Team:SUSTC-Shenzhen/gRNA Design

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       title=gRNA-mCherry-UAS Carried Plasmids Design and Construction|
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       title=gRNA Design|
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       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.

Latest revision as of 03:38, 18 October 2014

Team SUSTC-Shenzhen

gRNA Design

Not Only a Part of Modelling

Contents


(Here we take HIV-1 as an example)

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

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.

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

HIV-1 Quasi-Conservative gRNAs(Useful)
Sequence Rating(Zhang) Rank(Church) Free Energy(Approx.)
GTGTGGAAAATCTCTAGCAGTGG 71 - -1.4 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: $$\prod_{e\in{\mathcal{M}}}\left(1-\space W[e]\right)\times\frac{1}{\left(\frac{(19\space-\space\bar{d})}{19}\times 4\space+\space 1\right)}\times\frac{1}{n^2_{mm}} \\ M=[0,0,0.014,0,0,0.395,0.317,0,0.389,0.079,0.445,\\ 0.508,0.613,0.851,0.732,0.828,0.615,0.804,0.685,0.583]$$

Reference

  1. 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.
  2. 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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Licensed under CC BY 4.0.

  • ©2014 SUSTC-Shenzhen

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