Package: probabilistic2020
The Probabibilistic 20/20 test identifies genes with signficant oncogene-like and tumor suppressor gene-like mutational patterns for small coding region variants.
Putative signficant oncogenes are found through evaluating
missense mutation clustering andin silicopathogenicity scores. Often highly clustered missense
mutations are indicative of activating mutations.
While statistically signficant tumor suppressor genes (TSGs) are found by abnormally high proportion of inactivating mutations.Probabilistic 20/20 evaluates statistical significance by employing
monte carlo simulations, which incorporates observed mutation context. Monte carlo
simulations are performed within the same gene and thus avoid building a background
distribution based on other genes. This means that the statistical test can be applied
to either all genes in the exome from exome sequencing or to a certain target set of genes
from targeted sequencing.The Probabilistic 20/20 test has nice properties since it accounts
for several factors that could effect the significance of driver genes.gene lengthmutation contextgene sequence (e.g. codon bias)DocumentationPlease see thedocumentationon readthedocs for more details.CitationCollin J. Tokheim, Nickolas Papadopoulos, Kenneth W. Kinzler, Bert Vogelstein, and Rachel Karchin. Evaluating the evaluation of cancer driver genes. PNAS 2016 ; published ahead of print November 22, 2016, doi:10.1073/pnas.1616440113If you use the hotmaps1d command to find codons were missense mutations are significantly clustered, please cite the HotMAPS paper:Tokheim C, Bhattacharya R, Niknafs N, Gygax DM, Kim R, Ryan M, Masica DL, Karchin R (2016) Exome-scale discovery of hotspot mutation regions in human cancer using 3D protein structure Cancer Research. Apr. 28.pii: canres.3190.2015.InstallationPython Package InstallationUsing the python package installation, all the required python packages for the probabibilistic 20/20 test will automatically be installed for you.To install the package into python you can usepip. If you are installing to a system wide python then you may need to usesudobefore the pip command.$pipinstallprobabilistic2020The scripts for Probabilstic 20/20 can then be found inYour_Python_Root_Dir/bin. You can
check the installation with the following:$whichprobabilistic2020$probabilistic2020--helpLocal installationLocal installation is a good option if you do not have privilege to install a python package and already have the required packages.  The source files can also be manually downloaded from github athttps://github.com/KarchinLab/probabilistic2020/releases.Required packages:numpyscipypandas>=0.17.0pysamIf you don’t have the above required packages, you will need to install them. For the following commands to work you will needpip. If you are using a system wide python, you will need to usesudobefore the pip command. Also if you are using python 3.X then you likely will have to install pysam version >=0.9.0.$cdprobabilistic2020$pipinstall-rrequirements.txtIf you want the exact package version used for development on python 2.7, then instead use the requirements_dev.txt. Next you will need to build the Probabilistic 20/20 source files. This is can be accomplished in one command.$makebuildOnce finished building you can then use the scripts in theprobabilstic2020/prob2020/consoledirectory. You can check the build worked by the following:$pythonprob2020/cosole/probabilistic2020.py--help