FlyRNAi (http://www.flyrnai.org), the database and website of the Drosophila RNAi Screening Center (DRSC) at Harvard Medical School, serves a dual role, tracking both production of reagents for RNA interference (RNAi) screening in Drosophila cells and RNAi screen results. The database and website is used as a platform for community availability of protocols, tools, and other resources useful to researchers planning, conducting, analyzing or interpreting the results of Drosophila RNAi screens. Based on our own experience and user feedback, we have made several changes. Specifically, we have restructured the database to accommodate new types of reagents; added information about new RNAi libraries and other reagents; updated the user interface and website; and added new tools of use to the Drosophila community and others. Overall, the result is a more useful, flexible and comprehensive website and database.

To evaluate the specificity of long dsRNAs used in high-throughput RNA interference (RNAi) screens performed at the Drosophila RNAi Screening Center (DRSC), we performed a global analysis of their activity in 30 genome-wide screens completed at our facility. Notably, our analysis predicts that dsRNAs containing > or = 19-nucleotide perfect matches identified in silico to unintended targets may contribute to a significant false positive error rate arising from off-target effects. We confirmed experimentally that such sequences in dsRNAs lead to false positives and to efficient knockdown of a cross-hybridizing transcript, raising a cautionary note about interpreting results based on the use of a single dsRNA per gene. Although a full appreciation of all causes of false positive errors remains to be determined, we suggest simple guidelines to help ensure high-quality information from RNAi high-throughput screens.

A major objective of systems biology is to organize molecular interactions as networks and to characterize information flow within networks. We describe a computational framework to integrate protein-protein interaction (PPI) networks and genetic screens to predict the 'signs' of interactions (i.e., activation-inhibition relationships). We constructed a Drosophila melanogaster signed PPI network consisting of 6,125 signed PPIs connecting 3,352 proteins that can be used to identify positive and negative regulators of signaling pathways and protein complexes. We identified an unexpected role for the metabolic enzymes enolase and aldo-keto reductase as positive and negative regulators of proteolysis, respectively. Characterization of the activation-inhibition relationships between physically interacting proteins within signaling pathways will affect our understanding of many biological functions, including signal transduction and mechanisms of disease.

BACKGROUND: The Notch signaling pathway regulates a diverse array of developmental processes, and aberrant Notch signaling can lead to diseases, including cancer. To obtain a more comprehensive understanding of the genetic network that integrates into Notch signaling, we performed a genome-wide RNAi screen in Drosophila cell culture to identify genes that modify Notch-dependent transcription. RESULTS: Employing complementary data analyses, we found 399 putative modifiers: 189 promoting and 210 antagonizing Notch activated transcription. These modifiers included several known Notch interactors, validating the robustness of the assay. Many novel modifiers were also identified, covering a range of cellular localizations from the extracellular matrix to the nucleus, as well as a large number of proteins with unknown function. Chromatin-modifying proteins represent a major class of genes identified, including histone deacetylase and demethylase complex components and other chromatin modifying, remodeling and replacement factors. A protein-protein interaction map of the Notch-dependent transcription modifiers revealed that a large number of the identified proteins interact physically with these core chromatin components. CONCLUSIONS: The genome-wide RNAi screen identified many genes that can modulate Notch transcriptional output. A protein interaction map of the identified genes highlighted a network of chromatin-modifying enzymes and remodelers that regulate Notch transcription. Our results open new avenues to explore the mechanisms of Notch signal regulation and the integration of this pathway into diverse cellular processes.

Off-target effects have been demonstrated to be a major source of false-positives in RNA interference (RNAi) high-throughput screens. In this study, we re-assess the previously published transcriptional reporter-based whole-genome RNAi screens for the Wingless and Hedgehog signaling pathways using second generation double-stranded RNA libraries. Furthermore, we investigate other factors that may influence the outcome of such screens, including cell-type specificity, robustness of reporters, and assay normalization, which determine the efficacy of RNAi-knockdown of target genes.

Drosophila melanogaster has become a system of choice for functional genomic studies. Many resources, including online databases and software tools, are now available to support design or identification of relevant fly stocks and reagents or analysis and mining of existing functional genomic, transcriptomic, proteomic, etc. datasets. These include large community collections of fly stocks and plasmid clones, "meta" information sites like FlyBase and FlyMine, and an increasing number of more specialized reagents, databases, and online tools. Here, we introduce key resources useful to plan large-scale functional genomics studies in Drosophila and to analyze, integrate, and mine the results of those studies in ways that facilitate identification of highest-confidence results and generation of new hypotheses. We also discuss ways in which existing resources can be used and might be improved and suggest a few areas of future development that would further support large- and small-scale studies in Drosophila and facilitate use of Drosophila information by the research community more generally.

BACKGROUND: High-throughput screening using RNAi is a powerful gene discovery method but is often complicated by false positive and false negative results. Whereas false positive results associated with RNAi reagents has been a matter of extensive study, the issue of false negatives has received less attention. RESULTS: We performed a meta-analysis of several genome-wide, cell-based Drosophila RNAi screens, together with a more focused RNAi screen, and conclude that the rate of false negative results is at least 8%. Further, we demonstrate how knowledge of the cell transcriptome can be used to resolve ambiguous results and how the number of false negative results can be reduced by using multiple, independently-tested RNAi reagents per gene. CONCLUSIONS: RNAi reagents that target the same gene do not always yield consistent results due to false positives and weak or ineffective reagents. False positive results can be partially minimized by filtering with transcriptome data. RNAi libraries with multiple reagents per gene also reduce false positive and false negative outcomes when inconsistent results are disambiguated carefully.

Cellular signaling networks have evolved to enable swift and accurate responses, even in the face of genetic or environmental perturbation. Thus, genetic screens may not identify all the genes that regulate different biological processes. Moreover, although classical screening approaches have succeeded in providing parts lists of the essential components of signaling networks, they typically do not provide much insight into the hierarchical and functional relations that exist among these components. We describe a high-throughput screen in which we used RNA interference to systematically inhibit two genes simultaneously in 17,724 combinations to identify regulators of Drosophila JUN NH(2)-terminal kinase (JNK). Using both genetic and phosphoproteomics data, we then implemented an integrative network algorithm to construct a JNK phosphorylation network, which provides structural and mechanistic insights into the systems architecture of JNK signaling.

We present a resource of high quality lists of functionally related Drosophila genes, e.g. based on protein domains (kinases, transcription factors, etc.) or cellular function (e.g. autophagy, signal transduction). To establish these lists, we relied on different inputs, including curation from databases or the literature and mapping from other species. Moreover, as an added curation and quality control step, we asked experts in relevant fields to review many of the lists. The resource is available online for scientists to search and view, and is editable based on community input. Annotation of gene groups is an ongoing effort and scientific need will typically drive decisions regarding which gene lists to pursue. We anticipate that the number of lists will increase over time; that the composition of some lists will grow and/or change over time as new information becomes available; and that the lists will benefit the scientific community, e.g. at experimental design and data analysis stages. Based on this, we present an easily updatable online database, available at www.flyrnai.org/glad, at which gene group lists can be viewed, searched and downloaded.

RNA interference (RNAi) has become a powerful tool for genetic screening in Drosophila. At the Drosophila RNAi Screening Center (DRSC), we are using a library of over 21,000 double-stranded RNAs targeting known and predicted genes in Drosophila. This library is available for the use of visiting scientists wishing to perform full-genome RNAi screens. The data generated from these screens are collected in the DRSC database (http://flyRNAi.org/cgi-bin/RNAi_screens.pl) in a flexible format for the convenience of the scientist and for archiving data. The long-term goal of this database is to provide annotations for as many of the uncharacterized genes in Drosophila as possible. Data from published screens are available to the public through a highly configurable interface that allows detailed examination of the data and provides access to a number of other databases and bioinformatics tools.

Characterizing the extent and logic of signaling networks is essential to understanding specificity in such physiological and pathophysiological contexts as cell fate decisions and mechanisms of oncogenesis and resistance to chemotherapy. Cell-based RNA interference (RNAi) screens enable the inference of large numbers of genes that regulate signaling pathways, but these screens cannot provide network structure directly. We describe an integrated network around the canonical receptor tyrosine kinase (RTK)-Ras-extracellular signal-regulated kinase (ERK) signaling pathway, generated by combining parallel genome-wide RNAi screens with protein-protein interaction (PPI) mapping by tandem affinity purification-mass spectrometry. We found that only a small fraction of the total number of PPI or RNAi screen hits was isolated under all conditions tested and that most of these represented the known canonical pathway components, suggesting that much of the core canonical ERK pathway is known. Because most of the newly identified regulators are likely cell type- and RTK-specific, our analysis provides a resource for understanding how output through this clinically relevant pathway is regulated in different contexts. We report in vivo roles for several of the previously unknown regulators, including CG10289 and PpV, the Drosophila orthologs of two components of the serine/threonine-protein phosphatase 6 complex; the Drosophila ortholog of TepIV, a glycophosphatidylinositol-linked protein mutated in human cancers; CG6453, a noncatalytic subunit of glucosidase II; and Rtf1, a histone methyltransferase.

Genetic screens in the yeast Saccharomyces cerevisiae have identified many proteins involved in the secretory pathway, most of which have orthologues in higher eukaryotes. To investigate whether there are additional proteins that are required for secretion in metazoans but are absent from yeast, we used genome-wide RNA interference (RNAi) to look for genes required for secretion of recombinant luciferase from Drosophila S2 cells. This identified two novel components of the secretory pathway that are conserved from humans to plants. Gryzun is distantly related to, but distinct from, the Trs130 subunit of the TRAPP complex but is absent from S. cerevisiae. RNAi of human Gryzun (C4orf41) blocks Golgi exit. Kish is a small membrane protein with a previously uncharacterised orthologue in yeast. The screen also identified Drosophila orthologues of almost 60% of the yeast genes essential for secretion. Given this coverage, the small number of novel components suggests that contrary to previous indications the number of essential core components of the secretory pathway is not much greater in metazoans than in yeasts.

RNA interference has re-energized the field of functional genomics by enabling genome-scale loss-of-function screens in cultured cells. Looking back on the lessons that have been learned from the first wave of technology developments and applications in this exciting field, we provide both a user's guide for newcomers to the field and a detailed examination of some more complex issues, particularly concerning optimization and quality control, for more advanced users. From a discussion of cell lines, screening paradigms, reagent types and read-out methodologies, we explore in particular the complexities of designing optimal controls and normalization strategies for these challenging but extremely powerful studies.