Difference between revisions of "Team:Peking/Modeling"

Search for guide sequences of gRNA candidates

Recall the structure of Paired dCas9 Reporter (PC Reporter) System, a protospacer adjacent motif (PAM) sequence in the form of 5’-NGG-3’ at 3’ end of guide sequence, usually 20bp, on the non-complementary strand. (Figure 1) As it was showed in our experimental results that PAM-out orientation (5’-CCNN20-…-N20NGG-3’) was highly efficient for PC Reporter system to work, thus our model would focus on this orientation. (However, it can be more convenient to adjust our program for guide sequence design also with other orientations. See more in Supplementary 1 )

Figure 1. Schematic illustration of guide design in PAM-out orientation. Note the 20nt guide sequence is identical to target non-complementary strand.

We took advantage of Python 3.4.3 build-in regular expression to search for left guide sequences of gRNA (‘(?<=cc).(?=.{20})’) and right guide sequences of gRNA (‘(?<=.{20}).(?=gg)’) separately, which would be paired later for PC reporter system to function.

Specificity test for each candidate

Specificity of guide sequence of gRNA here is defined as the probability of the gRNA binds to the corresponding target site instead of other similar non-target sites. It is measured by taking both quantity of potential off-target sites and similarity between off-target sites and the unique target site into consideration. Since sputum sample is commonly used in MTB detection, we compared our guide sequence candidates with Human Oral Meta-Genome (HOMG), and reserved the specific sequences orthogonal to oral meta-genome to avoid false positive signals in MTB detection. Here we adopted a BLAST-based 2-step filter approach to realize it. In general, the two steps are: a) Filter out guides with off-targets that have 12 bp PAM-proximal sequence identical to corresponding target; b) Score the reserved gRNA on specificity. The principle of the score-rule is that higher specificity should get higher score (see the detail below). Thus we can easily filter out guides with high off-target probability, which is indicated by a low score.

b) Score filtration

Our initial filtration significantly reduce computation by limiting guide sequence score operation to only most possible off-target sites rather than entire subject. We then use Zhang Lab^[4][5] score methods to give an off-target effects measurement of the guide sequences. For each guide candidates, scoring process includes two steps: firstly，each of the individual off-target sites is assigned an “individual score”, then all individual scores are aggregated to an overall score of the given guide sequence.The algorithm used to score single off-target sites is as below.^[4]

In the first term, e runs over the mismatch positions set M between the guide and the off-target site, with W representing the experimentally-determined effect of mismatch position on targeting.^[5] Term two refers to the effect caused by mean pairwise distance between mismatches(d). Term three represent a dampening penalty for highly mismatched off-targets, where n_m refers to the number of mismatches.

Figure 2. Schematic illustration of an off-target site. The 20 bps are numbered sequentially from the one most distant to PAM, and the red 7, 12, 19 represents the mismatches in the off-target sites.

Here we show an example (Figure 2) to help readers understand this formula better. Nucleotides are numbered sequentially from the one most distant to PAM, and mismatches in the off-target sites are emphasized by red sign. In this case, W(7)= 0.317, W(12)=0.508, W(19)=0.583; d_mean=(d_(7,12)+d_(7,19)+d_(12,19))/3=7; n_m=3. Therefore, the off-target site shown above has an individual score of 0.004415278985074628. A higher individual score for an off target site indicates a higher similarity to the target, and thus a higher likelihood of the CRISPR/Cas9 complex binding to the off-target site. Once individual off-target have been scored, each guide is assigned an overall score:

h runs over the potential off-target set T of guide sequence

A higher overall score indicates a better guide with few or weak potential off-targets. Guides with individual off-target sites score 2% or above are eliminated, the remaining are ranked in the order of overall score from 100% to 0%, with a list of off-targets presented in individual score descending order. Guides having an overall score of greater than 45% are reserved finally while others were filtered out in case their lack of specificity.

Left gRNA candidates (CCNN20)	54417
Right gRNA candidates (CCNN20)	54288

Table 2. The number of gRNA candidates left after the first filtration in Mycobacterium Tuberculosis genome

Figure 3. Analysis of the guide sequences scores after score filtration. (a)Overall score distribution of guide sequences after the second step filtration. Most sequences have scores more than 97. (b)Ratio of reserved guides (i.e. the number of guide sequences reserved after score filtration / the number of guide sequences after first filtration) is approximately 1 on higher score gRNA and 0 on lower score gRNA.ure 1. Schematic illustration of guide design in PAM-out orientation. Note the 20nt guide sequence is identical to target non-complementary strand.

Pair left and right target sites with optimal spacer length

All reserved left and right target sites after two-step filtration are considered to be qualified for pairing. In this step, a left site and a right site with appropriate spacer length will be paired. The best spacer length is 19-23bp for split-luciferase dCas9 fusion system according to our experimental data (Figure 4, Link to CRISPR). Single sites that cannot pair with any other sites within the given range of the spacer length would be eliminated.

Figure 4. The effect of spacer length variation on the performance of PC reporter system. Orientation of sgRNA binding-site pairs used in the test is "PAM-out" and the spacer sequence length varies from 5bp to 107bp.

Spacer Length	19	20	21	22	23	19-23
Marker Number	690	836	746	720	759	3751

The number of gRNA pairs under the condition of different spacer length between left and right pair from 19 to 23 bps in Mycobacterium Tuberculosis genome

Design PCR fragments

We provide two methods for determining PCR fragments. For the first, fix pair number k per fragment, search the adjacent but non-overlapped k pairs. Sort the results with fragment length. For another, fix maximal PCR fragment length, sort the results with pair numbers per fragment. Sorted results will be presented on user interface, enabling users to select fragments by themselves as needed. Selecting overlapping fragment is not allowable for array design.

Figure 5. Schematic illustration of PCR fragment determination method, taking 2 pairs per fragment as an example. The top xxx shows all left and right targets on given segment of pathogen genome, and the chart aside lists all pairs. Only adjacent but non-overlapped pairs can be deposited on one fragment. Users can choose one through the four optional PCR fragment listed below, since they are overlapped.

Figure 6. The result of 72 sequence markers chosen in MTB genome. They are divided into 9 fragments for PCR as shown above