gespeR is a novel model to estimate gene-specific phenotypes from off-target confounded RNAi screens. The observed phenotype for each siRNA is modeled as a the weighted linear combination of gene-specific phenotypes from the on- and all off-target genes. This deconvolution approach yields highly reproducible phenotypes, essential for unbiased analyses of siRNA screening data.
Fabian Schmich, Ewa Szczurek, Saskia Kreibich, Sabrina Dilling, Daniel Andritschke, Alain Casanova, Shyan Huey Low, Simone Eicher, Simone Muntwiler, Mario Emmenlauer, Pauli Ramo, Raquel Conde-Alvarez, Christian von Mering, Wolf-Dietrich Hardt, Christoph Dehio and Niko Beerenwinkel. gespeR: a statistical model for deconvoluting off-target-confounded RNA interference screens Genome Biology, 2015.
In addition to the phenotypic readout, gespeR requires siRNA-to-gene target relation matrices, quantifying how strongly each siRNA downregulates transcript genes via on- and off-targeting. These matrices can be computed with miRNA target prediction tools, such as for instance TargetScan.
Below, we provide pre-computed matrices for all libraries used in Schmich et al., 2015. Wrapper scripts to run TargetScan in batch mode for the prediction of siRNA-to-gene target relation matrices are also available on GitHub, including a README with step-by-step instructions. All pre-computed siRNA-to-gene target relation matrices are stored in .rds files using R's serialization interface for single objects. Load the data into R by using the function readRDS(). Note that loading target relation matrices can require up to 5GB of RAM. - Ambion - Dharmacon - Dharmacon deconvoluted - Qiagen - Qiagen kinome - Validation - Schultz et al., 2011
Step-by-step instructions demonstrating how to download, pre-process and deconvolute pathogen infection screen phnotypes is available in form of an R/Vignette.
Fabian Schmich fabian.schmich (at) bsse.ethz.ch
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