Benchling’s Biomolecule Design capabilities not only allow for the import of previously designed gRNAs as oligos, but also has built-in gRNA design and HR template design tools so you can conduct CRISPR experiments.
The CRISPR tool allows you to:
- design and select gRNAs based off of binding site and on- and off- target scores
- design HR templates for use with different gRNAs.
Design gRNAs
Learn how to use the guide RNA tool on Benchling.
We will walk you through an example, designing and analyzing example guide RNAs for the Brca2 gene.
1. Click the Global create button on the left side panel, and then select the CRISPR option and choose CRISPR Guides
2. In the module that opens, search for your gene of interest (in this example BRCA2)
3. Ensure all settings match your experimental needs (genome from organism of interest and correct transcript are selected)
4. Update the project folder to where you want the gene to be stored and click Next
4. Next, you’ll be prompted to define the guide parameters. Select the guide that is relevant to you, in this case we will choose Single guide
5. If necessary, change the guide length, and use the dropdowns to update the genome, and PAM sequence based on the CAS you are using, then click Finish
- To specify your custom PAM, select the “Custom PAM” option in the “PAM” dropdown selection
- CRISPR guide design offers flexibility in the Advanced Settings, letting you adjust whether you mask repeated regions, on and off target scoring method, what cuts are displayed and more.
6. This will take you to the sequence map for the gene you are targeting. Use the split workspace view so that you can see both the sequence map and the design CRISPR tab.
7. Highlight the target sequence in the sequence map. If your region of interest is already annotated, use the Features tab to quickly navigate to that region. Then highlight the region of interest in the sequence map.
8. Highlighting the sequence in the sequence map will populate that region in the target region boxes on the design CRISPR tab. Once you are happy with the target, click the + button to confirm the target region.
9. Below your target region you should now see a list of guide sequences.
10. You can sort all the sequences by on-target or off-target score by clicking on the appropriate column. Use these scores to rank guides relative to each other. Use the check boxes at the left to select the guide(s) that are the best balance between on- and off-target scores.
Note: On-target scores are calculated based on Doench, Fusi et al. 2016. On-target scores above 60 are considered to be good guides. Off-target scores above 50 are considered to be good guides (see http://crispr.mit.edu/about#score).
11. Once you have picked one or more suitable guides, hit Save right above the sequence list to save the guides as Oligios.
- Choose a Folder location to store the oligos
- If you have schema in the registry for oligos select it so that they can be registered later on
- If you anticipate performing bulk actions on this set of oligos again add them to a Worklist (INCLUDE LINK TO WORKLIST ARTICLE)
12. After saving the guides you should save the whole CRISPR design. Go to the top of the Design CRISPR tab and use the text box to select an appropriate name for your design then hit Save.
Assemble (gRNAs) into plasmids
We can now assemble the guide RNAs we created earlier into plasmids.
1. If you don’t already have the CRISPR analysis open, open it by clicking the CRISPR panel on the right and choosing the saved CRISPR design
2. Use the checkbox to select the guides you want to clone into plasmids then click Assemble
3. This should open up a new tab where you can select a vector source. Select a plasmid vector from your folder or upload a new one. Once you have chosen a plasmid and specified the insertion region click Next
4. Select a folder you want your resultant assembly to be saved to then Click Assemble to assemble your gRNAs into the plasmid.
From the message that appears,you can create a notebook entry of your assembled gRNAs by clicking create a notebook entry.
View the entry, along with the linked protocol, that is added to your notebook in Training. You can edit this protocol to customize it for your particular experiment.
Design homologous recombination (HR) templates
In Benchling you can also design HR templates using gRNA.
1. Choose your gRNA from your guide RNA design list
2. Click the CRISPR icon on the side panel, and then Design HR template (ssODN)
3. Select the appropriate genome and PAM, and select Create a copy of this sequence
4. In the sequence map tab introduce the changes (e.g. point mutations, insertions, deletions) you want to make to the genome)
- In this example we will make a point mutation at 32319199 from A to T, changing the encoding amino acid from Threonine (T) to Serine (S). Tip: You can use ctrl/cmd + g to jump to a specific base pair
5. At the top of the page select the Design HR Template Tab and you should see that it has recorded all the changes you have made to the sequence. Verify the changes are correct and click Next
6. Adjust the length of HR arms on both sides by dragging the highlighted selection on the sequence map. Then click Next
7. Copy the sequence of the gRNA you selected earlier into the Guide box. You will see a table listing all possible silent mutations to change the target site in your HR template. To avoid the HR template being degraded by Cas9, it is most effective to mutate the PAM sequence. The wizard will automatically select the best mutation for you
8. Click Next, and you’ve now designed a HR template that is ready to be copied into your clipboard for de novo synthesis
For more tips and tricks on HR template design, visit this blog post.
Off target scores
When choosing guide RNAs, look at both the on-target and off-target scores. When analyzing gRNAs, Benchling calculates two types of scores associated with off-target assessment:
- Aggregated off-target score
- Potential off-target site score
The meaning of the score depends on which off-target score you're referencing.
| Aggregated off-target scores | Potential off-target site scores | |
| Definition |
Each gRNA has one aggregated off-target score. It is computed by the formulae 100/(100 + ∑(scores)). The aggregation scoring approach is described in Hsu et al. 2013, but was also applied for the Doench method to provide a uniform set of scores. |
Benchling numbers and scores all potential off-target sites in selected genomes. This means each gRNA can have multiple potential off-target sites, each with their own score. |
| How to view | In the list of gRNA in the Design CRISPR tab | In the pop-up window after clicking into each gRNA’s aggregate off-target score |
| Meaning | Higher aggregated off-target scores are generally better guides. |
High scoring off-target sites indicate a high likelihood the gRNA will interact with that off-target site, which is generally not wanted. In general, A “good” gRNA with the highest aggregate off-target score should have either or both:
|
Benchling searches for all of the off-targets however it pares down the list to the top 49 for users. In effect, Benchling shows you the top possible off-targets so you only see a finite number of off-target sites.
To set the genomic region for off-target score calculations, click on the blue text under genome region to manually set the genomic location.
Genomic indices are imported with the gene if you import the full sequence, but are removed if you import only cDNA.
Export a table of on-target or off-target scores and sites from your CRISPR experiment
Click the Export button above the table of potential guides and then select whether you want to export all or just the selected ones. The guides will then be copied in tab-delimited format, which can be pasted into Excel.
Frequently Asked Questions
Why are my paired nickase scores lower than my single guide scores?
We calculate paired nickase scores using the same calculations as crispr.mit.edu.
Paired scores should be compared relative to other paired scores, while the single guide scores should be compared to other single guide scores. It is a bit unintuitive that the score is lower for the paired compared to single, considering that the nickase pair will have a lower off-target cut probability overall biologically.
Why am I getting the error "Distinct type IIS enzyme cut sites were not found at the specified start and end"?
Ensure you are using only one Type IIS restriction enzyme, not two distinct ones (one of BsaI will work; one of BsaI and one of BsmBI will not work)
Then, check that your insertion region is correct:
1. Find out where the cut-sites are located on the plasmid (using the restriction digest tool) and highlight in between the two cutsites. Take note of the start and end locations.
2. Return to your CRISPR analysis, select your good guides and click assemble. Choose your plasmid
3. When choosing your "Insertion Region," insert the start and end values you identified in step 1, taking into account and adjusting for overhangs correctly.
Now your assembly should work properly