Computational Regulatory Genomics


Computational Regulatory Genomics

We develop and use computational and genomics approaches to understand the biology of gene regulation in eukaryotic organisms.

Gene regulation — the set of mechanisms that guides an organism to activate and use its genes at the right place and time — is a fundamental process for proper development. Changes or variation of gene regulation can predispose or lead to disease. We are an integrated interdisciplinary lab, whose members aim to understand the gene regulatory code through high throughput experiments and computational approaches. To this end, we want to find out…

  • Where are the genetic switches that control the activity of genes at the DNA and RNA level?
  • Where are the functionally relevant sequence patterns in those switches?
  • What do all the different switches do that control one gene, and how do the patterns and switches work together?
  • Can we change or design switches to achieve a defined activity pattern?


We adapt and apply genomics approaches, and collaborate extensively, to obtain new types of molecular data at ever increasing resolution. We develop new computational methods to analyze and integrate new types of data. We design interpretable, predictive machine learning methods — from sparse linear models to deep neural networks — to understand different mechanisms of gene regulation on the DNA and RNA level. These days, our focus is on:

  • Explainable artificial intelligence to understand the gene regulatory code, and to quantify the impact of sequence variation
  •  Generative machine learning to go from decoding to adapting and designing sequences to achieve distinct functions
  •  Zebrafish and mammalian cell lines as developmental model system that allow for high throughput single cell experiments at different levels of complexity
  •  Mid- and large-scale perturbation and reporter experiments to dissect distinct aspects of gene regulation

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Ohler lab 

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