Team:SUSTC-Shenzhen/gRNA Design

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       title=gRNA Design|
       subtitle=Not Only a Part of Modelling}}
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'''(Here we take HIV-1 as an example)'''  

We used a method derived from the method described in the paper by Feng Zhang<sup>[http://www.nature.com/nbt/journal/v31/n9/abs/nbt.2647.html ZhangFgRNA]</sup>.

=== Conserved Sequence Analysis ===

We first tried to extract all conserved regions from the NIH HIV-1 Reference Genome using BioEdit.
In this step, we found around 10 alternatives for the next process. Here all screening processes are done in a per-strain basis because of the high mutability of the HIV-1 virus.

{| class="wikitable table-striped table-bordered table-hover"
! colspan="11" | Supplementary Table 1 - Base Percentage of HIV-1 Aligned Genome 730bp-752bp
|-
| 
| A %
| G %
| C %
| T %
| Empty %
| Non Empty %
| A(Corrected)
| G(Corrected)
| C(Corrected)
| T(Corrected)
|-
| 730
| 0
| 0
| 0
| 56.47
| 43.53
| 56.47
| 0.00%
| 0.00%
| 0.00%
| 100.00%
|-
| 731
| 0
| 55.88
| 0
| 0.59
| 43.53
| 56.47
| 0.00%
| 98.96%
| 0.00%
| 1.04%
|-
| 732
| 0
| 0
| 0
| 56.47
| 43.53
| 56.47
| 0.00%
| 0.00%
| 0.00%
| 100.00%
|-
| 733
| 0
| 54.71
| 0
| 1.18
| 43.53
| 55.89
| 0.00%
| 97.89%
| 0.00%
| 2.11%
|-
| 734
| 0
| 0
| 0
| 58.24
| 41.76
| 58.24
| 0.00%
| 0.00%
| 0.00%
| 100.00%
|-
| 735
| 56.47
| 0.59
| 0.59
| 0.59
| 41.76
| 58.24
| 96.96%
| 1.01%
| 1.01%
| 1.01%
|-
| 736
| 0
| 1.18
| 57.06
| 0
| 41.76
| 58.24
| 0.00%
| 2.03%
| 97.97%
| 0.00%
|-
| 737
| 1.18
| 57.06
| 0
| 0.59
| 41.18
| 58.83
| 2.01%
| 96.99%
| 0.00%
| 1.00%
|-
| 738
| 60
| 0
| 0
| 0
| 40
| 60
| 100.00%
| 0.00%
| 0.00%
| 0.00%
|-
| 739
| 0.59
| 0
| 58.82
| 0
| 40
| 59.41
| 0.99%
| 0.00%
| 99.01%
| 0.00%
|-
| 740
| 0
| 0
| 0
| 0
| 100
| 0
| 
| 
| 
| 
|-
| 741
| 0
| 0
| 0
| 0
| 100
| 0
| 
| 
| 
| 
|-
| 742
| 0.59
| 0
| 1.18
| 58.24
| 40
| 60.01
| 0.98%
| 0.00%
| 1.97%
| 97.05%
|-
| 743
| 0
| 0
| 60
| 0
| 40
| 60
| 0.00%
| 0.00%
| 100.00%
| 0.00%
|-
| 744
| 0
| 1.18
| 58.82
| 0
| 40
| 60
| 0.00%
| 1.97%
| 98.03%
| 0.00%
|-
| 745
| 0
| 58.82
| 1.18
| 0
| 40
| 60
| 0.00%
| 98.03%
| 1.97%
| 0.00%
|-
| 746
| 0.59
| 0
| 59.41
| 0
| 40
| 60
| 0.98%
| 0.00%
| 99.02%
| 0.00%
|-
| 747
| 0.59
| 59.41
| 0
| 0
| 40
| 60
| 0.98%
| 99.02%
| 0.00%
| 0.00%
|-
| 748
| 0.59
| 59.41
| 0
| 0
| 40
| 60
| 0.98%
| 99.02%
| 0.00%
| 0.00%
|-
| 749
| 0
| 58.82
| 0.59
| 0.59
| 40
| 60
| 0.00%
| 98.03%
| 0.98%
| 0.98%
|-
| 750
| 0.59
| 0.59
| 58.24
| 0.59
| 40
| 60.01
| 0.98%
| 0.98%
| 97.05%
| 0.98%
|-
| 751
| 60
| 0
| 0
| 0
| 40
| 60
| 100.00%
| 0.00%
| 0.00%
| 0.00%
|-
| 752
| 59.41
| 0.59
| 0
| 0
| 40
| 60
| 99.02%
| 0.98%
| 0.00%
| 0.00%
|}
Table 1. Base-wise Statistics of One Designed Sequence

As we can see from Table 1, this sequence is highly conserved among about 50% of HIV-1 strains.

=== Strip out sequences without PAM ===

Sequences without PAM cannot bind with Cas9 protein and are thus of no use here.
Thus we used a small JavaScript script to strip out them.

=== Select gRNA sequences with the best theoretical quality ===

{| class="table"
! colspan="5" | HIV-1 Quasi-Conservative gRNAs(Useful)
|-
| Sequence
| Rating(Zhang)
| Rank(Church)
| Free Energy(Approx.)
| 
|-
| GTGTGGAAAATCTCTAGCAGTGG
| 71
| -
| -1.4
| rowspan="2" | HIV1_REF_2010
|-
| TCTAGCAGTGGCGCCCGAACAGG
| 97
| -
| -1.3
|}

In this step, we used the tools from Feng Zhang and George Church to analyze off-target activity.
Still, we did BLAST ourselves to verify the results.

We have selected gRNA sequences with the best theoretical quality using the experimental formula by Feng Zhang:
<html>
$$\prod_{e\in{\mathcal{M}}}\left(1-\space W[e]\right)\times\frac{1}{\left(\frac{(19\space-\space\bar{d})}{19}\times 4\space+\space 1\right)}\times\frac{1}{n^2_{mm}} \\ M=[0,0,0.014,0,0,0.395,0.317,0,0.389,0.079,0.445,\\ 0.508,0.613,0.851,0.732,0.828,0.615,0.804,0.685,0.583]$$
</html>
=== Reference ===

# Hsu, P. D., Scott, D. A., Weinstein, J. A., Ran, F. A., Konermann, S., Agarwala, V., ... & Zhang, F. (2013). DNA targeting specificity of RNA-guided Cas9 nucleases. Nature biotechnology, 31(9), 827-832.
# Mali, P., Yang, L., Esvelt, K. M., Aach, J., Guell, M., DiCarlo, J. E., ... & Church, G. M. (2013). RNA-guided human genome engineering via Cas9. Science, 339(6121), 823-826.


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