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.
Mascia DL, Karchin R (2016) PLoS Computational Biology. 12(5):e1004725. Article
Niknafs N, Guthrie VB, Naiman DQ, Karchin R (2015) PLoS Computational Biology 11(10):e1004416 Article
Tokheim C Bhattacharya R, Niknafs N, Gygax DM, Kim R, Ryan M, Masica DL, Karchin R. May 2016 Article
KarchinLab is recruiting new Ph.D students. If you are interested, please read the application instructions.
Dr. Karchin presents Evaluating the Evaluation of Cancer Driver Genes at Canceromatics III Tumor Heterogeneity in Madrid, Spain.
New MuPIT protein structure variant viewer released. Redesigned interface and fast 3D graphics.
Collin Tokheim presents Mutational hotspots in cancer: a protein structure-driver relationship to the JHU Lecture Series in Computational Biophysics.