Hindawi Publishing Corporation
Journal of Biomedicine and Biotechnology
Volume 2006, Article ID 65052, Pages 1–8
DOI 10.1155/JBB/2006/65052

Review Article
Essential Notes Regarding the Design of Functional siRNAs
for Efficient Mammalian RNAi
Kumiko Ui-Tei,1 Yuki Naito,2 and Kaoru Saigo2
1 Department of Biophysics and Biochemistry, Graduate School of Science and Undergraduate Program for Bioinformatics

and Systems Biology, School of Science, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-0033, Japan
2 Department of Biophysics and Biochemistry, Graduate School of Science, The University of Tokyo, 7-3-1 Hongo,

Bunkyo-ku, Tokyo 113-0033, Japan
Received 25 January 2006; Accepted 17 April 2006
Short interfering RNAs (siRNAs) are widely used to bring about RNA interference (RNAi) in mammalian cells. Numerous siRNAs
may be designed for any target gene though most of which would be incapable of eﬃciently inducing mammalian RNAi. Certain
highly functional siRNAs designed for knockout of a particular gene may render unrelated endogenous genes nonfunctional.
These major bottlenecks should be properly eliminated when RNAi technologies are employed for any experiment in mammalianfunctional genomics. This paper thus presents essential notes and findings regarding the proper choice of siRNA-sequence selection
algorithms and web-based online software systems.
Copyright © 2006 Kumiko Ui-Tei et al. This is an open access article distributed under the Creative Commons Attribution License,
which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

INTRODUCTION
RNA interference (RNAi) is the process of nucleotide-sequence-specific post-transcriptional gene silencing [1–5]. In
the case of lower eukaryotes such as Drosophila and Caenorhabditis elegans, long dsRNA may be used as an RNAi inducer [6–15], while, possibly owing to interferon response[16–20], short interfering RNA (siRNA), a Dicer digestion
product of long dsRNA, is widely used for knocking down
mammalian genes through RNAi [21–23]. Interferon response can be brought about even by siRNA transfection[24–28] and may be permitted in most cultured cell experiments, in which siRNA concentration is equal to or lessthan 100 nM [29]. In therapeutic application, low siRNAdependent interferon response would be a matter of concern[17, 18].
Theoretically, (n-20) siRNAs targeting for a gene n bp
in length can be designed. In Drosophila, more than 90% of
these siRNAs are capable of reducing target gene activity bymore than 80% [29]. The design of siRNAs in the case of
Drosophila as well as other lower eukaryotes would thus not
involve any real diﬃculty. But about 80% of theoretically designable siRNAs would not be highly functional in the case of
mammalian RNAi [29, 30]. With certain target genes rich in
GC, nonfunctional siRNAs may increase by 95% or more of
the total designable siRNAs [Y N et al, unpublished].

Mismatched siRNA may occasionally inactivate genes
other than the target, an undesired side eﬀect designated as
the “oﬀ-target eﬀect” [31, 32]. The molecular basis for thisremains to be clarified [33] though mRNA cleavage, the climax of the RNAi reaction [34–38], requires a nearly strict nucleotide sequence identity between the mRNA target portionand sense strand (SS) of siRNA [33, 39]. Thus, at least somefraction of undesirable siRNAs, giving rise to the oﬀ-target
eﬀect through destabilization of mRNAs other than the target, may be eliminated by computer-based homology search[40–45].
In the design of highly functional siRNAs for mammalianRNAi, suitable sequence conditions or good algorithms forselection of highly functional siRNAs and good computer
software suitable for genome-wide short-sequence homology search to minimize the oﬀ-target eﬀect are indispensable.
Too many websites are available for functional siRNAsearch for mammalian RNAi as partly listed in Table 1. Thesewebsites may incorporate one or a few algorithms for functional siRNA selection previously determined based on biological validation data. Considerable mammalian RNAi data
are presently available so that, in some websites, original algorithms may have been replaced with those modified tobe more eﬀective yet do not appear in scientific journals,
thus making diﬃcult the evaluation of individual website

2

Journal of Biomedicine and Biotechnology
Table 1: siRNA search websites.

WebsiteURL

Reference or company

BLOCK-iT RNAi Designer
DEQOR
Gene specific siRNA selector
OptiRNAi
RNAi Central
RNAi Design
Sfold
SiDE
siDESIGN Center
siDirectsiRNA Design Software

https://rnaidesigner.invitrogen.com/
http://cluster-1.mpi-cbg.de/Deqor/deqor.html
http://bioinfo.wistar.upenn.edu/siRNA/siRNA.htm
http://bioit.dbi.udel.edu/rnai/
http://katahdin.cshl.org:9331/RNAi web/
http://www.idtdna.com/Scitools/SciTools.aspx
http://sfold.wadsworth.org/
http://side.bioinfo.ochoa.fib.es/
http://www.dharmacon.com/sidesign/
http://design.RNAi.jp/
http://www.cs.hku.hk/ sirna/

Invitrogen
[46]
[47]
[48]
Hannon Lab
Integrated DNA Technologies
[49]
[50]
Dharmacon Research, Inc
[40]
[51]

siRNA Design Tool

http://www1.qiagen.com/Products/GeneSilencing/
CustomSiRna/SiRnaDesigner.aspx

Qiagenhttp://jura.wi.mit.edu/bioc/siRNA/
http://bioinfo.clontech.com/rnaidesigner/
http://www.promega.com/siRNADesigner/
http://www.genscript.com/rnai.html
http://www.ambion.com/techlib/misc/siRNA finder.html
http://www.sirnawizard.com/
http://sisearch.cgb.ki.se/
http://www.cellbio.unige.ch/RNAi.html[52]
Clontech
Promega[53]
AmbionInvivoGen
[42]
[54]

siRNA Selection Server
siRNA Sequence Selector
siRNA Target Designer
siRNA Target FindersiRNA Target FindersiRNA Wizard
siSearch
TROD

reliability. Consequently, the present study directs attention
to basic frameworks and some related application problems of algorithms for the selection of highly functional siRNAs.
RNAi-INDUCING ACTIVITY AS AN INTRINSICPROPERTY OF THE siRNA SEQUENCE
RNAi activity induced in mammalian cells is highly dependent on the particular sequence of siRNA used [29,30] and may vary depending on transfected cell types
or transfection eﬃciency. To examine these factors, various siRNAs targeting for the firefly luciferase gene (luc)were synthesized and transfected with luc encoding plasmid DNA into a variety of mammalian cell lines, which include human HeLa, HEK293, and colo205, Chinese hamster CHO-K1, and mouse E14TG2A ES cells [55]. The
concentration of siRNA used in these experiments was 5–50 nM. siRNA-dependent RNAi activity was also examined in chicken embryos [29]. The transfection eﬃciency
of colo205 is quite low and about 1/100 times as highas that of HeLa [55]. Neither diﬀerence in animal species
from which cell lines or embryos were derived nor thatin transfection eﬃciency had any significant eﬀect on induced RNAi activity [29, 55]. RNAi activity induced in
mammalian and chicken cells upon siRNA transfection may
thus be determined primarily by the transfected siRNA sequences themselves as far as RNAi due to 10–50 nM siRNA isconcerned.

THREE BASIC ALGORITHMS FOR SELECTINGFUNCTIONAL siRNAs BASED ON
BIOLOGICAL VALIDATION
Many experiments have been conducted to clarify possible sequence requirements of functional siRNAs for mammalian RNAi [29, 56–61]. Only three representative algorithms, which may be widely used for functional siRNAsearch for mammalian RNAi, are presented and discussed in
the following.
Algorithm 1. This algorithm was developed by Ui-Tei et al[29]. As shown in Figure 1(a1), all siRNAs satisfying the following four sequence conditions are defined as class I siRNAs
in Algorithm 1: (1) the 5 antisense-strand (AS) end, A or U,(2) the 5 SS end, G or C, (3) the 5 -terminal one-third of
AS, A/U-rich, and (4) a long G/C stretch, absent from the 5 terminal two thirds of SS. Validation data obtained using luc
as a target indicated all of 40 class I siRNAs arbitrarily chosen
to be capable of reducing target gene activity by more than
70% [29]. All RNAi experiments were conducted at 50 nM
siRNA.
Algorithm 1 siRNAs with features completely the opposite to those of class I siRNAs except for condition (4) aredefined as class III siRNAs (Figure 1(a2)). Validation indicated that all of 15 class III siRNAs arbitrarily chosen are incapable of inducing eﬃcient mammalian RNAi [29]. Thus,most, if not all, class I siRNAs may possibly serve as siRNAs highly functional in mammalian cells. Class III siRNAs

Kumiko Ui-Tei et al

3
G/C

AU rich

SS
5¼
AS 3¼

A/U
5¼

No long GC stretch

3¼

GC rich

SS
5¼
AS 3¼

5¼

A/U

3¼

G/C

(a1) Class I(a2) Class III

Not G
A

U

AU rich

SS
5¼
AS 3¼

A
5¼

3¼

Not U

Not GC72 GC2 10

A

Not G

5¼
SS
AS 3¼

5¼

G/C

1
Tm < 20 C
4

3¼

A/U
A/U diﬀerential

(b)

(c)

100% = 43747193 targets in RefSeq, cds
72.6%
Algorithm 1: 13.5%
0.7%

1.8%

3.7%

7.4%

5.1%

8%
0.7%

Algorithm 2: 10.2%

Algorithm 3: 19.8%(d)Figure 1: Three algorithms for siRNA design for functional RNAi in mammalian cells. (a) Algorithm 1. Highly functional class I siRNAs
simultaneously satisfy the following four conditions: A/U at the 5 AS end, G/C at the 5 SS end, more than four A/U nucleotides in the5 -terminal one-third of AS, and lacking a long G/C stretch in the 5 -terminal two-thirds of SS. Ineﬀective class III siRNAs possess features
opposite to class I siRNAs. (b) Algorithm 2. There are 8 requirements for this algorithm: low G/C contents (30–52%), three or more A/U at
the five 3 -terminal base pairs of SS, low internal stability lacking stable inverted repeats, and base preferences at SS positions 3, 10, 13, and19. (c) Algorithm 3. A/U content in the 5 AS end should be higher than that in the 5 SS end. Base preferences are also required at positions
indicated. (d) Diﬀerence in functional siRNA prediction between three Algorithms, 1, 2, and 3. 43747193 siRNA sequences were collectedfrom human RefSeq sequences and classified using three algorithms.

are nearly incapable of inducing eﬀective mammalian RNAi.
With the luc, the total number of theoretically designable siRNAs is 1631 and class I siRNAs represent about 17%, which
is roughly identical to the percentage (25%) of highly functional siRNAs estimated from validation data [29], class I siRNAs may thus constitute most, if not all, of siRNAs highly
functional in mammalian RNAi.
Algorithm 2. This algorithm was proposed by Reynolds et al[59, Figure 1B] who carried out analysis of 180 siRNAs targeting mRNA of two genes and found the following characteristics associated with siRNA functionality: low G/C content, preference for low internal stability at the 3 -terminus ofSS, and absence of inverted repeats. Furthermore, SS is presumed to preferably use A, U, and A at SS positions 3, 10,
and 19, respectively. The 5 AS terminal should not be G/C.
G may not be present at position 13 (Figure 1(b)). In morethan half of class I siRNAs, there are no base preferences at
position 3 and 10 [29, 55], so that Algorithms 1 and 2, respectively, may predict considerably diﬀerent siRNA sets to
be functional.
Algorithm 3. This algorithm was proposed by Amarzguioui
and Prydz [60] who carried out statistical analysis on 46 siRNAs and found Algorithm 3 to require the following features
for functional siRNAs. The 5 AS terminus and its SS partner
are A/U and the 5 SS terminus and its AS partner, G/C. An
opposite combination of terminal bases may give rise to inadequate functionality. These authors also found that thereis asymmetry in siRNA duplex end stability; that is, the A/Ucontent diﬀerential for the three terminal nucleotides at bothends of the duplex may be considered essential to siRNAfunctionality. Furthermore, they noted A to prefer position
6 of functional siRNAs (Figure 1(c)), although only a small

4fraction of class I siRNAs is associated with A at SS position
6 [29].
To examine in greater detail, relationships among thethree algorithms, that the percentage of siRNAs considered
functional by Algorithm 1 (class I) can be repredicted asfunctional by Algorithms 2 or 3 or vice versa, was determined(see [55, Figure 1D]. Based on the three algorithms, total possible siRNA sequences (4.4 × 107 ) designed using RefSeq human sequences (version 11) were found to be nonfunctionalby as much as 73%. Class I siRNAs constituted 14% of thetotal theoretically predictable siRNAs, whereas Algorithms 2
and 3, respectively, predict 10 and 20% as functional siRNAs.
Nearly 90% of class I siRNAs could be repredicted as functional by Algorithm 2 or 3 or both. Eighty four percent of siRNAs simultaneously predicted as functional by Algorithms
2 and 3 could be repredicted as functional or class I siRNAs by Algorithm 1. More than 50% of siRNAs predicted asfunctional by Algorithm 2 could not be predicted to be functional by Algorithm 3. Seventy seven percent of Algorithm 3
functional siRNAs could not be repredicted as functional by
Algorithm 2. These findings may indicate that Algorithm 1 iscapable of predicting the functionality of siRNAs more reliably than Algorithms 2 or 3.
ALIGNMENT ALGORITHM FOR SHORTNUCLEOTIDE SEQUENCES
Rapid homology comparison of the entire mRNA sequenceswith siRNA AS/SS sequences is indispensable for identifying oﬀ-target genes. BLAST [62] may not be a good software
for making such comparison, since a number of oﬀ-target
candidates are overlooked and too, considerable time is required for BLAST-based calculation. The Smith-Waterman
local alignment algorithm [63] is accurate but time consuming to execute. Recently, Yamada and Morishita have developed a very rapid and accurate alignment algorithm for short
nucleotide sequences [41] and this software can process 60
million siRNA sequences of 21 nucleotides in length in 10
hours when executed in parallel on ten inexpensive PCs. The
hardware of Snøve Jr and Holen [64] provides similar performance although the number of processing units is not clearlyspecified. Websites using the Yamada-Morishita software or
hardware of Snøve Jr and Holen should thus prove much
more rapid and reliable compared to BLAST.
The base mismatch introduction studies indicate thattransfected siRNAs occasionally cause phosphodiester-bondcleavage not only of the authentic mRNA target but also mutated targets with 1-2 base mismatches [33, 39]. But mutated targets with three or more mismatches may not undergo cleavage by transfection of the same siRNA [Y N etal, unpublished]. siRNAs less than 84 (16/19 × 100)% homology in sequence to any part of total mRNAs other than
the target should thus be used for RNAi, which would reduce the number of available functional siRNAs to 1/10 of
the input. That is, only 10% of class I siRNAs or less than 2%of total siRNAs theoretically designable using human RefSeq sequences becomes available in mammalian RNAi when

Journal of Biomedicine and Biotechnology
oﬀ-target eﬀects due to mRNA destability are considered.
Computational analysis indicated that, even so few available
siRNAs, at least one functional class I siRNA can be assigned
to more than 99% of human mRNA sequences (RefSeq sequences) [Y N et al, unpublished].
miRNAs involved in posttranscriptional gene silencing
through translational regulation [65–73] possess less homology with the target, indicating siRNAs with lesser homology
in some cases to possibly be involved in some oﬀ-target reactions [74]. The elimination of a large number of siRNA withlow homology to mRNAs other than the target may rendergenome-wide gene silencing in mammalian cells quite diﬃcult. The simultaneous use of a few to several siRNAs targeting for an identical gene (target gene) may possibly solve thisproblem since, in most cases, oﬀ-target targets would not be
identical to each other [31, 32].
EXPERIMENTAL PARAMETERS POSSIBLY
AFFECTING FUNCTIONALITY OF siRNAs
siRNA-mediated RNAi activity may vary significantly depending on not only the particular siRNA sequence but also
parameters such as siRNA concentration, duration of siRNA
exposure, and possibly target mRNA concentration and secondary structure within cells [29, 75]. Functional siRNAs insome cases have actually been found to induce maximum
RNAi activity 1 day after transfection, whereas other siRNAs to express maximum activity on 2 or 3 days following transfection. Usually, functional-siRNA-dependent RNAi
persists 1-2 weeks, whereas virtually no RNAi is induced
within cells even after a long incubation with nonfunctional
siRNAs. Class I siRNAs, capable of inducing highly functional RNAi when transfected at 50 nM, were considerably
heterogeneous in capability of bringing about RNAi whenused for 1-day transfection at the concentration of 50 pM(see [29] by Ui-Tei et al). Reduction in target gene activity varied from 20 to 60% depending on the sequences ofclass I siRNAs used. Thus, additional sequence conditions
may possibly be found so as to define a subclass of class
I with more functionality but in such a case, nearly complete genome-wide gene silencing might no longer be possible.
Recently, Kim et al [76] showed that a 27 bp long dsRNA
with blunt ends is much greater in functionality than 21 bp
long siRNA and suggested that short Dicer substrate dsRNA
may be generally much more functional compared to authentic siRNAs 21 bp long. However, it was subsequently
found that this is not a general feature of 27 bp long bluntended dsRNA [77]. In the absence of 3 overhang, Dicer digests dsRNA uncontrollably, generating many products varying in length, most of which may not be as functional as 21 bp
long highly functional siRNAs [77]. RNAi-inducing activity would thus appear to depend primarily on the presenceof considerable highly functional siRNAs in the digestion
products and so, consequently, 27 bp long blunt-end dsRNA
would not be necessarily a good choice for highly eﬃcientRNAi.

Kumiko Ui-Tei et alsiRNA-OLIGOMER-DEPENDENT RNAiIN MAMMALIAN CELLSLong dsRNA possessing 2-nucleotide 3 overhangs at bothends is cleaved by Dicer from these ends to generate siRNAs having definite nucleotide sequences [28, 77–80]. Thus,
should nearly all siRNAs produced by Dicer digestion belong
to class I and the interferon response due to dsRNAs equivalent in length to siRNA oligomers not being significant,the induction of eﬀective multiple-gene knockout in mammalian cells may occur with transfection of siRNA oligomers
and this was recently found to be the case [28]. Throughuse of class-I-siRNA oligomers multiple-target gene knockout was clearly shown to take place.
DNA/shRNA-MEDIATED RNAi
RNAi can be induced by introducing DNA encoding both
SS and AS of siRNA into mammalian cells. Both RNA polymerase III and II promoters, respectively, are used to express short hairpin RNA (shRNA) and longer RNA including shRNA sequence in the middle [81–90]. The primarytranscript of RNA polymerase III is a mixture of shRNAs
with two to several consecutive U’s at its 3 overhang [81–88]. Dicer cleavage sites of shRNAs vary depending on thelength of 3 overhangs [89] and accordingly, several diﬀerent
species of siRNAs are expected to be generated from shRNAs
transcribed by polymerase III [88]. Thus, the presence ofhighly functional siRNAs in these Dicer digestion products isrequired for successful RNAi due to a polymerase-III-based
system. In addition, four consecutive U’s or A’s should not be
included in the nonoverhang sequences of AS and SS, respectively, since these sequences stimulate premature terminationof polymerase-III-dependent transcription [88].
In polymerase II-driven expression systems, the primary transcript is long polyadenylated RNA (pri-miRNAlike RNA), which is recognized and cleaved by the nuclear microprocessor complex [91, 92]. This complex contains Drosha, an RNase III-type RNase that cleaves the primiRNA-like RNA to generate shRNA with a 2-nucleotide
3 overhang [93]. The shRNA thus produced is converted
mainly to two overlapping siRNAs through Dicer digestion(see [28]), indicating that successful RNAi requires the involvement of highly functional siRNAs in these siRNA products.
POSSIBLE MOLECULAR BASES OF ASYMMETRICSEQUENCE REQUIREMENTS IN
FUNCTIONAL siRNAs
Each mammalian Argonaute proteins (eIF2Cs) is comprisedof a PRP motif and two domains: PAZ and PIWI [94]. Structural analysis of the Argonaute protein crystals from Pyrococcus farious indicated that the PIWI domain has essentiallythe same three-dimensional structure as ribonuclease H andthat Argonaute may function as a slicer of mRNA [95]. PAZ
and PIWI domains may recognize separately two ends ofsiRNA. The crystal structure of the PAZ domain from human5
Argonaute 1 suggested that the PAZ domain is anchored to
the 2-nucleotide 3 overhang of the siRNA duplex [96]. The
PIWI domain from Archaeoglobus fulgidus contains a highly
conserved metal-binding site that may recognize the 5 nucleotide of AS of siRNA in a manner not dependent on sequence [97].
Algorithms 1 and 3 predict functional siRNAs to possess A/U and G/C at the 5 AS and SS ends, respectively[29, 55, 60]. The GC pair is thermodynamically much morestable than the AU pair and thus, diﬀerences in stability interminal base pair of the siRNA duplex may determine terminal sequence preference in highly functional and nonfunctional siRNAs, most probably by stimulating asymmetric binding of PIWI and PAZ domains to siRNA ends.
The 5 -terminal one-third of AS of functional class I siRNAs is A/U-rich, possibly due to preferable siRNA unwinding from its AS end [29, 56]. A one-step motor function of
the putative siRNA helicase may unwind several base pairs
from the A/U-rich siRNA end to stimulate formation of active RISC lacking SS of siRNA. Should this be the case, theintroduction of base mismatches into the 3 -terminal third
SS of siRNA may significantly increase the induced RNAi activity. Studies with Drosophila extracts showed a significant
base-mismatch-dependent increase in RISC formation [56].
But, to date there are no data clearly confirm this in mammalian cultured cell experiments. Recently a part of RISC
has been shown to be activated through cleavage of SS ofsiRNA at its center [98]. The presence of base mismatches
in SS might be unfavorable to SS cleavage and this negativeeﬀect might partially prevent siRNA from being unwound.
ACKNOWLEDGMENTS
This work was partially supported by Special Coordination
Fund for Promoting Science and Technology to K S, andGrants from the Ministries of Education, Culture, Sports,
Science and Technology of Japan to K S and K U-T.
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