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.
Tokheim CJ et al. (2016) Proc Natl Acad Sci U S A Dec 13;113(50):14330-14335. ArticleMascia DL, Karchin R (2016) PLoS Computational Biology. 12(5):e1004725. Article
Masica DL, et al.Jan 2017 Journal of the American Medical Informatics Assoc. Article
Dr. Karchin will teach Foundations of Computational Biology and Bioinformatics II during Spring semester 2017. Three sections are available for both undergrad and graduate students: BME 580.488, BME 580.688, CS 600.488.
Read about Ph.D student Collin Tokheim's latest paper in Science Daily.
Ph.D students Noushin Niknafs, Rohit Bhattacharya and Postdoc Violeta Beleva-Guthrie do computational modeling of non-small cell lung cancer resistance to immunotherapy in Evolution of the Neoantigen Landscape During Immune Checkpoint Blockade in Non-Small Cell Lung Cancer published in Cancer Discovery.
Dr. Karchin and Dr. Masica will present tools developed in our lab at the Gene Variation 3D meeting at the Institute for Systems Biology in Seattle, WA in February.