MOTIVATION: High-throughput screening (HTS) is an important method in drug discovery in which the activities of a large number of candidate chemicals or genetic materials are rapidly evaluated. Data are usually obtained by measurements on samples in microwell plates and are often subjected to artefacts that can bias the result selection. We report here a novel edge effect correction algorithm suitable for RNA interference (RNAi) screening, because its normalization does not rely on the entire dataset and takes into account the specificities of such a screening process. The proposed method is able to estimate the edge effects for each assay plate individually using the data from a single control column based on diffusion model, and thus targeting a specific but recurrent well-known HTS artefact. This method was first developed and validated using control plates and was then applied to the correction of experimental data generated during a genome-wide siRNA screen aimed at studying HIV-host interactions. The proposed algorithm was able to correct the edge effect biasing the control data and thus improve assay quality and, consequently, the hit-selection step.

MOTIVATION: High-throughput screening is a powerful technology principally used by pharmaceutical industries allowing the identification of molecules of interest within large libraries. Originally target based, cellular assays provide a way to test compounds (or other biological material such as small interfering RNA) in a more physiologically realistic in vitro environment. High-content screening (HCS) platforms are now available at lower cost, giving the opportunity for universities or research institutes to access those technologies for research purposes. However, the amount of information extracted from each experiment is multiplexed and hence difficult to handle. In such context, there is an important need for an easy-to-use, but still powerful software able to manage multidimensional screening data by performing adapted quality control and classification. HCS-analyzer includes: a user-friendly interface specifically dedicated to HCS readouts, an automated approach to identify systematic errors potentially occurring during screening and a set of tools to classify, cluster and identify phenotypes of interest among large and multivariate data. AVAILABILITY: The application, the C# .Net source code, as well as detailed documentation, are freely available at the following URL: http://hcs-analyzer.ip-korea.org.

The automated analysis of images is an active field of research in image processing and pattern recognition. In many applications, the first issue is to face illuminations artifacts that can appear due to bad imaging conditions. These artifacts often have direct consequences on the efficiency of the image analysis algorithms but also on the quantitative measures. This paper presents a fully automated nonuniformity correction based on empirical mode decomposition. The performances are outlined using both synthetic and real data.