74
views
0
recommends
+1 Recommend
0 collections
    0
    shares
      • Record: found
      • Abstract: found
      • Article: found
      Is Open Access

      Analysis of cell-based RNAi screens

      product-review
      1 , , 2 , 3 , 2 ,
      Genome Biology
      BioMed Central

      Read this article at

      Bookmark
          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

          Abstract

          cellHTS is a new method for the analysis and documentation of RNAi screens.

          Abstract

          RNA interference (RNAi) screening is a powerful technology for functional characterization of biological pathways. Interpretation of RNAi screens requires computational and statistical analysis techniques. We describe a method that integrates all steps to generate a scored phenotype list from raw data. It is implemented in an open-source Bioconductor/R package, cellHTS ( http://www.dkfz.de/signaling/cellHTS). The method is useful for the analysis and documentation of individual RNAi screens. Moreover, it is a prerequisite for the integration of multiple experiments.

          Related collections

          Most cited references31

          • Record: found
          • Abstract: found
          • Article: not found

          A large-scale RNAi screen in human cells identifies new components of the p53 pathway.

          RNA interference (RNAi) is a powerful new tool with which to perform loss-of-function genetic screens in lower organisms and can greatly facilitate the identification of components of cellular signalling pathways. In mammalian cells, such screens have been hampered by a lack of suitable tools that can be used on a large scale. We and others have recently developed expression vectors to direct the synthesis of short hairpin RNAs (shRNAs) that act as short interfering RNA (siRNA)-like molecules to stably suppress gene expression. Here we report the construction of a set of retroviral vectors encoding 23,742 distinct shRNAs, which target 7,914 different human genes for suppression. We use this RNAi library in human cells to identify one known and five new modulators of p53-dependent proliferation arrest. Suppression of these genes confers resistance to both p53-dependent and p19ARF-dependent proliferation arrest, and abolishes a DNA-damage-induced G1 cell-cycle arrest. Furthermore, we describe siRNA bar-code screens to rapidly identify individual siRNA vectors associated with a specific phenotype. These new tools will greatly facilitate large-scale loss-of-function genetic screens in mammalian cells.
            Bookmark
            • Record: found
            • Abstract: found
            • Article: not found

            3' UTR seed matches, but not overall identity, are associated with RNAi off-targets.

            Off-target gene silencing can present a notable challenge in the interpretation of data from large-scale RNA interference (RNAi) screens. We performed a detailed analysis of off-targeted genes identified by expression profiling of human cells transfected with small interfering RNA (siRNA). Contrary to common assumption, analysis of the subsequent off-target gene database showed that overall identity makes little or no contribution to determining whether the expression of a particular gene will be affected by a given siRNA, except for near-perfect matches. Instead, off-targeting is associated with the presence of one or more perfect 3' untranslated region (UTR) matches with the hexamer or heptamer seed region (positions 2-7 or 2-8) of the antisense strand of the siRNA. These findings have strong implications for future siRNA design and the application of RNAi in high-throughput screening and therapeutic development.
              Bookmark
              • Record: found
              • Abstract: found
              • Article: not found

              Genome-wide RNAi analysis of growth and viability in Drosophila cells.

              A crucial aim upon completion of whole genome sequences is the functional analysis of all predicted genes. We have applied a high-throughput RNA-interference (RNAi) screen of 19,470 double-stranded (ds) RNAs in cultured cells to characterize the function of nearly all (91%) predicted Drosophila genes in cell growth and viability. We found 438 dsRNAs that identified essential genes, among which 80% lacked mutant alleles. A quantitative assay of cell number was applied to identify genes of known and uncharacterized functions. In particular, we demonstrate a role for the homolog of a mammalian acute myeloid leukemia gene (AML1) in cell survival. Such a systematic screen for cell phenotypes, such as cell viability, can thus be effective in characterizing functionally related genes on a genome-wide scale.
                Bookmark

                Author and article information

                Journal
                Genome Biol
                Genome Biology
                BioMed Central (London )
                1465-6906
                1465-6914
                2006
                25 July 2006
                : 7
                : 7
                : R66
                Affiliations
                [1 ]Signaling and Functional Genomics, German Cancer Research Center, Im Neuenheimer Feld 580, 69120 Heidelberg, Germany
                [2 ]EMBL - European Bioinformatics Institute, Cambridge CB10 1SD, UK
                [3 ]Centre for Chemical and Biological Engineering, IST, Technical University of Lisbon, Av. Rovisco Pais, P-1049-001 Lisbon, Portugal
                Article
                gb-2006-7-7-r66
                10.1186/gb-2006-7-7-r66
                1779553
                16869968
                ae6e0bcb-7f26-4918-a6e5-42adea2ed958
                Copyright © 2006 Boutros et al.; licensee BioMed Central Ltd.

                This is an open access article distributed under the terms of the Creative Commons Attribution License ( http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

                History
                : 27 March 2006
                : 7 June 2006
                : 25 July 2006
                Categories
                Software

                Genetics
                Genetics

                Comments

                Comment on this article