Karchin Lab Johns Hopkins University

We develop computational models to interpret and predict the impact of individual variation in the genome, transcriptome, and proteome.

Our tools are designed for high throughput analysis of large data sets.

Featured Research

New tools for exploring immunogenic neoepitopes in cancer.

Identifying neoepitopes that elicit an adaptive immune response is a major bottleneck to developing personalized cancer immunotherapy therapies. Experimental validation of candidate neoepitopes is extremely resource intensive, and the vast majority of candidates are non-immunogenic, making their identification a needle-in-a-haystack problem. To address this challenge, we developed BigMHC, a deep learning method that predicts MHC-I epitopes and identifies immunogenic neoepitopes with high precision.

BigMHC preprint
BigMHC code and data

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Dr. Rachel Karchin

Professor, Johns Hopkins University. Institute for Computational Medicine, Department of Biomedical Engineering, Department of Oncology, Department of Computer Science.

About the Karchin Lab

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.

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