Prioritizing Variants in Personal Genomes, with particular application to cancer
My talk will focus on prioritizing genetic variants associated with
cancer, to identify key variants driving cancer progression. First, I
will look at the overall functional impact of the variants in cancer
genomes, ranking them in terms of impact, for both coding and
non-coding regions. For the coding analysis, we use the ALoFT and
frustration tools, and for the noncoding analysis, we use FunSeq.
Then, I will look at the recurrence of variants within cancer cohorts.
Here we develop two approaches: one parametric (LARVA) and the other
non-parametric (MOAT). These both depend on background
mutation rate, which, in turn, is linked to genomic features such
as replication timing and TADs. I will discuss tools for analyzing these.
Finally, I will put all these methods together
through application to kidney and prostate cancers.