TumorSTOPpy
We designed a machine learning method to distinguish two types of T cell receptor hypervariable CDR3 sequences!
This particular region is of high interest in immunology because it is binding to antigens that are presented on the surface of human cells. This interaction will eventually trigger adaptive immune responses which eliminates the pathogens, such as virus infection, or sometimes cancer cells.
In order to understand which CDR3 sequences may bind to cancer antigens, we have prioritized 20,000 cancer-specific CDR3s, which may possess distinct biochemical features from the non-cancer CDR3s. Your task is to develop a predictor that is able to distinguish the cancer-specific CDR3s from non-cancer CDR3s. I have provided an amino acid index matrix, which contains 544 features for each of the 20 amino acid. This matrix may help you to convert the amino acid characters into continuous values, which will be much more straightforward to design predictors.
What is ?
Overview Diagram
How to use
TumorSTOp.py
Software Workflow Diagram
File structure diagram
Define paths, variable names, etc
Installing from Github
git clone https://github.com/NCBI-Hackathons/<this software>.git- Edit the configuration files as below
sh server/<this software>.shto test- Add cron job as required (to execute .sh script)
Configuration
Examples here
Testing
We tested four different tools with . They can be found in server/tools/ .