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Z2: Data management, synthesis, and integration

Prof. Dr. Zoran Nikoloski

Institute for Biochemistry and Biology, University of Potsdam, Karl-Liebknecht-Str. 24-25, 14476 Potsdam-Golm

Tel. +49-331-9776305, zoran.nikoloskiuni-potsdamde

https://www.mpimp-golm.mpg.de/8360/nikoloski

https://www.uni-potsdam.de/en/ibb-bioinformatik/index

 

Prof. Dr. Christoph Lippert

Chair for Digital Health-Machine Learning, Hasso-Plattner-Institute for Digital Engineering, Prof.-Dr.-Helmert-Straße 2-3, 14482 Potsdam

Tel. +49-331-5509-4850, Christoph.Lipperthpide

 

Summary

Z2 (Lippert, Nikoloski) will provide standardized approaches and pipelines to store, manage, analyse, and integrate the big data sets needed to study phenotypic plasticity as an evolving trait. To do so, the project will (i) generate a set of metadata templates compatible with the needs of the CRC and with NFDI DataPlant; (ii) create a set of customized scripts to facilitate reproducible analysis of reaction norms and their genetic basis; and (iii) develop cutting-edge computational approaches for data synthesis and use them to address overarching questions in plasticity research.

 

Project-related publications

Lippert, C., Listgarten, J., Liu, Y., Kadie, C. M., Davidson, R. I., & Heckerman, D. (2011). FaST linear mixed models for genome-wide association studies. Nature Methods, 8(10), 833-835.

Cao, J., Schneeberger, K., Ossowski, S., Günther, T., Bender, S., Fitz, J., Koenig, D., Lanz, C., Stegle, O., Lippert, C., ... & Weigel, D. (2011). Whole-genome sequencing of multiple Arabidopsis thaliana populations. Nature Genetics, 43(10), 956-963.

Listgarten, J., Lippert, C., Kadie, C. M., Davidson, R. I., Eskin, E., & Heckerman, D. (2012). Improved linear mixed models for genome-wide association studies. Nature Methods, 9(6), 525-526.

Zou, J., Lippert, C., Heckerman, D., Aryee, M., & Listgarten, J. (2014). Epigenome-wide association studies without the need for cell-type composition. Nature Methods, 11(3), 309-311.

Casale, F. P., Rakitsch, B., Lippert, C., & Stegle, O. (2015). Efficient set tests for the genetic analysis of correlated traits. Nature Methods, 12(8), 755-758.

Listgarten, J., Lippert, C., Kang, E. Y., Xiang, J., Kadie, C. M., & Heckerman, D. (2013). A powerful and efficient set test for genetic markers that handles confounders. Bioinformatics, 29(12), 1526-1533.

Lippert, C., Xiang, J., Horta, D., Widmer, C., Kadie, C., Heckerman, D., & Listgarten, J. (2014). Greater power and computational efficiency for kernel-based association testing of sets of genetic variants. Bioinformatics, 30(22), 3206-3214.

Listgarten, J., Lippert, C., & Heckerman, D. (2013). FaST-LMM-Select for addressing confounding from spatial structure and rare variants. Nature Genetics, 45(5), 470-471.

Grimm, D. G., Roqueiro, D., Salomé, P. A., Kleeberger, S., Greshake, B., Zhu, W., Liu, C., Lippert, C., ... & Borgwardt, K. M. (2017). easyGWAS: a cloud-based platform for comparing the results of genome-wide association studies. The Plant Cell, 29(1), 5-19.