Simultaneous analysis of large-scale RNAi screens for pathogen entry

Pauli Rämö, Anna Drewek, Cécile Arrieumerlou, Niko Beerenwinkel, Houchaima Ben-Tekaya, Bettina Cardel, Alain Casanova, Raquel Conde-Alvarez, Pascale Cossart, Gábor Csúcs, Simone Eicher, Mario Emmenlauer, Urs Greber, Wolf Dietrich Hardt, Ari Helenius, Christoph Kasper, Andreas Kaufmann, Saskia Kreibich, Andreas Kühbacher, Peter KunsztShyan Huey Low, Jason Mercer, Daria Mudrak, Simone Muntwiler, Lucas Pelkmans, Javier Pizarro-Cerdá, Michael Podvinec, Eva Pujadas, Bernd Rinn, Vincent Rouilly, Fabian Schmich, Juliane Siebourg-Polster, Berend Snijder, Michael Stebler, Gabriel Studer, Ewa Szczurek, Matthias Truttmann, Christian von Mering, Andreas Vonderheit, Artur Yakimovich, Peter Bühlmann, Christoph Dehio

Research output: Contribution to journalArticlepeer-review

3 Citations (Scopus)
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BACKGROUND: 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.

RESULTS: 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.

CONCLUSIONS: 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.

Original languageEnglish
Article number1162
Number of pages18
JournalBMC Genomics
Publication statusPublished - 22 Dec 2014


  • High-throughput high-content RNAi screening
  • Pathogen entry
  • Linear mixed model
  • Hit detection

ASJC Scopus subject areas

  • Biotechnology
  • Genetics


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