CHASM @ Karchin lab

CHASM (Cancer-specific High-throughput Annotation of Somatic Mutations)

is a machine learning method that predicts the functional significance of somatic missense mutations observed in the genomes of cancer cells, allowing mutations to be prioritized in subsequent functional studies, based on the probability that they give the cells a selective survival advantage. Current release is CHASM 3.0.  Dependency: SNVBox 3.0    last updated on 05/01/2014.

CHASM software is intended for those with substantial bioinformatics and Linux system expertise and access to a large-memory Linux server. Other interested users can run CHASM on the CRAVAT web server. CHASM is free for non-commercial use. For more details please refer to our Software License. Commercial users should contact the Johns Hopkins Technology Transfer office .

Documentation for the user

This section provides the documentation for the user, including instructions to download and install the source code.

Documentation for the developer

This section provides documentation for developers.

Example workflow

Short tutorial on how to run CHASM from the command line

Demo

Example running CHASM on real data

Primary citations

If you use CHASM software for a publication, please cite the following:


Carter H, Chen S, Isik L, Tyekucheva S, Velculescu VE, Kinzler KW, Vogelstein B, Karchin R.(2009) Cancer-specific high-throughput annotation of somatic mutations: computational prediction of driver missense mutations.Cancer Research. 69(16):6660-7

Carter H, Samayoa J, Hruban RH, Karchin R (2010) Prioritization of driver mutations in pancreatic cancer using cancer-specific high-throughput annnotation of somatic mutations (CHASM). Cancer Biology & Therapy. Sep 31;10(6):582-7.

Software primary contact/developer

Hannah Carter  hcarte10 at gmail dot com

For issues with CHASM run on the web with CRAVAT, please contact Rick Kim  rkim at insilico dot us dot com

This is a beta version of the CHASM documentation page. Please contact the primary developer with feedback, suggestions, and requests to improve this page.