Computational models to interpret and predict the impact of individual variation in the genome, transcriptome, and proteome. ...read more
Genetic variation is critical to our susceptibility to diseases and response to medications. Yet the functional consequences of most genetic variants are unknown. We are working to predict these consequences using computation, by integrating information from molecular modeling and sequence analysis with clinical patient data and in vitro functional studies, through collaborations with physicians, genetic counselors, and experimental biologists. We are particularly interested in inherited cancer susceptibilities and gain of function mutations in tumor genomes.
Carter H, Chen S, Isik L, Tyekucheva S, Velculescu VE, Kinzler KW, Vogelstein B, Karchin R.(2009) Cancer-specific high-throughput annotation of somatic mutations: computational prediction of driver missense mutations.Cancer Research. 69(16):6660-7
Carter H, Samayoa J, Hruban RH, Karchin R (2010) Prioritization of driver mutations in pancreatic cancer using cancer-specific high-throughput annnotation of somatic mutations (CHASM). Cancer Biology & Therapy. Sep 31;10(6):582-7.
Masica DL and Karchin R (2011) Correlation among somatic mutation expression identifies genes important in human glioblastoma progression and survival. Cancer Research. Jul 1;71(13):4550-61
Ryan M, Diekhans M, Lien S, Liu Y, Karchin R. (2009) LS-SNP/PDB: annotated non-synonymous SNPs mapped to Protein Data Bank structures. Bioinformatics. 25(11):1431-2
Wong WC, Kim D, Carter H, Diekhans M, Ryan M and Karchin R (2011) CHASM and SNVBox: toolkit for detecting biologically important single nucleotide mutations in cancer. Bioinformatics. 27(15):2147-8
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The Patient Rating score is an average of all responses to physician related questions on the national CG-CAHPS Medical Practice patient experience survey through Press Ganey. Responses are measured on a scale of 1 to 5, with 5 being the best score. Comments are also gathered from our CG-CAHPS Medical Practice Survey through Press Ganey and displayed in their entirety. Patients are de-identified for confidentiality and patient privacy.