Our group examines mechanistic relationships between genetic information and crop quality traits. Utilizing deep learning on extensive sequence and omics datasets, we identify and functionally characterize genetic sequence features that affect gene expression and translate to macro-phenotypes. Our strategy integrates multi-modal data, including genomic, transcriptomic, metabolomic, and phenomic information.
H. Redestig, J. Szymanski, M. Y. Hirai, J. Selbig, L. Willmitzer, Z. Nikoloski, K. Saito, Data integration, metabolic networks and systems biology. Annual Plant Reviews, 261–316 (2018).
J. Szymanski, Y. Levin, A. Savidor, D. Breitel, L. Chappell-Maor, U. Heinig, N. Töpfer, A. Aharoni, Label-free deep shotgun proteomics reveals protein dynamics during tomato fruit tissues development. Plant J.90, 396–417 (2017).
J. Szymanski, Y. Brotman, L. Willmitzer, Á. Cuadros-Inostroza, Linking gene expression and membrane lipid composition of Arabidopsis. Plant Cell. 26, 915–928 (2014).
+49 2461 618688
Institute of Bio- and Geosciences - Bioinformatics (IBG-4)
RG Network Analysis and Modelling
Leibniz Institute of Plant Genetics and Crop Plant Research