cSSMD: assessing collective activity for addressing off-target effects in genome-scale RNA interference screens.

MOTIVATION: Off-target activity commonly exists in RNA interference (RNAi) screens and often generates false positives. Existing analytic methods for addressing the off-target effects are demonstrably inadequate in RNAi confirmatory screens. RESULTS: Here, we present an analytic method assessing the collective activity of multiple short interfering RNAs (siRNAs) targeting a gene. Using this method, we can not only reduce the impact of off-target activities, but also evaluate the specific effect of an siRNA, thus providing information about potential off-target effects. Using in-house RNAi screens, we demonstrate that our method obtains more reasonable and sensible results than current methods such as the redundant siRNA activity (RSA) method, the RNAi gene enrichment ranking (RIGER) method, the frequency approach and the t-test. CONTACT: xiaohua_zhang@merck.com SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Hit selection with false discovery rate control in genome-scale RNAi screens.

RNA interference (RNAi) is a modality in which small double-stranded RNA molecules (siRNAs) designed to lead to the degradation of specific mRNAs are introduced into cells or organisms. siRNA libraries have been developed in which siRNAs targeting virtually every gene in the human genome are designed, synthesized and are presented for introduction into cells by transfection in a microtiter plate array. These siRNAs can then be transfected into cells using high-throughput screening (HTS) methodologies. The goal of RNAi HTS is to identify a set of siRNAs that inhibit or activate defined cellular phenotypes. The commonly used analysis methods including median +/- kMAD have issues about error rates in multiple hypothesis testing and plate-wise versus experiment-wise analysis. We propose a methodology based on a Bayesian framework to address these issues. Our approach allows for sharing of information across plates in a plate-wise analysis, which obviates the need for choosing either a plate-wise or experimental-wise analysis. The proposed approach incorporates information from reliable controls to achieve a higher power and a balance between the contribution from the samples and control wells. Our approach provides false discovery rate (FDR) control to address multiple testing issues and it is robust to outliers.

The use of strictly standardized mean difference for hit selection in primary RNA interference high-throughput screening experiments.

RNA interference (RNAi) high-throughput screening (HTS) has been hailed as the 2nd genomics wave following the 1st genomics wave of gene expression microarrays and single-nucleotide polymorphism discovery platforms. Following an RNAi HTS, the authors are interested in identifying short interfering RNA (siRNA) hits with large inhibition/activation effects. For hit selection, the z-score method and its variants are commonly used in primary RNAi HTS experiments. Recently, strictly standardized mean difference (SSMD) has been proposed to measure the siRNA effect represented by the magnitude of difference between an siRNA and a negative reference group. The links between SSMD and d+-probability offer a clear interpretation of siRNA effects from a probability perspective. Hence, SSMD can be used as a ranking metric for hit selection. In this article, the authors investigated both the SSMD-based testing process and the use of SSMD as a ranking metric for hit selection in 2 primary siRNA HTS experiments. The analysis results showed that, as a ranking metric, SSMD was more stable and reliable than percentage inhibition and led to more robust hit selection results. Using the SSMD -based testing method, the false-negative rate can more readily be obtained. More important, the use of the SSMD-based method can result in a reduction in both the false-negative and false-positive rates. The applications presented in this article demonstrate that the SSMD method addresses scientific questions and fills scientific needs better than both percentage inhibition and the commonly used z-score method for hit selection.

A miniaturized cell-based fluorescence resonance energy transfer assay for insulin-receptor activation.

This report describes the development, optimization, and implementation of a miniaturized cell-based assay for the identification of small-molecule insulin mimetics and potentiators. Cell-based assays are attractive formats for compound screening because they present the molecular targets in their cellular environment. A fluorescence resonance energy transfer (FRET) cell-based assay that measures the insulin-dependent colocalization of Akt2 fused with either cyan fluorescent protein or yellow fluorescent protein to the cellular membrane was developed. This ratiometric FRET assay was miniaturized into a robust, yet sensitive 3456-well nanoplate assay with Z' factors of approximately 0.6 despite a very small assay window (less than twofold full activation with insulin). The FRET assay was used for primary screening of a large compound collection for insulin-receptor agonists and potentiators. To prioritize compounds for further development, primary hits were tested in two additional assays, a biochemical time-resolved fluorescence resonance energy transfer assay to measure insulin-receptor phosphorylation and a translocation-based imaging assay. Results from the three assays were combined to yield 11 compounds as potential leads for the development of insulin mimetics or potentiators.