Uwe Ohler's Research Group

Research & Software

Computational Biology of Gene Regulation Resources Page

Latest papers:

Rach, Winter et al, Transcription initiation patterns reflect divergent nucleosome promoter architecture, PLoS Genetics


Majoros & Ohler, Phylogenetic Pair HMM approach to enhancer evolution, PLoS Computational Biology

All hereditary information is essentially encoded in the genome of an organism, and much of our work is focused on comparative DNA and RNA sequence analysis. Here, we develop and apply probabilistic models of coding and non-coding eukaryotic genes and their regulatory regions and elements. We look at gene regulation steps on both transcriptional and post-transcriptional level — mainly, dealing with promoters and microRNAs in plants and flies (but also mammals). Increasingly, our work in this area has dealt with the evolution of regulatory regions, and how to be able to model conservation and divergence of regulatory elements.

Our second focus is on what we call "image expression analysis" — using microscopy datasets to derive gene expression levels and patterns. Microscopy allows us to obtain information on gene expression with high spatiotemporal resolution, and enables us to pursue a number of topics on the dynamics and evolution of regulatory expression patterns. We currently concentrate our efforts here on images from plants and fruit flies.

These two areas fit nicely together under the umbrella of systems biology: For one, we are developing approaches to first identify regulatory elements and connections; and then to use image analysis to study the quantitative properties under different conditions which are encoded in them. And in turn, using large-scale image analysis, we can use this high-resolution expression data to infer regulatory connections in combination with sequence data.

To the right, you will find links to some of our ongoing research projects. They provide access to data and computational tools to analyze and identify eukaryotic genes and their regulatory regions, as well as their expression levels.