DFG-Projekt: Computational Detection of Bacterial ncRNAs and their Targets

Description of the project:
Small non-coding RNAs (ncRNAs) in bacteria provide another layer to regulate gene expression. The main problems in this context are the detection of ncRNAs and the determination of the mRNA targets. In this project, both problems are considered. Current approaches for the detection of mRNA targets are not sufficient to get reliable predictions. A system will be developed that uses additional features known to be important in bacteria to improve prediction reliability and to determine the mode of regulation. Machine learning will be used to detect these features and to estimate their significance. The interactions will be experimentally validated by the cooperating scientists. Secondly, we will search for unknown ncRNAs and will investigate their properties. Current approaches look for structurally conserved regions, which alone is not sufficient to detect ncRNAs. We will generate models of bacterial regulatory elements (like terminator signals or protein binding sites) that can be found in most of the bacterial ncRNAs, and integrate this additional knowledge in our existing comparative approach. We will use this system to detect new ncRNAs as well as common structural/functional features of known ncRNAs and their targets.

contact person: Steffen Heyne
Email: heyne@informatik.uni-freiburg.de
Start of project: 01.05.2007
End of project: 31.05.2010
Project Management:
Albert-Ludwigs-University Freiburg
Prof. Dr. Rolf Backofen

Actual Research Report
  • DFG SPP 1258/1