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.
matsnprank is developed and tested on 64-bit Linux (Ubuntu), but should work on any platform supported by Matlab (Matlab R2010a tested).
A GPU can be used for accelerated matrix computations. Jacket, a commercial Matlab GPU engine, is required. See the Jacket site for more details on supported hardware and software environments.
To run snprank, open the Matlab environment and run:
snprank('gain-matrix.txt')
Additional parameters include:
gamma, the damping factor (default is 0.85)capturedata, when enabled plots and output are savedshowgraphs, when enabled plots are displayedusegpu, enables GPU computing (requires Jacket by Accelereyes)
See AUTHORS file.
[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