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.
Niknafs N et al. (2019) Nat Comm. Nov;10(1):5435 Article
Springer S, Masica D et al. (2019) Sc. Trans. Med Jul 17(11(501) Article
Shao XM et al. (2019) Cancer Immunology Research Dec 23(CIR-19-0464) Article
OpenCRAVAT web server now available!
Talk by Kym about OpenCRAVAT at the Walter J. Burdette Trainee Symposium on Human Genomics, Yale University, New Haven CT. Dec. 2
Talk by Rachel about tools for computational immuno-oncology at UCSD Genetics, Bioinformatics and Systems Biology Symposium on January 9. San Diego.
Webinar by Kym about OpenCRAVAT National Cancer Institute Data Science Seminar Series on January 29.
Talk by Rachel at JHMI Cancer Chemical and Structural Biology meeting Evaluating Drivers with HotMAPS. Feb 25.
OpenCRAVAT invited workshop at American Society for Human Genetics 2020 meeting t San Diego Convention Center on Oct 31.