Computational RNA Biology



Gene regulation occurs at several levels and is a highly controlled process in space and time, whose alterations contribute to the genesis and progression of complex diseases, such as metabolic disorders and cancer. Many newly discovered ncRNAs, as well as RNA Binding Proteins (RBPs) have been suggested to constitute a new hidden layer of gene regulation that is necessary to establish complex regulatory programs in higher organisms. 

However, the fact that the function of most of these regulators remains elusive so far calls for the need to develop new computational approaches to pinpoint ncRNAs as a new class of biomarkers or therapeutic targets in biomedical and biotechnological applications.

Our group focuses on the development of both supervised and unsupervised machine learning methods to analyze high-dimensional genomic data, such as genetic variation,  CLIP-seq, RNA-seq data and RNA sequences, in order to contribute to bridge the gap between the high number of annotated ncRNAs and RBPs and their (lack of) of function. A main pillar of our work is also the computational integration of different omics data with clinical data to design machine learning methods to make sense of multi-dimensional observations, as well as generate testable predictions in collaboration with both molecular biologists and medical doctors. The long term goal is to decipher complex post-transcriptional regulatory networks and understand how their perturbation leads to disease phenotypes on a genome-wide scale.