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Fluorescence microscopy is one of the most powerful tools to investigate complex cellular processes such as cell division, cell motility, or intracellular trafficking. The availability of RNA interference (RNAi) technology and automated microscopy has opened the possibility to perform cellular imaging in functional genomics and other large-scale applications. Although imaging often dramatically increases the content of a screening assay, it poses new challenges to achieve accurate quantitative annotation and therefore needs to be carefully adjusted to the specific needs of individual screening applications. In this review, we discuss principles of assay design, large-scale RNAi, microscope automation, and computational data analysis. We highlight strategies for imaging-based RNAi screening adapted to different library and assay designs.
Pubmed ID: 20176920 RIS Download
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Software package for control of automated microscopes. Cross-platform desktop application, to control motorized microscopes, scientific cameras, stages, illuminators, and other microscope accessories.
View all literature mentionsSoftware repository for R packages related to analysis and comprehension of high throughput genomic data. Uses separate set of commands for installation of packages. Software project based on R programming language that provides tools for analysis and comprehension of high throughput genomic data.
View all literature mentionsSoftware tool to enable biologists without training in computer vision or programming to quantitatively measure phenotypes from thousands of images automatically. It counts cells and also measures the size, shape, intensity and texture of every cell (and every labeled subcellular compartment) in every image. It was designed for high throughput screening but can perform automated image analysis for images from time-lapse movies and low-throughput experiments. CellProfiler has an increasing number of algorithms to identify and measure properties of neuronal cell types.
View all literature mentionsOpen tools to support data management for biological light microscopy produced by a multi-site collaborative effort among academic laboratories and a number of commercial entities. Designed to interact with existing commercial software, all OME formats and software are free, and all OME source code is available under the GNU General public license or through commercial license from Glencoe Software. OME is developed as a joint project between research-active laboratories at the Dundee, NIA Baltimore, and Harvard Medical School and LOCI. In addition, OME has active collaborations with many imaging and informatics groups. While many other applications could use OME''s architecture and design, their specific implementation is focused on biological and biomedical imaging. Those interested in applying OME''s technology to other applications should contact the developers. OME work is divided into several different standards and software projects: * Bio-Formats: A Java-based library for reading and writing over 90 microscopy file formats. * OMERO Software: The Java-based OMERO software project, which currently includes tools for storing, visualizing, managing, and annotating microscopic images and metadata. * OME-XML & OME-TIFF: The OME-XML and OME-TIFF file format specifications, which are open file formats for sharing microscope image data. * OME Server: This was the original OME server project which has now ended and is a legacy product. It implements image-based analysis of cellular dynamics and image-based screening of cellular localization or phenotypes, and included a fully developed version of the 2003 version of OME-XML Schema language.
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