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Simultaneous analysis of large-scale RNAi screens for pathogen entry

Rämö, Pauli and Drewek, Anna and Arrieumerlou, Cécile and Beerenwinkel, Niko and Ben-Tekaya, Houchaïma and Cardel, Bettina and Casanova, Alain and Conde-Alvarez, Raquel and Cossart, Pascale and Csúcs, Gábor and Eicher, Simone and Emmenlauer, Mario and Greber, Urs and Hardt, Wolf-Dietrich and Helenius, Ari and Kasper, Christoph and Kaufmann, Andreas and Kreibich, Saskia and Kühbacher, Andreas and Kunszt, Peter and Low, Shyan Huey and Mercer, Jason and Mudrak, Daria and Muntwiler, Simone and Pelkmans, Lucas and Pizarro-Cerdá, Javier and Podvinec, Michael and Pujadas, Eva and Rinn, Bernd and Rouilly, Vincent and Schmich, Fabian and Siebourg-Polster, Juliane and Snijder, Berend and Stebler, Michael and Studer, Gabriel and Szczurek, Ewa and Truttmann, Matthias and von Mering, Christian and Vonderheit, Andreas and Yakimovich, Artur and Bühlmann, Peter and Dehio, Christoph. (2014) Simultaneous analysis of large-scale RNAi screens for pathogen entry. BMC Genomics, 15 (1). p. 1162.

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Official URL: http://edoc.unibas.ch/dok/A6337657

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Abstract

Large-scale RNAi screening has become an important technology for identifying genes involved in biological processes of interest. However, the quality of large-scale RNAi screening is often deteriorated by off-targets effects. In order to find statistically significant effector genes for pathogen entry, we systematically analyzed entry pathways in human host cells for eight pathogens using image-based kinome-wide siRNA screens with siRNAs from three vendors. We propose a Parallel Mixed Model (PMM) approach that simultaneously analyzes several non-identical screens performed with the same RNAi libraries.; We show that PMM gains statistical power for hit detection due to parallel screening. PMM allows incorporating siRNA weights that can be assigned according to available information on RNAi quality. Moreover, PMM is able to estimate a sharedness score that can be used to focus follow-up efforts on generic or specific gene regulators. By fitting a PMM model to our data, we found several novel hit genes for most of the pathogens studied.; Our results show parallel RNAi screening can improve the results of individual screens. This is currently particularly interesting when large-scale parallel datasets are becoming more and more publicly available. Our comprehensive siRNA dataset provides a public, freely available resource for further statistical and biological analyses in the high-content, high-throughput siRNA screening field.
Faculties and Departments:05 Faculty of Science > Departement Biozentrum > Infection Biology > Molecular Microbiology (Dehio)
05 Faculty of Science > Departement Biozentrum > Services Biozentrum > Research IT (Podvinec)
UniBasel Contributors:Dehio, Christoph and Rämö, Pauli and Arrieumerlou, Cécile and Ben Tekaya, Houchaima and Casanova, Alain and Eicher, Simone and Emmenlauer, Mario and Kasper, Christoph and Low, Shyan Huey and Pujadas, Eva and Podvinec, Michael
Item Type:Article, refereed
Article Subtype:Research Article
Bibsysno:Link to catalogue
Publisher:BioMed Central
e-ISSN:1471-2164
Note:Publication type according to Uni Basel Research Database: Journal article
Language:English
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Last Modified:25 Jun 2018 11:42
Deposited On:06 Feb 2015 09:59

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