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      PICKLES: the database of pooled in-vitro CRISPR knockout library essentiality screens

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      , ,
      Nucleic Acids Research
      Oxford University Press

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          Abstract

          The adaptation of CRISPR/Cas9 systems for pooled library genetic knockout screens in mammalian cells has substantially advanced the state of the art in human functional genomics. Screening panels of cell lines for genes whose knockout imposes a significant fitness defect has dramatically expanded our catalog of high-confidence essential genes, and has already proven useful in identifying tumor-specific essential genes for the development of targeted therapies. However, nonexperts currently lack an easy to use way to access this data and to identify whether their genes of interest are essential across different genetic backgrounds. The volume of screening data is expected to grow massively, making the problem more intractable. Here we describe PICKLES, the database of Pooled In vitro CRISPR Knockout Library Essentiality Screens, where end users can display and download raw or normalized essentiality profiles for more that 18 000 protein-coding genes across more than 50 cell lines. An additional data set with 15,000 genes targeted by pooled library shRNA in over 100 cell lines is also included. Researchers can see at a glance the relative fitness defect and tissue specificity of their genes of interest, generate and save figures locally, and download all raw data. The database is available at http://pickles.hart-lab.org.

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          Most cited references14

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          Epigenome editing by a CRISPR/Cas9-based acetyltransferase activates genes from promoters and enhancers

          Technologies that facilitate the targeted manipulation of epigenetic marks could be used to precisely control cell phenotype or interrogate the relationship between the epigenome and transcriptional control. Here we have generated a programmable acetyltransferase based on the CRISPR/Cas9 gene regulation system, consisting of the nuclease-null dCas9 protein fused to the catalytic core of the human acetyltransferase p300. This fusion protein catalyzes acetylation of histone H3 lysine 27 at its target sites, corresponding with robust transcriptional activation of target genes from promoters, proximal enhancers, and distal enhancers. Gene activation by the targeted acetyltransferase is highly specific across the genome. In contrast to conventional dCas9-based activators, the acetyltransferase effectively activates genes from enhancer regions and with individual guide RNAs. The core p300 domain is also portable to other programmable DNA-binding proteins. These results support targeted acetylation as a causal mechanism of transactivation and provide a new robust tool for manipulating gene regulation.
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            Full-genome RNAi profiling of early embryogenesis in Caenorhabditis elegans.

            A key challenge of functional genomics today is to generate well-annotated data sets that can be interpreted across different platforms and technologies. Large-scale functional genomics data often fail to connect to standard experimental approaches of gene characterization in individual laboratories. Furthermore, a lack of universal annotation standards for phenotypic data sets makes it difficult to compare different screening approaches. Here we address this problem in a screen designed to identify all genes required for the first two rounds of cell division in the Caenorhabditis elegans embryo. We used RNA-mediated interference to target 98% of all genes predicted in the C. elegans genome in combination with differential interference contrast time-lapse microscopy. Through systematic annotation of the resulting movies, we developed a phenotypic profiling system, which shows high correlation with cellular processes and biochemical pathways, thus enabling us to predict new functions for previously uncharacterized genes.
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              Project DRIVE: A Compendium of Cancer Dependencies and Synthetic Lethal Relationships Uncovered by Large-Scale, Deep RNAi Screening

              Elucidation of the mutational landscape of human cancer has progressed rapidly and been accompanied by the development of therapeutics targeting mutant oncogenes. However, a comprehensive mapping of cancer dependencies has lagged behind and the discovery of therapeutic targets for counteracting tumor suppressor gene loss is needed. To identify vulnerabilities relevant to specific cancer subtypes, we conducted a large-scale RNAi screen in which viability effects of mRNA knockdown were assessed for 7,837 genes using an average of 20 shRNAs per gene in 398 cancer cell lines. We describe findings of this screen, outlining the classes of cancer dependency genes and their relationships to genetic, expression, and lineage features. In addition, we describe robust gene-interaction networks recapitulating both protein complexes and functional cooperation among complexes and pathways. This dataset along with a web portal is provided to the community to assist in the discovery and translation of new therapeutic approaches for cancer.
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                Author and article information

                Journal
                Nucleic Acids Res
                Nucleic Acids Res
                nar
                Nucleic Acids Research
                Oxford University Press
                0305-1048
                1362-4962
                04 January 2018
                25 October 2017
                25 October 2017
                : 46
                : Database issue , Database issue
                : D776-D780
                Affiliations
                Department of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
                UTHealth Graduate School of Biomedical Sciences, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
                Author notes
                To whom correspondence should be addressed. Email: traver@ 123456hart-lab.org
                Author information
                http://orcid.org/0000-0002-1880-3341
                Article
                gkx993
                10.1093/nar/gkx993
                5753353
                29077937
                53b3fd2e-9150-4288-a997-a60ce7f0f459
                © The Author(s) 2017. Published by Oxford University Press on behalf of Nucleic Acids Research.

                This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( http://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.

                History
                : 12 October 2017
                : 22 September 2017
                : 14 August 2017
                Page count
                Pages: 5
                Categories
                Database Issue

                Genetics
                Genetics

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