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Sequence determinants of improved CRISPR sgRNA design.
Xu, Han; Xiao, Tengfei; Chen, Chen-Hao; Li, Wei; Meyer, Clifford A; Wu, Qiu; Wu, Di; Cong, Le; Zhang, Feng; Liu, Jun S; Brown, Myles; Liu, X Shirley.
Afiliação
  • Xu H; Department of Biostatistics and Computational Biology, Dana-Farber Cancer Institute and Harvard School of Public Health, Boston, Massachusetts 02115, USA; Center for Functional Cancer Epigenetics, Dana-Farber Cancer Institute, Boston, Massachusetts 02215, USA;
  • Xiao T; Department of Biostatistics and Computational Biology, Dana-Farber Cancer Institute and Harvard School of Public Health, Boston, Massachusetts 02115, USA; Center for Functional Cancer Epigenetics, Dana-Farber Cancer Institute, Boston, Massachusetts 02215, USA; Department of Medical Oncology, Dana-Fa
  • Chen CH; Program in Biological and Biomedical Sciences, Harvard Medical School, Boston, Massachusetts 02215, USA;
  • Li W; Department of Biostatistics and Computational Biology, Dana-Farber Cancer Institute and Harvard School of Public Health, Boston, Massachusetts 02115, USA; Center for Functional Cancer Epigenetics, Dana-Farber Cancer Institute, Boston, Massachusetts 02215, USA;
  • Meyer CA; Department of Biostatistics and Computational Biology, Dana-Farber Cancer Institute and Harvard School of Public Health, Boston, Massachusetts 02115, USA; Center for Functional Cancer Epigenetics, Dana-Farber Cancer Institute, Boston, Massachusetts 02215, USA;
  • Wu Q; Department of Biostatistics and Computational Biology, Dana-Farber Cancer Institute and Harvard School of Public Health, Boston, Massachusetts 02115, USA; Center for Functional Cancer Epigenetics, Dana-Farber Cancer Institute, Boston, Massachusetts 02215, USA; Department of Bioinformatics, School of
  • Wu D; Department of Statistics, Harvard University, Cambridge, Massachusetts 02138, USA;
  • Cong L; Broad Institute of MIT and Harvard, Cambridge, Massachusetts 02142, USA; McGovern Institute for Brain Research, Department of Brain and Cognitive Sciences, Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, USA;
  • Zhang F; Broad Institute of MIT and Harvard, Cambridge, Massachusetts 02142, USA; McGovern Institute for Brain Research, Department of Brain and Cognitive Sciences, Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, USA;
  • Liu JS; Department of Statistics, Harvard University, Cambridge, Massachusetts 02138, USA;
  • Brown M; Center for Functional Cancer Epigenetics, Dana-Farber Cancer Institute, Boston, Massachusetts 02215, USA; Department of Medical Oncology, Dana-Farber Cancer Institute and Harvard Medical School, Boston, Massachusetts 02115, USA; Department of Medicine, Brigham and Women's Hospital and Harvard Medica
  • Liu XS; Department of Biostatistics and Computational Biology, Dana-Farber Cancer Institute and Harvard School of Public Health, Boston, Massachusetts 02115, USA; Center for Functional Cancer Epigenetics, Dana-Farber Cancer Institute, Boston, Massachusetts 02215, USA;
Genome Res ; 25(8): 1147-57, 2015 Aug.
Article em En | MEDLINE | ID: mdl-26063738
ABSTRACT
The CRISPR/Cas9 system has revolutionized mammalian somatic cell genetics. Genome-wide functional screens using CRISPR/Cas9-mediated knockout or dCas9 fusion-mediated inhibition/activation (CRISPRi/a) are powerful techniques for discovering phenotype-associated gene function. We systematically assessed the DNA sequence features that contribute to single guide RNA (sgRNA) efficiency in CRISPR-based screens. Leveraging the information from multiple designs, we derived a new sequence model for predicting sgRNA efficiency in CRISPR/Cas9 knockout experiments. Our model confirmed known features and suggested new features including a preference for cytosine at the cleavage site. The model was experimentally validated for sgRNA-mediated mutation rate and protein knockout efficiency. Tested on independent data sets, the model achieved significant results in both positive and negative selection conditions and outperformed existing models. We also found that the sequence preference for CRISPRi/a is substantially different from that for CRISPR/Cas9 knockout and propose a new model for predicting sgRNA efficiency in CRISPRi/a experiments. These results facilitate the genome-wide design of improved sgRNA for both knockout and CRISPRi/a studies.
Assuntos

Texto completo: 1 Base de dados: MEDLINE Assunto principal: RNA Guia de Cinetoplastídeos / Biologia Computacional / Repetições Palindrômicas Curtas Agrupadas e Regularmente Espaçadas Idioma: En Ano de publicação: 2015 Tipo de documento: Article

Texto completo: 1 Base de dados: MEDLINE Assunto principal: RNA Guia de Cinetoplastídeos / Biologia Computacional / Repetições Palindrômicas Curtas Agrupadas e Regularmente Espaçadas Idioma: En Ano de publicação: 2015 Tipo de documento: Article