The KnownLeaf literature curation system captures knowledge about Arabidopsis leaf growth and development and facilitates integrated data mining

D. Szakonyi*, S. van Landeghem*, K. Baerenfaller, L. Baeyens, J. Blomme, R. Casanova-Saéz, S. De Bodt, D. Esteve-Bruna, F. Fiorani, N. Gonzalez, J. Grønlund, R.G.H. Immink, S. Jover-Gil, A. Kuwabara, T. Muñoz-Nortes, A.D.J. van Dijk, D. Wilson-Sánchez, V. Buchanan-Wollaston, G.C. Angenent, Y. Van de PeerD. Inzé, J.L. Micol, W. Gruissem, S. Walsh, P. Hilson*

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

7 Citations (Scopus)


The information that connects genotypes and phenotypes is essentially embedded in research articles written in natural language. To facilitate access to this knowledge, we constructed a framework for the curation of the scientific literature studying the molecular mechanisms that control leaf growth and development in Arabidopsis thaliana (Arabidopsis). Standard structured statements, called relations, were designed to capture diverse data types, including phenotypes and gene expression linked to genotype description, growth conditions, genetic and molecular interactions, and details about molecular entities. Relations were then annotated from the literature, defining the relevant terms according to standard biomedical ontologies. This curation process was supported by a dedicated graphical user interface, called Leaf Knowtator. A total of 283 primary research articles were curated by a community of annotators, yielding 9947 relations monitored for consistency and over 12,500 references to Arabidopsis genes. This information was converted into a relational database (KnownLeaf) and merged with other public Arabidopsis resources relative to transcriptional networks, protein–protein interaction, gene co-expression, and additional molecular annotations. Within KnownLeaf, leaf phenotype data can be searched together with molecular data originating either from this curation initiative or from external public resources. Finally, we built a network (LeafNet) with a portion of the KnownLeaf database content to graphically represent the leaf phenotype relations in a molecular context, offering an intuitive starting point for knowledge mining. Literature curation efforts such as ours provide high quality structured information accessible to computational analysis, and thereby to a wide range of applications. DATA: The presented work was performed in the framework of the AGRON-OMICS project (Arabidopsis GRO wth Network integrating OMICS technologies) supported by European Commission 6th Framework Programme project (Grant number LSHG-CT-2006-037704). This is a data integration and data sharing portal collecting all the all the major results from the consortium. All data presented in our paper is available here.
Original languageEnglish
Pages (from-to)1-11
JournalCurrent Plant Biology
Publication statusPublished - 2015


  • Arabidopsis
  • Data integration
  • Leaf growth
  • Literature curation


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