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Nucleic Acids Res. 2019 Oct 23. pii: gkz925. doi: 10.1093/nar/gkz925. [Epub ahead of print]

AraPheno and the AraGWAS Catalog 2020: a major database update including RNA-Seq and knockout mutation data for Arabidopsis thaliana.

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Machine Learning and Computational Biology Lab, Department of Biosystems Science and Engineering, ETH Zürich, Basel, Switzerland.
Swiss Institute of Bioinformatics, Basel, Switzerland.
Gregor Mendel Institute of Molecular Plant Biology, Vienna, Austria.
Center for Computational and Theoretical Biology, University Würzburg, Würzburg, Germany.
Max Planck Institute for Developmental Biology, Tübingen, Germany.
Google, Mountain View, USA.
Technical University of Munich, TUM Campus Straubing for Biotechnology and Sustainability, Bioinformatics, Straubing, Germany.
Weihenstephan-Triesdorf University of Applied Sciences, Bioinformatics, Straubing, Germany.


Genome-wide association studies (GWAS) are integral for studying genotype-phenotype relationships and gaining a deeper understanding of the genetic architecture underlying trait variation. A plethora of genetic associations between distinct loci and various traits have been successfully discovered and published for the model plant Arabidopsis thaliana. This success and the free availability of full genomes and phenotypic data for more than 1,000 different natural inbred lines led to the development of several data repositories. AraPheno ( serves as a central repository of population-scale phenotypes in A. thaliana, while the AraGWAS Catalog ( provides a publicly available, manually curated and standardized collection of marker-trait associations for all available phenotypes from AraPheno. In this major update, we introduce the next generation of both platforms, including new data, features and tools. We included novel results on associations between knockout-mutations and all AraPheno traits. Furthermore, AraPheno has been extended to display RNA-Seq data for hundreds of accessions, providing expression information for over 28 000 genes for these accessions. All data, including the imputed genotype matrix used for GWAS, are easily downloadable via the respective databases.


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