Featured Research

New tools for exploring rare cancer driver mutations.

Large-scale cancer sequencing studies of patient cohorts have statistically implicated many cancer driver genes, with a long-tail of infrequently mutated genes. Our group has developed CHASMplus, a computational method to predict driver missense mutations, which is uniquely powered to identify rare driver mutations within the long-tail. CHASMplus substantially outperforms comparable methods across a wide variety of benchmark sets.


View Current Research »


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.

Lab Members and More Lab Info »


Featured Software Tool Tutorial

Selected Publications

Niknafs N et al. (2019) Nat Comm. Nov;10(1):5435 Article

Shao XM et al. (2019) Cancer Immunology Research Dec 23(CIR-19-0464) Article

Newest Publication

Pagel K et al. (2020) Journal of Clinical Oncology CCI. Mar;4:310-317 Article

View All Publications »

Lab News Archive »

Software and Tools

openCRAVAT  [url]  [github]  [pub]
A modular annotation tool for genomic variants 

CHASMplus  [github]  [pub]
Cancer-specific driver mutation prediction 

MHCnuggets  [github]  [pub]
Neoantigen prediction with deep learning 

HotMAPS  [github]  [pub]
3D hotspot mutation identification 

20/20+  [github]  [pub]
Cancer driver gene prediction 

SCHISM  [github]  [pub]
Infer clonal evolutionary history of tumors 

VEST  [url]  [pub]
Predict pathogenic variants 

MOCA  [github]  [pub]
Genotype-phenotype correlations 

CLUMP  [github]  [pub]
Cluster Mendelian variants in 1D 

NAPA  [github]  [pub]
Network analysis of multiple mutations 

CRAVAT  [url]  [pub]
Annotation of cancer somatic variants 

MuPIT  [url]  [pub]
Visualize mutations on 3D protein structures