New tools for exploring rare cancer driver mutations.
Large-scale cancer sequencing studies of patient cohorts have statistically implicated many cancer driver genes, with a long-tail of infrequently mutated genes. Our group has developed CHASMplus, a computational method to predict driver missense mutations, which is uniquely powered to identify rare driver mutations within the long-tail. CHASMplus substantially outperforms comparable methods across a wide variety of benchmark sets.
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.
Guthrie VB et al. (2018) Molecular Biology and Evolution. 35(6):1507-1519 Article
Douville C et al. (2018) Proc Natl Acad Sci U S A 115(8):1871-1876. Article
Kuboki Y, Fischer CG, Guthrie VB et al.Article
Autumn 2018/Winter 2019
KarchinLab is recruiting new Ph.D students. If you are interested, please read the application instructions
Hear Rachel talk about CHASMplus at the Integrative Omics meeting in Santa Fe, New Mexico
OpenCRAVAT beta released! Modular, open tools for Custom Ranked Analysis of VAriants Toolkit.