The use of high-throughput methodologies and large cohorts has permitted the identification of many genes and genetic variants associated to congenital diseases. Nevertheless, we have not seen the expected breakthrough: causative genes only explain a small proportion of cases, and most identified variants are of unknown significance. Therefore, we need to develop novel bioinformatics approaches for interrogating the vast amount of available genetic variation data.

 

Cardiovascular diseases encompass many diverse conditions with very dissimilar origins; e.g., congenital, environmental, or as a complication of diseases such as kidney failure or diabetes mellitus. Therefore the comprehension of the pathophysiology of diseases not only relies on the identification of the genetic causes and the influence of external factors, but also on the understanding of the molecular consequences caused by any anomaly. This highlights the necessity for approaches that permit modelling the systemic changes (from transcriptional variation to protein degradation) undergoing within the cell.

Transcriptional and translational response to stress

Yeast is a very useful model organism for studying basic global processes such as translational control and stress response. We have been collaborating extensively with the Pavitt, Grant, Ashe, and Hubbard labs in the study of some of these processes.