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.
Missense variants in CFTR nucleotide-binding domains predict quantitative phenotypes associated with cystic fibrosis disease severity. Human Mol. Genetics. 24(7):1908-17 Article
A probabilistic model to predict clinical phenotypic traits from genome sequencing. PLoS Computational Biology. Sep 4. 10(9):e1003825 Article
June 2015. SubClonial Hierarchy Inference from Somatic Mutations (SCHISM) automatic reconstruction of cancer evolutionary trees from multi-region next generation sequencing (Niknafs et al.) is in press at PLoS Computational Biology. Preprint on BioRxiv
May 2015 Special issue of the Human Genetics journal on Computational Molecular Medicine, edited by Dr. Karchin and Dr. Melissa Cline published by Springer.
New version of mutation analysis pipeline CRAVAT released May 2015. Includes VEST pathogenicity scores for all non-silent mutation consequence types, improved report layouts, faster processing times.
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.