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.
Identifying Mendelian disease genes with the Variant Effect Scoring Tool. BMC Genomics. 14(3) 1-16. Article
SubClonal Hierarchy Inference from Somatic Mutations: automatic reconstruction of cancer evolutionary trees from multi-region next generation sequencing. BioRxiv Nov 26. 2014 [preprint]
A probabilistic model to predict clinical phenotypic traits from genome sequencing. PLoS Computational Biology. Sep 4. 10(9):e1003825 Article
New release of Mutation Position Imaging Toolbox (MuPIT) interactive features visualization of clustered Cancer Genome Atlas somatic mutations in 14 tumor types and completely redesigned interface. Dec. 22, 2014
New paper by David Masica published Dec 8, 2014 in Human Molecular Genetics from Karchin Lab project with Cystic Fibrosis patient individualized care (inCF) initiative. Featured here in Johns Hopkins Engineering magazine.
Visualization of Ebola mutations. Mutation Position Imaging Toolbox for Ebola virus proteins released, in collaboration with the UCSC Ebola Portal Nov 15, 2014.