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VIRsiRNAdb: a curated database of experimentally
validated viral siRNA/shRNA
Nishant Thakur, Abid Qureshi and Manoj Kumar*
Bioinformatics Centre, Institute of Microbial Technology, Council of Scientific and Industrial Research (CSIR),
Sector 39-A, Chandigarh-160036, India
Received August 15, 2011; Revised October 4, 2011; Accepted November 9, 2011
ABSTRACT
RNAi technology has been emerging as a potential
modality to inhibit viruses during past decade. In
literature a few siRNA databases have been
reported that focus on targeting human and mam-
malian genes but experimentally validated viral
siRNA databases are lacking. We have developed
VIRsiRNAdb, a manually curated database having
comprehensive details of 1358 siRNA/shRNA target-
ing viral genome regions. Further, wherever avail-
able, information regarding alternative efficacies of
above 300 siRNAs derived from different assays has
also been incorporated. Important fields included in
the database are siRNA sequence, virus subtype,
target genome region, cell type, target object, ex-
perimental assay, efficacy, off-target and siRNA
matching with reference viral sequences. Database
also provides the users with facilities of advance
search, browsing, data submission, linking to
external databases and useful siRNA analysis tools
especially siTarAlign which align the siRNA with ref-
erence viral genomes or user defined sequences.
VIRsiRNAdb contains extensive details of siRNA/
shRNA targeting 42 important human viruses
including influenza virus, hepatitis B virus, HPV
and SARS Corona virus. VIRsiRNAdb would prove
useful for researchers in picking up the best viral
siRNA for antiviral therapeutics development and
also for developing better viral siRNA design tools.
The database is freely available at http://crdd.osdd
.net/servers/virsirnadb.
INTRODUCTION
Viral diseases remain one of the public health problems
due to emerging and reemerging nature of viruses such as
influenza, hepatitis, Human Immunodeficiency Virus
(HIV), Human Papillomavirus (HPV) & Severe Acute
Respiratory Syndrome (SARS) etc. (1). Combating
majority of these viruses is compromised due to lack of
effective vaccines and antiviral drugs (2). Besides, devel-
opment of new vaccines and antiviral drugs, there are con-
tinuous efforts to search for alternative therapeutic
interventions. Lately, RNA interference has emerged as
a potential approach in the battle against pathogenic
viruses (3,4) and other human diseases (5,6).
RNAi was first reported by Fire et al.(7) when authors
showed a potent gene silencing effect after injecting double
stranded RNA into C. elegans. In RNA silencing
pathway, long dsRNA is processed by RNase III family
member, dicer, to a 19–21 nucleotide long double stranded
siRNA, with 2-nucleotide unphosphorylated 30overhangs
(8). The double stranded siRNA is composed of a guide
(antisense) strand and a passenger (sense) strand.
Unwinding of the siRNA duplex is catalyzed by
argonaute. After the unwinding step, the guide strand is
incorporated into the RNA Induced Silencing Complex
(RISC), while the passenger strand is released. Using the
antisense strand RISC targets, the complementary mRNA
resulting in the cleavage of the latter (9).
Using RNA silencing mechanism, researchers have
reported considerable decrease in the expression of
targeted viral genes (10,11). For example, siRNAs
directed against the influenza virus nucleocapsid (NP)
and RNA transcriptase (PA) genes inhibited its transcrip-
tion and replication (12). Similarly, siRNAs against the
hepatitis B virus polyadenylation (PA), precore (PreC)
and surface (S) regions inhibited the viral replication
(13). In another study, siRNAs synthesized to target the
E, M and N genes of SARS-CoV effectively down
regulated the target genes expression by over 80% in a
dose-dependent manner (14). Inhibition of virus replica-
tion for several human viruses using RNAi strategy has
been reviewed (3,15,16).
RNAi approach offers several advantages for antiviral
therapeutics development. It has ability to target all types
*To whom correspondence should be addressed. Tel: +91 172 6665453; Fax: +91 172 2690632; Email: manojk@imtech.res.in;
manojkumardelhi@yahoo.co.in
The authors wish it to be known that, in their opinion, the first two authors should be regarded as joint First Authors.
Nucleic Acids Research, 2011, 1–7
doi:10.1093/nar/gkr1147
ßThe Author(s) 2011. Published by Oxford University Press.
This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/
by-nc/3.0), which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.
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of viral genomes [ssDNA, dsDNA, RNA(+), RNA()
and dsRNA] which makes this versatile mechanism to be
harnessed as broad-spectrum antiviral therapy (4).
Further, RNAi targets a short stretch of viral nucleic
acids instead of a functional domain of a viral protein,
therefore, even a small viral genome offers many potential
targets (11). Even more, multiple antiviral siRNAs can be
expressed simultaneously or pooled in a way similar to
current drug combination anti-viral therapy of infected
individuals to sustain prolonged effect (17,18).
In the past decade, a number of RNAi therapeutic
programs with focus on cancer, metabolic diseases, re-
spiratory disorders, retinal degeneration, dominantly
inherited brain and skin diseases and infectious dis-
eases have entered the clinical practice (6,19,20).
Simultaneously, several RNAi based antiviral therapeutic
projects have also reached at clinical trial stages (21), for
example, RSV (Phase II) (22), HBV (Phase I) (23), HCV
(Phase II) (24) and HIV (Phase I) (25). Ongoing clinical
trials further emphasize the need for development of the
viral RNAi resources.
There is no dedicated viral siRNA database, except
HIVsirDB, an HIV specific siRNA database (26).
However, there are a few other siRNA databases
reported in literature like HuSiDa (27) and siRNAdb
(28) which provide sequences of published functional
siRNA targeting human genes while siRecords (29)
focused on siRNA data of mammalian RNAi experiments
and DSTHO (30) on human oncogenes. VIRsiRNAdb is
an attempt to provide comprehensive details of the experi-
mentally validated viral siRNA targeting the diverse
genome regions of as many as 42 important human
viruses at one platform to help researchers working in
the field of siRNA based antiviral therapeutic
development.
DATABASE CONTENT
Data acquisition
Exhaustive literature search was carried out to extract the
relevant articles from PubMed. This was accomplished by
searching queries having combination of two keywords: (i)
terms most commonly used for gene silencing viz: RNA
interference, RNAi, silencing, siRNA(s), shRNA(s), small
interfering RNA(s), short interfering RNA(s), small
hairpin RNA(s) and short hairpin RNA(s) and (ii) virus
names including their common names, aliases & abbrevi-
ations (like Severe acute respiratory syndrome, SARS,
Corona Virus, SARS-CoV). Full text search using the
above two keywords combinations was performed for
each of the human viruses individually. The search
results are given in Supplementary Table S1. Around
4000 abstracts were screened so as to select the articles
likely to contain relevant viral siRNA information.
Reviews, general methodological and non-English
articles were not considered. After initial screening,
around 1000 remaining potential articles were examined
in detail to retrieve the viral siRNA information. Articles
of siRNAs targeting the host genome regions were
excluded. Further, articles that did not have individual
siRNA sequence or its experimental efficacies were also
not included. After this extensive filtering, 221 research
articles were shortlisted to collect the siRNA data. In
our database, complete siRNA data of almost all human
viruses reported in the literature have been included.
Database architecture
The database provides comprehensive information of
experimentally validated viral siRNAs which includes:
(i) siRNA sequence, (ii) family of virus, (iii) virus
subtype, (iv) target gene, (v) siRNA location,
(vi) GenBank accession, (vii) design algorithm, (viii) pro-
duction method, (ix) siRNA concentration, (x) cell type,
(xi) transfection method, (xii) incubation time, (xiii)
PubMed ID, (xiv) object used (i.e. mRNA protein, virus
load etc), (xv) efficacy, (xvi) efficacy assay (e.g. Western
blot, PCR, plaque number, ELISA) and (xvii) references.
Further, wherever available, extended information regard-
ing alternative efficacy assays has also been provided.
Architecture of the database is depicted in the Figure 1.
Structure of each siRNA predicted by Mfold (31) was also
displayed in the data. In addition, we have also provided
information of viral siRNA off-targets in human and the
siRNA sequence matching with the reference viral genome
sequences.
Database statistics
VIRsiRNAdb database provides information of 1358 ex-
perimentally validated siRNAs pertaining to 42 important
human viruses belonging to 19 different virus families and
targeting as many as 150 different viral genome regions.
For HBV, HCV, SARS and Coxsackievirus many genome
regions were being targeted by siRNAs as given in
Table 1. The database entries contain siRNA experiments
based on 71 different cell lines but Huh-7, 293T, MDCK,
HepG2.2.15 and HeLa cell lines were mostly used
(Figure 2a). In the database, 45% of the total siRNAs
were highly effective with >70% inhibition efficacy and
9% siRNA have >90% efficacy. siRNAs (23%) have
moderate efficacy of 50–70% whereas 32% of siRNAs
were less effective with efficacy rating <50% (Figure 2b).
One of the major hindrances in RNAi based therapeut-
ics is the lack of siRNA specificity. Besides, directly affect-
ing the expression of the desired genes, a siRNA may
affect regulation of unintended transcripts which possess
complementarity to the siRNA sequence. siRNA
off-target effect was initially reported in 2003 (32) and
later Amanda Birmingham et al.(33) reported that
off-targeting is associated with the presence of one or
more perfect 30untranslated region (UTR) matches with
the hexamer or heptamer seed region (positions 2–7 or
2–8) of the antisense strand of the siRNA. Seed based
siRNA off-target was experimentally demonstrated by
others also (34,35). The impact of non-specific siRNA
off-target effect in therapeutic application was further
reviewed (36).
We have predicted the off-targets in human for all the
siRNAs present in our database, using three algorithms:
(i) BLAST (37), (ii) Seed Locator (33) and (iii)
SpecificityServer (38). Result outputs of each algorithm
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are given against respective siRNA record as link under
off-target column. BLAST algorithm was commonly used
to detect possible off-target effects of a siRNA by
searching it against the human Unigene or transcriptome
database (28). We have also used BLAST (37) with e
1000; q4; r5 parameters and found that around
13% of siRNA having off-targets in the human genome.
Seed Locator output include total genes with at least one
seed match and multiple seed matches in the 30-UTR.
Finally, results of SpecificityServer which is designed to
identify potential non-specific matches to siRNA showed
that 113 siRNAs are not specific for both siRNA strands
while 101 siRNA have off-targets for the sense strand and
remaining does not have any off-target.
As we know that viruses exhibit greater genetic variabil-
ity, therefore it is important to know that in how many
viral genome sequences, siRNA sequence is matching.
This analysis is helpful for users in selecting such siRNA
which is having high matching with maximum reference
viral strains. Significance of selection of conserved regions
targeted by siRNA in HIV-1 has been discussed by Naito
et al. (39,40). We have checked the siRNA sequence
matching with the reference viral genome sequences avail-
able at NCBI. For this purpose, we have used ALIGN0
algorithm (41), which computes the alignment of two
DNA sequences without penalizing for end-gaps. Pie
chart result displayed the number of nucleotide differences
or mismatches (0, 1, 2, 3, >3) between of each siRNA and
respective viral reference genome sequences in the align-
ment. Cumulative results of all the siRNA showed that
2% of siRNAs were fully (100%) matching with respective
viral genome sequences and 16% matched with 90–99%
viral genomes while 61% were having <50% matching as
shown in Figure 2c.
There are reports of escape mutants generated by the
virus in the siRNA target site to overcome the effect of
RNAi. These escape mutations in the target sequence de-
creases the potency of siRNA gene silencing (42). Wilson
(43) observed maximum escape mutations at 12th and
18th residues for HCV NS5B while Konishi (44)
reported appreciable mutation at the 15th residue for
HCV NS5A gene. In another study, Jun (45) recorded
changes in Coxsackie virus at positions 10 and 13. We
have collected such 57 siRNA escape sequences having
52 substitutions; 2 deletions; 1 insertion and 2 substitu-
tion/deletion mutations. Position of these escape substitu-
tions mutations among 57 escape sequences are shown in
Figure 2d.
Tools
Viral siRNA database allows the users to take advantage
of useful tools like siTarAlign, siRNAmap and
siRNAblast. siTarAlign aligns the siRNA sequence with
the respective virus/family reference genomes sequences
using either BLAST (37) or Smith–Waterman algorithm
from EMBOSS suite (46) The output shown below
displays a list of flaviviruses and influenza A viruses
targeted by respective siRNA (Figure 3). Viral/family ref-
erence genomes were taken from the NCBI viral genome
resources as summarized in the Supplementary Table S2.
In siTarAlign, user defined viral genome sequences can
also be uploaded to align the siRNA sequence with user
provided sequences also.
The ‘siRNAmap’ is a simple tool to display the perfectly
matching siRNA available in our database to the user
provided viral sequence. So, it helps the user to know
that against the user provided viral sequence, how many
siRNAs are available in VIRsiRNAdb. Additionally, the
Figure 1. VIRsiRNAdb database architecture.
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Table 1. Number of siRNAs for 42 viruses and targeted genome regions
Virus Target gene No. of
siRNAs
Virus Target gene No. of
siRNAs
BK polyomavirus T-Ag(2) 2 Influenza A virus M(33), NP(26), PB(19), PA(11), NS(6),
C(1)
96
Chikungunya virus E1(1), NSP3(1) 2 Influenza B virus PB(33), NP(17), M(11), NS(10), PA(10) 81
Dengue virus [DENV] E(6), NS5(6), 30-UTR(5), 50-UTR(2), C(1),
NS3(1), PreM(1)
22 Japanese encephalitis virus [JE] NS1(4), E(2) 6
Ebolavirus [EBOV] ZNP(2), ZT(2), ZL(1) 5 John Cunningham virus [JCV] T-Ag (1) 1
Encephalomyocarditis virus EMCV-IRES(1) 1 Junı
´n virus Z(4) 4
Enterovirus [EV]
3D Pol(10), VP1(3), 2C(2), 3C pro(2),
50NTR(2), 30-UTR(1), MET-2C(1),
VP2(1)
22 La Crosse virus M(G2)(7), L(4), S(3) 14
Epstein–Barr virus [EBV] EBNA1(25), LMP1(10), PR(4), BKRF3(2),
LMP2A(1), Zta(1)
43 Lassa virus GPC(1), L(1), NP(1), Z(1) 4
Hazara nairovirus L(4), M(4), S(4) 12 Lymphocytic choriomeningitis virus L(1), Z(1) 2
Hendra virus N(2) 2 Marburg virus VP30(2), NP(1) 3
Henipavirus L(4), N(4) 8 Measles virus N(16), L(8) 24
Hepatitis A virus [HAV]
2C(2), 3D(2), 3A(1), 3C(1)
6 Polio virus Capsid(1), 5NC(1) 2
Hepatitis B virus [HBV] S(60), X(48), C(25), P/S(24), P(17), C/P(11),
ORF-C(3), ORF-S(3), P/X(3), NLS(2),
PRE(2), preS/P(2), PA(1), PreS1(1),
HBeAg(7)
227 Rabies N(3) 3
Hepatitis C virus [HCV]
50-UTR(38), 30-UTR(26), NS5B(22),
Core(19), E(11), NS3(8), E2(7), IRES
(50-UTR)(6), NS5A(3), NS4B(2), NAa(1),
NS4A(1)
145 Reovirus mNS(7), sNS(4), m2(2) 13
Hepatitis delta virus [HDV] Delta Ag(16) 16 Rotavirus NSP5(2) 2
Hepatitis E virus [HEV] ORF2(4), RdRp(4), Helicase(2),
Replicase(2), 3CAE region(1)
13 SARS coronavirus ORF9a, N-protein(31), ORF5,
M-protein(23), ORF4, E-protein(22),
ORF2, Spike(18), Replicase(16),
RDRP(11), ORF1a(8), ORF1b(7),
ORF3a(7), (6), 3A(3), NSP1(3), ORF7(3),
30-UTR(2), 50-UTR(1), Leader(1), TRS(1)
163
Herpes simplex virus [HSV] U51(4), UL39(4), UL40(4), DNA polymer-
ase(3), gD(3), UL29(3), UL5(3), Vp
16(3), gB(2), UL27(2), UL38(2), gE(1),
K13(1), ORF75(1)
36 Semliki forest virus Cold(7), Hot(7) 14
Human coxsackievirus [CV]
50-UTR(29), 3D(25), 2A(6), VP1(6),
1B(5), 1D(5), 2C(5), 1C(4), 3C(3), Rev(3),
30-UTR(2), RdRP(2), MET-2C(2),
50NTR(2), 3A(1), AUG start Codon
region(1), POL(1)
103 Sendai VIRUS HN(5) 5
Human metapneumovirus L(26), N(11), M(9), F(8), P(4) 58 St. Louis encephalitis [SLE] E(1), C(2), NS5(2) 5
Human papillomavirus [HPV] E6(42), E7(39), E6/E7(8) 89 Vaccinia E3L(4) 4
Human respiratory syncytial
virus [HRSV]
NS1(4), P(4), NS2(1) 9 West Nile virus [WNV] E(18), NS5(17), Core(7), C(4), NS1(4),
NS4B(4), PrM/M(3), 30-UTR(2), NS3(2),
CAP(1), NS2A(1), NS4A/B(1)
64
Human rhinovirus
3D(4), 2C(3), 5-UTR(3), VP3(3), 3C(2),
VP1(2), VP2(2), 2A(1), 3A(1), VP4(1)
22 Yellow fever virus NS5(2), E(1), NS1(1) 4
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siRNAblast allows alignment of a user provided siRNA
sequence against all the siRNA sequences available in our
database. This helps the user to confirm whether a given
siRNA sequence or similar one has already been reported
or not.
Data retrieval
It is possible to perform a quick search based on various
database fields i.e. Virus name, siRNA sequence, target
region, cell line and Pubmed ID. We have included a
separate search option to retrieve siRNA with efficacy;
greater than, equal to and lower than for a given value.
Database also has qualitative efficacy of some siRNAs
(where numerical values were not available) in three
categories viz. ‘High’ (>70%), ‘Medium’ (50–70%) and
‘Low’ (<50%). The efficacy search will also fetch
siRNAs with qualitative efficacies.
In the search output we have implemented the sorting
and filtering functions. By clicking the heading of the
given field, user can sort the displayed data.
Simultaneously, by entering the desired keyword in the
designated field, user can filter the siRNA data. Multiple
filtering can be accomplished by entering desired keyword
in different fields one after another. ‘Advanced Search
page’ allows for more flexible queries using logical oper-
ators (AND, OR). These options enable the user to readily
find appropriate siRNA data. External links pointing to
the GenBank accession of the siRNA target sequence,
Pubmed ID and International Committee on Taxonomy
of Viruses (ICTV) are given for each siRNA record.
Data submission
Authors generating experimental viral siRNA data are
encouraged to submit the data directly into viral siRNA
database. For this purpose, a web form for data submis-
sion is provided. Submitted information will be included
in the database update after ascertaining its authenticity.
Implementation
VIRsiRNAdb database is implemented on Red Hat Linux
with MySQL (5.0.51b) and Apache (2.2.17) in back-end
and front-end of web interface is implemented with PHP
(5.2.14).
Future developments
As increasing number of articles are being published in the
area of viral RNAi, therefore, in future our main priority
would be to update the existing viral siRNA data as well
as to include siRNA information for new viruses once
appropriate data is available. We would also include
virus specific siRNA design tool to further help the
researchers.
Figure 2. Database statistics (a) Cell line used (b) siRNA efficacy (c) siRNA sequence matching with reference viral genomes (d) Positions of the
escape mutations.
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SUPPLEMENTARY DATA
Supplementary Data are available at NAR Online:
Supplementary Tables 1 and 2.
FUNDING
Council of Scientific and Industrial Research, and
Department of Biotechnology, Government of India.
Funding for open access charge: Institute of Microbial
Technology (CSIR), Sector 39-A, Chandigarh, India.
Conflict of interest statement. None declared.
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