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Protocols

GLIDE specifications

Information


Unique identifier OMICS_10118
Name GLIDE
Software type Package/Module
Interface Command line interface
Restrictions to use None
Operating system Unix/Linux
Parallelization CUDA
Computer skills Advanced
Stability Stable
Maintained Yes

Versioning


No version available

Maintainer


  • person_outline Karsten Borgwardt

Publication for GLIDE

GLIDE citations

 (3)
library_books

gammaMAXT: a fast multiple testing correction algorithm

2015
BioData Min
PMCID: 4654922
PMID: 26594243
DOI: 10.1186/s13040-015-0069-x

[…] Among the numerous software designed for pair-wise or higher-order SNP-SNP interactions, we recall BOOST [], BiForce [], epiGPU [], EpiBlaster [], GLIDE [], Multifactor Dimensionality Reduction (MDR) [, ] and Model-Based Multifactor Dimensionality Reduction (MB-MDR) [, ]. The following comparison of these approaches is mainly inspired from [] wh […]

call_split

The Challenges of Genome Wide Interaction Studies: Lessons to Learn from the Analysis of HDL Blood Levels

2014
PLoS One
PMCID: 4203717
PMID: 25329471
DOI: 10.1371/journal.pone.0109290
call_split See protocol

[…] .r-project.org/web/packages/coxme/) and adjusted for PCs. In the discovery and filtering stage, the HDL levels after adjustment for sex and age were normalised around zero as this is a requirement of GLIDE. To compare the βint in the discovery and filtering stage with the ßint in the replication stage, we also calculated the βint in the Rotterdam Study cohorts without scaling around zero for the m […]

call_split

Discovering epistasis in large scale genetic association studies by exploiting graphics cards

2013
Front Genet
PMCID: 3848199
PMID: 24348518
DOI: 10.3389/fgene.2013.00266
call_split See protocol

[…] Upon searching the literature for epistasis detection methods on GPUs, we were able to locate and download programs for eight published methods, which we list as EpiGPU(Hemani et al., ), GLIDE (Kam-Thong et al., ), GBOOST(Yung et al., ), cuGWAM(Kwon et al., ), EPIBLASTER(Kam-Thong et al., ), EpiGPUHSIC(Kam-Thong et al., ), SHEsisEpi(Hu et al., ), and MDRGPU(Greene et al., ). We evalua […]

Citations

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GLIDE institution(s)
Machine Learning and Computational Biology Research Group, Max Planck Institutes Tübingen, Germany

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