Python implementation of SNPrank
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AUTHORS
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README.md
example_data.tab
snprank.py

README.md

pysnprank

SNP ranking algorithm

Description

SNPrank[1] is an eigenvector centrality algorithm that ranks the importance of single nucleotide polymorphisms (SNPs) in a genetic association interaction network (GAIN) [2]. Each SNP is ranked according to its overall contribution to the phenotype, including its main effect and second- and higher-order gene-gene interactions.

This software was created as a bioinformatics tool for usage by our research group, In Silico, as well as other researchers and interested parties.

Dependencies and Usage

pynprank is developed and tested on 64-bit Linux (Ubuntu), but should work on any platform supported by Python (Python version 2.6.5 tested).

pysnprank requires NumPy for matrix computations (tested with NumPy version 1.3.0).

A GPU can be used for accelerated matrix computations. CUDAMat, an open source library for GPU matrix calculations, is required. See the CUDAMat site for more details on prerequisites.

To run pysnprank from command-line:

./snprank.py -i gain-matrix.txt -o output.txt

Additional parameters:

Usage: snprank.py [OPTIONS]

Options:
  --version             show program's version number and exit
  -h, --help            show this help message and exit
  -i INFILE, --input=INFILE
                        read data from INFILE
  -o OUTFILE, --output=OUTFILE
                        output to OUTFILE
  -g GAMMA, --gamma=GAMMA
                        gamma value (default: .85)
  -n, --gpu             enable GPU

Contributors

See AUTHORS file.

References

[1]N.A. Davis, J.E. Crowe, Jr., N.M. Pajewski, and B.A. McKinney. Surfing a genetic association interaction network to identify modulators of antibody response to smallpox vaccine. Genes and Immunity, 2010, doi: 10.1038/gene.2010.3. open access

[2]B.A. McKinney, J.Guo, J.E. Crowe, Jr., and D. Tian. Capturing the spectrum of interaction effects in genetic association studies by simulated evaporative cooling network analysis. PLoS Genetics 2009, 5(3): e1000432. doi:10.1371/journal.pgen.1000432. open access