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      Genome-wide functional analysis using the barcode sequence alignment and statistical analysis (Barcas) tool

      research-article
      1 , 2 , 2 , 3 , 4 , 1 , 2 ,
      BMC Bioinformatics
      BioMed Central
      The 27th International Conference on Genome Informatics
      3-5 October 2016
      Barcode sequencing, Trie data structure based imperfect matching algorithm, Pooled library screen analysis, shRNA, sgRNA, Barcoded yeast deletion strains

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          Abstract

          Background

          Pooled library screen analysis using shRNAs or CRISPR-Cas9 hold great promise to genome-wide functional studies. While pooled library screens are effective tools, erroneous barcodes can potentially be generated during the production of many barcodes. However, no current tools can distinguish erroneous barcodes from PCR or sequencing errors in a data preprocessing step.

          Results

          We developed the Barcas program, a specialized program for the mapping and analysis of multiplexed barcode sequencing (barcode-seq) data. For fast and efficient mapping, Barcas uses a trie data structure based imperfect matching algorithm which generates precise mapping results containing mismatches, shifts, insertions and deletions (indel) in a flexible manner. Barcas provides three functions for quality control (QC) of a barcode library and distinguishes erroneous barcodes from PCR or sequencing errors. It also provides useful functions for data analysis and visualization.

          Conclusions

          Barcas is an all-in-one package providing useful functions including mapping, data QC, library QC, statistical analysis and visualization in genome-wide pooled screens.

          Electronic supplementary material

          The online version of this article (doi:10.1186/s12859-016-1326-9) contains supplementary material, which is available to authorized users.

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          Most cited references13

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          Genome-wide recessive genetic screening in mammalian cells with a lentiviral CRISPR-guide RNA library.

          Identification of genes influencing a phenotype of interest is frequently achieved through genetic screening by RNA interference (RNAi) or knockouts. However, RNAi may only achieve partial depletion of gene activity, and knockout-based screens are difficult in diploid mammalian cells. Here we took advantage of the efficiency and high throughput of genome editing based on type II, clustered, regularly interspaced, short palindromic repeats (CRISPR)-CRISPR-associated (Cas) systems to introduce genome-wide targeted mutations in mouse embryonic stem cells (ESCs). We designed 87,897 guide RNAs (gRNAs) targeting 19,150 mouse protein-coding genes and used a lentiviral vector to express these gRNAs in ESCs that constitutively express Cas9. Screening the resulting ESC mutant libraries for resistance to either Clostridium septicum alpha-toxin or 6-thioguanine identified 27 known and 4 previously unknown genes implicated in these phenotypes. Our results demonstrate the potential for efficient loss-of-function screening using the CRISPR-Cas9 system.
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            Highly parallel identification of essential genes in cancer cells.

            More complete knowledge of the molecular mechanisms underlying cancer will improve prevention, diagnosis and treatment. Efforts such as The Cancer Genome Atlas are systematically characterizing the structural basis of cancer, by identifying the genomic mutations associated with each cancer type. A powerful complementary approach is to systematically characterize the functional basis of cancer, by identifying the genes essential for growth and related phenotypes in different cancer cells. Such information would be particularly valuable for identifying potential drug targets. Here, we report the development of an efficient, robust approach to perform genome-scale pooled shRNA screens for both positive and negative selection and its application to systematically identify cell essential genes in 12 cancer cell lines. By integrating these functional data with comprehensive genetic analyses of primary human tumors, we identified known and putative oncogenes such as EGFR, KRAS, MYC, BCR-ABL, MYB, CRKL, and CDK4 that are essential for cancer cell proliferation and also altered in human cancers. We further used this approach to identify genes involved in the response of cancer cells to tumoricidal agents and found 4 genes required for the response of CML cells to imatinib treatment: PTPN1, NF1, SMARCB1, and SMARCE1, and 5 regulators of the response to FAS activation, FAS, FADD, CASP8, ARID1A and CBX1. Broad application of this highly parallel genetic screening strategy will not only facilitate the rapid identification of genes that drive the malignant state and its response to therapeutics but will also enable the discovery of genes that participate in any biological process.
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              Quality control, modeling, and visualization of CRISPR screens with MAGeCK-VISPR

              High-throughput CRISPR screens have shown great promise in functional genomics. We present MAGeCK-VISPR, a comprehensive quality control (QC), analysis, and visualization workflow for CRISPR screens. MAGeCK-VISPR defines a set of QC measures to assess the quality of an experiment, and includes a maximum-likelihood algorithm to call essential genes simultaneously under multiple conditions. The algorithm uses a generalized linear model to deconvolute different effects, and employs expectation-maximization to iteratively estimate sgRNA knockout efficiency and gene essentiality. MAGeCK-VISPR also includes VISPR, a framework for the interactive visualization and exploration of QC and analysis results. MAGeCK-VISPR is freely available at http://bitbucket.org/liulab/mageck-vispr. Electronic supplementary material The online version of this article (doi:10.1186/s13059-015-0843-6) contains supplementary material, which is available to authorized users.
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                Author and article information

                Contributors
                kimsy@kribb.re.kr
                Conference
                BMC Bioinformatics
                BMC Bioinformatics
                BMC Bioinformatics
                BioMed Central (London )
                1471-2105
                23 December 2016
                23 December 2016
                2016
                : 17
                Issue : Suppl 17 Issue sponsor : Publication of this supplement has not been supported by sponsorship. Information about the source of funding for publication charges can be found in the individual articles. The articles have undergone the journal's standard peer review process for supplements. The Supplement Editors declare that they have no competing interests.
                : 475
                Affiliations
                [1 ]Korea Research Institute of Bioscience and Biotechnology (KRIBB), Personalized Genomic Medicine Research Center, Daejeon, Republic of Korea
                [2 ]ISNI 0000 0004 1791 8264, GRID grid.412786.e, Department of Functional Genomics, , University of Science and Technology, ; Daejeon, Republic of Korea
                [3 ]Korea Research Institute of Bioscience and Biotechnology (KRIBB), Aging Research Center, Daejeon, Republic of Korea
                [4 ]ISNI 0000 0001 0722 6377, GRID grid.254230.2, Graduate School of New Drug Discovery and Development, , Chungnam National University, ; Yusong-gu, Daejeon, South Korea
                Article
                1326
                10.1186/s12859-016-1326-9
                5260075
                1bc1b6f9-4cad-43b4-8f9e-6bdb985efc6e
                © The Author(s). 2016

                Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License ( http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

                The 27th International Conference on Genome Informatics
                Shanghai, China
                3-5 October 2016
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                © The Author(s) 2016

                Bioinformatics & Computational biology
                barcode sequencing,trie data structure based imperfect matching algorithm,pooled library screen analysis,shrna,sgrna,barcoded yeast deletion strains

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