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

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

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


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

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

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

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

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

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

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

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VEST  [url]  [pub]
Predict pathogenic variants 

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MOCA  [github]  [pub]
Genotype-phenotype correlations 

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

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

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

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