ABSTRACT
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.
Subject(s)
Genomics/methods , RNA Interference , RNA, Small Interfering/genetics , Cell Line , Gene Library , Genomics/standards , High-Throughput Screening Assays , Host-Pathogen Interactions/genetics , Humans , ROC Curve , Reproducibility of ResultsABSTRACT
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.
Subject(s)
Database Management Systems , Databases, Genetic , Information Storage and Retrieval/methods , Internet , RNA Interference , Sequence Analysis, RNA/methods , Software , AlgorithmsABSTRACT
This paper describes the effort to deploy a Medical Data Management service on top of the EGEE grid infrastructure. The most widely accepted medical image standard, DICOM, was developed for fulfilling clinical practice. It is implemented in most medical image acquisition and analysis devices. The EGEE middleware is using the SRM standard for handling grid files. Our prototype is exposing an SRM compliant interface to the grid middleware, transforming on the fly SRM requests into DICOM transactions. The prototype ensures user identification, strict file access control and data protection through the use of relevant grid services. This Medical Data Manager is easing the access to medical databases needed for many medical data analysis applications deployed today. It offers a high level data management service, compatible with clinical practices, which encourages the migration of medical applications towards grid infrastructures. A limited scale testbed has been deployed as a proof of concept of this new service. The service is expected to be put into production with the next EGEE middleware generation.