|Title||permGPU: Using graphics processing units in RNA microarray association studies.|
|Publication Type||Journal Article|
|Year of Publication||2010|
|Authors||Shterev, Ivo D., Sin-Ho Jung, Stephen L. George, and Kouros Owzar|
|Date Published||2010 Jun 16|
|Keywords||Gene Expression Profiling, Genetic Association Studies, Humans, Microarray Analysis, Neoplasms, RNA, Software|
BACKGROUND: Many analyses of microarray association studies involve permutation, bootstrap resampling and cross-validation, that are ideally formulated as embarrassingly parallel computing problems. Given that these analyses are computationally intensive, scalable approaches that can take advantage of multi-core processor systems need to be developed.RESULTS: We have developed a CUDA based implementation, permGPU, that employs graphics processing units in microarray association studies. We illustrate the performance and applicability of permGPU within the context of permutation resampling for a number of test statistics. An extensive simulation study demonstrates a dramatic increase in performance when using permGPU on an NVIDIA GTX 280 card compared to an optimized C/C++ solution running on a conventional Linux server.CONCLUSIONS: permGPU is available as an open-source stand-alone application and as an extension package for the R statistical environment. It provides a dramatic increase in performance for permutation resampling analysis in the context of microarray association studies. The current version offers six test statistics for carrying out permutation resampling analyses for binary, quantitative and censored time-to-event traits.
|Alternate Journal||BMC Bioinformatics|
|Original Publication||permGPU: Using graphics processing units in RNA microarray association studies.|
|PubMed Central ID||PMC2910023|
|Grant List||P01 CA142538 / CA / NCI NIH HHS / United States |
CA33601 / CA / NCI NIH HHS / United States
CA142538 / CA / NCI NIH HHS / United States
permGPU: Using graphics processing units in RNA microarray association studies.