Genomics of Gene Expression


After the great success of the SMODIA14 workshop ( we are proud to announce the¨Statistical Methods for Omics Data Integration and Analysis 2015”  Valencia, Spain, September 14-16, 2015. SMODIA15

Host and Venue: Centro de Investigación Príncipe Felipe (CIPF)

Prof. Dr. Lennart Martens, works at the Computational Omics and Systems Biology Group, Department of Medical Protein Research, VIB, Ghent, Belgium
 His group focus on three key points, aimed at enabling systems biology modeling:
– Data collection and integration across the various Omics fields

– (Semi-)automatic quality control of the obtained data using configurable expert systems
– Development of computational Omics to help set up and guide experiments based on a user-supplied list of target entities.

Tittle of his presentation: “Dig deep and dig greedily: what we awaken in omics data analyses”
Abstract: Omics data analyses are among the most powerful analytic techniques available today. The methods and instruments employed provide the capability to acquire data for a very large amount of molecules in a single analysis, while also spanning a broad dynamic range. Of course, this impressive performance is a two-edged blade: omics approaches open up enormous possibilities while creating highly complex issues. Typical problems are the incomplete interpretation of the large amount of data acquired, difficulties in reproducibility especially at the lower end of the dynamic range, and the unavoidable presence of false signals and/or false interpretations. As a result, the downstream integration of results derived from omics datasets is particularly tricky. Not only do these results cover different phases in the flow of  biological information (DNA-RNA-protein-metabolites), there are also domain-specific issues with the results that act synergistically (in the bad sense here) to confound efforts to harmonize the findings. Here, a few interesting analyses on omics data are presented, illustrating the promises of omics and across-omics analyses, but also highlighting the issues encountered when attempting to fulfill these promises. The focus will be on metabolomics, proteomics and the combination of genomics and proteomics data.