Chen MX, Zhu XD, Zhang H et al. SMRI: A new method for siRNA design for COVID-19 therapy. JOURNAL OFCOMPUTER SCIENCE AND TECHNOLOGY 37(4): 991–1002 July 2022. DOI 10.1007/s11390-021-0826-x

SMRI: A New Method for siRNA Design for COVID-19 Therapy
Meng-Xin Chen1 (•‰!), Student Member, CCF, Xiao-Dong Zhu2 (Á¡Á), Hao Zhang3 (Ü Ó)Zhen Liu3,4 (4), and Yuan-Ning Liu3,∗ (4 w), Member, CCF
1

College of Software, Jilin University, Changchun 130012, China

2

Key Laboratory of Symbolic Computation and Knowledge Engineering of the Ministry of Education, Jilin University
Changchun 130012, China

3

College of Computer Science and Technology, Jilin University, Changchun 130012, China

4

Graduate School of Engineering, Nagasaki Institute of Applied Science, Nagasaki 851-0193, Japan

E-mail: chenmx19@mails.jlu.edu.cn; {zhuxd, zhangh}@jlu.edu.cn; liu zhen@nias.ac.jp; liuyn@jlu.edu.cn
Received July 23, 2020; accepted August 31, 2021.
Abstract First discovered in Wuhan, China, SARS-CoV-2 is a highly pathogenic novel coronavirus, which rapidly spread
globally and became a pandemic with no vaccine and limited distinctive clinical drugs available till March 13th, 2020.
Ribonucleic Acid interference (RNAi) technology, a gene-silencing technology that targets mRNA, can cause damage to
RNA viruses effectively. Here, we report a new efficient small interfering RNA (siRNA) design method named Simple
Multiple Rules Intelligent Method (SMRI) to propose a new solution of the treatment of COVID-19. To be specific, thisstudy proposes a new model named Base Preference and Thermodynamic Characteristic model (BPTC model) indicating
the siRNA silencing efficiency and a new index named siRNA Extended Rules index (SER index) based on the BPTC model
to screen high-efficiency siRNAs and filter out the siRNAs that are difficult to take effect or synthesize as a part of the
SMRI method, which is more robust and efficient than the traditional statistical indicators under the same circumstances.
Besides, to silence the spike protein of SARS-CoV-2 to invade cells, this study further puts forward the SMRI method tosearch candidate high-efficiency siRNAs on SARS-CoV-2’s S gene. This study is one of the early studies applying RNAitherapy to the COVID-19 treatment. According to the analysis, the average value of predicted interference efficiency ofthe candidate siRNAs designed by the SMRI method is comparable to that of the mainstream siRNA design algorithms.
Moreover, the SMRI method ensures that the designed siRNAs have more than three base mismatches with human genes,
thus avoiding silencing normal human genes. This is not considered by other mainstream methods, thereby the five candidate
high-efficiency siRNAs which are easy to take effect or synthesize and much safer for human body are obtained by our SMRI
method, which provide a new safer, small dosage and long efficacy solution for the treatment of COVID-19.
Keywords

1

COVID-19, SARS-CoV-2, RibonucleicAcid interference

Introduction

Coronaviruses are the positive-stranded RNA
viruses with the largest RNA viral genomes ranging
from 27 kb to 32 kb [1, 2] . The word of coronavirus
is derived from its unique crown appearance, which
may be attributed to its envelope with long petaloid
spines [3] . Coronaviruses can infect various parts of thebody, particularly the upper respiratory tract and gastrointestinal tract tissues, liver and the central nervous

system (CNS) [2, 4] . For humans, coronaviruses mainly
cause respiratory diseases, such as severe acute respiratory syndrome (SARS) and middle east respiratory
syndrome (MERS) [2, 5, 6] .
In December 2019, a new type of pneumonia wasreported in Wuhan, Hubei Province, China [7–9] . Soon
after, these patients were found with a novel coronavirus that was subsequently named severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) byRegular Paper
The work was supported by Jilin Provincial Key Laboratory of New Biometrics Technology, the National Natural Science Foundation of China under Grant No. 61471181, and the Jilin Province Industrial Innovation Special Fund Project under Grant Nos. 2019C053-2
and 2019C053-6.
∗ Corresponding Author©Institute of Computing Technology, Chinese Academy of Sciences 2022

992

the World Health Organization (WHO). Additionally,
the new pneumonia was named as coronavirus disease2019 (COVID-19). SARS-CoV-2 causes typical flu-like
symptoms. As reported by a recent study, most patients have fever or cough, a third of patients develop
shortness of breath, and other symptoms include diarrhoea, chest pain, confusion, headache, and muscle
ache [10] . On March 11th, 2020, the WHO declared
COVID-19 as a global pandemic. According to oursurvey, no vaccine has been developed to successfully
prevent the COVID-19 virus infection till March 13th,
2020, and there are limited distinctive clinical drugs
available.
RNA interference (RNAi), an endogenous gene expression regulation mechanism, was first found in plants
to act as a natural immune mechanism to resist virus
infections [11–16] . RNAi is widely employed by many
creatures to resist virus infections, such as caenorhabditis elegans, arabidopsis, chlamydomonas, drosophila, filamentous fungi, plants and other organisms [17] . RNAi is mediated by small RNAs, consisting of small interfering RNAs (siRNAs) and microRNAs (miRNAs) [18] . siRNAs, the double-stranded RNA(dsRNA) sequences about 21nt–23nt in length, candown-regulate specific target genes with an accuratecomplementary sequence and is formed by the cleavage of dsRNA by the Dicer enzyme [19–21] . siRNAs canbe used to resist all types of viral genomes, regardlessof whether they are double-stranded or single-stranded
DNA/RNA viruses. In addition, the use of multiple
siRNAs simultaneously can achieve an enhanced antiviral effect [22] . RNAi has shown a potential application
prospect in antiviral therapy [23–26] .
This study proposes a simple multiple-rule intelligent method (referred to as the SMRI method) for
the design of high-efficiency siRNAs for SARS-CoV2. There are mainly two types of research at present:
based on statistical methods and based on machinelearning. Compared with the research based on statistical methods, the SMRI method enhances the ability of screening efficient siRNAs by incorporating machine learning methods. Compared with the researchbased on machine learning, the SMRI method can effectively filter out candidate siRNAs which are difficult
to take effect or synthesize by integrating rules. At thesame time, compared with the complex neural networkmodel, the complexity of the SMRI method is lower.
Different from previous studies [27–35] that only usedstatistical methods or machine learning methods, theSMRI method integrates the advantages of statistical

J. Comput. Sci. & Technol., July 2022, Vol.37, No.4

methods and machine learning methods. Besides, theSMRI method ensures that the candidate siRNAs havea mismatch of over three bases in the Basic Local Alignment Search tool (BLAST) homology analysis of thehuman genome, so as to avoid silencing the human gene.
This is not found in other mainstream methods, thereby
the siRNAs designed by the SMRI method are muchsafer for human body. The advantage of the SMRI
method is that it is highly efficient, safe to human andcan effectively filter out the candidate siRNAs that are
difficult to take effect or synthesize. Moreover, we innovatively propose a new index named siRNA Extended
Rules index (SER index) based on integration rules tomeasure the silencing efficiency of candidate siRNAs.
Its advantage lies in that it has stronger and more robust ability to screen high-efficiency siRNA than a single index. The main contributions of this study are as
follows.
• This study proposes a new model named Base
Preference And Thermodynamic Characteristic model(BPTC model) regarding the siRNA silencing efficiency.
• Based on the BPTC model, this study proposes a
new efficient and robust index named SER index to extract the rules in methods summarizing the characteristic properties of high-efficiency siRNAs and to measure
the silencing efficiency of candidate siRNAs.
• This study proposes a new efficient and safe siRNAdesign method named the SMRI method for SARSCoV-2. The SER index is a part of the SMRI method.
And the SMRI method uses the SER index to effectively filter out the candidate siRNAs that are difficult
to take effect or synthesize. And five potential highefficiency siRNAs for the treatment of COVID-19 are
obtained by the SMRI method.
The rest of this paper is organized as follows. Section 2 summarizes the prediction algorithms of siRNAinterference efficiency. Section 3 introduces the specific mechanism of the RNAi therapy in mammals andthe factors related to the design of siRNAs with a highsilencing efficiency. Section 4 gives the precise definition of the BPTC model, a new model regarding thesiRNA silencing efficiency, the SER index, a new index to screen siRNAs with a high silencing efficiency,
and the SMRI method to design siRNAs against SARSCoV-2. Section 5 shows the data source, experimental
process and results. Section 6 analyzes the screening
ability of the SER index, the contribution of each stepin the SMRI method, and the screening ability of the
SMRI method, and describes the antiviral process andMeng-Xin Chen et al.: SMRI: A New Method for siRNA Design for COVID-19 Therapy

advantages of our proposed method. Section 7 concludes the paper.
2

Related Work

The premise of the siRNA formation is to selectgene substrings of target genes. Nonetheless, not every
siRNA formed by an arbitrary substring of the target
gene can induce target gene silencing [36] . Many studies
have explored the related factors affecting the design ofsiRNAs with a high silencing efficiency. These studies
are classified as follows.
2.1

Methods Summarizing the Characteristic
Properties of High-Efficiency siRNAs

Elbashir et al. [27] proposed a set of guidelines aiming at designing the effective siRNAs. These principles
include some characteristic attributes of siRNAs witha high silencing efficiency, like the selection of siRNAs
with the C and G contents between 30% and 70%.
Reynolds et al. [28] identified eight features related
to the siRNA silencing efficiency, such as the shortageof reverse repeats. Based on the above-mentioned characteristics, they proposed a principle that significantly
improved the predicted silencing efficiency of siRNAs,
commonly known as the Reynolds rule.
Amarzguioui and Prydz [29] identified various characteristics of the double-stranded siRNAs with a silencing efficiency of over 70%, like the steadiness of thesiRNA ends. Combining these findings, they proposed
an effective selective algorithm to design siRNAs targeting the new endogenous human genes.
Hsieh et al. [30] detected a significant nucleotide preference at specific positions in siRNAs with a silencing
efficiency of over 70%, especially G or C at the 11thbase position and C at the nineteenth base position.
Takasaki et al. [31] defined a priority score based on
single nucleotide’s position characteristics to measuregene degradation and developed an effective method toselect the target sequence of siRNA.
Katoh and Suzuki [32] proposed that the RNAi activity was dramatically affected by the precise base inevery three positions of its siRNAs. Specifically, whenthe 3n+1 (4, 7, 10, 13, 16, 19) bases in siRNAs were thecorresponding optimized bases, their activities would
be enhanced. On account of the above-mentioned observations, they developed an algorithm (“siExplorer”)
to calculate the silencing efficiency of siRNAs targetingthe endogenous human genes.

993

Researches aiming to summarize the characteristic properties of siRNAs with high silencing efficiency
have proposed numerous good siRNAs screening algorithms, such as Elbashir et al. [27] , Reynolds et al. [28] ,Amarzguioui and Prydz [29] , Hsieh et al. [30] , Takasaki etal. [31] , and Katoh and Suzuki [32] . These algorithms are
effective on the siRNA design to some extent. However,
only limited shared properties of siRNAs with a high silencing efficiency are concluded from a single study. As
a result, a large number of candidate siRNAs conformto these rules. Moreover, some ineffective or low silencing efficiency siRNAs also comply with these rules, limiting the accuracy of the siRNAs screening algorithm
developed by this research.
In this study, inspired by integrated learning, weinnovatively propose a new index SER to measure thesilencing efficiency of candidate siRNAs based on themethods summarizing the characteristic properties ofhigh-efficiency siRNAs proposed by Reynolds et al. [28] ,Amarzguioui and Prydz [29] and Hsieh et al. [30] . According to our analysis, the SER index performs better
than any of the above-mentioned three methods [28–30]
in screening siRNA. Moreover, the SMRI method further enhances the ability to screen high-efficiency siRNAs by combining with machine learning methods.
2.2

Methods Adopting Machine Learning
Algorithms

Vert et al. [34] captured the essential factors related to siRNA silencing efficiency with Least Absolute
Shrinkage and Selection Operator (LASSO) regression
and proposed a linear model Designer of Small Interfering RNA (DSIR) to predict the siRNA silencing efficiency. Their analysis suggested that DSIR achieved acomparable accuracy to other mainstream algorithms.
Besides, it had more advantages than other mainstreamalgorithms in explaining the siRNA efficiency mechanism.
Ichihara et al. [35] built a linear regression model ona dataset containing 2 431 siRNAs, and proposed an
intuitive algorithm inhibitory-Score (i-Score) to predict siRNAs silencing efficiency. The algorithm only
considers the nucleotide preference of each position.
Moreover, based on a validation dataset containing 419
siRNAs, the i-Score attained a comparable prediction
accuracy compared with other mainstream algorithms
where more parameters were used.
Qureshi et al. [36] studied a new dataset containing 1 725 viral siRNAs with experimentally verified si-

994

J. Comput. Sci. & Technol., July 2022, Vol.37, No.4

lencing efficacy. A high-accuracy siRNAs silencing efficiency prediction model was constructed by consideringvarious siRNA characteristic parameters, including binary pattern, thermodynamic properties and secondary
structure mono to pentanucleotide frequencies.
Compared with methods aiming to summarize thecharacteristic properties of siRNAs with a high silencing efficiency, the prediction accuracy of researchesmodeling by the machine learning methods has a great
improvement, such as Vert et al. [33] , Ichihara et al. [34]and Qureshi et al. [35] . However, some complex machine learning models are challenging to assist in clarifying the mechanism of siRNA silencing efficiency. In
addition, the related factors affecting the siRNA silencing efficiency selected by each model have great influence on the silencing efficiency of siRNAs designed bythis model. Moreover, there are many candidate siRNAs that are difficult to take effect or synthesize in thescreening results.
In this study, the SMRI method can effectively filter out the candidate siRNAs that are difficult to takeeffect or synthesize by using the SER index to screen
siRNAs. The SER index is proposed in this study to
extract the rules in methods summarizing the characteristic properties of high-efficiency siRNAs.

is cleaved is broken down by the cellular exonucleases,
resulting in a post-transcriptional gene silencing [37–39] .
3.2

Factors of High-Efficiency siRNAs
Many factors are involved in the success of RNAi.

3.2.1

Schubert et al. [40] proposed that the complexity of
secondary structure in the target region is negatively
correlated with siRNA silencing efficiency. The RNAregion can be classified into the stem region, loop region, stem loop region and multi-branch loop region.
It is concluded that the siRNA silencing effect is goodin the stem region but poor in the multi-branch loop
region.
3.2.2

3.1

Backgroud
Mechanism of RNAi Therapy in Mammals

The RNAi process in mammals consists of twostages, including an initiation and an execution
phase [37] . In the initial phase, a long dsRNA is introduced from an exogenous source or from a transgene
or a virus. Then, the long dsRNA is specifically recognized and cleaved by the Dicer enzyme in an energyconsuming process into a double-stranded siRNA. The
double-stranded siRNA contains a sense strand and an
antisense strand, having the length of 21nt–23nt. The 3’
ends of the above two strands contain two overhanging
bases [37–39] . In the execution phase, the RNA-induced
silencing complex (RISC), which is in charge of unwinding the double-stranded siRNA to form a sense
strand and an antisense strand, is formed by the doublestranded siRNA, helicase, and other required enzymes
in an energy-consuming way. Then, the RISC unwinds
the double strands of siRNA. Afterwards, an endogenous mRNA with a complementary sequence containing the antisense strand is searched under the guidanceof the antisense strand. Later, the target mRNA which

Thermodynamic Stability of siRNA

When siRNA duplexes assemble into RISC, they aretransformed into a single-stranded form [41] . The entryof the antisense strand into RISC plays a crucial rolein the cleavage of target mRNA. Relatively, the unstable siRNAs antisense strands at 5’ end enter RISC
advantageously [36] . In addition, the G-C base pair isthermodynamically more stable than the A-U base pair.
3.2.3

3

Secondary Structure of Target Sequence

Selection of Target Sequence

The base matching degree between siRNA andthe target sequence based on the gene complementation principle is crucial to the gene silencing effect ofsiRNA [42] . Overall, the gene silencing effect of siRNA
will be considerably reduced, even if there is only one
different base in its antisense strand from the complementary base sequence of the target mRNA sequence.
4

BPTC Model,
Method

SER

Index

and

SMRI

In this section, Subsection 4.1 introduces the BPTC
model regarding the siRNA silencing efficiency. In Subsection 4.2, the SER index for screening siRNAs withhigh silencing efficiency is proposed based on the BPTC
model. Subsection 4.3 illustrates the SMRI method
to design siRNAs against SARS-CoV-2, which filtersroughly invalid siRNAs by the SER index.
4.1

BPTC ModelIn this study, a mathematical model named BPTC
model is established to measure the silencing efficiencyof siRNA. The factors considered are as follows: theMeng-Xin Chen et al.: SMRI: A New Method for siRNA Design for COVID-19 Therapy

base preference of each base position in the sense strand
and the free energy of the secondary structure in thesense strand. The specific model is as follows:
W a1 W a2 · · · W a19
 Wu Wu ··· Wu 
1
2
19 

(1)
w=
.
 W c1 W c2 · · · W c19 
W g1 W g2 · · · W g19
Wai , Wui , Wci , and Wgi (i = 1, 2, 3, . . . , 19) represent the base position weight values at the first to the19th base positions in the sense strand of siRNA, respectively. Wai indicates the contribution to the whole
siRNA silencing efficiency when the A nucleotide appears at the i-th position. Similarly, Wui , Wci , andWgi can be deduced in the same way.

Ea1 Eu1 Ec1 Eg1
 Ea Eu Ec Eg 
2
2
2
2 



..
..
..  .
e =  ..
 .
.
.
. 


Ea19 Eu19 Ec19 Eg19
Eai , Eui , Eci , and Egi (i = 1, 2, 3, . . . , 19) represent
the existence status of a corresponding type of base at
the first to the 19th base positions in the sense strandof siRNA, respectively. 0 indicates that the base at
the base position is not a corresponding type of bases,
while 1 suggests that the base at the base position is acorresponding type of bases.
W = w · e.
w stands for the base position weight matrix and e995

denotes the base existence status matrix.
 4
X



Wi,i + 1, if F > t,
SED = i=1
4
X




Wi,i ,
otherwise.

i=1

F (kcal/mol) is indicative of the free energy of thesecondary structure in the sense strand; t (kcal/mol)
represents the threshold of the F ’s value.
4.2

Model Parameters of the SER Index

After analyzing the different empirical rules forthe siRNA design, our present study finds that previous studies mainly summarized the properties of highefficiency siRNAs from different aspects. However,there are many candidate siRNAs conforming to a single empirical rule, which are often not limited to theeffective siRNAs [43] .
Therefore, if different rules defined in previous studies are combined to form a new index, the selective advantages of different rules can be integrated to improve
the screening efficiency of high-efficiency siRNAs.
By integrating the rules of the three methods
proposed by Reynolds et al. [28] , Amarzguioui andPrydz [29] and Hsieh et al. [30] and removing some of
their common evaluation rules, a new index SER is proposed to efficiently screen siRNAs based on the abovementioned ideas.
Here, based on our SER index implementation, theparameters of the BPTC model established in Subsection 3.1 are shown below:−1 −1 0 0 0 1 0 0 0 0 0 0 1 0 0 0 1 1 2
 −2 −1 −1 0 0 0 0 0 0 1 0 0 0 0 0 0 1 1 2 t = −0.9, w = .
 1 0 0 0 0 −1 0 0 0 0 1 0 0 0 0 0 0 0 −1 
1 0 0 0 0 0 0 0 0 0 1 0 −1 0 0 1 0 0 −2
As shown in (2) with reference to (1) in Subsection 4.1, lines 1–4 in (2) represent the position weightvalues of A, U, C and G nucleotides. For example, thenumber in the first row indicates that if the A nucleotide
appears at the 1st or 2nd position of the siRNA sequence, its contribution at this position to the silencing
efficiency score of the siRNA is all −1. If the nucleotide
at positions of the 6th, 13th, 17th or 18th of the siRNAsequence is A, every A at the above base positions willcontribute 1 to the siRNA silencing efficiency score. If
the A nucleotide appears at the 6th or 13th or 17th or(2)

18th position of the siRNA sequence, its contribution
at this position to the silencing efficiency of the siRNAis all 1. If the A nucleotide appears at the 19th positionof the siRNA sequence, its contribution at this position
to the silencing efficiency score of the siRNA is 2.
4.3

SMRI Method

Also, we propose a new siRNA design method,
namely, the SMRI method. In the process of designingsiRNAs, we need to screen a large number of candidate

996

J. Comput. Sci. & Technol., July 2022, Vol.37, No.4

siRNAs to get potential high-efficiency siRNAs. The
flow chart of the SMRI method is shown in Fig.1.
As a matter of routine, there are 21 bases in thesense strand and the antisense strand of siRNAs designed by us, including 19 paired and two unpaired
bases at the 3’ end of each strand. The SMRI method
is described in detail as follows.
Step 1. The BLAST analysis is performed on thetarget gene fragment of all viral strains to obtain theconservative sequences of the target gene fragment.
Step 2. According to the principle of gene complementation, 19 consecutive nucleotides are searched on
the target gene fragment, and the corresponding siR-

NAs double strands are obtained.
Step 3. The candidate siRNAs whose sense strand
with a G/C content is beyond the range of 36%–53%
are excluded.
Step 4. The candidate siRNAs whose 50 end free energy is higher than the 30 end free energy of the sense
strand are excluded.
Step 5. The values of SER proposed in Subsection 3.1 for all the candidate siRNAs are calculated.
According to the SER value, all candidate siRNAs are
sorted in descending order, and candidate siRNAs with
the SER index value less than 4 are excluded.
Step 6. Candidate siRNAs are scored according to

Step 1: Obtaining Conservative Regions of the Target Gene Fragment
Acquisitionof All siRNAs
Step 2: Obtaining All the 19nt Sequences and siRNAs of the Target Gene Fragment

Steps 3 & 4: Screening siRNAs by G, C Content, 5' and 3' End Free Energy

Step 5: Excluding siRNAs with SER Index Value Less than 4

MultipleIndex Screening

Step 6: Excluding siRNAs with Reynolds Index Value Less than 6

Steps 7 & 8: siRNAs Ranked Top 50 in the Top 50% of i-Score Index Are Retained

Step 9: In BLAST Analysis, siRNAs with More than 3 Mismatched Bases Are Retained

Step 10: Excluding siRNAs Targeting Variation Regions and Adding UU Bases Pairs to siRNAs

Step 11(a): The Silencing Efficiency of siRNAs with UU Base Pairs Is Predicted
Predictionof siRNAs
Activity
Step 11(b): siRNAs with Silencing Efficiency Not Less than 97% Were Taken as the Result

Fig.1. Flow chart of the SMRI method for screening siRNAs.

Meng-Xin Chen et al.: SMRI: A New Method for siRNA Design for COVID-19 Therapy

Reynolds et al.’s rule [28] , and candidate siRNAs with
Reynolds index value less than 6 are excluded.
Step 7. According to the i-Score method proposedby Ichihara et al. [34] , the candidate siRNAs are scoredand sorted in descending order according to the i-Score
value, and then the top 50% siRNAs are screened out.
Step 8. If more than 50 siRNAs are selected in theabove steps, we rank them in a descending order according to the i-Score value, and retain the top 50 siRNAs;
otherwise, we retain all the candidate siRNAs.
Step 9. All the candidate siRNAs are confirmed tohave more than three base mismatches in BLAST homology analysis of human genome to avoid silencing
human genes.
Step 10. Candidate siRNAs whose target sequences
are not in the conserved region of the target gene fragment are excluded. Meanwhile, overhanging UU basepairs are added to the 3’ end of the sense strand andthe antisense strand.
Step 11. The silencing efficiency of candidate siRNAs is predicted, and the candidate siRNAs with thepredicted silencing efficiency equal to or greater than
97% are retained. In this study, 97% is used as thethreshold to screen an appropriate number of highefficiency siRNAs.
5
5.1

5.3

Acquisition of Conserved Regions in S
Gene of SARS-CoV-2

The S gene sequence starts at 21563 of the SARSCoV-2 genome and ends at 25381 of the SARS-CoV2 genome. According to the variation annotation of
SARS-CoV-2 of 2019nCoVR from National Genomics
Data Center till February 15th, 2020, the variation regions in the S gene of SARS-CoV-2’s cDNA are exhibited in Table 1. The distribution of variation regions in
2
S gene can be seen in the full version of Table 1○.
Table 1.
cDNA

Variation Regions in the S Gene of SARS-CoV-2’s

Position in Genome

Original Bases of Variation Regions

21644

T

21656

T

21707

C

21949

A

22224

C

22303

T

Experiment and Results
Data Sources

1
Altogether 98 samples○are obtained by searching the 2019 Novel Coronavirus Information Database(2019nCoVR) of National Genomics Data Center [44]to identify the whole-genome nucleotide sequences of
SARS-CoV-2 till February 2020.

5.2

997

22586

T

22622

A

22652

G

22661

G

23403

A

23569

T

23605

T

24034

C

24325

A

24947

G

24990

C

25060

A

25153

C

25347

A

Selection of the Target Sequences

According to the latest research, Hoffmann et al. [45]proved that SARS-CoV-2 utilized the spike protein tobind to angiotensin-converting enzyme2 (ACE2), the
SARS-CoV receptor [46] , thus entering the host cells.
The S gene sequence is a sequence that encodes the
spike protein located near the 3’ end of the wholegenome of SARS-CoV-2. Considering that virus mutation at the site on the target sequence can cause RNAi
failure, the target sequences are selected from the highly
conserved regions in the S gene sequence of the SARSCoV-2 genome.

5.4

Design of siRNAs

In this study, the target sequences of siRNAs are selected from the conserved regions in the S gene sequenceof the SARS-CoV-2 genome, and the SMRI method
described in Section 3 is employed to design siRNAs
based on the SARS-CoV-2 sequence NC 045512.2 obtained from Genbank. When step 10 in SMIR method
is finished, altogether 19 safe and potentially efficientsiRNAs are obtained, as shown in Table 2.
In this study, the siRNA scales are adopted to predict the prediction efficiency of the obtained siRNAs ac-

1○
https://workdrive.zohopublic.com.cn/file/lk8pq67c30baa9b3b487f9e4e2f9616018633, June 2022.
2○
https://workdrive.zoho.com.cn/file/lk8pqd1108f616c8a449b9fe04ddadb038eae, June 2022.

998

J. Comput. Sci. & Technol., July 2022, Vol.37, No.4
Table 2. Candidate siRNAs Obtained After Step 10 in the SMRI Method

Number Position in
Genome
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19

21785
22103
22180
22454
22844
23128
23138
23438
23517
24230
24421
24577
24655
24863
25124
25172
25229
25230
25239

Target Sequence

Sense

Predicted
Silencing
Efficiency (%)
GGTACTAAGAGGTTTGATA GGUACUAAGAGGUUUGAUAuu UAUCAAACCUCUUAGUACCuu
95
GGAAAACAGGGTAATTTCA GGAAAACAGGGUAAUUUCAuu UGAAAUUACCCUGUUUUCCuu
90
GCACACGCCTATTAATTTA GCACACGCCUAUUAAUUUAuu UAAAUUAAUAGGCGUGUGCuu
96
GAAACAAAGTGTACGTTGA GAAACAAAGUGUACGUUGAuu UCAACGUACACUUUGUUUCuu
95
GATTTTACAGGCTGCGTTA GAUUUUACAGGCUGCGUUAuu UAACGCAGCCUGUAAAAUCuu
97
AACTGTTTGTGGACCTAAA AACUGUUUGUGGACCUAAAuu UUUAGGUCCACAAACAGUUuu
95
GGACCTAAAAAGTCTACTA GGACCUAAAAAGUCUACUAuu UAGUAGACUUUUUAGGUCCuu
95
GCAGATCAACTTACTCCTA GCAGAUCAACUUACUCCUAuu UAGGAGUAAGUUGAUCUGCuu
96
GGGCTGAACATGTCAACAA GGGCUGAACAUGUCAACAAuu UUGUUGACAUGUUCAGCCCuu
95
GCAGGTGCTGCATTACAAA GCAGGUGCUGCAUUACAAAuu UUUGUAAUGCAGCACCUGCuu
95
CCAAAATGCACAAGCTTTA CCAAAAUGCACAAGCUUUAuu UAAAGCUUGUGCAUUUUGGuu
98
GACATATGTGACTCAACAA GACAUAUGUGACUCAACAAuu UUGUUGAGUCACAUAUGUCuu
94
GTGTGTACTTGGACAATCA GUGUGUACUUGGACAAUCAuu UGAUUGUCCAAGUACACACuu
89
CACTGGTTTGTAACACAAA CACUGGUUUGUAACACAAAuu UUUGUGUUACAAACCAGUGuu
97
GAGGTTGCCAAGAATTTAA GAGGUUGCCAAGAAUUUAAuu UUAAAUUCUUGGCAACCUCuu
98
GGAAAGTATGAGCAGTATA GGAAAGUAUGAGCAGUAUAuu UAUACUGCUCAUACUUUCCuu
94
GGCTTGATTGCCATAGTAA GGCUUGAUUGCCAUAGUAAuu UUACUAUGGCAAUCAAGCCuu
97
GCTTGATTGCCATAGTAAT GCUUGAUUGCCAUAGUAAUuu AUUACUAUGGCAAUCAAGCuu
92
CCATAGTAATGGTGACAAT CCAUAGUAAUGGUGACAAUuu AUUGUCACCAUUACUAUGGuu
94

cording to the method proposed by Matveeva et al. in2007 [47] . The input parameters of the method include
partial duplex stability, nucleotide position dependence
preference and total G/C content of the siRNAs duplex. Besides, linear regression fitting is adopted toachieve a faster calculation speed. It is found thatsiRNA scales achieved the same performance in identifying the efficient and inefficient siRNAs as that of thebest method identified at the moment [47] . Moreover,the siRNA scales [47] are utilized to predict the silencing efficiency of 19 candidate siRNAs obtained from the
BLAST analysis, as presented in Table 2.
According to Table 2, the prediction silencing efficiency of almost all the screened candidate siRNAs isgreater than 90%, indicating that our SMRI method
is quite effective. The candidate siRNAs with the predicted silencing efficiency greater than or equal to 97%
are finally selected as the designed siRNAs for SARSCoV-2, as shown in Table 3. In previous studies, it
is generally considered that siRNAs with silencing efficiency larger than 70% are high-efficiency siRNAs.
Therefore, these five siRNAs are mostly high-efficiency.
However, the actual silencing efficiency of these five siRNAs needs to be synthesized and tested experimentally.

Antisense

6

Experimental Analysis

In this subsection, we analyze the performance of
the SER index to screen siRNAs with high silencing
efficiency in Subsection 6.1. And we explore the rationality and the performance to screen siRNAs withhigh silencing efficiency of the SMRI method in Subsection 6.2 and Subsection 6.3 respectively. Last but
not least, we introduce the antiviral process and advantages of the SMRI method in Subsection 6.4.
6.1

SER Index Screening Performance
Analysis

As shown in Fig.2, we screen siRNAs with four different indexes based on 3 802 21nt siRNAs on the S genesequence of SARS-CoV-2, and then predict the silencing efficiency of the screened siRNAs with the siRNAscales [47] . Thereafter, the average value of the predicted silencing efficiency of siRNAs is used as the standard to measure the screening effect of each index. In
the chart, the abscissa represents the sample capacity,and the ordinate stands for the average value of thepredicted sample silencing efficiency.

Table 3. siRNAs Designed for SARS-CoV-2
Number
5
11
14
15
17

Position in
Genome
22844
24421
24863
25124
25229

Sense

Antisense

GAUUUUACAGGCUGCGUUAuu
CCAAAAUGCACAAGCUUUAuu
CACUGGUUUGUAACACAAAuuGAGGUUGCCAAGAAUUUAAuu
GGCUUGAUUGCCAUAGUAAuu

UAACGCAGCCUGUAAAAUCuu
UAAAGCUUGUGCAUUUUGGuuUUUGUGUUACAAACCAGUGuuUUAAAUUCUUGGCAACCUCuu
UUACUAUGGCAAUCAAGCCuu

Predicted Silencing
Efficiency (%)
97
98
97
98
97

Meng-Xin Chen et al.: SMRI: A New Method for siRNA Design for COVID-19 Therapy
Amarzguioui and Prydz[29]Hsieh et al.[30]Reynolds et al.[28]
SER

96
94
92
90
88
86
84
82
80
10

20

30

40

50

60

70

80

90

100

100
Average of Predicted Silencing
Efficiency of siRNAs (%)

Mean Value of siRNA
Prediction Efficiency (%)

98

Number of siRNA Samples (Piece)

6.2

Rationality Analysis of SMRI

The siRNA scales [47] are utilized to predict the silencing efficiency of candidate siRNAs in the screening
process of the SMRI method. As shown in Fig.3, theabscissa represents the number of execution steps in the
SMRI method. Steps 1 and 2 in the SMRI method do
not screen the candidate siRNAs. As a result, they are
not drawn in Fig.3.

90

80

70

60

50
5
6
7
8
9
10
11
3
4
Number of the Execution Algorithm Step of SMRI

Fig.2. Screening performance chart for different indicators.

To measure the ability of the proposed new index SER in screening the high-efficiency siRNAs, ourmethod is compared with those proposed by Reynolds
et al. [28] , Amarzguioui and Prydz [29] , and Hsieh etal. [30] based on 3 802 21nt siRNAs on the S gene sequence of SARS-CoV-2. Four methods are used to sort
all the candidate siRNAs in the descending order, thenmultiple samples were taken, and the siRNA scales [47]are utilized to predict the sample silencing efficiency.
The comparison results of the screening abilities of thefour methods are displayed in Fig.2.
As shown in Fig.2, the prediction efficiency and thesilencing efficiency of siRNAs screened by our SER index decrease with the increase of the number of predicted siRNAs, indicating that the index attains efficient screening performance. Therefore, the relatively
inefficient siRNAs with a lower ranking in the numberof predicted siRNAs reduces the overall average prediction efficiency. In addition, compared with the siRNAs
screened by other indexes, the siRNAs screened by the
SER index have evident advantages in the prediction silencing efficiency of the top 10 siRNAs. Moreover, thesiRNAs screened by our SER index exhibit the highest
prediction efficiency on the whole, which indicates that
our SER index outperforms the other three methods inscreening high-efficiency siRNAs.

999

Fig.3. Trend chart of average prediction efficiency of candidate
siRNAs in the screening process using the SMRI method.

It is observed from Fig.3 that, after screening thethree indexes, the prediction efficiency of candidate siRNAs increases. Besides, the selection steps of our index
effectively utilize the screening ability of each index andsuccessfully screen out the high-efficiency siRNAs layer
by layer. Firstly, the SER index with more evaluation
features is used to preliminarily screen the candidate
siRNAs. Secondly, the Reynolds rule [28] is employed
to more rigorously and precisely screen the candidate
siRNAs. Thirdly, the i-Score method [34] with a highscreening capability is utilized to screen siRNAs withhigh silencing efficiency. By adopting this order, ourproposed SMRI method achieves excellent performancein screening siRNAs.
6.3

Screening Performance Analysis of SMRI

The whitehead siRNA selection web server [48] is an
siRNA design tool developed by the Whitehead Institute at MIT, which has the characteristics of flexible customization. As shown in Table 4, we simply record this method as Whitehead. In the present
study, this tool is adopted to retrieve the S genome
sequence of SARS-CoV-2 with a typical AA(N19)UU
format, in which N indicates any nucleotide [29] . Candidate siRNAs which have a mismatch of over threebases in the BLAST homology analysis of the humangenome were retained. After the BLAST analysis,nine candidate siRNAs are obtained. The remaining
settings are the same as the default settings of theWhitehead [48] . Then, the siRNA scales [47] are utilized
to predict the siRNAs silencing efficiency and calculate the average value to obtain the items presented
in Table 4. Additionally, Designer of Small Interfering RNA (DSIR) [33] and inhibitory-Score (i-Score) [34]1000

J. Comput. Sci. & Technol., July 2022, Vol.37, No.4

are the precise second-generation siRNA design methods. This study uses these two methods to screen all
the candidate siRNAs with the same siRNA sequencelength as siRNAs obtained after step 10 in the SMRI
method on the S gene of SARS-CoV-2, respectively.
Then, as shown in Table 4, the average predicted silencing efficiency of siRNAs screened by DSIR and i-Score
with the same number of siRNAs obtained after step
10 using the SMRI method is calculated.

analysis on the target sequences of siRNAs, the siRNAs designed using our method have more than threemismatched bases with the human genome. This minimizes the miss target effect of siRNAs and ensures thesafety and reliability of siRNAs for the human body.
Most importantly, compared with other siRNA design
algorithms, siRNAs designed by our new method havea high silencing efficiency, which will render them withstrong antiviral effect.

Table 4. Comparison of the Screening Ability for Effective siRNAs of Different siRNA Design Algorithms

7

MethodSample Size

Average Value of Predicted
Interference Efficiency (%)

Whitehead

974.555 6

i-Score19

93.947 3DSIR

1995.052 6SMRI

19

94.842 1

It is obviously observed from Table 4 that, ourmethod outperforms the typical siRNA design method
Whitehead in searching for target sequences conforming
to the AA(N19)UU format [29] , and the performance ofour method is comparable to that of the advanced highprecision siRNA design methods, such as i-Score and
DSIR. Moreover, considering the off-target effect, siRNAs designed by the SMRI method do not target thehuman genome, and are safe for human body, whichcannot be achieved by siRNAs designed by the otherthree methods.
6.4

Antiviral Process and Advantages of SMRI

In this study, in order to maintain the reliability ofthe efficacy of siRNAs in the case of virus mutation,
the target region of designed siRNAs selects the conserved regions in the S gene of SARS-CoV-2. As mentioned in Section 2, if the designed siRNAs were applied to the patients, they cleaved the RNA sequencesof SARS-CoV-2, resulting in the functional silencingof the SARS-CoV-2 S gene. This further blocks thetranslation of the spike glycoprotein, which is utilized
by SARS-CoV-2 for cellular invasion, thereby significantly reducing the threat and harm induced by thevirus to the human body. In addition, an enhanced
antiviral effect can be acquired by using multiple siRNAs simultaneously [22] . With the cooperation of boththe immune system and other drugs, patients will overcome the residual SARS-CoV-2 and regain their health
hopefully. Compared with other therapies, RNAi is safeand only specific to SARS-CoV-2. Through the BLAST

Conclusions

Compared with the existing research, this study
proposed a new mathematical model named BPTC
model regarding the siRNA silencing efficiency, a newindex SER to extract the rules in methods summarizing
the characteristic properties of high-efficiency siRNAs
and to efficiently screen high-efficiency siRNAs basedon the BPTC model. It also presented a new, efficientmethod named SMRI to efficiently design siRNAs using the SER index to effectively filter out the candidate
siRNAs that are difficult to take effect or synthesize.
That is, the SER index is part of the SMRI method.
Moreover, this study successfully designed five potentially efficient siRNAs using the SMRI method. Findings in this study provided an effective support for thesubsequent research and development of RNAi for thetreatment of COVID-19.
Moreover, there are several interesting directions in
the research of RNAi we want to expand upon, such as
developing new siRNA interference efficiency prediction
algorithms using deep learning methods, and explaining the related factors of effective RNAi using machinelearning methods.
References[1] Saif L J, Wang Q, Vlasova A N et al. Coronaviruses.
In Diseases of Swine (11th edition), Zimmerman J
J, Karriker L A, Ramirez A, Schwartz K J, Stevenson G W, Zhang J Q (eds.), 2019, pp.488-523. DOI:
10.1002/9781119350927.ch31.
[2] Cui J, Li F, Shi Z L. Origin and evolution of pathogenic
coronaviruses. Nature Reviews Microbiology, 2018, 17(3):181-192. DOI: 10.1038/s41579-018-0118-9.
[3] Li F. Structure, function, and evolution of coronavirus spike
proteins. Annual Review of Virology, 2016, 3(1): 237-261.
DOI: 10.1146/annurev-virology-110615-042301.
[4] Richman D D, Whitley R J, Hayden F G. Clinical Virology(3rd edition). ASM Press, 2009.
[5] Drosten C, Günther S, Preiser W et al. Identification of anovel coronavirus in patients with severe acute respiratory
syndrome. The New England Journal of Medicine, 2003,
348: 1967-1976. DOI: 10.1056/NEJMoa030747.

Meng-Xin Chen et al.: SMRI: A New Method for siRNA Design for COVID-19 Therapy[6] Yin Y, Wunderink R G. MERS, SARS and other coronaviruses as causes of pneumonia. Respirology, 2017, 23(2):130-137. DOI: 10.1111/resp.13196.
[7] Shereen M A, Khan S, Kazmi A et al. COVID-19 infection:
Origin, transmission, and characteristics of human coronaviruses. Journal of Advanced Research, 2020, 24: 91-98.
DOI: 10.1016/j.jare.2020.03.005.
[8] Zhu N, Zhang D, Wang W et al. A novel coronavirus
from patients with pneumonia in China, 2019. New England Journal of Medicine, 2020, 382(8): 727-733. DOI:
10.1056/NEJMoa2001017.
[9] Wang C, Horby P W, Hayden F G et al. A novel coronavirus outbreak of global health concern. The Lancet, 2020,
395(10223): 470-473. DOI: 10.1016/S0140-6736(20)301859.
[10] Chen N, Zhou M, Dong X et al. Epidemiological and clinical characteristics of 99 cases of 2019 novel coronavirus
pneumonia in Wuhan, China: A descriptive study. The
Lancet, 2020, 395(10223): 507-513. DOI: 10.1016/S01406736(20)30211-7.
[11] Lindbo J A, Silva-Rosales L, Proebsting W M et al. Induction of a highly specific antiviral state in transgenic
plants: Implications for regulation of gene expression andvirus resistance. The Plant Cell, 1993, 5(12): 1749-1759.
DOI: 10.1105/tpc.5.12.1749.
[12] Vance V, Vaucheret H. RNA silencing in plants—Defense
and counterdefense. Science, 2001, 292(5525): 2277-2280.
DOI: 10.1126/science.1061334.
[13] Montgomery M K, Xu S, Fire A. RNA as a target of doublestranded RNA-mediated genetic interference in Caenorhabditis elegans. Proceedings of the National Academy of Sciences of the United States of America, 1998, 95(26): 1550215507. DOI: 10.1073/pnas.95.26.15502.
[14] Voinnet O, Pinto Y M, Baulcombe D C. Suppression ofgene silencing: A general strategy used by diverse DNA
and RNA viruses of plants. Proceedings of the National
Academy of Sciences of the United States of America, 1999,
96(24): 14147-14152. DOI: 10.1073/pnas.96.24.14147.
[15] Nishikura K. A short primer on RNAi: RNA-directed RNA
polymerase acts as a key catalyst. Cell, 2001, 107(4): 415418. DOI: 10.1016/S0092-8674(01)00581-5.
[16] Tijsterman M, Ketting R F, Plasterk R H A. The genetics
of RNA silencing. Annual Review of Genetics, 2002, 36(1):489-519. DOI: 10.1146/annurev.genet.36.043002.091619.
[17] Lippman Z, Martienssen R. The role of RNA interferencein heterochromatic silencing. Nature, 2004, 431(7006): 364370. DOI: 10.1038/nature02875.
[18] Siomi H, Mikiko C, Siomi M C. On the road to reading theRNA-interference code. Nature, 2009, 457(7228): 396-404.
DOI: 10.1038/nature07754.
[19] McManus M T, Sharp P A. Gene silencing in mammals
by small interfering RNAs. Nature Reviews Genetics, 2002,
3(10): 737-747. DOI: 10.1038/nrg908.
[20] Thomas M, Greil J, Heidenreich O. Targeting leukemic
fusion proteins with small interfering RNAs: Recent advances and therapeutic potentials 1. Acta Pharmacologica Sinica, 2006, 27(3): 273-281. DOI: 10.1111/j.17457254.2006.00282.x.

1001[21] Ichim T E, Li M, Qian H et al. RNA interference:
A potent tool for gene-specific therapeutics. American
Journal of Transplantation, 2004, 4(8): 1227-1236. DOI:
10.1111/j.1600-6143.2004.00530.x.
[22] Qureshi A, Tantray V G, Kirmani A R et al. A review on current status of antiviral siRNA. Reviews in
Medical Virology, 2018, 28(4): Article No. e1976. DOI:
10.1002/rmv.1976.
[23] Shi Y, Yang D H, Xiong J et al. Inhibition of genes expression of SARS coronavirus by synthetic small interfering RNAs. Cell Research, 2005, 15(3): 193-200. DOI:
10.1038/sj.cr.7290286.
[24] Barouch-Bentov R, Einav S. Turning up your nose for a flaviviral encephalitis cure. Cell Host & Microbe, 2018, 23(4):427-429. DOI: 10.1016/j.chom.2018.03.014.
[25] Wu C J, Chan Y L. Antiviral applications of RNAi for coronavirus. Expert Opinion on Investigational Drugs, 2006,
15(2): 89-97. DOI: 10.1517/13543784.15.2.89.
[26] Thakur N, Qureshi A, Kumar M. VIRsiRNAdb: A curated
database of experimentally validated viral siRNA/shRNA.
Nucleic Acids Research, 2012, 40(D1): D230-D236. DOI:
10.1093/nar/gkr1147.
[27] Elbashir S, Harborth J, Weber K, Tuschl T. Analysis of genefunction in somatic mammalian cells using small interfering
RNAs. Methods, 2002, 26(2): 199-213. DOI: 10.1016/S10462023(02)00023-3.
[28] Reynolds A, Leake D, Boese Q, Scaringe S, MarshallW S, Khvorova A. Rational siRNA design for RNA interference. Nat. Biotechnol., 2004, 22(3): 326-330. DOI:
10.1038/nbt936.
[29] Amarzguioui M, Prydz H. An algorithm for selection of functional siRNA sequences. Biochem. Biophys. Res. Commun., 2004, 316(4): 1050-1058. DOI:
10.1016/j.bbrc.2004.02.157.
[30] Hsieh A C, Bo R, Manola J, Vazquez F, Bare O, Khvorova
A, Scaringe S, Sellers W R. A library of siRNA duplexes
targeting the phosphoinositide 3-kinase pathway: Determinants of gene silencing for use in cell-based screens. Nucleic
Acids Res, 2004, 32(3): 893-901. DOI: 10.1093/nar/gkh238.
[31] Takasaki S, Kotani S, Konagaya A. An effective method forselecting siRNA target sequences in mammalian cells. Cell
Cycle, 2004, 3(6): 790-5. DOI: 10.4161/cc.3.6.892.
[32] Katoh T, Suzuki T. Specific residues at every third position of siRNA shape its efficient RNAi activity. Nucleic Acids Res., 2007, 35(4): Article No. e27. DOI:
10.1093/nar/gkl1120.
[33] Vert J P, Foveau N, Lajaunie C et al. An accurate and interpretable model for siRNA efficacy prediction. BMC Bioinformatics, 2006, 7(1): Article No. 520. DOI: 10.1186/14712105-7-520.
[34] Ichihara M, Murakumo Y, Masuda A et al. Thermodynamic
instability of siRNA duplex is a prerequisite for dependable prediction of siRNA activities. Nucleic Acids Research,
2007, 35(18): Article No. e123. DOI: 10.1093/nar/gkm699.
[35] Qureshi A, Thakur N, Kumar M. VIRsiRNApred: A web
server for predicting inhibition efficacy of siRNAs targeting
human viruses. Journal of Translational Medicine, 2013,
11: Article No. 305. DOI: 10.1186/1479-5876-11-305.
[36] Pei Y, Tuschl T. On the art of identifying effective and specific siRNAs. Nature Methods, 2006, 3(9): 670-676. DOI:
10.1038/nmeth911.

1002

J. Comput. Sci. & Technol., July 2022, Vol.37, No.4

[37] Geley S, Müller C. RNAi: Ancient mechanism with apromising future. Experimental Gerontology, 2004, 39(7):985-998. DOI: 10.1016/j.exger.2004.03.040.
[38] Martinez J, Patkaniowska A, Urlaub H, Lührmann R,
Tuschl T. Single-stranded antisense siRNAs guide target
RNA cleavage in RNAi. Cell, 2002, 110(5): 563-574. DOI:
10.1016/s0092-8674(02)00908-x.
[39] Novina C D, Sharp P A. The RNAi revolution. Nature,
2004, 430(6996): 161-164. DOI: 10.1038/430161a.
[40] Schubert S, Grünweller A, Erdmann V A et al. Local
RNA target structure influences siRNA efficacy: Systematic analysis of intentionally designed binding regions. Journal of Molecular Biology, 2005, 348(4): 883-893. DOI:
10.1016/j.jmb.2005.03.011.
[41] Preall J B, Sontheimer E J. RNAi: RISC gets loaded. Cell,
2005, 123(4): 543-545. DOI: 10.1016/j.cell.2005.11.006.
[42] Holen T, Amarzguioui M, Wiiger M T, Babaie E, Prydz H.
Positional effects of short interfering RNAs targeting thehuman coagulation trigger tissue factor. Nucleic Acids Res.,
2002, 30(8): 1757-1766. DOI: 10.1093/nar/30.8.1757.
[43] Yiu S M, Wong P W H, Lam T W et al. Filtering of ineffective siRNAs and improved siRNA design tool. Bioinformatics, 2005, 21(2): 144-151. DOI: 10.1093/bioinformatics/bth498.
[44] Zhao W M, Song S H, Chen M L et al. The 2019 novel coronavirus resource. Hereditas (Beijing), 2020, 42(2): 212-221.
DOI: 10.16288/j.yczz.20-030. (in Chinese)[45] Hoffmann M, Kleine-Weber H, Schroeder S et al. SARSCoV-2 cell entry depends on ACE2 and TMPRSS2 and isblocked by a clinically proven protease inhibitor. Cell, 2020,
181(2): 271-280. DOI: 10.1016/j.cell.2020.02.052.
[46] Li W, Moore M J, Vasilieva N et al. Angiotensin-converting
enzyme 2 is a functional receptor for the SARS coronavirus. Nature, 2003, 426(6965): 450-454. DOI: 10.1038/nature02145.
[47] Matveeva O, Nechipurenko Y, Rossi L, Moore B, Saetrom
P, Ogurtsov A Y, Atkins J F, Shabalina S A. Comparisonof approaches for rational siRNA design leading to a new
efficient and transparent method. Nucleic Acids Res., 2007,
35: Article No. e63. DOI: 10.1093/nar/gkm088.
[48] Yuan B, Latek R, Hossbach M, Tuschl T, Lewitter F. siRNA
selection server: An automated siRNA oligonucleotide
prediction server. Nucleic Acids Res., 2004, 32(suppl 2):
W130-W134. DOI: 10.1093/nar/gkh366.

Meng-Xin Chen received his B.S.
degree in chemical engineering andtechnology from Changzhou University,
Changzhou, in 2019. He is currently
studying for his M.S. degree in College of Software of Jilin University,
Changchun. His research interests aremachine learning, bioinformatics, andRNA interference.

Xiao-Dong Zhu received his B.S.
degree in computer science and technology from Peking University, Beijing,
his M.S. degree in computer applicationand technology and his Ph.D. degree
in communication information system,
both from Jilin University, Changchun.
He is currently a professor of computerscience of Jilin University, Changchun.
His researchinterests include software engineering, RNA interference,pattern recognition, and machine learning techniques forbiometrics.
Hao Zhang received his B.S. degree
in computer applications, M.S. degree
in biomechanics and Ph.D. degree, allfrom Jilin University, Changchun. He
is currently a professor of computerscience of Jilin University, Changchun.
His current research interests include
machine learning, bioinformatics, andRNA interference.
Zhen Liu received his B.S. degree,
M.S. degree, both in computer applications, from Jilin University, Changchun,and his Ph.D. degree in information
science from Tohoku University, Sendai,in 1998. He is a professor in the Graduate School of Engineering, Nagasaki
Institute of Applied Science, Nagasaki.
His current research interests include RNA interference,
big data and data mining, and embedded computing. He
is a member of IEEE Computer Society.
Yuan-Ning Liu received his B.S.
degree, M.S. degree in computer
application and Ph.D. degree in communication information system from
Jilin University, Changchun. He is currently a professor of computer scienceof Jilin University, Changchun. His
current research interests are software
engineering, RNA interference, pattern recognition, andbioinformatics.