CRISPR-DO for genome-wide CRISPR design and optimization

Ma, Jian; Köster, Johannes; Qin, Qian; Hu, Shengen; Li, Wei; Chen, Chen-Hao; Cao, Qingyi; Wang, Jinzeng; Mei, Shenglin; Liu, Qi; Liu, Xiaole Shirley

doi:10.1093/bioinformatics/btw476

Cited by 47 publications

(39 citation statements)

References 19 publications

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“…A number of solutions have been presented for the first task of search and filter, including Cas-OFFinder 14 , CRISPOR 15 , CHOP-CHOP 16 , e-CRISPR 17 , CRISPR-DO 18 , CROP-IT 19 and COSMID 20 , which differ in the algorithms used to search as well as the completeness of the search. Completeness is dictated by options such as maximum number of mismatches, allowed protospacer adjacent motifs (PAMs) and the search algorithm used.…”

Section: Introductionmentioning

confidence: 99%

“…A list of available gRNA design services that perform one or both of these steps is shown in Table 1. The only existing tools that return aggregation scores are the MIT web server 21 , CRISPOR 15 and CRISPR-DO 18 , the latter two which re-implement the MIT web server rules. CHOP-CHOP 16 counts the number of potential off-targets without scoring them; CROP-IT 19 uses a hand-crafted series of rules and has been shown to be substantially outperformed by the MIT web server.…”

Section: Introductionmentioning

confidence: 99%

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Prediction of off-target activities for the end-to-end design of CRISPR guide RNAs

et al. 2018

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The CRISPR-Cas9 system provides unprecedented genome editing capabilities. However, off-target effects lead to sub-optimal usage and additionally are a bottleneck in the development of therapeutic uses. Herein, we introduce the first machine learning-based approach to off-target prediction, yielding a state-of-the-art model for CRISPR-Cas9 that outperforms all other guide design services. Our approach, Elevation, consists of two interdependent machine learning models—one for scoring individual guide-target pairs, and another which aggregates these guide-target scores into a single, overall summary guide score. Through systematic investigation, we demonstrate that Elevation performs substantially better than competing approaches on both tasks. Additionally, we are the first to systematically evaluate approaches on the guide summary score problem; we show that the most widely-used method performs no better than random at times, whereas Elevation consistently outperformed it, sometimes by an order of magnitude. We also introduce an evaluation method that balances errors between active and inactive guides, thereby encapsulating a range of practical use cases; Elevation is consistently superior to other methods across the entire range. Finally, because of the large scale and computational demands of off-target prediction, we have developed a cloud-based service for quick retrieval. This service provides end-to-end guide design by also incorporating our previously reported on-target model, Azimuth. (https://crispr.ml:please treat this web site as confidential until publication).

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Prediction of off-target activities for the end-to-end design of CRISPR guide RNAs

et al. 2018

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“…Numerous computational tools are freely available to aid sgRNA design for a wide spectrum of PAM sequences, as well as for on-target efficiency and off-target cleavage predictions: Broad GPP Portal 24 , Cas-Database 25 , Cas-OFFinder 26 , CasOT 27 , CCTop 28 , COSMID 29 , CHOPCHOP 30,31 , CRISPRdirect 32 , CRISPR-DO 33 , CRISPR-ERA 34 , CRISPR-P 35 , CROP-IT 36 , DNA Striker 12 , E-CRISP 37 , flyCRISPR 38 , GUIDES 39 , GuideScan 40 , GT-scan 41 , MIT CRISPR design tool 8 , WU-CRISPR 42 , CRISPRseek 43 , sgRNAcas9 (ref. 44), and CRISPR multiTargeter 45 , as well as others offered by companies such as Deskgen 46 and Benchling 46 .…”

Section: Introductionmentioning

confidence: 99%

Integrated design, execution, and analysis of arrayed and pooled CRISPR genome-editing experiments

et al. 2018

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CRISPR (clustered regularly interspaced short palindromic repeats) genome-editing experiments offer enormous potential for the evaluation of genomic loci using arrayed single guide RNARNARNAs (sgRNAs) or pooled sgRNA libraries. Numerous computational tools are available to help design sgRNAs with optimal on-target efficiency and minimal off-target potential. In addition, computational tools have been developed to analyze deep-sequencing data resulting from genome-editing experiments. However, these tools are typically developed in isolation and oftentimes are not readily translatable into laboratory-based experiments. Here, we present a protocol that describes in detail both the computational and benchtop implementation of an arrayed and/or pooled CRISPR genome-editing experiment. This protocol provides instructions for sgRNA design with CRISPOR (computational tool for the design, evaluation, and cloning of sgRNA sequences), experimental implementation, and analysis of the resulting high-throughput sequencing data with CRISPResso (computational tool for analysis of genome-editing outcomes from deep-sequencing data). This protocol allows for design and execution of arrayed and pooled CRISPR experiments in 4–5 weeks by non-experts, as well as computational data analysis that can be performed in 1–2 d by both computational and noncomputational biologists alike using web-based and/or command-line versions.

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“…This RNA guided interference mechanism has been successfully employed in eukaryotic cells (both in vitro and in vivo) by Zhang [4] and Charpentier [5] groups. It is now established that sgRNAs (single guide RNAs) can be engineered to target a 17-20 bp stretch of DNA sequence preceding a protospacer adjacent motif (PAM) [6]. CRISPR/Cas9 system has been crucial in multiple disciplines but is especially useful for understanding the gene function by manipulating precise genomic locations [6][7][8].…”

Section: Introductionmentioning

confidence: 99%

“…It is now established that sgRNAs (single guide RNAs) can be engineered to target a 17-20 bp stretch of DNA sequence preceding a protospacer adjacent motif (PAM) [6]. CRISPR/Cas9 system has been crucial in multiple disciplines but is especially useful for understanding the gene function by manipulating precise genomic locations [6][7][8]. This emerging technology enables the re-investigation of multilayered functional dependency of regulating molecules such as kinases, transcription factors (TFs), long non-coding RNAs (lncRNAs), and other protein coding gene groups, to provide specific perturbations and to study their comprehensive genome wide effects [7,[9][10][11][12].…”

Section: Introductionmentioning

confidence: 99%

SliceIt: A genome-wide resource and visualization tool to design CRISPR/Cas9 screens for editing protein-RNA interaction sites in the human genome

Vemuri

Srivastava

Mir

et al. 2019

Preprint

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Several protein-RNA cross linking protocols have been established in recent years to delineate the molecular interaction of an RNA Binding Protein (RBP) and its target RNAs. However, functional dissection of the role of the RBP binding sites in modulating the post-transcriptional fate of the target RNA remains challenging. CRISPR/Cas9 genome editing system is being commonly employed to perturb both coding and noncoding regions in the genome. With the advancements in genome-scale CRISPR/Cas9 screens, it is now possible to not only perturb specific binding sites but also probe the global impact of protein-RNA interaction sites across cell types. Here, we present SliceIt (http://sliceit.soic.iupui.edu/), a database of in silico sgRNA (single guide RNA) library to facilitate conducting such high throughput screens. SliceIt comprises of ~4.8 million unique sgRNAs with an estimated range of 2-8 sgRNAs designed per RBP binding site, for eCLIP experiments of >100 RBPs in HepG2 and K562 cell lines from the ENCODE project. SliceIt provides a user friendly environment, developed using advanced search engine framework, Elasticsearch. It is available in both table and genome browser views facilitating the easy navigation of RBP binding sites, designed sgRNAs, exon expression levels across 53 human tissues along with prevalence of SNPs and GWAS hits on binding sites. Exon expression profiles enable examination of locus specific changes proximal to the binding sites. Users can also upload custom tracks of various file formats directly onto genome browser, to navigate additional genomic features in the genome and compare with other types of omics profiles. All the binding site-centric information is dynamically accessible via "search by gene", "search by coordinates" and "search by RBP" options and readily available to download. Validation of the sgRNA library in SliceIt was performed by selecting RBP binding sites in Lipt1 gene and designing sgRNAs. Effect of CRISPR/Cas9 perturbations on the selected binding sites in HepG2 cell line, was confirmed based on altered proximal exon expression levels using qPCR, further supporting the utility of the resource to design experiments for perturbing protein-RNA interaction networks. Thus, SliceIt provides a one-stop repertoire of guide RNA library to perturb RBP binding sites, along with several layers of functional information to design both low and high throughput CRISPR/Cas9 screens, for studying the phenotypes and diseases associated with RBP binding sites.

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CRISPR-DO for genome-wide CRISPR design and optimization

Abstract: CRISPR-DO is available at http://cistrome.org/crispr/ CONTACT: qiliu@tongji.edu.cn or hanxu@jimmy.harvard.edu or xsliu@jimmy.harvard.eduSupplementary information: Supplementary data are available at Bioinformatics online.

Cited by 47 publications

References 19 publications

Prediction of off-target activities for the end-to-end design of CRISPR guide RNAs

Prediction of off-target activities for the end-to-end design of CRISPR guide RNAs

Integrated design, execution, and analysis of arrayed and pooled CRISPR genome-editing experiments

SliceIt: A genome-wide resource and visualization tool to design CRISPR/Cas9 screens for editing protein-RNA interaction sites in the human genome

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