Computational analysis of genomic data to aid medical decision making.
In the new "post-genome" era of personalized medicine, many variants critical to disease susceptibilities and drug sensitivies will be identified and increased numbers of people will undergo genetic testing. 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.
Collections of simultaneously altered genes as biomarkers of cancer cell drug response Cancer Research. 73(6):1699-70. Article
A hybrid likelihood model for sequence-based disease association studies PLoS Genetics. 9(1): e1003224 Article
Correlation among somatic mutation expression identifies genes important in human glioblastoma progression and survival. Cancer Research. Jul 1;71(13):4550-61 Article
Exome sequencing identifies frequent inactivating mutations in BAP1, ARID1A and PBRM1 in intrahepatic cholangiocarcinomas. Nat Genet. 2013 Dec; 45(12):1470-3 Article
Dr. Karchin is teaching Foundations of Computational Biology and Bioinformatics II, BME 580.688, CS 600.688 during Spring semester 2014.
CRAVAT and MuPIT web tools are now part of National Cancer Institutes Informatics Tools for Cancer Research Initiative and are supported by NIH NCI U01-CA180956-01
Lab alumnus Dr. Hannah Carter (Ph.D. 2012) has received the NIH Director's Early Independence Award Dr. Carter is an Assistant Professor in the Department of Medicine and Human Genetics at University of California, San Diego.
Dr. Karchin has received a National Science Foundation Advances in Biological Informatics Innovation Grant.