Welcome to DESKGEN Cloud documentation. Inside, you'll find answers to commonly asked questions and manuals describing how to use the software.
What is DESKGEN Cloud?
DESKGEN Cloud is a free sgRNA design tool available instantly upon sign-up at DESKGEN.com. The suite provides a great first step for any CRISPR experiment, simplifying and aggregating many of the tools and services available around the web in one intuitive interface. DESKGEN includes three “modes,” or tools, which are separated based on experimental intent: Knockout Mode, Knockin Mode and Guide Picker. Below you’ll find links out to all of our Resources and videos we’ve produced to help you use DESKGEN to design efficient CRISPR genome editing experiments.
What is Knockout Mode?
The focus of Knockout (KO) Mode is to design guides which will permanently disrupt gene function. KO offers a built-in genome browser to precisely locate your gene or locus of interest; we’ve put together a guide on how to do that here. KO mode also provides access to a range of Cas9 and Cpf1 ortholog designs and their unique PAM sequences. KO mode is useful for designing guides targeting exonic or intronic regions in any one of a gene’s transcripts.
Guides can be filtered based on on-target score and then analyzed for off-target activity. Off-target activity is captured both by a single specificity score (based on the work in Hsu et al. 2013) and by a full specificity report documenting potential off-target cut sites around the genome. Guide sequences can be saved in the My Projects tool or copy-pasted into a text document for later use.
For_Streptococcus pyogenes_Cas9 (SpCas9), the most widely used CRISPR nuclease for the moment, scores in Knockout and Knockin mode include Doench 2016on-target activity, Hsu 2013 specificity and percent GC content. Other nucleases found in Knockout and Knockin mode are scored with Doench 2014, Hsu 2013 and percent GC content. An additional set of scoring functions and sgRNA design parameters are available in Guide Picker (below).
It is important to note that although on- (Doench 2014) and off-target (Hsu 2013) scores are provided for other nucleases (e.g. SaCas9, Cpf1), these algorithms were trained on SpCas9. Therefore, the scores provided likely do not reflect the behavior and specificity of nucleases other than SpCas9. Knockout and Knockin Modes can still be used to design guides for these nucleases based on complementarity and enzyme-dependent PAM sites, but the efficacy and specificity of these guides are unpredictable for the time being.
What is Knockin Mode?
Knockin (KI) Mode functions in a similar manner to KO Mode, but with the additional purpose of designing gene knockin experiments. A built-in genome browser allows the user to search for and precisely target regions of interest in a given gene transcript. Like KO, KI offers an array of genome and nuclease options for sgRNA design.
The primary difference between the KI and KO modes is that the Knockin genome browser can visualize amino acid sequences for a given gene’s open reading frame. These amino acids can be selected and mutated according to the needs of the investigator. This mutation will be processed by DESKGEN’sdonordesign tool which automates the design of single-stranded oligo donor (ssODN) of varying homology arm lengths and symmetry. Donors and guide sequences can then be saved in theMy Projects tool or in a text file.
As with KO, we have put together a comprehensive tutorial on how to use Knockin Mode, in addition to an article on the basics of performing a CRISPR knockin experiment. We’ve also written articles on best practices for donor design in KI experiments, including the potential advantages of asymmetric donor design in mammalian cell lines.
What is Guide Picker?
Guide Picker (Hough, Kancleris et al. 2017) offers an unprecedented method for evaluating large populations of guide RNAs in a simple visual interface. Side by side x/y plots depict all guide RNA designs for the coding regions of a gene of interest. Each x/y variable can be set to represent one of ten pre-calculated sgRNA design parameters, allowing the user to apply four scores to a single population of guides simultaneously.
Guides can be further filtered and selected for based on score thresholds, and the resulting list of sequences can be stored for future synthesis purposes.
Our paper on Guide Picker was published in BMC Bioinformatics, which you can find here. We’ve also written a full tutorial on the use of Guide Picker, including descriptions of the scores available for guide design. Building on this, we also put together some of the ways users can compare scoring functions to design optimal guide RNAs for their experiment.
What is Genome Editor?
Genome Editor will allow you to visualize variants in your model organism or cell line and design guides accordingly. Recognizing variants can adversely influence guide activity and therefore should be taken into account when designing high efficiency CRISPR experiments. Genome Editor is now in Beta and is open to the public. This manual should help you get started using the tool to design guides across a range of model genomes.