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Probabilistic 20/20

Project description

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 and in silico pathogenicity 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 length

  • mutation context

  • gene sequence (e.g. codon bias)


Documentation Status

Please see the documentation on readthedocs for more details.


Collin 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.1616440113

If 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.


Python Package Installation

Using 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 use pip. If you are installing to a system wide python then you may need to use sudo before the pip command.

$ pip install probabilistic2020

The scripts for Probabilstic 20/20 can then be found in Your_Python_Root_Dir/bin. You can check the installation with the following:

$ which probabilistic2020
$ probabilistic2020 --help

Local installation

Local 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 at

Required packages:

  • numpy

  • scipy

  • pandas>=0.17.0

  • pysam

If you don’t have the above required packages, you will need to install them. For the following commands to work you will need pip. If you are using a system wide python, you will need to use sudo before the pip command. Also if you are using python 3.X then you likely will have to install pysam version >=0.9.0.

$ cd probabilistic2020
$ pip install -r requirements.txt

If 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.

$ make build

Once finished building you can then use the scripts in the probabilstic2020/prob2020/console directory. You can check the build worked by the following:

$ python prob2020/cosole/ --help

Project details

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probabilistic2020-1.2.2.tar.gz (185.6 kB view hashes)

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