Dr Gabriele Schweikert

Principal Investigator/Senior Lecturer

Computational Biology, School of Life Sciences

Portrait photo of Gabriele Schweikert
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+44 (0)1382 388895


Gabriele previously completed a PhD at the Max Planck Institute Tuebingen (Schoelkopf, Weigel, Raetsch labs) developing machine learning techniques for computational gene finding. She then moved on to the Wellcome Trust Center for Cell Biology in Edinburgh, joining the lab of Adrian Bird, one of the pioneers of epigenomic research. She later held a Marie Curie and an EMBO Fellowship at the School of Informatics, University of Edinburgh prior to coming to Dundee/Tuebingen. 


Dr Gabriele Schweikert is a joint appointment between the Division of Computational Biology in the School and Cyber Valley in Tuebingen. Gabriele uses Machine Learning Tools to better understand important molecular processes in living cells, with a particular interest in epigenetic mechanisms. 

“All cells in our body share the same genetic code, the DNA. But what makes a liver cell a liver cell, what makes neurons communicate with other neurons, while white blood cells defend our body against germs and pathogens? Epigenetic mechanisms may provide the living cell with a system to efficiently use the specific information required for each cells’ specific needs,” Gabriele explained. “Currently, we have only limited understanding of its architecture and working, however, with modern high-throughput technologies we can register epigenomic snapshots of current states of cells. The data is very complex, high-dimensional, redundant and dynamic. A truly interdisciplinary approach is required that combines expert knowledge of epigenomic mechanisms with machine learning technologies. The impact of understanding these epigenetic processes holds immense promise for medical applications. For instance, malfunctioning of the epigenetic machinery are increasingly recognised as important contributors to tumourigenesis (e.g. in leukaemia).”

Selected Publications

  • Lagger, S, Connelly, JC, Schweikert, G, Webb, S, Selfridge, J, Ramsahoye, BH, Yu, M, He, C, Sanguinetti, G, Sowers, LC, Walkinshaw, MD & Bird, A (2017) MeCP2 recognizes cytosine methylated tri-nucleotide and di-nucleotide sequences to tune transcription in the mammalian brain.  PLoS Genetics, vol. 13, no. 5, e1006793. https://doi.org/10.1371/journal.pgen.1006793
  • Mayo, TR, Schweikert, G & Sanguinetti, G 2015, 'M 3 D: A kernel-based test for spatially correlated changes in methylation profiles', Bioinformatics, vol. 31, no. 6, pp. 809-816. https://doi.org/10.1093/bioinformatics/btu749
  • Van Nues, R, Schweikert, G, De Leau, E, Selega, A, Langford, A, Franklin, R, Iosub, I, Wadsworth, P, Sanguinetti, G & Granneman, S (2017) Kinetic CRAC uncovers a role for Nab3 in determining gene expression profiles during stress.  Nature Communications, vol. 8, no. 1, 12. https://doi.org/10.1038/s41467-017-00025-5
  • Varshney, D, Lombardi, O, Schweikert, G, Dunn, S, Suska, O & Cowling, V (2018) mRNA cap methyltransferase, RNMT-RAM, promotes RNA pol II-dependent transcription.  Cell Reports, vol. 23, no. 5, pp. 1530-1542.  https://doi.org/10.1016/j.celrep.2018.04.004
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Media availability

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Areas of expertise

  • AI/automation

PhD Projects

Principal supervisor

Second supervisor


Award Year
Personal Fellowships / UKRI Future Leaders Fellowship 2020