Theme: Computational Regulomics and Cancer
Methodology: algorithms, machine learning, data analytics
We study gene regulation and disruptions involved in cancer to identify drivers of the disease beyond genomic variation and discover new targets for treatment.
We develop computational approaches to characterize the regulatory landscape and capture patterns of disruption from genome-wide profiles of regulatory elements (transcription factor binding, chromatin organization, histone marks, ...), and transcriptional and proteomic responses (ChIP-seq, RNA-seq, MS, ...). We are also very interested in harnessing the potential of large-scale perturbation screens (siRNA, CRISPR) to decipher regulatory relationships, understand gene essentiality, and uncover synthetic lethal interactions. Check our website for additional information.