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\begin{document}

\title{Prediction of activity of CRISPR-Cpf1 guide RNA via in vivo
high-throughput screening}



\author[1]{gancheng wang}%
\author[1]{dan zhu}%
\author[1]{juan li}%
\author[1]{junyi wang}%
\author[1]{jianzhong xi}%
\affil[1]{Peking University College of Engineering}%


\vspace{-1em}



  
  \date{May 07, 2024}


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\selectlanguage{english}
\begin{abstract}
Background:~CRISPR-cpf1 is a single RNA-guided endonuclease system,
becoming a promising tool in both prokaryotic and eukaryotic~genome
engineering. The editing efficiency of Cpf1 based engineering still
requires improvements. However, limited information regarding the
relationship between guide RNA sequence and on-target activity is
available. To address these challenges, we developed a screening
platform based on the association of Acidaminococcus sp. Cpf1(AsCpf1)
DNA cleavage with cellular lethality. Major results:~In total, we
measured the activities of 12,544 guide RNAs, and observed a substantial
variation of the editing efficiency depending on the design of the
sequence. Based on this large-scale dataset, we designed and implemented
a comprehensive computational model to predict activities of guide RNAs.
Through comparison using simulated and experimental data, our approach
outperformed existing algorithms, enabling selection of efficient~guide
RNAs. Conclusions:~We refine on-target design rules and isolate the
important sequence features that contribute to DNA cleavage, that is, AH
dimers at position1-8 of protospacer promoting Cas12a activity while TK,
GB dimer playing an inhibitory role. We validate guide RNA affinities
designed by our optimized rules in both E.coli and 293T cells.%
\end{abstract}%



\sloppy


\textbf{Introduction}

The class 2 clustered regularly interspaced short palindromic repeats
(CRISPR)-CRISPR-associated proteins (Cas), which are derived from
prokaryotic immune system, were identified as programmable, RNA-guided
nucleases.{[}1-7{]} Generally, each CRISPR-Cas system is composed of Cas
proteins and a guide RNA. In a broad spectrum of eukaryotic and
prokaryotic species, CRISPR/Cas9 and CRISPR/Cpf1 could be expressed
heterologously with relative guide RNAs to target complementary DNA
sequences, exhibiting many advantages as powerful genome editing
tools.{[}8-11{]} Cpf1 was reported with several differences from Cas9:
first, Cpf1 processes its own guide RNAs and does not require a
tracrRNA; second, there is a longer distance between the seed sequence
and cleavage site; third, Cpf1 recognizes thymidine-rich PAM sequence;
fourth, Cpf1 generates cleavage with 5'overhangs.{[}12,13,14{]} These
features make Cpf1 expand the toolkit for genome editing.{[}15,16,17{]}

A general issue for the application of Cpf1 appears to be the
unpredictable success of guide RNA design.{[}18,19{]} However, limited
information of the relationship between guide RNAs sequence and activity
is available. There is a number of tools and applications developed to
predict guide RNA performance of Cas9.{[}20-28{]} It may seem that the
guide RNA design for Cpf1 would benefit from these information and
strategies. Recent studies for Cpf1 attempted to describe the guide RNA
sequence-activity relationship and present algorithms to predict the
activity of Cpf1 guide RNAs.{[}20-22{]}

Nevertheless, such approaches were developed in mammalian cell lines
where Cpf1 activities at endogenous sites were found to be affected by
chromatin accessibility as well as target sequence composition. And the
known nonhomologous end-joining (NHEJ) pathway preference for different
DSB substrates may also reshape the guide RNA activity landscape

To exclude these factors and gain more general insights into the
relationship of guide RNA sequence and activity, we launched
high-throughput screening experiments and collect large-scale datasets
in \emph{E.coli} cells, in which NHEJ molecular machinery is entirely
absent.

In this paper, we described a library of \textgreater{}12,500 target
sequence and guide RNA pairs and evaluated guide RNA activity in\emph{E.
coli} by associating CRISPR/Cpf1-induced DNA cleavage with cellular
lethality. The guide RNA activity revealed significant diversity. It's
worth noting that the current guide RNA activity prediction models
showed Spearman correlations of only 0.56 when tested with our data. We
therefore proposed a computational approach to design Cpf1 guide RNAs
allowing the prediction of efficient and inefficient guide RNAs with an
improved performance with Spearman correlation of 0.80. Lastly, our
model identified important guide RNA sequence features that contribute
to DNA cleavage.

\textbf{Materials and Methods}

\textbf{Strains and media}

\emph{Escherichia coli} Trans1-T1 (Transgene) was used as host for
cloning, plasmid propagation and library construction. \emph{Escherichia
coli} BL21(DE3) was used as host for library screening and validation.
Bacteria were generally cultured on Luria--Bertani (LB) liquid medium
(10 g/L Tryptone, 5 g/L Yeast extract, 10 g/L NaCl, PH adjusted to 7.0)
, incubated at 37 \selectlanguage{ngerman}°C.

When required, media were supplemented with chemical substrates, and the
working concentrations of which were as follows: ampicillin (100 mg/l),
chloramphenicol (35 mg/l), anhydrotetracycline (30 mg/l), L-arabinose(10
mM).

\textbf{Oligo pool design and library}

We designed each the oligonucleotide to contain a guide RNA encoding
sequence and its target sequence flanked by the correct PAM(TTTV) , in a
total length of 140 bases(Fig1.a) 12,044 guide RNA sequences were
generated targeting human genes, and 500 were generated
targeting\emph{E. coli} genome.

Each oligonucleotide included two constant 20-base sequences at either
end for PCR amplification; A unique 8-base barcode sequence was inserted
in each oligonucleotide.

Oligonucleotides were synthesized electrochemically on arrays (Custom
Array, GenScript). These oligonucleotides (140 bases each) were
amplified by PCR using Q5 Polymerase (NEB) and gel-purified by using a
QIAquick Purification Kit (QIAGEN). Purified PCR products were assembled
with the PUC19 vector using a NEBuidler HiFi DNA assembly kit (NEB).
Assembly reaction products were transformed into Trans1-T1 competent
cells (Transgene). Transformed cells were cultured onto Luria--Bertani
(LB) agar plate with 100 \selectlanguage{greek}μ\selectlanguage{english}g/ml ampicillin. The resulting number of
colonies yielded more than 60\selectlanguage{ngerman}× library coverage. The total plasmids were
extracted with a Plasmid Maxiprep Kit (Biomed).

Library sequences are given in TABLE.(excel)

\textbf{Electrocompetent cell preparation and transformation}

Electrocompetent cells are prepared with the following protocol: (1)
inoculate a single colony in a 10 mL tube for overnight growth; (2)
transfer 1\% V/V strains into a 500 mL shake flask with 100 mL LB liquid
medium; (3) when optical density at 600 nm (OD600) of the bacterial
culture reaches 0.4-0.6, place it on ice and chill for 30 min; (4) spin
in centrifuge at 4000 rpm for 10 min, discard the supernatant, and add
40 ml ddH20 to wash the cell pellet; (5) spin in centrifuge at 4000 rpm
for 10 min, discard the supernatant, add 40 ml 10\% V/V glycerol to wash
the pellet, and place it on ice for 10 min; (6) repeat step (5) with 20
ml then 10 ml 10\% V/V glycerol, successively; (7) spin in centrifuge at
6000 rpm for 10 min, discard the supernatant, and add 2 ml 10\% V/V
glycerol to resuspend the pellet; (8) distribute the final dilutions
(100 \selectlanguage{greek}μ\selectlanguage{english}l each) into the sterile 1.5 ml tubes, freeze it immediately with
liquid nitrogen and store at - 80 \selectlanguage{ngerman}°C.

About 250ng of plasmid was pipetted into 100 \selectlanguage{greek}μ\selectlanguage{english}l thawed electrocompetent
cells, and transferred to a pre-cooled 1 mm cuvette (Bio-Rad micro
pulser, 2.5 kV, 100 \selectlanguage{greek}Ω, \selectlanguage{english}25 \selectlanguage{greek}μ\selectlanguage{english}F). After pulse, we added 600 \selectlanguage{greek}μ\selectlanguage{english}L pre-warmed
LB liquid medium (containing 30 \selectlanguage{greek}μ\selectlanguage{english}g/ml anhydrotetracycline or 10mM
L-arabinose if necessary) and recovered it in tube at 37 \selectlanguage{ngerman}°C for 1.5
hours. Finally, we spread this culture onto LB agar plate containing 100
\selectlanguage{greek}μ\selectlanguage{english}g/ml ampicillin and incubated it at 37 \selectlanguage{ngerman}°C overnight. The transformation
efficiency was calculated as the number of colony forming units per 1 \selectlanguage{greek}μ\selectlanguage{english}g
of plasmid (cfu/\selectlanguage{greek}μ\selectlanguage{english}g).

\textbf{Screening experiments in \emph{E coli}}

For the guide RNA activity screening experiment, competent cells were
prepared as described above (Electrocompetent cell preparation and
transformation). We collected clones per sample to achieve a proper
(\textgreater{}60\selectlanguage{ngerman}×) coverage over the design library.

Because the DSB lethality caused by Cpf1 cleavage made the direct
calculation of transformation efficiency impossible, the respective
control group without inducer was used to evaluate the transformation
efficiency for Cpf1 transformed with gene-targeting crRNA library.

Two screening experiments (SE1 and SE2) were conducted parallelly. In
each experiment, this library was delivered to the competent cells
expressing Cpf1 driven by an inducible promoter to evaluated guide RNA
activity. After the \emph{E.coli} cells were cultured with/without
inducer, the remaining cells were collected as select/control. In SE1,
Cpf1 was driven by a strong promoter (tet promoter) while in SE2 by a
weak promoter. Through a synthetical consideration of two groups of
results, we could eliminate the influence of the known leakage
expression of tet promoter.

\textbf{Next-Generation Sequencing (NGS) and pooled screen analysis}

Plasmid DNA was isolated using a Plasmid Maxiprep Kit (Biomed).
Integrated guide RNA target pair sequences were PCR-amplified using Q5
polymerase (NEB) for frequency analysis. PCR products were purified
using a QIAquick Purification Kit (QIAGEN). For each sample, 1 microgram
of purified PCR amplicons were used as a library template. Sequencing
libraries were obtained from the replicates using a NEBNext Ultra II RNA
Kit (NEB)and purified with SPRI beads(Beckman). The sequencing libraries
were prepared following the manufacturer's protocol(Illumina).Library
size and purity was verified by Agilent 2100(Agilent) before sequencing
on a Nova seq (Illumina) using a Reagent Kit S2 (Novaseq) (2 × 150 bp).

\textbf{Statistical analysis}

The whole collection of NGS data-set were showing in Table1.a.While the
control1 and selective1 were referring to data generated from SE1
control group and SE1 experiment group; the control2 and selective2 were
referring to data generated from SE2 control group and SE2 experiment
group.The reads were the raw sequencing data and the counts were the
filtered data.

For a specific crRNA sequence, its abundance (crRNA frequency) in a
certain group could be calculated by its divided by the summation of all
crRNA counts.(Table1.b)Further, the fold-change of a specific crRNA
sequence across a screening experiment (SE1 or SE2), representing the
activity of a crRNA, could be calculated the ratio between the frequency
in the post selection sample (selective) and the no-selection sample
(control). (Table1.b)

\textbf{T7 endonuclease I assay for genome modification}

293T Cells were collected after 48\selectlanguage{english} h post-transfection for genomic DNA
extraction. The genomic region flanking the target site of each gene was
PCR-amplified, and products were purified using DNA Clean Kit following
the manufacturer's protocol. A total of \textasciitilde{}\selectlanguage{english} 200\selectlanguage{english} ng
purified PCR amplicons was mixed with 1\selectlanguage{english} \selectlanguage{greek}μ\selectlanguage{english}l NEBuffer 2 and diluted in
ddH2O to 10 \selectlanguage{greek}μ\selectlanguage{english}l, then subjected to a re-annealing process to form a
heteroduplex according to the reported procedure {[}44{]}. After
re-annealing, the products were treated with T7EI following recommending
protocol, and 2.5\% agarose gels were used for further analysis. Indels
were calculated via band intensities based on previously reported method
{[}45{]}.

\textbf{Results}

\textbf{Design of a library of guide RNA target sequence pairs library
and screening experiments}

Biotechnologies based on CRISPR/Cpf1 have greatly facilitated the
genetic manipulation on model and non-model bacteria for higher editing
efficiency and specificity.{[}29-32{]}However, Cpf1 editing in mammalian
cells were still challenging.{[}34-35{]}We ascribed it to the following
factors. (i) NHEJ pathway is absent in most bacteria, such
as\emph{E.coli,} once a single-copy gene was destroyed by Cpf1-induced
DNA cleavage, it can lead to cell death for lack of redundancy.(ii) The
binding of Cpf1-crRNA complex to target gene can interfere with gene
expression. Consequently, we intended to avoid these effects by choosing
sequences non-existing in \emph{E.coli} genome when designing guide RNAs
and using multi-copy plasmids instead of genome sites as targets.

A pool of 12,544 oligonucleotides containing the target sequence and the
corresponding guide RNA sequence was synthesized by a DNA microarray,
amplified by PCR, and cloned into a guide RNA expression plasmid
backbone using Gibson assembly (Fig.1a,b).Among this library, 12,044
pairs were designed for human genes and 500 pairs with targets
across\emph{E. coli} genome as internal controls.

Next generation sequencing (NGS) data showed that 12.434(99.12\%) pairs
were included in the bacteria library among the 12,544 designed pairs
(Table1.a). An activity score for each member of the library was
calculated by quantifying the representation of each sequence with and
without Cpf1 expression (Table1.b). The activity of a guide RNA may be
related to cellular survival for the successful DNA cleavage leads to
the loss of resistance to antibiotics that a more positive score
indicates lower activity.

The screening experiments and data processing were showing in Methods
and Materials.

\textbf{The diversity of activity among guide RNAs}

The guide RNA activity distributions of negative control(selective1) and
screening experiment(selective2) were shown (Fig.1c). As there is almost
no selection pressure of DNA cleavage, the guide RNA activity in the SE1
nearly obeyed a normal distribution with mean approximately equal to 1
as expected, while in SE2 there was significant variability of guide RNA
activities. Concretely, the majority of guide RNAs in the designed
library exhibited high activities that the best of which resulted in
1000-fold slowdown in growth rate of E. coli cells. To validate such
results from the screening experiment, we chose a series of guide RNAs
consisting of 9 with high scores and 2 with low scores to retest their
activities individually by measuring colony number after transformation,
and the results were observed in a very good consistency with which in
the screening experiment (Fig.1d). Thus, it suggested that our screening
method to evaluate guide RNA activity was reliable and such activity
dataset could be used for further analysis.

\textbf{A computational method predicting guide RNA activities}

According to our results above, quite a fraction of guide RNAs showed
moderate or no activity that about 50\% of guide RNAs in the library got
activity scores \textgreater{} 0.1 (lgscore\textgreater{}-1), which
indicates guide RNAs designed without a rational method could not be
successfully used in genome editing. It is time and labor consuming to
test each guide RNA before a gene-editing experiment, thus an in silico
method for guide RNA efficacy prediction is in need, and recently
several works have been developed to facilitate it.{[} {]}As we firstly
created a dataset of Cpf1 guide RNA activity in prokaryotes, a novel
predicting method based on our results could be a supplement to current
studies and test the generalization ability of those methods based on
eukaryotic datasets.

We filtered our results by removing data of low quality, and established
a dataset based on selective2. We randomly separated the dataset (90\%
for training and 10\% for testing) in order to avoid overfitting, and
used 10-fold cross-validation to retest the capacity of the trained
model (Fig.2a). Then, we defined a series of featurization considering
the DNA sequence of the protospacer, PAM and fraction of base that
convert each sequence in our library into more than 350 binary and
continuous feature information as inputs. Using features extracted from
each guide RNA, we built a regression model to predict its activity.
(Fig.2b) We re-separated the dataset after randomly shuffling to do the
training 10 times in total, and there was no significant difference at
the performance of all trained models, suggesting that our model is
robust and no bias have been introduced by separating the dataset
(Fig.2c Fig2d).

We compared our predictive results with deepCpf1, the most-cited work,
to evaluate the performance of our model. {[}36{]} We found weak
correlation between experiment results and the predictions from
deepCpf1, while our model is much more predictive with Spearman
correlation coefficient of 0.80 on average(Fig.2c Fig2d). It indicated
that the models trained with data from mammalian cells provided limited
comprehension of the guide RNA sequence features contributing to
cleavage activity on the Impact of chromatin structures and the NHEJ
repair pathway.

To verify our founding and explore the mechanism of different predictive
power between two models, we further tested their performance on
forecasting the most efficient guide RNAs as well as the inefficient
ones, since the selection of guide RNA for efficient genome editing is
in critical demand in research and clinical. After we have processed the
data of every sequence in our library using our model and deepCpf1, each
sequence got a prediction score corresponding to its experimental
activity. Then, we accessed the ability of each model to distinguish
efficient guide RNAs from inefficient ones individually, by comparing
the experimentally measured activity scores of a group of sequence in
high prediction scores with which in low scores (Fig2.e). According to
the scores predicted by our model, there was significant difference of
measured activities between predictive high-score group and low-score
group, while no significant difference of which by deepCpf1 in contrary,
confirming that our model has a better predictive capacity Furthermore,
we investigated the reason that the improvement of our model may be
attributed to. This time we divided the test library into high-score
group and low-score group according to their experimental activity and
compared those prediction scores processed by each model (Fig2.f) As
expected, our model performed much better than deeCpf1 in both efficient
and inefficient guide RNA predictions. Interestingly, the deepCpf1 model
was proven to be of almost no ability to predict efficient guide RNAs,
but of weak ability to predict inefficient ones accurately. It revealed
the disability in characterization of high-activity guide RNA as a
primary reason why deepCpf1 underperformed in prokaryotic. We tried to
make an explanation for why deepCpf1 is not sensitive when working with
data from more efficient guide RNA, later. Overall, our model makes
predictions of guide RNA activity better than current approaches and is
extremely good at predicting efficient ones, at least in prokaryotic
where developed.

We next investigated the sequence features contributing to guide RNA
activity. We mainly focus on the sequence composition of protospacer
besides some other factors reported including GC content, melting
temperature. A linear model was used here to plot the coefficients of
position-dependent dimers and trimers respectively(Fig.3a,b,c ). Results
of T7 endonuclease I (T7EI) assay showed that guide RNA sequence
features could affect genome editing in both human cells (Fig. 3d ) . It
is notable that approximately equal effect of dimer/trimer each position
in seed region of protospacer was observed considering their distance
from the PAM, while the first single nucleotide was used to be known as
a stronger factor. We also observed the promotional effect of AH dimers,
AHN trimers and the inhibitory role of GB, TK dimers, GBN trimers at
certain positions. Our findings were in consistence with the results of
another activity profiling screening independent, although we obtained a
larger scale library and provided a more comprehensive and convincing
model.{[}33{]} These effects may be attributed to the expression level
or stability of guide RNA as well as the interaction of Cpf1-crRNA
complex with its DNA substrate.

\textbf{Discussion}

CRISPR-Cpf1-based genome editing technology was invented as another
promising tool following the success of CRISPR-Cas9 and have been
applied for a broad spectrum of species including eukaryotic and
prokaryotic organisms. Cpf1 has several advantages over Cas9 such as
guided by a single and shorter RNA, requires a T-rich PAM and exhibits
better specificity, making it a complementary and alternative gene
editing tool.{[}37{]} However, predicting the activity of guide RNA is a
challenge due to the lack of the knowledge.

In this study, we made a large-scale library containing 12544 designed
guide RNAs .Take advantage of the library, we carry out screening
experiments. and presented an efficient and comprehensive computational
model for prediction of Cpf1 guide RNA activity trained by the results
of which. Consequently, we systematically studied the guide RNA activity
in bacteria for the first time, broadening the application of Cpf1 in
genome editing. We observed various activities of guide RNAs and tested
the results by independent validation experiments. Our trained model was
developed to select optimized guide RNAs better-performing than previous
approaches and analyzed the features contributed to guide RNA
activities. We propose that deepCpf1 and other models trained in
mammalian cells shows a higher false negative rate processing data from
in prokaryotic organisms on account of misattributing influences of NHEJ
repairs, chromatin structure, or lentivirus transduction to guide RNA
incapacity, hence our model may be applied to other bacteria or
unexplored host, potentially. Furthermore, our unbiased screening
methods may be applied in other developing CRISPR system utilization
scenario

However, there may be still several defects in our model that the
mechanism of Cpf1-RNA complex interacting with DNA target is unclear and
the generalization capacities are unproven. To further improve the
accuracy and efficiency of guide RNA design, we expect that more
datasets from well-designed experiments across species could be
generated, and more comprehensive algorithms such as machine and deep
learning (MDL) methods could be applied.{[}38-40{]}In addition,
improvement of the specificity of guide RNA to reduce off-target effects
is a clinical demand, thus our platform combining high-throughput
screening and analysis of computational methods may be used to address
this issue.

Currently, novel cpf1 orthologs and several other CRISPR nucleases were
harnessed for genome editing in mammalian and bacteria cells, while
relationship between guide RNA sequence and on-target activity was
underexplored. {[}41{]}Moreover, several Cpf1 variants of AsCpf1 were
described with increased activity and expanded protospacer-adjacent
motif (PAM) preferences.{[}42,43{]}The method described in this paper
could deal with such issues and make progress towards optimizing the
design of guide RNAs.

\textbf{Acknowledgments}

We thank Luo yufeng for suggestions on the experiments.

\textbf{Data Availability Statement:} The data sets used in this study
are provided as supplementary table 'guide RNA activities data-1' and
'guide RNA activities data-2'. All custom scripts used are provided as a
.txt file'Code'. DeepCpf1 codes are available on GitHub (at
https://github.com/MyungjaeSong/Paired-Library).

\textbf{Declaration of Conflicting Interests}

The authors declared no potential conflicts of interest with respect to
the research, authorship, and/or publication of this article.

\textbf{Funding}

This research was supported by the National Natural Science Foundation
of China (81827809 to Jianzhong Xi, SQ2018YFA010021 to Jianzhong Xi).

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\textbf{Figure and Table}

\textbf{Fig1}\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image1/image1}

\end{center}
\end{figure}\selectlanguage{english}
\begin{figure}[H]
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\includegraphics[width=0.70\columnwidth]{figures/image2/image2}

\end{center}
\end{figure}\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image3/image3}

\end{center}
\end{figure}\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image5/image5}

\end{center}
\end{figure}

Figure 1\textbar{} Library preparation and high-throughput evaluation of
Cpf1 guide RNA activity.

(a) Representation of an oligonucleotide containing pairs of target and
guide RNA sequence.

(b) A schematic of the high-throughput evaluation of Cpf1 guide RNA
activity.

(c) The distribution of guide RNA activity in screening expriment1 and
screening expriment2

(d) The pooled screening results were confirmed by cloning 11 guide RNAs
(9 of high activity and 2 of low activity) individually and measured via
transformation assay.

\textbf{Fig2}\selectlanguage{english}
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\includegraphics[width=0.70\columnwidth]{figures/image7/image7}

\end{center}
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\includegraphics[width=0.70\columnwidth]{figures/image8/image8}

\end{center}
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\includegraphics[width=0.70\columnwidth]{figures/image9/image9}

\end{center}
\end{figure}\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image10/image10}

\end{center}
\end{figure}\selectlanguage{english}
\begin{figure}[H]
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\includegraphics[width=0.70\columnwidth]{figures/image11/image11}

\end{center}
\end{figure}\selectlanguage{english}
\begin{figure}[H]
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\includegraphics[width=0.70\columnwidth]{figures/image12/image12}

\end{center}
\end{figure}

Figure 2\textbar{} Computational model predicting guide RNA activities

(a) (b) The schematic of datasets and algorithm used

(c) ROC curves comparing the predictive power of our model and deepCpf1.

(d) A quantization of (c) and the bar shows the mean \selectlanguage{ngerman}±s.d. between
predicted and measured guide activity scores (n=10)

(e) Comparison of the different models. A smaller scale library of 6,265
guide RNAs was processed by each model, and the top 1,000 scored ones
were chosen as an efficient group, in comparison to bottom 1,000 ones as
an inefficient group. Their corresponding experimental activity scores
were compared.

(f) The former library was re-chosen into high score and low score group
by measured guide RNA activities (1,000 members from 6,265).

\textbf{Fig3}\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image13/image13}

\end{center}
\end{figure}\selectlanguage{english}
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\includegraphics[width=0.70\columnwidth]{figures/image14/image14}

\end{center}
\end{figure}\selectlanguage{english}
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\includegraphics[width=0.70\columnwidth]{figures/image16/image16}

\end{center}
\end{figure}

Figure 3 \textbar{}The top sequence features for predicting guide RNA
activities

(a)The features for adjacent dimers of guide RNAs are plotted as
heatmaps based on their position in seed region, and positive
coefficients suggest positive contributions to guide RNA activity.

(b)The features for adjacent trimers of guide RNAs are plotted as
heatmaps based on their position in seed region, and positive
coefficients suggest positive contributions to guide RNA activity.

(c) The features for adjacent nucleotides of guide RNAs are plotted as
columns based on their position in seed region, and positive
coefficients suggest positive contributions to guide RNA activity.

(d) T7EI analysis of targeted indel frequencies induced by the 6 guide
RNAs with different sequence features.

\textbf{FigS1}\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image18/image18}

\end{center}
\end{figure}

Figure s1 Workflow of the screening experiments in \emph{E.coli}

Table 1\textbar{} The numbers of pairs in the oligonucleotide pool and
NGS data processing.\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image19/image19}

\end{center}
\end{figure}

(a) Raw NGS data from designed library and each screening experiment.

(b) For each condition, the activity of each guide RNA was calculated
via these equations

\selectlanguage{english}
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