Who should I contact if I encounter a problem?

If you believe your problem may be encountered by other users, please post the question on biostars. Check to make sure your question has not been already answered by looking at posts with the tag CHASMplus. Otherwise, create a new post with the CHASMplus tag. We will be checking biostars for questions. You may also contact me directly at ctokhei1 AT alumni DOT jh DOT edu.

Does CHASMplus support targeted gene panels?

Yes! We have added CHASMplus modules into OpenCRAVAT that support targeted gene panel sequencing. The first iteration was done for the MSK-IMPACT gene panel, but others may be supported upon request. Please see the OpenCRAVAT instructions for how to install “annotators” (GUI, command line). The MSK-IMPACT version of CHASMplus will have “MSK-IMPACT” (GUI version) or “mski” (command line version) in the name.

Can I get custom scores based on my own data from targeted gene panels?

Yes. However, you will need to run the source code version of CHASMplus to customize the predictions to your data.

Where can I obtain the training data for CHASMplus?

You can obtain the set of mutations used for training from here.

I want to compare my method to CHASMplus. How should I do it?

I recommend using the precomputed scores available through OpenCRAVAT [see Quick start (OpenCRAVAT & CHASMplus)]. Scores in the precompute were generated using gene-hold out cross-validation, so there is no issue when evaluating performance about training set overlap leading to overfitting. However, the scores do reflect training based on data from The Cancer Genome Atlas (TCGA). If a new method is trained using more data than is available from the TCGA, then it is recommended to create a new CHASMplus model based on the larger data set by using the CHASMplus source code.

I want to apply CHASMplus to new data. How should I do it?

For small datasets it is recommended that pre-computed scores obtained from OpenCRAVAT are used. Large datasets may also use the pre-computed scores, but won’t benefit from predictions that are customized to your data. If a cancer type you are interested in does not have precomputed scores or you have collected a large number of cancer samples, than it is recommended to use the CHASMplus source code to perform tailored predictions for your data.