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MicroRNA Detection and Target Prediction: Integrationof Computational and Experimental Approaches
KEYA CHAUDHURI and RAGHUNATH CHATTERJEE
ABSTRACT
In recent years, microRNAs (miRNAs), a class of 19–25 nucleotides noncoding RNAs, have been shown to play
a major role in gene regulation across a broad range of metazoans and are important for a diverse biological
functions. These miRNAs are involved in the regulation of protein expression primarily by binding to one ormore target sites on an mRNA transcript and causing cleavage or repression of translation. Computer-based
approaches for miRNA gene identification and miRNA target prediction are being considered as indispensable
in miRNA research. Similarly, effective experimental techniques validating in silico predictions are crucial to
the testing and finetuning of computational algorithms. Iterative interactions between in silico and experi-
mental methods are now playing a central role in the biology of miRNAs. In this review, we summarize the
various computational methods for identification of miRNAs and their targets as well as the technologies that
have been developed to validate the predictions.
INTRODUCTION
M ICRORNAS (MIRNAS) are an abundant class of endoge-
nous, small, noncoding RNAs typically 19 to 25 nucleo-
tide (nt) long expressed in a wide variety of organisms from
plants, viruses, and animals (Bartel and Bartel, 2003; Lim et al.,
2003a; Pfeffer et al., 2004, 2005). Many miRNAs are highly
conserved across species (Bartel, 2004). The numbers of miRNA
entries are increasing almost exponentially over the past 5 years.
Depending on the degree of complementarity between miRNA
and its target transcript, miRNAs are known to exercise post-
transcriptional control over most eukaryotic genomes, causing
degradation of target transcript or translational repression (Lagos-
Quintana et al., 2001; Lau et al., 2001; Lee and Ambros, 2001;
Moss and Poethig, 2002; Bartel, 2004).
The founding members of this class of noncoding RNAs are
the lin-4 and let-7 gene products ofCaenorhabditis elegans (Lee
et al., 1993; Reinhart et al., 2000). Both lin-4 and let-7RNAs act
as repressor of their respective target genes lin-14, lin-28, and
lin-41 (Lee et al., 1993; Moss et al., 1997; Slack et al., 2000).
In all these cases repression was mediated by the presence of
complementary miRNA sequences in the 30 untranslated regions(UTRs) of the target mRNAs (Slack et al., 2000; Lewis et al.,
2003).
The participation of several miRNAs in essential biological
processes have been identified; for example, the roles of animal
miRNAs have been identified in developmental timing, cell
death, cell proliferation control, hematopoiesis and patterning
of nervous system, pancreatic cell insulin secretion, adipocyte
development (Ambros, 2004; Harfe, 2005). Recent findings in-
dicate that mRNA profiles are changed in a large number of hu-
man cancers (Xie et al., 2005; Calin and Croce, 2006) and that
the forced over expression of miRNAs can lead to the devel-
opment of tumors (He et al., 2005).
The biogenesis of miRNAs has been studied by several lab-
oratories, and we are beginning to understand the process of
transcription, cytoplasmic transport, and maturation of miRNAs
(Bartel, 2004; Kim, 2005). The miRNA genes encoded in the
genomes of most eukaryotic organisms are transcribed by RNA
polymerase II into primary miRNAs. These structured RNAs
are then processed by the RNase III endonuclease Drosha to
form one or more precursor miRNA (~60–100 nt) having capa-
bility to form stem–loop-type secondary structure. Specific
RNA cleavage by Drosha predetermines the mature miRNA
sequence and provides the substrate for subsequent processing
events. The pre-miRNAs are transported to the cytoplasm
by Exportin-5, in a Ran GTP-dependent manner. Once in the
cytoplasm, a second RNase III endonuclease, Dicer, acts on
Molecular & Human Genetics Division, Indian Institute of Chemical Biology, Kolkata, India.
DNA AND CELL BIOLOGYVolume 26, Number 5, 2007# Mary Ann Liebert, Inc.Pp. 321–337DOI: 10.1089/dna.2006.0549
321
the pre-miRNA and subsequently forms mature double stranded
miRNA (~19–25 nt). One strand of the miRNA duplex is sub-
sequently incorporated into an effector complex termed RNA-
induced silencing complex that mediates target gene expression.
Direct cloning and sequencing of short RNA molecules has
enabled the identification of many miRNAs; however, highly
constrained tissue- and time-specific expression patterns, pres-
ence of degradation products from mRNAs, and other noncoding
RNAs, has made it difficult and incomplete to clone miRNAs
(Lai et al., 2003; Lim et al., 2003b). This led to the development
of increasingly sophisticated computational approaches to predict
miRNAs and their target mRNAs complemented by the biolog-
ical validation techniques. An overview of the current advances
in this area is presented here with a view to stimulate the reader to
explore the diverse and exciting field of miRNA research.
COMPUTATIONAL IDENTIFICATIONOF miRNAs
The basic principle of the computational approaches is
simple—they rely on the known characteristics of miRNAs and
search those in other organisms. miRNA detection in animals
relies on (1) conservation of miRNAs in the genomes of related
species, (2) formation of stable stem–loop structure by pre-
miRNAs, and (3) the presence of mature miRNAs in the stem
and not in the loop of pre-miRNAs. However, the prediction
approaches are more challenging in the case of viruses and
plants, as viral miRNAs are rarely evolutionarily conserved
and the level of sequence conservation of miRNA precursor is
lower in plants. The length of hairpin structures is also more
variable in plants compared to that of animals. The secondary
structures, generated using RNA Fold (Hofacker, 2003), of
representative pre-miRNAs from Arabidopsis thaliana, Homo
sapiens, and Epstein Barr virus is shown in Figure 1.
miRNA PREDICTION ALGORITHMS
Computational procedure MiRscan (Lim et al., 2003a, 2003b)
was developed to identify miRNA genes conserved in more than
one genome. The program uses an RNA folding algorithm
RNAFold (Schuster et al., 1994) to locate potential hairpin
structures in sequences that are evolutionarily conserved among
C. elegans and C. briggsae. Briefly, it slides a 110-nt window
along both strands of the C. elegans genome and folding the
window with RNAfold to identify predicted stem–loop struc-
tures (>25 bp) and a folding free energy of at least�25 kcal/mol.
Each conserved hairpin, considered as a potential pre-miRNA,
was further evaluated for the location of miRNA within it by
passing a 21-nt window along each stem–loop, assigning a log-
likelihood score to each position for its similarity to known
miRNAs, the training set for the algorithm being 50 published
miRNAs from C. elegans and C. briggsae.
This approach successfully identified 35 novel miRNAs in
C. elegans (from ~35,000 hairpins conserved between C. ele-
gans and C. briggsae), of which 16 were experimentally veri-
fied, the accuracy level was calculated to be � 0.7. From
~15,000 hairpins conserved among humans, mice, and puffer
fish, the algorithm identified a high-scoring set of 188 human
loci. This set included 81 of the 109 members of a reference set
of known human miRNA loci, for a sensitivity of 0.74 (Lim et
al., 2003a).
Later, MiRscan algorithm has been used along with stem–
loop finder to identify miRNAs in human cytomegalovirus ge-
nome (Grey et al., 2005). Out of the potential 406 stem–loops
identified, 110 were conserved between chimpanzee and hu-
man cytomegaloviruses, and of these, 13 exhibited a significant
score from MiRscan. Five of these miRNAs were found to be
expressed during infection.
The accuracy of MiRscan was further improved by the same
group (Ohler et al., 2004). A highly significant sequence motif,
with consensus CTCCGCCC, was found to be present in about
200 bp upstream of almost all independently transcribed nem-
atode miRNA genes which were incorporated in the algorithm.
With this improvement, the total number of confidently iden-
tified nematode miRNAs now approaches 100. Using a poly-
merase chain reaction (PCR)-sequencing protocol, 9 new C.
elegans miRNA gene candidates were validated in their study.
The computationalmiRNAdetection programmiRseeker (Lai
et al., 2003) analyzed the completed euchromatic sequences of
twoDrosophila species,D. melanogaster andD. pseudoobscura
for conserved sequences that adopt an extended stem–loop
structure and display a pattern of nucleotide divergence char-
acteristic of known miRNAs using Mfold (Zuker, 2003). The
sensitivity of this computational procedure was demonstrated
by the presence of 75% (18 of 24) of previously identified
Drosophila miRNAs within the top 124 candidates. In total, 48
novel miRNA candidates identified by miRseeker were strongly
conserved in more distant insect, nematode, or vertebrate ge-
nomes. Of these, the expression of a total of 24 novel miRNA
genes was experimentally verified. MiRseeker estimated around
110 miRNA genes in Drosopila genomes.
Additional C. elegans small RNAs with properties similar
to miRNAs and siRNAs were identified by cDNA sequencing
and comparative genomics (Ambros et al., 2003). The novel
miRNAs were then identified by Mfold (Zuker, 2003). Their
method identified 21 new C. elegans miRNAs and estimated
that C. elegansmight contain 100 miRNAs, 30% of which were
conserved in vertebrates. Another informatic approach, based
on sequence conservation and structural similarity to known
miRNAs, predicted 214 candidate miRNAs in C. elegans ge-
nome (Grad et al., 2003). Northern blotting and sensitive PCR-
based experimental approaches were used to confirm the expres-
sion of some new miRNAs. They estimated that C. elegansmay
encode140–300miRNAsandpotentiallymanymore.Their strat-
egy was similar to MiRscan (Lim et al., 2003a, 2003b) but used
different selection criteria.
Rodriguez et al. (2004) annotated the positions of mammalian
miRNAs, obtained from microRNA registry (Griffiths-Jones,
2004), in the human and mouse genomes to derive a global
perspective on the transcription of miRNAs in mammals. Their
results showed that more than half of all known mammalian
miRNAs were within introns of either protein-coding or non-
coding transcription units, whereas ~10% were encoded by
exons of long nonprotein-coding transcripts (mRNA-like non-
coding RNAs).
Berezikov et al. (2005) used phylogenetic shadowing ap-
proach (Boffelli et al., 2003) to predict novel candidate miRNAs
in humans. Phylogenetic shadowing overcomes the limitation
322 CHAUDHURI AND CHATTERJEE
of classical phylogenetic footprinting and allows unambiguous
sequence alignments and accurate conservation determination
at single nucleotide resolution level. The authors sequenced
122 miRNAs in 10 primate species and found that nucleotides
in the stem of miRNA hairpin precursors are significantly more
conserved compared to loop sequences and sequences flanking
the hairpins. Using this distinctive property in conjunction with
other known properties of miRNAs, they predicted 976 can-
didate miRNAs by scanning whole-genome human/mouse and
human/rat alignments, most of the novel miRNA candidates
being conserved in other vertebrates (dog, cow, chicken, opos-
sum, and zebrafish). Northern blot analysis confirmed the ex-
pression of mature miRNAs for 16 out of 69 representative
candidates. Their results suggested that the numbers of miRNAs
in the human genome would be significantly higher than pre-
viously estimated, although the risk of false positives might be
higher.
For computational prediction of miRNAs, Sewer et al. (2005)
exploited the property that miRNAs are occasionally clustered.
The fraction of clustered miRNA genes in D. melanogaster has
been estimated to be ~50% (Bartel, 2004), while a total of 37%
of the known humanmiRNA genes analyzed in a study appeared
in clusters of two or more, with pairwise chromosomal distances
of at most 3000 nucleotides (Altuvia et al., 2005). Starting with
the known human, mouse, and rat miRNAs, the authors ana-
lyzed 20 kb of flanking genomic regions for the presence of
putative pre-miRNAs. Cross-species comparisons were then
used to make conservative estimates of the number of novel
miRNAs. In this way they predicted between 50 and 100 novel
pre-miRNAs for each of the conserved species. Around 30% of
FIG. 1. Hairpin secondary structures of miRNAs of Arabidopsis thaliana (ath-mir-156c), Homo sapiens (has-let-7d) and EpsteinBarr virus (EBV-mir-BHRF1-1). PremiRNA sequences were obtained from miRbase (Griffiths-Jones, 2004; Griffiths-Jones et al.,2006) and secondary structures were generated from Vienna RNAfold web server (Hofacker, 2003).
MICRORNA DETECTION AND TARGET PREDICTION 323
their predicted miRNA had experimental support in a large set
of cloned mammalian small RNAs.
The algorithm ProMiR (Nam et al., 2005) introduced a
probabilistic colearning model based on the paired hidden
Markov model (HMM) for miRNA gene finding. The authors
combine both sequence and structure of pre-miRNA in a prob-
abilistic framework and simultaneously decide the presence of
pre-miRNA and mature miRNA by detecting the signals for the
site cleaved by Drosha. This approach is expected to identify
novel miRNAs in addition to those which are abundantly ex-
pressed or close homologs of previously identified miRNAs.
On screening human chromosomes 16, 17, 18, and 19, ProMiR
detected at least 23 novel miRNA gene candidates, which do
not bear sequence similarity to the known miRNA genes.
Of these, nine candidate genes were experimentally validated
by determining/examining the accumulation of pre-miRNAs by
real-time quantitative RT-PCR in the cells depleted of Drosha,
indicating that ProMiR may successfully predict at least with
40% accuracy.
ProMiR was further improved by the same group (Nam et al.,
2006) to ProMiRII integrating additional filtering criteria like
G/C ratio, conservation score, entropy, and free energy of can-
didate sequences. Low and high stringency prediction of con-
served and nonconserved miRNA genes are possible by
adjusting the filtering criteria. With appropriate training data
set, this method can be applied to all species.
An integrative approach called PalGrade (Bentwich et al.,
2005) has been developed combining computational predic-
tions with microarray analysis and sequence-directed cloning
for miRNA detection which does not rely on sequence con-
servation. In this approach, the folding of noncoding region of
the entire human genome was carried out with RNAFold (Hof-
acker, 2003) yielding 11 million hairpins, from which PalGrade
selected a set of 5300 high scoring candidates, which were then
passed through microarray experiments yielding 359 expressed
candidates and were sequenced, finally yielding 89 novel val-
idated human miRNAs. Thus, PalGrade could identify a large
number of miRNAs that are unique to primates and are unde-
tected by other prediction algorithms and proposed that the
total number of miRNAs in humans could be at least 800.
Legendre et al. (2005) used a profile-based strategy im-
plemented in an ERPIN program with a view to estimate how
many miRNAs could be recovered. ERPIN represents RNA
alignments as weight matrices or profiles (Gautheret and Lam-
bert, 2001), and identifies matching sequences using a combined
dynamic programming/profile scan algorithm, thus capturing
both primary and secondary structure information, which is
particularly well adapted to pre-miRNA identification. Their ap-
proach produced 265 new miRNA candidates that were not
previously found in miRNA databases. The authors suggested
that the Profile-based RNA detection will be an important com-
plement of similarity search programs in the completion of
miRNA collections.
Comparative methods, based on the idea that miRNAs are
conserved across species, have been used by some groups (We-
ber, 2005; Xie et al., 2005; Pedersen et al., 2006) for identifi-
cation of miRNAs.
Xie et al. (2005) performed a comparative analysis of the
human, mouse, rat, and dog genomes to create a systematic
catalog of common regulatory motifs in promoters and 30-UTRs
which identified 106 new motifs (8-mers) in 30-UTR. The
neighborhood of each motif was then evaluated by RNAfold
(Hofacker, 2003) for secondary structure and the selected stable
stem–loops were further evaluated based on several observed
features of known miRNAs: higher conservation in the 22 bp
stem, lower conservation in the loop and surrounding regions,
and appropriate base-pairing and bulges in the stem region. No-
tably, roughly one-half of the discovered motifs in the 30 UTRswere related to miRNAs, leading to the identification of several
new miRNA genes. Their results suggested that previous esti-
mates of the number of human miRNA genes were low, and that
miRNAs regulate at least 20% of human genes.
Weber (2005) performed a systematic search for poten-
tial human orthologues of known mouse miRNAs and mouse
orthologues of known human miRNAs deposited in the miRNA
Registry (Griffiths-Jones, 2004). His algorithm consisted of
miRNA tracks written to visualize miRNAs in human andmouse
genomes on the UCSC Genome Browser. With this tool, the
author systematically determined the position and orientation of
miRNA genes relative to known transcriptional units, examined
the conservation of miRNA gene localization between the hu-
man and mouse genomes, and made a comprehensive list of
miRNA clusters. The hairpin structures of the sequences cor-
responding to potential new miRNA precursors were assessed
with the MFOLD (Zuker, 2003) program, finally leading to the
identification of 35 human and 45mouse putativemiRNA genes.
The comparative genomic method of Pedersen et al. (2006) is
based on phylogenetic analysis of multiple alignments. Their al-
gorithm EvoFold makes use of phylogenetic stochastic context-
free grammar and is a combined probabilistic model of RNA
secondary structure and sequence evolution. Screening the re-
gions of the human genome that are under strong selective
constraints, the algorithm yielded a set of 48,479 candidate RNA
structures containing various types of genetic regulatory ele-
ments including 195 miRNAs. The false positives are high,
estimated to be around 62%. Among the highest-scoring can-
didates, the screen predicted 169 new miRNAs.
A basic learning approach based on the Naı̈ve Bayes clas-
sifier has recently been proposed for the prediction of miRNA
genes (Yousef et al., 2006). This method automatically gener-
ates a model from the training data, which consists of sequence
and structure information of known miRNAs from a variety of
species, allowing prediction of nonconserved miRNAs. This
was followed by comparative analysis over multiple species to
reduce the number of false positives. The resulting algorithm
exhibits higher specificity and similar sensitivity compared to
currently used algorithms using conserved gnomic regions (Grad
et al., 2003; Lai et al., 2003; Lim et al., 2003a, 2003b). The
major novelty of the approach lies in the integration of data from
multiple species which stabilize the learning process, and more
importantly, construct a model that is more likely to be appli-
cable to a variety of genomes.
A support vector machine (SVM) based approaches has re-
cently been proposed by two groups (Helvik et al., 2006; Hertel
and Stadler, 2006). The program RNAmicro (Hertel and Stadler,
2006) used an SVM-based approach in conjunction with a
nonstringent filter for consensus secondary structures and could
detect miRNA precursors in multiple sequence alignments.
Helvik et al. (2006) presented a SVM-classifier called Micro-
processor SVM, which predicts 50 Drosha processing sites in
324 CHAUDHURI AND CHATTERJEE
hairpins of candidate miRNAs. The prediction was correct for
50% of known human 50 miRNAs. Using another classifier
trained on the output from theMicroprocessor SVM, the authors
performed an analysis on 130 reported miRNAs and showed that
some miRNAs may have been mis-annotated. The authors sug-
gest that expressed hairpins should not be annotated as miRNAs
until they are verified to be Drosha and Dicer substrates.
Li et al. (2006) has recently proposed a scanning method
which examined ESTs and intronic sequences to identify novel
miRNAs using the srnaloop program (Grad et al., 2003). The
output was passed through sequence and structure filters like
GC content, core, and hairpin minimum-free energies and their
ratio. Using 130 newly updated premiRNA and randomly se-
lected sequences, the sensitivity and specificity of the method
was 85 and 49%, respectively.
Chatterjee and Chaudhuri (2006) developed a computational
approach miRsearch based on the criteria that miRNAs are usu-
ally highly conserved in the genomes of related organisms, their
pre-miRNA transcript forms extended stem–loop structure, and
the mature miRNAs are present in the long helical arm of the
stem–loop structure. miRsearch relies on searching the homologs
of all knownmiRNAs of one organism in the genome of a related
organism allowing few mismatches depending on the evolution-
ary distance between them, followed by assessing for the capa-
bility of formation of stem–loop structure using MFOLD (Zuker,
2003). The approach identified 91 probable candidate miRNAs
along with pre-miRNAs in Anopheles gambiae using known D.
melanogaster miRNAs and selecting the cutoff free energy of
MFOLD based on known D. melanogaster pre-miRNAs.
COMPUTATIONAL IDENTIFICATIONOF PLANT miRNAs
For plant miRNA detection various computational methods
have been developed, which are mostly focused on Arabidopsis
thaliana and Oryza sativa genomes. The algorithm MiRFinder
(Bonnet et al., 2004) was based on the conservation of short
sequences between the genomes of A. thaliana andO. sativa and
on properties of the secondary structure of the miRNA pre-
cursor. The method was fine tuned to take into account the
variable length of the miRNA precursor sequences. Out of the
identified 91 potential miRNA genes, 58 had at least one nearly
perfect match with an Arabidopsis mRNA, constituting the
potential targets of those miRNAs.
Another comparative genomic approach (Jones-Rhoades and
Bartel, 2004) identified novel miRNAs in Arabidopsis of which
23 miRNA candidates, representing seven newly identified gene
families, were experimentally validated.
A single genome computational approach, findMiRNA (Adai
et al., 2005) was proposed, which relies on the rigid comple-
mentarity between plant miRNAs and their targets, and stem–
loop formation. From this data set, they selected 13 potential
new miRNAs for experimental verification and detected the
expression of 8 of the candidate miRNAs. This method thus
provides an important alternative as it uses a single genome, and
looks for conserved miRNA target sites in transcripts in addi-
tion to looking for conserved miRNA sequences.
Wang et al. (2004) presented a computational method for
genome-wide prediction of A. thaliana miRNAs using charac-
teristic features of known plant miRNAs as criteria to search for
miRNAs conserved between Arabidopsis and O. sativa. They
predicted 95 ArabidopsismiRNAs including 83 new sequences.
The expression of 19 new miRNAs was confirmed by Northern
blot hybridization. Their results suggested that at least some
miRNA precursors are polyadenylated at certain stages.
Strategies for miRNA identification based on Expressed
Sequence Tags (EST) analysis was taken up by some groups
(Williams et al., 2005; Zhang et al., 2005). ESTs represent true
gene expression; the analysis based on ESTs could thus provide
more evidence and confidence in the discovery of new potential
miRNAs. Williams et al. (2005) used the nonannotated EST
database (Yamada et al., 2003) and developed a computational
screen based on the properties of miRNAs to exclusively iden-
tify the candidate miRNAs. Zhang et al. (2005) also used EST
analysis and DNA database analysis in detail and reported 338
new miRNAs in 60 plant species.
COMPUTATIONAL IDENTIFICATIONOF VIRAL miRNAs
Pfeffer et al. (2004) first identified miRNAs in Epstein-Barr
virus (EBV), a large DNA virus of the Herpes family that pref-
erentially infects human B cells. The miRNAs were found to
be clustered in two different regions of the genome and were
detectable by Northern blotting. They concluded that EBV,
through miRNA, might exploit RNA silencing as a convenient
method for gene regulation of host and viral genes in a non-
immunogenic manner. In contrast to most eukaryotic miRNAs,
these viral miRNAs do not have close homologs in other viral
genomes or in the genome of the human host. So, in a later study
by the same authors miRNA genes in the herpesvirus family
were identified by combining a new miRNA gene prediction
method with small-RNA cloning from several virus-infected
cell types (Pfeffer et al., 2005).
An algorithm VirMir was developed for the detection of
likely pre-miRNAs in the small genome (<300 kb) (Sullivan
et al., 2005). VirMir could identify miRNAs encoded by SV40,
and this study also defined their functional significance for viral
infection. A refined version of VirMir named as VMir was
presented by the same group (Grundhoff et al., 2006). VMir
features an updated scoring algorithm and the incorporation of
several quality filters designed to reduce the complexity of the
prediction. Candidate hairpins were then synthesized as oligo-
nucleotides on microarrays, hybridized with small RNAs from
infected cells and miRNAs scoring positive on the arrays were
then subjected to Northern blot analysis. Using this approach,
10 of the known and 1 novel Kapos sarcoma-associated her-
pesvirus (KSHV) pre-miRNAs were identified.
Recently, a computational method (Cui et al., 2006) has been
proposed to screen the complete genome ofHSV-1 for sequences
that adopt an extended stem–loop structure and display a pattern
of nucleotide divergence characteristic of known miRNAs.
Using this method, 11 HSV-1 genomic loci were predicted to
encode 13 miRNA precursors and 24 miRNA candidates. Eight
of the HSV-1 miRNA candidates were predicted to be con-
served in HSV-2. The precursor and the mature form of one
HSV-1 miRNA candidate was detected during infection of
Vero cells by Northern blot hybridization suggesting a possible
MICRORNA DETECTION AND TARGET PREDICTION 325
role for this miRNA in regulation of viral and host gene ex-
pression.
EXPERIMENTAL VALIDATIONOF CANDIDATE miRNAs
Computational predictions contribute a lot to the miRNA gene
discovery, but the existence of a candidate miRNA needs ex-
perimental validation. Experimental detection of miRNAs is
technically challenging because of their small size, sequence
similarity among various members, low level, and tissue-specific
or developmental stage-specific expression. Fortunately in the
past 2 years there has been a significant progress in performance,
execution, and fine tuning of several validation approaches re-
sulting in high sensitivity, high throughput and high comparative
capabilities.
Northern blot
Northern blot analysis has been widely used to determine the
expression of both the mature and precursor miRNAs cloned
from size-fractionated cDNA libraries (Lagos-Quintana et al.,
2001; Lau et al., 2001; Lee andAmbros, 2001;Calin et al., 2002).
The disadvantages are its low throughput and limited sensitivity
for detecting rare miRNAs, and consequently, a large amount of
total RNA per sample is required which may not be feasible
especially for diseased tissues. However, Northern blots are sill
regarded as golden standard for miRNA validation, and quan-
tification (Ambros et al., 2003) and is used for confirmation of
high-throughput data (Sempere et al., 2004).
The sensitivity of detection of miRNAs by Northern blot
has been increased by about 10-fold using locked nucleic acid
(LNA)-modified oligonucleotide probes (Valoczi et al., 2004).
LNA probes exhibit unprecedented thermal stability and show
improved hybridization properties against complementary RNA
targets. This strategy has been used in the detection of miRNAs
in the mouse, A. thaliana, and Nicotiana benthamiana (Valoczi
et al., 2004).
Other hybridization techniques include RNase protection as-
say (RPA) (Lee et al., 2002), primer extension (Seitz et al.
2004), and a signal-amplifying ribozyme method (Hartig et al.,
2004). In RPA, a labeled antisense RNA probe complementary
to the sequence of interest is synthesized through an in vitro
transcription and hybridized to total RNA followed by digestion
with a single-strand-specific RNase to degrade unhybridized
probe and target. The remaining protected probe:target hybrid
is separated on a denaturing polyacrylamide gel and detected by
methods specific to the label on the probe. The primer exten-
sion approach detects the miRNA by hybridizing a labeled DNA
primer to the 30-portion of the RNA, followed by template-
directed incorporation of nucleotides by reverse transcrip-
tase. The primer is a few nucleotides shorter than the predicted
miRNA target. Polyacrylamide gel electrophoresis is then used
to detect the extended products. The miRNA detection by ribo-
zyme method is based upon hairpin ribozymes that cleave a
short RNA substrate labeled with a fluorophor at the 30-endand a quencher at the 50-end, as a function of the presence or
absence of a miRNA effector followed by real-time monitoring.
Cloning and sequencing approaches
Initially random cloning and sequencing of size-fractionated
RNA was the main approach for identification of miRNAs
(Lagos-Quintanaetal.,2002).Later, informaticspredictionswere
carried out in parallel.
Bentwich et al. (2005) used a sequence-directed cloning
and sequencing approach. A biotin-labeled oligonucleotide
(~22–30 nt long, biotin at the 50-end) was designed and used to
capture the homologous miRNA from a cDNA library enriched
for small RNAs. The captured cDNA library molecules were
then PCR amplified, cloned, and sequenced.
In the PCR-based cloning approach used earlier (Lim et al.
2003a, 2003b), a primer specific to the predicted 30-terminus of
the candidate miRNA and a universal primer corresponding to
the 50-adapter was used to amplify the specific cDNA clone from
a cDNA library constructed from 18–26 nt RNAs. PCR products
were then cloned and sequenced.
A more sensitive PCR-based cloning approach called mRAP
procedure (Takada et al., 2006) has been described recently.
Isolated small-RNA molecules are first ligated at their 30-end toa 30 adaptor and reverse-transcribed with a primer complemen-
tary to the 30 adaptor. After the annealing of a 50 adaptor to the
poly(C) overhang (added earlier) of the cDNAs, PCR is per-
formed to amplify the cDNAs. The isolated cDNAs are then
cloned and sequenced.
RT-PCR and real-time RT-PCR
The reverse-transcriptase polymerase chain reaction (RT-
PCR) is a widely used method to detect the expression of mRNA
and other RNA molecules. Real-time quantitative RT-PCR has
been successfully used to detect the expression of miRNA pre-
cursors (Schmittgen et al., 2004). Fu et al. (2006) have proposed
a poly(A)-tailed RT-PCR, to detect the expression of mature
miRNAs. Total RNA was polyadenylated by poly(A) polymer-
ase, cDNA was synthesized by an RT primer and reverse tran-
scriptase using the poly(A)-tailed total RNA as templates. The
expression of mature miRNAs was comparable to that deter-
mined by Northern blotting. Recently, Tang et al. (2006) ana-
lyzed miRNAs in single cell by using a real-time PCR-based
220-plex miRNA expression profiling method. This method re-
quires about 0.015 ng of starting total RNA.
Macroarray
Krichevsky et al. (2003) determined the expression profile of
44 miRNAs in mice brain by an oligonucleotide array (~52–74
nucleotides antisense) on nylon membranes. The array was
probed with a radioisotope-labeled low molecular weight frac-
tion (<60 nt) of total RNA and analyzed by phosphor imaging.
This approach is less suited for high throughput applications
and suffers from the drawbacks like unequal hybridization ef-
ficiency of individual probes and targets and use of single data
points for normalization impairing accurate quantification.
In situ hybridization
In situ hybridization methods for detection of miRNAs have
been developed recently (Wienholds et al., 2005; Kloosterman
et al., 2006a, 2006b; Nelson et al., 2006). Technical challenges
include fixing of small RNAs which diffuses fast and could be
326 CHAUDHURI AND CHATTERJEE
lost during hybridization or washing. Use of LNA-modified
oligonucleotide probes successfully detected conserved verte-
brate miRNAs in zebrafish, mice, and frog embryos (Wien-
holds et al., 2005; Kloosterman et al., 2006a, 2006b). Nelson
et al. (2006) demonstrated coordinated miRNA expression on
archival formalin-fixed paraffin-embedded (FFPE) humanbrains
and oligodendroglial tumors by using RAKE (RNA-primed,
array-based, Klenow Enzyme) miRNA microarray platform in
conjunction with LNA-based in situ hybridization. Deo et al.
(2006) have recently improved the specificity by using RNA
oligonucleotide probes linked to a fluorescein hapten and highly
specific washing conditions with tetra-methyl ammonium chlo-
ride (TMAC). The method could directly detect mature miR-
NAs in tissue sections from developing mouse embryos, adult
brain, and the eye. In situ hybridization can be successfully used
to determine the spatio-temporal expression patterns of candi-
date miRNAs, thus having immense application to functional
studies.
Microarray technology
To validate hundreds of computationally predicted novel
miRNAs and also to determine their comparative expression
profile, miRNA profiling by microarray is emerging as a pow-
erful and popular tool because of its automation and high
throughput nature.
Liu et al. (2004) published the first report of genome wide
miRNAexpression profiling by amicroarray in human andmouse
tissues. The miRNAs were reverse transcribed with biotinylated
random primers and hybridized to oligonucleotide spotted
arrays. miRNA levels were then detected using streptavidin-
bound fluorophores. Their results were confirmed with North-
ern blot, real-time RT-PCR, and literature search. The study
measured the expression of pre-miRNA (~70 nt) rather than
~22-nt mature miRNA. Moreover, labeling of highly structured
pre-miRNA with random primers may be susceptible to strong
biases in efficiency.
The miRNA microarray techniques was improved by specifi-
cally targeting the mature 22-nt miRNA sequence and ligase-
based labeling (Thomson et al., 2004). They adapted a labeling
method using T4 RNA ligase to couple the 30-end of RNAs to a
fluorescent modified dinucleotide. Their microarray data strongly
correlated with Northern blot analysis. However, the possibility
of crosshybridization between closely related miRNAs and po-
tential biases in the expression data from different ligation effi-
ciencymay not be eliminated. The authors reported the expression
profiles of 124 miRNAs in adult mouse tissues and embryonic
stages.
Miska et al. (2004) developed a microarray for mature
miRNA expression analysis, in which miRNAs were ligated to
30 and 50 adaptor oligonucleotides followed by reverse tran-
scription and amplified by PCRwith Cy3-labeled primer to label
the sense strand of PCR products. Their data correlated well
with Northern blot analysis. Since RNA is amplified before
hybridization, a relatively low amount of starting material is
needed. A similar PCR amplification and adapter ligation strat-
egy has also been presented (Barad et al., 2004), where labeling
of cDNA was done after PCR amplification. This methodology
was utilized to profile the expression of 150 known human
miRNAs in HeLa cells and five human tissues.
In another approach Tm-normalized DNA oligonucleotides,
antisense to the given small RNA sequence, were used as
probes in the microarray experiment, target miRNAs were PCR
amplified with a fluorescently labeled primer (Axtell and Bartel,
2005; Baskerville and Bartel, 2005). A synthetic reference li-
brary was used for internal normalization.
Sun et al. (2004), however, generated unlabeled cDNA first
with random hexamer. After alkaline hydrolysis of the template
RNA, the single-stranded cDNA was 30 labeled with biotin-
ddUTP using terminal deoxynucleotidyl transferase.
In contrast to the above methods labeling small RNAs prior to
hybridization, a new procedure called the RNA primed array-
based Klenow enzyme (RAKE) assay has been described (Nel-
son et al., 2004). DNA oligonucleotide probes having 30-endcomplementary to specific miRNAs and a universal spacer se-
quence at the 50-end are synthesized and covalently crosslinked
at the 50 termini on to glass microarray slides. The RNA sam-
ples containing miRNAs are then hybridized and treated with
exonuclease I to degrade single-stranded, unhybridized probes.
The Klenow fragment of DNA polymerase I and biotinylated
dATP were then added. The hybridized miRNAs act as primers
and immobilized DNA probes as templates generating biotin
labeled double-stranded fragments, which were visualized by
streptavidin-conjugated fluorophore. Another innovative ad-
vancement made by this group was to isolate small RNAs from
FFPE sections of tissue samples to use on their microarrays.
RAKE does not involve the generation of the cDNA library
or amplification of the RNA sample, and avoids sample RNA
manipulation altogether. This method can distinguish nucleotide
mismatches at the 30-end where miRNA homologs commonly
share the greatest sequence disparity.
Direct chemical methods of labeling have been tried by dif-
ferent groups (Babak et al., 2004; Liang et al., 2005). Babak
et al. (2004) usedUlysisAlexaFluor (Molecular Probes, Eugene,
OR), which reacts with the N7 of guanine to form a stable coor-
dination complex. Total RNA was fluor-labeled and hybridized
directly to an array antisense to 154 mouse miRNAs and 206
other noncoding RNAs included as controls. miRNA expression
was analyzed across 17 mouse organs and tissues and detected
78 miRNAs. Since miRNAs may contain varying G-residues,
the labeling efficiencies are expected to vary and miRNAs
lacking G residuals cannot be labeled.
In the method by Liang et al. (2005) miRNAs were oxidized
with sodium periodate to convert 30 terminal adjacent hydroxyl
groups (20 and 30 position of ribose) into dialdehyde, which was
then reacted with biotin-X-hydrazide through a condensation re-
action resulting in biotinylated miRNA. The biotinylated miR-
NAs were captured on the microarray by oligonucleotide probes
in hybridization. Quantum dots (QD) were labeled on the cap-
tured miRNAs through the strong specific interaction of strepta-
vidin and biotin. QDs have a high extinction coefficient and a high
quantum yield, so trace amounts of miRNAs are easily detected
with a laser confocal scanner. The detection limit of this micro-
array for miRNA was higher than the previous methods using
a model microarray, the authors reported the profiling of 11
miRNAs from leaf and root of rice (Oryza sativa L.ssp.indica)
seedlings. miRNAs resulted from the analysis had a good repro-
ducibility and were consistent with the Northern blot result.
To avoid using high-cost detection equipment, the authors
have used a colorimetric gold–silver detection method, in which
MICRORNA DETECTION AND TARGET PREDICTION 327
the captured miRNAs were labeled with streptavidin-conjugated
gold followed by silver enhancement. During silver enhance-
ment, the gold nanoparticles bound to miRNAs catalyzed the
reduction of silver ions to metallic silver, which further auto-
catalyzed the reduction of silver ions to form metallic silver
precipitation on gold, resulting in a signal enhancement. This
process allowed straightforward detection of the miRNAs with
an ordinary charge coupled device (CCD) camera mounted on a
microscope.
The recent developments in microarray analysis involves
more sensitive and specific methods (Castoldi et al., 2006; Fang
et al., 2006; Wang et al., 2006). The microarray platform mi-
Chip (Castoldi et al., 2006) is based on LNA-modified, Tm-
normalized capture probes spotted onto NHS-coated glass slides
and can discriminate between miRNAs with single nucleotide
differences. Fang et al. (2006) detected miRNAs on LNA
microarrays down to a concentration of 10 fM using a combi-
nation of surface polyadenylation chemistry and nanoparticle-
amplified surface plasmon resonance imaging (SPRI) detection.
This approach was used to determine miRNA concentrations in
a total RNA sample from mouse liver tissue. Wang et al. (2006),
on the other hand, used multiplexed miRNA profiling assay
employing simple high-efficiency direct labeling of submicro-
gram quantities of total RNA, without amplification or size
fractionation. The assay had a low detection limit (<0.05 amol)
and also suitable for use with formalin-fixed paraffin-embedded
clinical samples.
Other approaches
Jonstrup et al. (2006) presented a simple miRNA detection
protocol based on padlock probes and rolling circle amplifica-
tion. Padlock probes are linear DNA probes where the terminal
sequences are designed to hybridize to two adjacent target se-
quences. Under right conditions, DNA ligase will ligate the
termini of the padlock probe on a perfectly matching RNA
template, accurately distinguishing matched and mismatched
substrates. The miRNA, used as a template, can subsequently be
used as primer for rolling circle amplification, thereby linearly
amplifying the target sequence in a quantitative manner. It can
be performed without specialized equipment, and has been
shown to measure specific miRNAs in a few nanograms of total
RNA.
Lu et al. (2005) presented a new, bead-based flow cytometric
miRNA expression profiling method and determined expression
profile of 217 mammalian miRNAs including multiple human
cancers. In this method, oligonucleotide-capture probes com-
plementary to miRNAs were coupled to carboxylated 5-micron
polystyrene beads impregnated with variable mixtures of two
fluorescent dyes (that can yield up to 100 colors), each repre-
senting a single miRNA. Following adaptor ligations of both
the 50-phosphate and the 30-hydroxyl groups of miRNAs, reverse
transcribed miRNAs were PCR amplified using a common bio-
tinylated primer, hybridized to the capture beads, and stained
with streptavidin-phycoerythrin. The beads were then analyzed
using a flow-cytometer. The results were comparable to North-
ern blot analysis. In contrast to traditional microarrays, bead-
based hybridization more closely approximates hybridization in
solution, raising the specificity, thus, it offers a less expensive
high-speed platform for miRNA validation.
A list summarizing putative and experimentally verified
miRNAs in various animals, plants, and viruses are presented in
Table 1.
COMPUTATIONAL PREDICTIONOF miRNA TARGETS
Computational prediction of miRNA targets are far more
challenging compared to miRNA prediction due to the lack of
strict base pairing between miRNA and its target mRNA se-
quences. The basic principles of these predictions, largely de-
rived from experimental studies, rely on: (1) complementarity of
miRNA sequence to the 30 UTR of target mRNA, complemen-
tarity being imperfect in animals with some exceptions but exact
in plants (illustrated in Fig. 2), (2) strong binding of the 50 end ofmiRNA to target compared to its 30-end, (3) thermodynamic
stability of miRNA-mRNA duplex, (4) conservation of target 30
UTR sites related genomes, (5) multiplicity and cooperativity of
miRNA-target interaction, and (6) lack of strong secondary
structure of target mRNAs at miRNA binding site.
MicroRNA TARGET PREDICTIONALGORITHMS
Target prediction in animals
Stark et al. (2003) screened conserved 30 UTR sequences
from the D. melanogaster genome for potential miRNA targets.
The procedure involves (a) search for sequences complemen-
tary to the first eight residues (allowing for G:U mismatches) of
themiRNAwithHMMer (Eddy, 1996), (b) evaluation of thermo-
dynamic stability and structure of the predicted miRNA–target
mRNA heteroduplex with MFOLD (Zuker, 2003). This ap-
proach identified previously validated targets and some new
targets. Three predicted targets each for miR-7 and the miR-2
family were experimentally verified.
Later, the same group systematically evaluated the minimal
requirements for a functional miRNA–target duplex in vivo
(Brennecke et al., 2005). Their study revealed two categories
of target sites in Drosophila: (1) 50 dominant sites base pairing
well with the 50 end of miRNA and may be canonical (pairing
at both 50 and 30 ends) or seed (pairing with the 50 end only); (2)30 compensatory sites (weak 50 base pairing but strong com-
pensatory pairing to 30 end). Their study showed that both
classes of sites are used in biologically relevant genes.
Further studies by the same group (Stark et al., 2005) com-
bined improved miRNA target prediction with information on
gene function and expression in Drosophila. They reported that
a large set of genes involved in basic cellular processes avoid
miRNA regulation due to short 30 UTRs lacking miRNA bind-
ing sites. For individual miRNAs, coexpressed genes avoided
miRNA sites, whereas target genes and miRNAs were prefer-
entially expressed in neighboring tissues. This mutually ex-
clusive expression argues that miRNAs confer accuracy to
developmental gene expression programs, thus ensuring tissue
identity and supporting cell-lineage decisions.
TargetScan (Lewis et al., 2003), a computational method for
the identification of targets of vertebrate miRNA, combines
328 CHAUDHURI AND CHATTERJEE
thermodynamics-based modeling of RNA:RNA duplex interac-
tions with comparative sequence analysis. Given an miRNA
conserved in multiple organisms and a set of orthologous 30
UTR sequences from these organisms, TargetScan (1) searches
the UTRs for segments of complementarity of a 7-nt seed (2–8
from the 50 end of miRNA), (2) extends each seed match al-
lowing G:U pairs, (3) optimizes base pairing using the RNA-
fold program (Hofacker et al., 1994), (4) assigns folding free
energy to each miRNA: target site interaction with RNAeval
(Hofacker et al., 1994), (5) score each UTR and assigns rank,
and (6) predicts as miRNA targets those genes with score and
rank above prechosen cutoff. TargetScan was applied to a non-
redundant pan-mammalian set of 79 miRNAs and a nonre-
dundant pan-vertebrate set of 55 miRNAs and predicted more
than 400 regulatory target genes, the estimated false positives
being ~22–31%. Experimental support for 11 out of 15 genes
was obtained using a HeLa cell reporter system.
TargetScan was further improved to TargetScanS by the same
group (Lewis et al., 2005). TargetScanS relaxed the seed nu-
cleotide match to 6 nt (2–7 from the 50 end), and it did not
consider thermodynamic stability of pairing, pairing outside
immediate vicinity of the seed, or presence of multiple com-
plementary sites per UTR. Chicken and dog genomes were in-
cluded in addition to the mouse, rat, and human genomes, thus
reducing the noise. Moreover, conserved positions immediately
upstream/downstream of the seed match were considered in-
creasing the specificity of prediction. The false positive rate was
estimated ~22% for targets conserved in mammals. In a four-
genome analysis of 30 UTRs, over one-third of human genes
were estimated to be conserved miRNA targets.
Enright et al. (2003) presented a computational method
miRanda for whole genome prediction of miRNA targets in
animals. Target identification inmiRanda involves: (1) sequence
matching, using a position-weighted local alignment algorithm,
TABLE 1. PUTATIVE AND EXPERIMENTALLY VERIFIED MIRNASa
Organism
Total
predicted
miRNA
miRNAs
experimentally
verified
miRNAs yet to
be verified
experimentally
Animals
Anopheles gambiae 84 0 84
Apis mellifera 25 0 25
Bombyx mori 21 0 21
Bos taurus 109 103 6
Caenorhabditis briggsae 79 0 79
Caenorhabditis elegans 115 114 1
Canis familiaris 6 6 0
Drosophila melanogaster 85 82 3
Drosophila pseudoobscura 74 0 74
Danio rerio 371 355 16
Fugu rubripes 133 0 133
Gallus gallus 165 95 71
Homo sapiens 541 444 97
Monodelphis domestica 111 0 111
Mus musculus 419 351 68
Pan troglodytes 39 35 4
Rattus norvegicus 261 174 87
Schmidtea mediterranea 85 85 0
Tetraodon nigroviridis 133 0 133
Xenopus tropicalis 196 0 196
Plants
Arabidopsis thaliana 133 127 6
Oryza sativa 242 98 144
Zea mays 96 0 96
Viruses
Epstein Barr virus 32 32 0
Human cytomegalovirus 14 14 0
Kaposi sarcoma-associated herpesvirus 17 17 0
Mareks disease virus 10 10 0
Mouse gammaherpesvirus 68 9 9 0
Rhesus lymphocryptovirus 22 22 0
Simian Virus 40 2 2 0
aNumbers obtained from miRbase release 9.0 at ftp://ftp.sanger.ac.uk/pub/mirbase/sequences/9.0/database_files/mirna_mature.txt.gz
MICRORNA DETECTION AND TARGET PREDICTION 329
(2) free-energy calculation estimating the energetics of this
physical interaction; and (3) filtering through evolutionary con-
servation. For validation, experimentally verified target genes
were used in a randomized background model. Application to
D. melanogaster, D. pseudoobscura, and A. gambiae genomes
identified target genes enriched in transcription factors and
distinct networks like control of cell fate, morphogenesis, and
nervous system function.
John et al. (2004) further applied miRanda for the human
miRNA target prediction. They reported about 2000 human
genes with miRNA target sites conserved in mammals and about
250 human genes conserved as targets between mammals and
fish. Overrepresented targets included transcription factors, pro-
teins involved in translational regulation, and components of
the miRNA/ubiquitin machinery, representing novel feedback
loops in gene regulation.
Rehmsmeier et al. (2004) presented an improved RNA
folding algorithm RNAhybrid that predicts multiple potential
miRNAs binding sites in target RNAs. This program finds the
energetically most favorable miRNA–mRNA hybridization
sites and does not allow miRNA–miRNA/mRNA–mRNA hy-
bridizations. Statistical significance was assessed with an ex-
treme value statistics of length normalized minimum free
energies, a Poisson approximation of multiple binding sites,
and the calculation of effective numbers of orthologous targets
in comparative genomics. In Drosophila, the algorithm recov-
ered some of the known targets, and suggested additional pos-
tulated targets.
Recently, some useful features like disallowance of G:U base
pairs in the seed region and seed-match speedup accelerates the
program (Kruger and Rehmsmeier 2006). This advanced RNA
hybrid predicted lin-39 as a strong target candidate for miR-241
which is a bona fide target site but has been missed by the
standard approaches (Enright et al., 2003; Krek et al., 2005;
Lewis et al., 2005).
Rajewsky and Socci (2004) presented a simple model for the
mechanism of miRNA target site recognition based on kinetic
and thermodynamic consideration. Application to a set of 74
D. melanogastermiRNAs and 30 UTR sequences of a predefined
set of fly mRNAs revealed highly scoring, conserved putative
target sites in several key developmental body-patterning tran-
scription factors such as fushi-tarazu and hairy.
The DIANA-microT algorithm (Kiriakidou et al. 2004) used
a combined bioinformatics and experimental approach for the
prediction of human miRNA targets. This program scores high
affinity interaction betweenmiRNA:mRNA recognition element
(MRE) with a dynamical programming algorithm and then uses
a MRE filter based on experimentally derived rules: (a) the
proximal (50-end) region of miRNA forms seven to nine base
pairs with MRE; the nucleotide may or may not participate; (b)
a central loop or bulge must exist; and (c) the distal (30-end)region of the miRNA must form at least five canonical/wobble
base pairs with MRE. DIANA-microT successfully recovered
all previously known prototypical C. elegans miRNA targets.
Smalheiser and Torvik (2004) carried out a population-wide
unbiased statistical analysis of how human miRNAs interact
complementarily with human mRNAs compared to scrambled
control sequences. The results demonstrate several novel fea-
tures of human miRNA–mRNA interactions that differ from
C. elegans and Drosophila, and identified a set of 71 mRNAs.
Unlike the case in C. elegans and Drosophila, many human
miRNAs exhibited long exact matches (�10 bases), and in
several cases, a miRNA hit multiple mRNAs belonging to same
functional class.
Krek et al. (2005) presented a probabilistic identification of
combinations of target sites (PicTar) for identification of targets
for both single and combinations of miRNAs. In PicTar prob-
abilistic model, miRNAs compete with each other and back-
ground for binding. The model accounts for synergistic effects
of multiple binding sites of one miRNA or several miRNAs
acting together, along with appropriate scoring of overlapping
sites. The probabilities assigned according to experimental and
computational results. The authors reported that each vertebrate
miRNA could target roughly 200 transcripts on average.
Later, Grun et al. (2005) exploited cross species comparison
and PicTar. They predicted that each D. melanogaster miRNA,
on average, could target 54 transcripts. They also predicted co-
ordinate regulation of target genes by clustered miRNAs. The
authors suggested that gene regulation by miRNAs was com-
parable between flies and mammals, but certain miRNAs might
function in clade-specific modes of gene regulation. The algo-
rithm could recover published miRNA targets and is estimated
to have a ~30% false-positive rate.
By using a new version of PicTar and sequence alignments
of three nematodes, Lall et al. (2006) predicted that miRNAs
regulate at least 10% of C. elegans genes through conserved
interactions. They devised a more elaborate way of assigning
emission probabilities based on the likely increased evolution-
ary conservation of functional sites.
FIG. 2. Complementarity of plant or animal miRNA to 30-UTR of the respective target mRNA. The mature miRNA hy-bridizes to its complementary site in the 30 UTR of targetmRNA. Plant miRNAs commonly pair with perfect comple-mentarity to the target, but animal miRNAs show imperfectcomplementarity although at least one animal miRNA showperfect complementarity (Yekta et al., 2004). When miRNAand mRNA target sites are perfectly complementary, mRNA isdegraded, and for imperfect complementarity leads to repres-sion of protein translation.
330 CHAUDHURI AND CHATTERJEE
Software program MovingTargets (Burgler and Macdonald,
2005) predicts miRNA targets inDrosophila. The approach cre-
ates a database of potential targets, and screens those for ad-
herence to constraints suggested by analysis of known miRNA/
target interactions. The program uses a set of biological con-
straints: number of target sites (usually multiple), strength of
miRNA–mRNA hybridization, number of miRNA 50 nucleo-tides involved in base pairing to the target and involved in G:U
base pairs. The authors identified 83 high likelihood miRNA
targets and verified 3 of these in cultured cells, including a target
for the Drosophila let-7 homolog. MovingTargets provides
flexibility in adjusting the values for the constraints leading to the
prediction of more refined sets of miRNA targets in future.
Saetrom et al. (2005) presented a miRNA target prediction
algorithm TargetBoost, an adaptation of the boosted genetic
programming algorithm (Saetrom 2004), which uses machine
learning to capture the binding characteristics between miRNAs
and their targets on a set of validated miRNA targets in lower
organisms. Given a miRNA and a potential target site, this
classifier returns a score that represents the likelihood of the site
being targeted by the miRNA. The program was used to predict
target sites in a set of genes important for fly body patterning in
D. melanogaster.
Yoon and De Micheli (2005) proposed a computational
method to predict miRNA regulatory modules. Their method
was based upon the observation that more than one miRNA
typically regulates one message, and that one miRNA may have
several target genes. The method was tested with human ge-
nome and a total of 431 regulatory modules were predicted.
Robins et al. (2005) developed an algorithm for predicting
targets that does not rely on evolutionary conservation. It con-
sists of: (a) matching 50 seven nucleotides, (b) scoring the match
of the entire miRNA, (c) incorporating 30 UTR structure of the
target, and (d) combining scores for multiple sites in the targets.
By usingDrosophilamiRNAs as a test case, they validated their
predictions in 10 of 15 genes tested. However, in contrast to
other studies, their computational and experimental data suggest
that miRNAs have fewer targets than previously reported.
Xie et al. (2005) performed comparative analysis of the human,
mouse, rat, and dog genomes and created a systematic catalog of
common regulatorymotifs in promoters and 30 UTRs. Although itis not a miRNA target prediction algorithm, the authors report a
large number of UTR motifs, many of which are likely to be
miRNA targets. Their conclusion was based on the following ob-
servations: (a) the motifs had strong directional bias with respect
to the DNA strand, (b) length distribution showed a strong peak at
8-base length, and (c) they ended with an adenosine comple-
mentary to the 50-end of a bindingmiRNA. The authors suggested
that in humans, miRNAs regulate at least 20% of genes.
Chan and colleagues (2005) have applied a computational
comparative genomic approach for identifying the targets of
miRNAs in closely related flies and worms. The target set of a
given miRNA was defined as the union of all conserved sets
corresponding to the highly conserved k-mers complementary to
its 50 extremity and randomly generated sequences were used to
create pseudotargets and used as control. This approach did not
require orthologous sequences to be aligned, and required only
two genomes. Using this strategy, a large number of target genes
for most of the knownmiRNAswere determined in these species.
Kim et al. (2006) presented miTarget, a SVM classifier to
predictmiRNA target genes. The algorithmmiTarget uses a radial
basis function kernel as a similarity measure for SVM features,
categorized by structural, thermodynamic, and position-based
features. The training data set was collected from the literature to
make a biologically relevant simulation. With this approach,
authors predicted significant functions for human miR-1, miR-
124a, and miR-373 using Gene Ontology (GO) analysis and re-
vealed the importance of pairing at positions 4, 5, and 6 in the 50
region of a miRNA from a feature selection experiment.
Wang (2006) implemented an algorithmMirTarget for animal
miRNA target prediction. The algorithm combines relevant
parameters for miRNA target recognition (seed sequence scan-
ning, cross-species conservation, miRNA-target site duplex sta-
bility, and limited seed extension) and heuristically assigns
weights according to their relative importance. A score calcu-
lation scheme is introduced to reflect the strength of each pa-
rameter. The authors also performed microarray time course
experiments to identify downregulated genes due to miRNA
overexpression.A significant downregulationofmanycell cycle-
related genes was observed following miR-124 over expression.
Watanabe et al. (2006) used an algorithm which combines
hybridization tendency of an miRNA–mRNA target duplex with
the conventionally used prediction criteria. The numbers of
perfectly complementary di-nucleotide sequences were counted
between known pairs of miRNA–mRNA in C. elegans and the
free energy within complementary base pairs of each dinucleo-
tide was calculated by sliding a 2-nt window along all nucleo-
tides of the miRNA–mRNA duplex. The analysis confirmed
strong base pairing at the 50-end of miRNAs (nts 1–8) in C.
elegans, the required central region mismatch (nt 9 or nt 10), and
found weak binding at the 30 region (nts 13–14) in addition.With
this approach, the group predicted 687 possible miRNA target
transcripts, many of which are thought to be involved in C.
elegans development.
Very recently, a pattern-based method rna22 (Miranda et al.,
2006) has been presented for identification of miRNA binding
sites and their corresponding miRNA–mRNA heteroduplexes.
Unlike the previous methods, rna22 does not use a cross-species
sequence conservation filter, allowing the discovery of miRNA
binding sites, that may not be present in closely related species.
Rna22 first finds putative miRNA binding sites in the sequence
of interest, then identifies the targeting miRNA. Computation-
ally, rna22 could identify most of the currently known hetero-
duplexes. Experimentally, with luciferase assays, the authors
demonstrated average repressions of 30% ormore for 168 of 226
tested targets. The results suggest that in a given genome the
true numbers of miRNA precursors, miRNA binding sites, and
affected gene transcripts may be substantially higher than cur-
rently hypothesized and that, in addition to 30 UTRs, numerous
binding sites likely exist in 50 UTRs and coding sequences.
TARGET PREDICTION INPLANTS AND VIRUSES
Rhoades et al. (2002) extracted Arabidopsis mRNA se-
quences from GenBank, and searched for complementary sites
MICRORNA DETECTION AND TARGET PREDICTION 331
for the 16 miRNAs using PatScan (Dsouza et al. 1997). They
predicted 49 regulatory targets of 14 miRNAs of 34 belonging
to transcription factor gene families involved in developmental
patterning or cell differentiation, and hence, the authors sug-
gested that many plant miRNAs function during cell differ-
entiation to clear key regulatory transcripts from daughter cell
lineage. A similar approach was used by Bonnet et al. (2004),
but they allowed mismatches in accordance with the length of
the potential miRNA. Their data was validated with the test
data set of Rhoades et al. (2002).
Jones-Rhoades and Bartel (2004) developed a comparative
genomic approach to systematically identify both miRNAs and
their targets conserved in A. thaliana and O. sativa. The algo-
rithm allowed for gaps and more mismatches in the mRNA:
miRNA duplex compared to earlier method (Rhoades et al.
2002). The authors confirmed 19 newly identified target candi-
dates and suggested that plant miRNAs have a strong propen-
sity to target genes controlling development, transcription
factors, and F-box proteins in particular, in addition to those of
superoxide dismutases, laccases, and ATP sulfurylases.
Target prediction by Wang et al. (2004) used extensive se-
quence complementarity between miRNAs and their target
mRNAs. Putative targets functionally conserved between A.
thaliana and O. sativa were identified for most newly identified
miRNAs. Independent microarray data showed that the expres-
sion levels of some mRNA targets anticorrelated with the ac-
cumulation pattern of their corresponding regulatory miRNAs.
The cleavage of three target mRNAs by miRNA binding was
validated in 50 RACE experiments.
A Web-based integrated computing system, miRU, has been
developed by Zhang (2005) for plant miRNA target gene pre-
diction in any plant whose genome sequence or a large number
of expressed sequence tags (ESTs) are available.
Li and Zhang (2005) proposed a computational pipeline and
detected 96 candidate Arabidopsis miRNAs which were pre-
dicted to target 102 transcription factor genes classified as 28
transcription factor gene families. The method searched for
short, perfectly complementary sequences, and considered RNA
secondary structures and sequence conversation between Ara-
bidopsis and O. sativa.
Zilberstein et al. (2006) presented a program miRNAXpress
which associates between miRNAs and conditions in which they
act. The program consists of a target prediction module (out-
put: Targets Matrix) and associating OperationF, operating on
predefined Expression Matrix, working in tandem. The program
was applied on A. thaliana as model containing 98 miRNAs
and 380 conditions. One interesting result stated that mir159C
activity could be a factor in the misresponse of nph4 mutants to
phototropic stimulations.
To identify targets of Epstein-Barr virus (EBV) miRNAs,
Pfeffer et al. (2004) used a similar computational method used
earlier for animals (Enright et al., 2003). Themajority of predicted
host cell targets had more than one binding site for viral miRNAs,
and ~50% of these had additional targets from host miRNAs. The
predicted viral miRNA targets included regulators of cell prolif-
eration and apoptosis, B cell-specific chemokines and cytokines,
transcriptional regulators, and components of signal transduction
pathways. The authors suggested that EBVmight exploit miRNA
silencing as a convenient method for gene regulation of host and
viral genes in a nonimmunogenic manner.
EXPERIMENTAL VALIDATIONOF miRNA TARGETS
In sharp contrast to the availability of the number of experi-
mentally validated miRNAs, there is a dearth of experimental
evidences identifying their corresponding targets. This is be-
cause validating predictions of miRNA targets is much more
challenging, and so far there is no simple, high-throughput
method for biologically validating miRNA targets. The most
commonly used method implements tissue culture assays using
luciferase reporter gene constructs fused to target sequences
(Lewis et al., 2003; Chang et al., 2004; Esau et al., 2004;
Mansfield et al., 2004; Burgler and Macdonald, 2005; Kir-
iakidou et al., 2005; Krek et al., 2005). These constructs are used
to transfect cells expressing the relevant miRNA, or sometimes
miRNA is experimentally overexpressed, along with vectors
carrying mutant versions of binding sites. If such a construct is
actively regulated by miRNAs already present in the transfected
cells, one might expect it to produce lower levels of the reporter
than the mutant construct.
Another method is to examine cells in which an miRNA has
been over expressed by transfection ofhomologous synthetic
short interfering RNAs or recombinant adenoviral infection for
stable target mRNA expression by microarray (Krutzfeldt et al.,
2005; Lim et al., 2005). This approach could be effective, as
many of the predicted genes can be tested in one experiment.
Loss-of-function studies have also been used in which an
miRNA is inhibited by 20-O-methyl- modified oligonucleotides,
and the inhibition of activity is assayed either by luciferase
activity in reporter assays or by gene expression analysis (Poy
et al., 2004; Chen et al., 2006; Schratt et al., 2006). Lall et al.
(2006) have developed an in vivo validation system that has the
key feature of using upstream sequence from each specific tar-
get, allowing us to drive reporter expression in a manner that
approximates expression of the endogenous transcript. With the
above techniques a small of targets have so far been validated.
Complications are due largely to multiplicity of miRNA targets
and to cooperative interactions of miRNAs with a target. Nev-
ertheless, the experimental results are encouraging, and have
confirmed that various target prediction engines are indeed cap-
able of identifying miRNA targets. Future development of
high-throughput target validation techniques will be necessary
to raise the specificity and sensitivity of microRNA target
prediction algorithms.
miRNA DATA RESOURCES
The increasing number of predicted miRNAs and their re-
spective targets has led to the development of several database
resources. miRBase is one such database focused on microRNA
data. It incorporates all published miRNA sequences with ge-
nomic location and annotation, predicted miRNA target genes,
and also it has a registry where data submissions can be done
prior to publication. Besides miRBase, there are other database
resources likemiRNAMAP, Tarbase, andArgonaut. A summary
of the available online resources like database resources, Web
sites for miRNA and target prediction algorithms, and Web
sites with precomputed predictions is presented in Table 2.
332 CHAUDHURI AND CHATTERJEE
TABLE 2. AVAILABLE ONLINE RESOURCES FOR MIRNA INFORMATION
Name URL Remarks References
Database resources
miRNA registry/
miRBase
http://microrna.sanger.ac.uk miRNA sequences,
annotations, and
predicted targets
(Griffiths-Jones, 2004,
Griffiths-Jones
et al., 2006)
miRNAMap http://mirnamap.mbc.nctu
.edu.tw
Genomic maps for
miRNA genes and targets
(Hsu et al., 2006)
Tarbase http://www.diana.pcbi
.upenn.edu
List of experimentally
supported miRNA targets
(Sethupathy et al., 2006)
Argonaute http://www.ma
.uni-heidelberg.de/apps/zmf/
argonaute/interface
Database for gene
regulation by
mammalian miRNAs
(Shahi et al., 2006)
Identification of miRNAs
MiRscan http://genes.mit.edu/mirscan miRNA gene scan webserver (Lim et al., 2003a, 2003b;
Ohler et al., 2004)
MiRseeker Dr. Pavel Tomancak;
Email: tomancak
@mpi-cbg.de
Program available
upon request
(Lai et al., 2003)
srnaloop http://arep.med.harvard.edu/
miRNA/pgmlicense.html
Source code available (Grad et al., 2003)
findMiRNA http://sundarlab.ucdavis.edu/
mirna/
Downloadable program (Adai et al., 2005)
ProMiR II http://cbit.snu.ac.kr/*ProMiR2/ Web server (Nam et al., 2006)
Bayes-MiRNAfind https://bioinfo.wistar.upenn.edu/
miRNA/miRNA/login.php
Webserver (Yousef et al., 2006)
Microprocessor SVM &
miRNA SVM
https://demo1.interagon.com/
miRNA/
Webserver (Helvik et al., 2006)
miR-abela http://www.mirz.unibas.ch RNA regulatory networks —
Identification of miRNA
targets
TargetScan/
TargetScanS
http://genes.mit.edu/
targetscan
Precomputed searchable
targets for human, mouse,
rat, dog
(Lewis et al., 2003, 2005)
PicTar http://pictar.bio.nyu.edu Precomputed searchable
targets for vertebrates
and flies
(Grun et al., 2005;
Krek et al., 2005)
miRanda http://www.microma.org Precomputed searchable
targets for human,
flies, and zebrafish
(Enright et al., 2003;
John et al., 2004)
DIANA-microT http://www.diana.pcbi.upenn
.edu/cgi-bin/micro_t.cgi
Webserver for target
prediction in human,
mouse, rat, flies, worm,
and Arabidopsis
(Kiriakidou et al., 2004)
RNAhybrid http://bibiserv.techfak
.uni-bielefeld.de/rnahybrid
Prediction of miRNA
binding sites
(Rehmsmeier et al., 2004)
miRU http://bioinfo3.noble.org/
miRNA/miRU.htm
Webserver for plant
miRNA target finder
(Zhang, 2005)
TargetBoost https://demo1.interagon.com/
demo
Webserver for target
prediction
(Saetrom et al., 2005)
RNA22 http://cbcsrv.watson
.ibm.com/rna22.html
Webserver for target
prediction
(Miranda et al., 2006)
miRNA target
prediction at EMBL
http://www.russell
.embl-heidelberg.de/
miRNAs/
Precomputed searchable
targets for Drosophila
(Stark et al., 2003, 2005;
Brennecke et al., 2005)
RNA folding programs
Mfold package http://www.bioinfo.rpi.edu/
applications/mfold/
RNA folding and
hybridization prediction
(Zuker, 2003)
Vienna package http://www.tbi.univie.ac.at/
*ivo/RNA/
RNA secondary structure
prediction and comparison
(Hofacker, 2003)
333
CONCLUDING REMARKS
Over the past few years, the complex and subtle roles of
miRNAs in gene regulation have been increasingly appreciated.
This review summarizes the recent research efforts in compu-
tational prediction and experimental validation techniques re-
lated to both miRNAs and their targets. Most miRNA prediction
algorithms combine information on sequence, structure, and
conservation and predict different numbers of candidate miRNA
genes, few of which have been experimentally validated. Pos-
sible explanations could be that these represent false-positives
or the gene is not simply expressed in the RNA sample exam-
ined. These algorithms so far been not been equipped with the
predictions on the orientation of the transcript (plus or minus
strand) with respect to genomic location, the position of the
processing sites within the hairpin structure, and the determi-
nation of which of the paired segments of the hairpin will
constitute the mature miRNA. Target prediction, again, has been
more complicated by multiplicity of interaction and by the fact
that hundreds of RNAs and thousands of the targets appear to
compose remarkably complex regulatory networks, mediating
many facets of eukaryotic cell function. Despite such uncer-
tainties, in silico prediction methods for miRNAs and their tar-
gets have already become a valuable tool. Sensitive biological
validation techniques are key factors in fine tuning informat-
ics prediction algorithms. And yet, developing such biological
techniques often depends on effective prediction algorithms.
An integrated detection approach, which combines computa-
tional prediction together with high-throughput biological val-
idation, has been most effective in discovery of miRNAs. Now
we know that the regulation of gene expressions by miRNA is
a widespread natural phenomenon regulating complex genetic
pathways, and these miRNAs are modulated in many human
diseases. Understanding the miRNA-guided network has enor-
mous possibility of providing a new window for diagnostics
and therapy of many human diseases. Many challenges remain
in understanding miRNAs and dissecting the affected pathways.
Integrative approaches with crosstalk between in silico and ex-
perimental methods will continue to push forward future de-
velopments in this exciting field.
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Address reprint requests to:
Keya Chaudhuri, Ph.D.
Molecular & Human Genetics Division
Indian Institute of Chemical Biology
4, Raja S. C. Mullick Road
Kolkata, 700 032
India
E-mail:keyachaudhuri@yahoo.com
kchaudhuri@iicb.res.in
Received for publication November 29, 2006; received in re-
vised form December 28, 2006; accepted January 12, 2007.
MICRORNA DETECTION AND TARGET PREDICTION 337
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