Disease Gene Prioritization with Algorithms
A gene is a sequence of DNA that codes for a protein that is associated with a particular biological function (you can think of genes and proteins as having a 1-1 relationship). A mutation, or aberration, that occurs in a gene and disrupts the normal function is called a disease gene.
In our project, we run three graph algorithms: PageRank, random walk with restart, and diffusion kernel. Each algorithm takes a set of known disease genes and uses this information to predict which other genes are associated with a given disease.
In order to use our software package, first clone the GitHub repository onto your development environment. Open your terminal/command prompt and use the following command
git clone https://github.com/oscardssmith/Disease-Gene-Network-Analysis.gitOnce cloned, use the command
cd Disease-Gene-Network-Analysisto make our repository your working directory. Now use the command
python3 run.pyto install dependencies, download appropriate files, and pull up an interface that allows you to interact with our code.
The interface will first ask for the task you would like to perform.
If you choose algorithm, you will be asked to choose which algorithm you would like to run (page rank, diffusion kernel, random walk with restart). Depending on the algorithm, you may also be asked to input a beta or R value. Suggested ranges exist within the interface. You will then be asked to select the PPI network you want to analyze. There will be one dataset by default unless you add a dataset of your own. You will then be asked to select the gene file you want to use. We have 9 gene files setup and ready to use. Finally, you will be asked to name your output file. More details on output are below.
If you choose validation, you will be asked which type of validation (ROC or leave one out). Once chosen, you will then go through the same process as above in terms of selecting an algorithm, parameters for the algorithm, dataset, gene file, and output file.
You will be asked to specify the name of your output file. This will appear in the results folder. There are three cases for the output files:
- Running Algorithms- output file will be a csv file of protein names with probability, in descending order.
- Area under the ROC curve- A picture of the ROC curve as a .png image
- Leave one out- a text file containing validation results
Copyright 2020 Carleton College
Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:
The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.