, Organisms & Methods

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Research Focus:benchmarking· 2 Research Focus:database / resource· 2 Organism:human· 2 Approach:statistical model· 2 Assay:whole-exome sequencing· 2 Assay:whole-genome sequencing· 2 Approach:benchmark study· 1 Assay:multiome· 1

Frequent Co-Authors

K. Joeri van der VeldeiD· 2 shared Morris A. SwertziD· 2 shared Aashish SrivastavaiD· 1 shared Ana RathiD· 1 shared Anders JemtiD· 1 shared Anthony J. BrookesiD· 1 shared Ashish Kumar SinghiD· 1 shared Birgit Sikkema‐RaddatziD· 1 shared

Activity by Year

2019-2025 · peak 1/yr

Co-Author Collaboration Network

10 top collaborators · 18 mutual co-author links

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2 shared papersGenetic & Genomic Medicine

K. Joeri van der VeldeORCID

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Author profile →

3 papers

Recent·Most cited

Recommendations for Bioinformatics in Clinical Practice

Ksenia Lavrichenko, Emilie Sofie Engdal, Rasmus L. Marvig, Anders Jemt, Jone Marius Vignes, Henrikki Almusa, Kristine Bilgrav Sæther, Eiríkur Briem, Eva Caceres, Edda María Elvarsdóttir, Magnús Halldór Gíslason, Maria K. Haanpää, Viktor Henmyr, Ronja Hotakainen, Eevi Kaasinen, , Frederik Otzen Bagger

This paper provides consensus recommendations for standardizing bioinformatics practices in clinical whole genome sequencing to ensure accuracy, reproducibility, and comparability of results.

Genome Medicine · Oct 17, 2025Peer-reviewedBioinformatics

A unified data infrastructure to support large-scale rare disease research

Lennart F. Johansson, Steven Laurie, Dylan Spalding, Spencer Gibson, David Ruvolo, Coline Thomas, Davide Piscia, Fernanda de Andrade, Gerieke Been, Marieke Bijlsma, Han G. Brunner, Sandi Cimerman, Farid Yavari Dizjikan, Kornelia Ellwanger, Marcos Fernández-Callejo, , Anthony J. Brookes

The paper describes a unified data infrastructure for rare disease research, enabling collaborative analysis of multi-omics data across institutions.

medRxiv · Dec 20, 2023 · PreprintGenetic & Genomic Medicine

Improving the diagnostic yield of exome-sequencing, by predicting gene-phenotype associations using large-scale gene expression analysis

Patrick Deelen, Sipko van Dam, Johanna C. Herkert, Juha M Karjalainen, Harm Brugge, Kristin M. Abbott, Cleo C. van Diemen, Paul A. van der Zwaag, Erica H. Gerkes, Evelien Zonneveld‐Huijssoon, Jelkje J. de Boer-Bergsma, Pytrik Folkertsma, Tessa E. Gillett, K. Joeri van der Velde, Roan Kanninga, , Lude Franke

This paper introduces GADO, a tool that uses gene expression data to predict gene-phenotype associations and improve diagnostic yield in exome sequencing by prioritizing variants in patients with unknown genetic diagnoses.

Nature Communications · Jun 28, 2019Peer-reviewedGenetics