一种用于单个、组合或多重基因敲除的CRISPR资源。

Erard Nicolas, Knott Simon R V, Hannon Gregory J

Cancer Research UK Cambridge Institute, University of Cambridge, Li Ka Shing Centre, Robinson Way, Cambridge CB2 0RE, UK.

Cancer Research UK Cambridge Institute, University of Cambridge, Li Ka Shing Centre, Robinson Way, Cambridge CB2 0RE, UK; Cold Spring Harbor Laboratory, 1 Bungtown Road, Cold Spring Harbor, NY 11724, USA; Cedars-Sinai Medical Institute, 8700 Beverly Boulevard, Los Angeles, CA 90048, USA.

Mol Cell. 2017 Jul 20;67(2):348-354.e4. doi: 10.1016/j.molcel.2017.06.030.

We have combined a machine-learning approach with other strategies to optimize knockout efficiency with the CRISPR/Cas9 system. In addition, we have developed a multiplexed sgRNA expression strategy that promotes the functional ablation of single genes and allows for combinatorial targeting. These strategies have been combined to design and construct a genome-wide, sequence-verified, arrayed CRISPR library. This resource allows single-target or combinatorial genetic screens to be carried out at scale in a multiplexed or arrayed format. By conducting parallel loss-of-function screens, we compare our approach to existing sgRNA design and expression strategies.

我们将机器学习方法与其他策略相结合，以优化CRISPR/Cas9系统的敲除效率。此外，我们还开发了一种多重sgRNA表达策略，该策略可促进单个基因的功能缺失，并允许进行组合靶向。这些策略已被整合起来，用于设计和构建一个全基因组范围的、经过序列验证的、阵列式CRISPR文库。该资源允许以多重或阵列形式大规模开展单靶点或组合基因筛选。通过进行平行的功能缺失筛选，我们将我们的方法与现有的sgRNA设计和表达策略进行比较。