Computational Biology

Over the last decade, large international efforts have revolutionized our understanding of how or genome functions in normal and disease states. The Computational Biology Team focusses on utilizing publicly available large datasets in combination with innovative computer algorithms to 1) improve diagnostic rates in children suffering from undiagnosed diseases and 2) discover new disease mechanisms and biomarkers in childhood cancers. 

Our aim is to understand how individual bases in our genome predispose, alter and interact in normal and disease contexts.

We focus on analyzing large public omics datasets using machine learning algorithms to understand dynamics of genome activity – which sequences are used when and where. Linking this data back to the genetic information of a patient is allowing us to discover the molecular causes of diseases and is key in predicting future disease and treatment outcomes.

Team Highlights – Research Published in Nature and Science

• Hon, Chung-Chau, et al. "An atlas of human long non-coding RNAs with accurate 5′ ends." Nature 543.7644 (2017): 199.
• Arner, Erik, et al. "Transcribed enhancers lead waves of coordinated transcription in transitioning mammalian cells." Science 347.6225 (2015): 1010-1014.
• Forrest, Alistair RR, et al. "A promoter-level mammalian expression atlas." Nature 507.7493 (2014): 462.
• Andersson, Robin, et al. "An atlas of active enhancers across human cell types and tissues." Nature 507.7493 (2014): 455.
• ENCODE Project Consortium. "An integrated encyclopedia of DNA elements in the human genome." Nature 489.7414 (2012): 57.
• Djebali, Sarah, et al. "Landscape of transcription in human cells." Nature 489.7414 (2012): 101.
• Maida, Yoshiko, et al. "An RNA-dependent RNA polymerase formed by TERT and the RMRP RNA." Nature 461.7261 (2009): 230.