What do we do?
At the Computational Cancer Genomics Group, we are interested in gene regulation both in healthy and diseased cells. Breakthroughs in genomics technologies have led to the production of large volumes of data that could potentially tell us something about how gene regulatory instructions are encoded in our DNA. Our group develops new algorithms, computer programs, web services and databases that will help us and others to extract knowledge and understanding from such data.
During the course of 2015, our team extended EPDNew to two new model organisms, i.e. S. cerevisiae (baker’s yeast) and S. pombe (fission yeast) for which we released comprehensive promoter collections.
Our group also gave an SIB course entitled “Chip-seq data analysis: from quality check to motif discovery and more - An introduction to the tools and databases of the EPD team”. The goal was to teach new and prospective users how to use their public resources in an efficient and effective manner.
A workshop entitled “Beyond Position weight matrices – towards next generation tools for predicting protein-DNA interactions” was also organized at the [BC]2 Basel Computational Biology Conference. Experts in the field discussed the opportunities and challenges of novel high-throughput technologies for profiling transcription factor binding specificity both in vitro and in vivo.
With regard to ChIP-seq tools on the Amazon cloud, we created a public AMI (Amazon Machine Image) for EC2 under the name ChIP-Seq-Tools_SIB (ID: ami-a9c3dc99).
Main publications 2015
- Ambrosini G et al. Principles of ChIP-seq data analysis illustrated with examples. Genomics Comp Biol 2015;1(1):e22.
- Dreos R et al. The Eukaryotic Promoter Database: expansion of EPDnew and new promoter analysis tools. Nucleic Acids Res 2015;43(Database issue):D92-6.