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.


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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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Featured Software Tool Tutorial

Selected Publications

Shao, XM et al. (2022) Ann Oncol. Jul;33(7):728-738 Article

Zheng, L et al. (2022) Bioinformatics Jun 1:btac367 Article

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

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Lab News Archive »

Job Openings

Genomics data scientist We are seeking an individual with experience in variant annotation to join the lab's OpenCRAVAT project, sponsored by NCI ITCR. The data scientist will work closely with our team of software engineers and collaborators to develop new annotation and visualization tools, with a focus on tools for structural variation. OpenCRAVAT is an open source variant meta-annotation software toolkit. Send CV to Dr. Rachel Karchin, [email protected]

Software and Tools

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

PICTograph  [github]  [pub]
Bayesian method to model tumor evolution 

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