SYSTEM AND METHOD FOR PREDICTING EFFICIENCY AND OUTCOME OF BASE EDITOR BY USING DEEP LEARNING
According to a system for predicting the efficiency and an outcome of a base editor by using deep learning, it is possible to select a base editor from among 63 base editors with various protospacer adjacent motif (PAM) compatibilities and sgRNA for efficient base editing, without extensive experiments. Therefore, the system may be usefully used in all fields where gene editing is applied, such as disease treatment by gene editing.
1 . A system for predicting efficiency and an outcome of a base editor by using deep learning, the system comprising:
a target sequence input unit configured to receive an input of target sequence data of the base editor; and
an outcome prediction unit configured to obtain a base editing efficiency output value and a base editing outcome proportion output value by applying the target sequence data that is input through the target sequence input unit, to a base editing efficiency prediction model and a base editing outcome proportion prediction model, respectively, and generate a base editing prediction score by multiplying the base editing efficiency output value by the base editing outcome proportion output value.
2 . The system of claim 1 , wherein the base editing efficiency prediction model is generated by:
receiving an input of base conversion activity data of the base editor through an information input unit; and
generating the base editing efficiency prediction model by performing deep learning based on a convolutional neural network (CNN) on the base conversion activity data that is input through the information input unit.
3 . The system of claim 2 , wherein the generating of the base editing efficiency prediction model by performing the deep learning based on the CNN further comprises linking CRISPR associated protein 9 (Cas9) activity data.
4 . The system of claim 3 , wherein the Cas9 activity data is obtained by performing a method comprising:
introducing Cas9 into a cell library containing oligonucleotides containing a nucleotide sequence that encodes sgRNA and a target nucleotide sequence targeted by the sgRNA;
performing deep sequencing by using DNA obtained from the cell library into which the Cas9 is introduced; and
analyzing efficiency of the Cas9 based on data obtained from the deep sequencing.
5 . The system of claim 4 , wherein the analyzing of the efficiency of the Cas9 comprises predicting an activity of the Cas9 based on a correlation between indel frequencies of the Cas9 in a particular target sequence by performing deep learning based on a CNN.
6 . The system of claim 1 , wherein the base editing outcome proportion prediction model is generated by:
receiving an input of base editing outcome data of the base editor through an information input unit; and
generating the base editing outcome proportion prediction model by performing deep learning based on a CNN on the base editing outcome data that is input through the information input unit.
7 . The system of claim 1 , further comprising an output unit configured to output efficiency and an outcome proportion of the base editor, which are predicted by the outcome prediction unit.
8 . The system of claim 3 , wherein the Cas9 is any one or more selected from a group consisting of SpCas9, VRQR variant, SpCas9-NG, SpCas9-NRRH, SpCas9-NRTH, SpCas9-NRCH, SpG, SpRY, and Sc++.
9 . The system of claim 1 , wherein the base editor is any one or more selected from a group consisting of YE1-BE4max, SsAPOBEC3B, ABE8e(V106W), ABE8.17-m+V106W, CGBE1, miniCGBE1, and APOBEC-nCas9-Ung.
10 . The system of claim 1 , wherein the base editing efficiency output value is calculated through Equation 1 below:
Base
editing
efficiency
(
%
)
=
Total
read
counts
of
intended
target
nucleotide
conversions
at
each
position
Total
read
counts
×
100
[
Equation
1
]
11 . The system of claim 1 , wherein the base editing outcome proportion output value is calculated through Equation 2 below:
Base
editing
outcome
proportion
=
Total
read
counts
of
unique
base
-
edited
outcome
sequence
Total
read
counts
of
converted
sequences
within
wide
windows
[
Equation
2
]
12 . A method of predicting efficiency and an outcome of a base editor by using deep learning, the method comprising:
designing a target sequence of the base editor; and
applying the designed target sequence to the system for predicting efficiency and an outcome of a base editor of claim 1 .
13 . A computer-readable recording medium having recorded thereon a program for causing a computer to execute the method of claim 12 .