Friday, May 31, 2019

Abstract for talk at the UNIVERSITY OF BERN


My talk will focus on how to leverage thousands of functional genomics datasets to deeply annotate the disease genome and perform data mining to discover disease-associated regulators and variations.


First, I will introduce our computational efforts to perform large-scale integration to accurately define distal and proximal regulatory elements (MatchedFilter) and then show how our extended gene annotation allows us to place oncogenic transformations in the context of a broad cell space; here, many normal-to-tumor transitions move towards a stem-like state, while oncogene knockdowns show an opposing trend.


Second, I will look at our comprehensive regulatory networks of both transcription factors and RNA-binding proteins (TFs and RBPs). I will showcase their value by highlighting how SUB1, a previously uncharacterized RBP, drives aberrant tumor expression and amplifies the effect of the well-known oncogenic TF MYC.


Third, I will introduce a workflow to prioritize key elements and variants. I will showcase the application of this prioritization to somatic burdening, cancer differential expression and GWAS (LARVA, MOAT & uORF tools). Targeted validations of the prioritized regulators, elements and variants demonstrate the value of our annotation resource.


Finally, I will put all these methods together through application to kidney and prostate cancers.



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