README.md
In cbg-ethz/gespeR: Gene-Specific Phenotype EstimatoR
gespeR: Gene-Specific Phenotype EstimatoR
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.
Reference:
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.
Installation
gespeR is hosted on GitHub and available through Bioconductor. The package is released under the GNU General Public License (GPL) version 3 and includes examples and a vignette.
Data
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.
Target Relation Matrices
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
Pathogen Infection Screen Phenotypes
High-content, image based phenotypes for pathogen infection RNAi screens from the InfectX consortium are hosted at PubChem.
Usage
Step-by-step instructions demonstrating how to download, pre-process and deconvolute pathogen infection screen phnotypes is available in form of an R/Vignette.
Contributions
Contact
Fabian Schmich
fabian.schmich (at) bsse.ethz.ch
cbg-ethz/gespeR documentation built on May 13, 2019, 2:02 p.m.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
gespeR: Gene-Specific Phenotype EstimatoR
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.
Reference:
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.
Installation
gespeR is hosted on GitHub and available through Bioconductor. The package is released under the GNU General Public License (GPL) version 3 and includes examples and a vignette.
Data
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.
Target Relation Matrices
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
Pathogen Infection Screen Phenotypes
High-content, image based phenotypes for pathogen infection RNAi screens from the InfectX consortium are hosted at PubChem.
Usage
Step-by-step instructions demonstrating how to download, pre-process and deconvolute pathogen infection screen phnotypes is available in form of an R/Vignette.
Contributions
Contact
Fabian Schmich
fabian.schmich (at) bsse.ethz.ch
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
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