Optimized sgRNA design to maximize activity and minimize off-target effects of CRISPR-Cas9

By John G Doench, Nicoló Fusi, Meagan E. Sullender, Mudra Hegde, Emma Vaimberg, Katherine F. Donovan, Ian Smith, Z. Tothova, Craig B. Wilen, R. Orchard and 3 more. Nature Biotechnology, 2016

4,033 citations recorded by the source corpus.

CRISPR-Cas9–based genetic screens are a powerful new tool in biology. By simply altering the sequence of the single-guide RNA (sgRNA), one can reprogram Cas9 to target different sites in the genome with relative ease, but the on-target activity and off-target effects of individual sgRNAs can vary widely. Here, we use recently devised sgRNA design rules to create human and mouse genome-wide libraries, perform positive and negative selection screens and observe that the use of these rules produced improved results. Additionally, we profile the off-target activity of thousands of sgRNAs and develop a metric to predict off-target sites. We incorporate these findings from large-scale, empirical data to improve our computational design rules and create optimized sgRNA libraries that maximize on-target activity and minimize off-target effects to enable more effective and efficient genetic screens and genome engineering.

Subfields: Molecular Biology

View Paper: https://www.semanticscholar.org/paper/b9e5fa707e804d6008e5011b058244437c656a93

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Citations in the corpus

The UCSC Genome Browser database: 2018 update (2017, neutral): “The list of scores provided includes a guide specificity score, an out-of-frame score (3) and efficiency scores (4,5).”
BioSeq-Analysis2.0: an updated platform for analyzing DNA, RNA and protein sequences at sequence level and residue level based on machine learning approaches (2019, background): “Of the four, the first one is of One-hot, where the residues are arranged in a particular order, and then the ith residue type is represented by four binary bits with the ith bit set as 1, and all the other bits are set as 0; the rest of the four are Position-specific-2 (18), Positionspecific-3 (18)”
Prediction of off-target activities for the end-to-end design of CRISPR guide RNAs (2018, methodology): “For our validation of the gRNA–target model, Elevation-score (trained on the CD33 and GUIDE-seq data), we applied it to two previously unseen data sets that were assayed with GUIDE-seq: (1) five gRNAs yielding a total of 103,040 potential off-targets, of which 53 are active, from ref.”
DeepCRISPR: optimized CRISPR guide RNA design by deep learning (2018, methodology): “As a result, our final trained off-target site prediction model was tested on the independent datasets for each of the two cell lines and compared with four of the current state-of-the-art off-target site prediction tools, CFD score [2], MIT score [6], CROP-IT [45], and CCTop [46] .”
A CRISPR Dropout Screen Identifies Genetic Vulnerabilities and Therapeutic Targets in Acute Myeloid Leukemia (2016, background): “Furthermore, the nucleotide biases observed in v1 were not observed with the v2 library (Figure 1D), indicating that on-target efficiency prediction (Doench et al., 2016; Wang et al., 2015) may not be necessary with the improved gRNA scaffold.”
Evaluation and Design of Genome-Wide CRISPR/SpCas9 Knockout Screens (2017, methodology): “Though a variety of small- and medium-scale experiments have been used to guide gRNA selection algorithms (Doench et al. 2016; Haeussler et al. 2016; Heigwer et al. 2014, 2016; Hsu et al. 2013; Liu et al. 2016; Park et al. 2016), to our knowledge our design is based on the largest available set of…”
Compact and highly active next-generation libraries for CRISPR-mediated gene repression and activation (2016, methodology): “Finally, we wanted to benchmark our hCRISPRi-v2 library against other recent genome-scale growth screens performed with nuclease-active Cas9 and novel second-generation libraries (Doench et al., 2016; Hart et al., 2015; Wang et al., 2015).”
CHOPCHOP v2: a web tool for the next generation of CRISPR genome engineering (2016, background): “A study from Doench et al . produced a large dataset to calculate efficiencies across a wide range of sgRNAs (14), and the rules for computationally-aided sgRNA design were recently further refined by the same group (13).”
Multimodal Analysis of Composition and Spatial Architecture in Human Squamous Cell Carcinoma (2020, methodology): “We used the STARS algorithm (Doench et al., 2016) to derive 38 hits, 18 of which were shared across at least two cell lines (Figure 7F; STAR Methods).”
CRISPOR: intuitive guide selection for CRISPR/Cas9 genome editing experiments and screens (2018, background): “Cutting frequency determination (CFD) score is now displayed along potential offtargets because it was shown to more reliably predict offtargets (7).”
CHOPCHOP v3: expanding the CRISPR web toolbox beyond genome editing (2019, methodology): “In addition, we have added efficiency scores for Cas12a/Cpf1 (21) and updated the ‘Doench’ efficiency score to the newest version (15), which is now the default scoring metric for Cas9 genomic targeting.”