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permGPU: Using graphics processing units in RNA microarray association studies.

dc.contributor.author Shterev, Ivo D
dc.contributor.author Jung, Sin-Ho
dc.contributor.author George, Stephen L
dc.contributor.author Owzar, Kouros
dc.coverage.spatial England
dc.date.accessioned 2011-06-21T17:27:56Z
dc.date.issued 2010-06-16
dc.identifier http://www.ncbi.nlm.nih.gov/pubmed/20553619
dc.identifier 1471-2105-11-329
dc.identifier.uri https://hdl.handle.net/10161/4333
dc.description.abstract 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.
dc.language eng
dc.language.iso en_US
dc.publisher Springer Science and Business Media LLC
dc.relation.ispartof BMC Bioinformatics
dc.relation.isversionof 10.1186/1471-2105-11-329
dc.subject Gene Expression Profiling
dc.subject Genetic Association Studies
dc.subject Humans
dc.subject Microarray Analysis
dc.subject Neoplasms
dc.subject RNA
dc.subject Software
dc.title permGPU: Using graphics processing units in RNA microarray association studies.
dc.title.alternative
dc.type Journal article
duke.contributor.id Jung, Sin-Ho|0285239
duke.contributor.id George, Stephen L|0115612
duke.contributor.id Owzar, Kouros|0298315
dc.description.version Version of Record
duke.date.pubdate 2010-6-16
duke.description.issue
duke.description.volume 11
dc.relation.journal Bmc Bioinformatics
pubs.author-url http://www.ncbi.nlm.nih.gov/pubmed/20553619
pubs.begin-page 329
pubs.organisational-group Basic Science Departments
pubs.organisational-group Biostatistics & Bioinformatics
pubs.organisational-group Duke
pubs.organisational-group Duke Cancer Institute
pubs.organisational-group Institutes and Centers
pubs.organisational-group School of Medicine
pubs.publication-status Published online
pubs.volume 11
dc.identifier.eissn 1471-2105


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