Ohler Lab

Ohler Lab

Computational Regulatory Genomics


According to current understanding, complexity in higher organisms is not achieved by a more complex repertoire of parts, i. e. genes, but instead by the more complex regulation of the parts.

The expression of genes is tightly controlled on several levels — a large number of protein and RNA factors and DNA and RNA sequence elements enable the precise regulation of interacting gene products. It is a key challenge to decipher these complex networks of players and interactions, and to understand biology via integrated, global approaches.

To this end, our lab develops and applies genomics and computational approaches to understand mechanisms of gene regulation in eukaryotic organisms. Computational biology, and machine learning in particular,  has become indispensable to analyze and ultimately make sense of large-scale data sets that look at the phenomenon of gene regulation from different angles.

Our long term goal is to investigate how regulatory networks enable the correct development of complex organisms, with their multitude of cell types that carry out different functions despite the same genome. This will help us to understand the impact of sequence variation on biological functions and disease.


Group Leader



Technical Assistants

PhD student

Graduate and Undergraduate

Research and Code

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

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


As computational lab, we develop a lot of new software that we make available to the scientific community. 

  • Older tools and code (prior to ~2019) can be found on the old lab website. Here, you can also find supplemental data and information for older publications.
  • Since then, all code can be found via our lab's github repository.