Deciphering the 3D genome of pediatric brain tumors

There is increasing evidence that three-dimensional (3D) genome folding can affect gene expression and regulatory processes, with consequences on development and cancer. In this project, we explore the 3D genome of pediatric brain tumors using a deep-learning model (Akita) that is capable of predicting 3D genome structure from sequencing data to uncover novel regulators and potential drivers of pediatric brain tumor formation.