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Zürich, Switzerland

Rouilly V.,University of Basel | Pujadas E.,University of Basel | Hullar B.,SyBIT | Hullar B.,Institute of Molecular Systems Biology | And 6 more authors.
Studies in Health Technology and Informatics | Year: 2012

We report on the implementation of a software suite dedicated to the management and analysis of large scale RNAi High Content Screening (HCS). We describe the requirements identified amongst our different users, the supported data flow, and the implemented software. Our system is already supporting productively three different laboratories operating in distinct IT infrastructures. The system was already used to analyze hundreds of RNAi HCS plates. © 2012 The authors and IOS Press. All rights reserved.


Quandt A.,ETH Zurich | Espona L.,ETH Zurich | Balasko A.,Laboratory of Parallel and Distributed Systems | Weisser H.,ETH Zurich | And 7 more authors.
EuPA Open Proteomics | Year: 2014

Tandem mass spectrometry and sequence database searching are widely used in proteomics to identify peptides in complex mixtures. Here we present a benchmark study in which a pool of 20,103 synthetic peptides was measured and the resulting data set was analyzed using around 1800 different software and parameter set combinations. The results indicate a strong relationship between the performance of an analysis workflow and the applied parameter settings. We present and discuss strategies to optimize parameter settings in order to significantly increase the number of correctly assigned fragment ion spectra and to make the analysis method robust. © 2014 The Authors.


Ramo P.,University of Basel | Drewek A.,ETH Zurich | Arrieumerlou C.,University of Paris Descartes | Beerenwinkel N.,ETH Zurich | And 41 more authors.
BMC Genomics | Year: 2015

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. © 2014 Rämö et al.; licensee BioMed Central.


Ramo P.,University of Basel | Drewek A.,ETH Zurich | Arrieumerlou C.,University of Paris Descartes | Beerenwinkel N.,ETH Zurich | And 41 more authors.
BMC Genomics | Year: 2014

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. © 2014 Rämö et al.. licensee BioMed Central.

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