Computational analysis of genomic and clinical data to aid medical decision making.
In the new "post-genome" era of personalized medicine, many variants critical to disease susceptibilities, prognosis and drug sensitivities will be identified and increased numbers of people will undergo DNA sequencing. We are developing algorithms and tools intended to facilitate this process.
We develop computational models to interpret and predict the impact of individual variation in the genome, transcriptome, and proteome. The models are being applied to cancer genomics, unclassified variants in Mendelian disease genes, and complex disease genetics. In collaboration with clinicians, pathologists, and experimental biologists, we aim to make significant improvements in individualized medicine within the next five years.
Douville C et al. (2018) Proc Natl Acad Sci U S A 115(8):1871-1876. Article
Guidugli L, Shimelis H, Masica DL et al. (2018) Am. Journal of Human Genetics. S0002-9297(17)30502-5 Article
Bailey MH, Tokheim C, Porta-Pardo E et al. (2018). Cell. in press.
Ph.D. student Collin Tokheim's paper on Driver Mutations in Cancer (co-first author with Matt Bailey and Eduard Porta-Pardo) is accepted by Cell.
Postdoc Violeta Beleva Guthrie's paper on network analysis of the functional impact of multiple adaptive mutations is accepted at Mol. Biol. Evo.!
Registration is open for Gordon Research Conference on Human Genetic Variation and Disease, chaired by Dr. Karchin to be held June 10-15, 2018
CRAVAT/MuPIT tutorial workshop at American Society of Human Genetics annual meeting 2018 in San Diego, CA. Time and location TBD.