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J Biomed Biotechnol. 2006; 2006: 95270. Published online 2006 June 28. doi: 10.1155/JBB/2006/95270. | PMCID: PMC1559940 |
Computational Prediction of MicroRNAs Encoded in Viral and Other Genomes Gard O. S. Thomassen,1 Øystein Røsok,2 and Torbjørn Rognes1, 3* 1Centre for Molecular Biology and Neuroscience (CMBN), Institute of Medical Microbiology, Rikshospitalet-Radiumhospitalet Medical Centre, 0027 Oslo, Norway 2Department of Immunology, Institute for Cancer Research, Rikshospitalet-Radiumhospitalet Medical Centre, 0310 Oslo, Norway 3Department of Informatics, University of Oslo, PO Box 1080 Blindern, 0316 Oslo, Norway Received February 1, 2006; Revised April 5, 2006; Accepted May 2, 2006. 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. We present an overview of selected computational methods for
microRNA prediction. It is especially aimed at viral miRNA
detection. As the number of microRNAs increases and the range of
genomes encoding miRNAs expands, it seems that these small
regulators have a more important role than has been previously
thought. Most microRNAs have been detected by cloning and Northern
blotting, but experimental methods are biased towards abundant
microRNAs as well as being time-consuming. Computational detection
methods must therefore be refined to serve as a faster, better,
and more affordable method for microRNA detection. We also present
data from a small study investigating the problems of
computational miRNA prediction. Our findings suggest that the
prediction of microRNA precursor candidates is fairly easy, while
excluding false positives as well as exact prediction of the
mature microRNA is hard. Finally, we discuss possible improvements
to computational microRNA detection. Since 2000 the interest in microRNAs (miRNAs) and their role as
gene expression regulators has grown immensely. Lee et al were the
first to identify such a small regulator: the lin-4 RNA in
Caenorhabditis elegans [ 1]. It has been shown that the 21 nt lin-4 RNA represses mRNA and controls part of the
C elegans larval development [ 1, 2]. The next small
regulatory RNA to be discovered was the let-7, which controls a
later stage in the development of C elegans [ 3, 4].
The lin-4 and let-7, previously known as small temporal RNAs
(stRNAs), are today recognized as the first of a large class of
small regulatory noncoding RNA molecules now called microRNAs
[ 5]. This class of molecules is not limited to development but regulates a wide range of biological processes [ 6]. The microRNAs have been reported to be encoded within noncoding
regions of genomes [ 5, 7, 8], and within protein coding genes
[ 9] as well as noncoding genes [ 10]. Primary precursor miRNAs (pri-miRNAs) are long
transcripts that contain one or more miRNA precursors (pre-miRNAs)
[ 11]. Subsequently the pri-miRNA is cut by the Drosha enzyme into one or more ~ 70 nt long pre-miRNA stem-loop
(hairpin) structure(s) while still in the nucleus [ 12]. The pre-miRNAs are transported by exportin-5 to the cytoplasm
[ 13– 15], where they are cut by the RNase III Dicer enzyme
into active ~ 22 nt long miRNAs [ 16– 18]
(). Usually only one side of the stem encodes
a mature miRNA [ 5, 19], however the process of selecting the
side and region of the pre-miRNA that becomes a mature miRNA is
still not fully understood. The mature miRNAs are then
incorporated as subunits of the micro-ribonucleoproteins (miRNPs)
[ 20]. The miRNP is able to repress the transcription of
target mRNAs by binding to or cleaving the mRNA. Thus the miRNA is
capable of posttranscriptional regulation [ 1– 4, 21– 23].
Such a posttranscriptional silencing complex is often called an
miRNA-initiated (or associated) RISC complex (RNA-induced
silencing complex), and is very similar to the small interfering
RNA-initiated RISC complexes [ 21, 24]. Detailed descriptions
of the stepwise maturation of microRNAs are presented by Chen and
Meister [ 25] and by Bartel [ 26]. Different miRNAs have been detected in a variety of organisms; including 114 C elegans miRNAs, 326 human miRNAs, and a total of 35 virus-encoded
miRNAs (miRBase release 7.1, October 2005) [ 27, 28]. It is estimated that as
much as 30% of human genes are regulated by miRNAs [ 29, 30]. COMPUTATIONAL DETECTION OF miRNAs IN SELECTED ORGANISMS Until 2003 miRNAs were identified almost exclusively by experimental molecular biology [ 31] because there were few computational miRNA prediction tools available (except for homology searches). According to Lai et al [ 32], three observations suggest that computational miRNA prediction approaches will be feasible. “First, miRNAs are generally derived from precursor transcripts of 70–100 nucleotides with extended stem-loop structure. Second, miRNAs are usually highly conserved
between the genomes of related species. Third, miRNAs display a characteristic
pattern of evolutionary divergence.” Already in 2001 Lee and Ambros used both bioinformatics and cDNA
cloning to identify potential C elegans miRNAs [ 7]. They searched the C elegans genome for sequences
conserved in C briggsae that also had characteristic
pre-miRNA features and a secondary structure similar to lin-4 and
let-7, as computed by the mfold program [ 37]. They reported 15 novel miRNAs, of which two were the results of the
computational screening, while the rest were derived from the cDNA
cloning. Table 1 contains an overview of computational miRNA prediction studies. | Table 1Overview, in chronological order, of approaches and results
of selected miRNA detection studies. Computational and experimental
approaches used as well as the total number of predicted candidates and
verified (more ...) |
Another computational tool for miRNAs identification is MiRscan,
described by Lim et al in 2003 [ 31]. MiRscan was designed to identify miRNA genes conserved between genomes, and was
initially applied to C elegans and C briggsae.
MiRscan was utilized together with extensive sequencing of clones,
resulting in the detection of 30 additional miRNAs. MiRscan starts out with two closely related genomes A and B. It scans genome A
for sequences that could form hairpin structures and then checks if the
sequences are conserved in genome B. This initial search aims at capturing most
of the homologous pre-miRNAs in the two genomes. The program uses the captured
miRNAs that are already experimentally verified as a training set, and then
computes a score for all the initially recognized sequences. Lim et al found 35 novel miRNA candidates in C elegans using MiRscan,
of which 16 were experimentally validated. In addition, the program used a
detection threshold that would have identified half (29) of the known (58)
miRNAs. This implies that in the worst case, the MirScan program would have a
sensitivity of 0.70 for miRNAs detection in this study. Lim et al also showed that the accuracy of MirScan
is lower than for programs designed to detect one special type of
RNA, such as tRNAs [ 38], but on the other hand it is at least as good as general computer algorithms for detection of bacterial
ncRNAs [ 39– 41]. Due to the homology criterion of MiRscan,
it may be questionable whether this program is suitable for the
detection of viral miRNAs as there are reports on viral
miRNAs not being conserved across species [ 33], as well as reports on the opposite [ 36]. MiRscan has proved itself able to detect a large number of miRNAs in vertebrate genomes with a
detection sensitivity of 0.74 [ 42]. In May 2003, Ambros et al reported on the testing of different methods for the
detection of miRNAs in C elegans [ 34]. This study was a follow up to their 2001 study, when only 10% of the C briggsae genome was
available [ 7]. Two computational approaches were based on sequence similarities and stem-loop structure features, but used slightly different algorithms. The algorithms were complementary in the way that the methods
uniquely identified miRNAs and in total these two approaches identified 9 new
miRNAs. Combined with a third approach, cDNA cloning followed by Northern blots,
they discovered a total of 21 novel miRNAs. Others have also screened the C elegans genome for miRNAs using
computational approaches based on hairpin structure searches, secondary
structure predictions, and interspecies sequence conservation. Grad et al suggested 214 miRNA candidates of which 14 were confirmed by expression analysis [ 43]. In 2003 Lai and colleagues described a computational
method for miRNA identification in Drosophilia
[ 32]. The approach was named miRseeker, and the initial step was to search the euchromatic DNA sequences of D melanogaster and D pseudoobscura for transcripts
potentially forming stem-loop structures and having a “pattern of
nucleotide divergence characteristic of known miRNAs.”
Subsequently they considered the conservation of this sequence in
more distantly related insects. Lai et al started by aligning 24
pre-miRNA sequences from the two Drosophilia species and
found the degree of conservation to be higher than in protein
coding regions. The candidates were then subjected to a stricter
selection procedure due to the many conserved possible pre-miRNA
stem-loops found. Further analysis proved that most divergence in
the orthologous Drosophila miRNAs originated in
loop-mutations. In more diverged species only the 21–24 nt
mature miRNAs were found to be preserved. The algorithm consists
of three steps. Initially it aligns all D melanogaster
and D pseudoobscura intronic and intergenic regions. It
then slides a window along the conserved regions and uses mfold
[ 37] to estimate the free energy of potential secondary structure formed by the sequence in the window. A minimum arm
length of 23 nt was required as well as a free
energy of at most −23.0 kcal/mol for one isolated miRNA
precursor arm. Both strands of the DNA sequence in the sliding
window were mfolded. Additional scoring of the stem-loops was also
applied. Finally, miRseeker attempts to fit all the remaining
miRNA-precursor candidates into one of six stem-loop pattern
classes defined by the initial 24 pre-miRNA training set. This
procedure left 208 miRNA candidates, including 18 (75%) from
the training set among the 124 highest scoring candidates. Out of
the 208 candidates 42 were also found to be conserved (by sequence
and structure) in a third species. In a selection of 38
candidates, 24 were confirmed as novel miRNA genes (20/27 of those
conserved in a third species and 4/11 of the Drosophila specific candidates). Lai and colleagues also estimated miRNAs to
make up about 1% of the total amount of genes in the
Drosophila genomes (94–124 miRNA genes), while Grad et al estimated C elegans to code for 140–300 miRNA genes [ 43]. As a concluding remark, Lai et al state that their algorithm excludes at least one known miRNA (miR-100). Another study exploiting both characteristic miRNA features and sequence
conservation was developed by Wang et al [ 44]. This approach was used in their search for Arabidopsis thaliana miRNAs. Their prediction identified 63% of known Arabidopsis miRNAs, and they claim identification of 83 novel miRNAs, of which 25 were verified. The computer
algorithm evaluated possible miRNA precursors based on their stem-loop
structure, the GC content of the mature miRNA, the loop length, mismatches in
the stem containing the mature miRNA and the conservation of mature miRNA
sequence in the Orysa sativa genome. Interestingly, 15 of the 19
already known unique Arabidopsis miRNAs have a loop ranging from
20–75 nt, which is much longer than in the known viral miRNAs [ 19, 33, 35, 36]. In plants, the alignment of the miRNA and its target mRNA contains
few mismatches. This fact has been successfully exploited in
combination with typical miRNA feature and conservation searches,
as described above, in a search for Arabidopsis thaliana
miRNA [ 45]. Yet another project combining bioinformatics and experimental
biology in the quest for A thaliana and Nicotiana
tabacum miRNA chose a “reverse” approach [ 46]. Billoud first created a cDNA library of all short N tabacum RNAs, then computational methods were used to identify potential miRNAs.
Their pattern matching program, Patbank, was used for finding
homologues and their MIRFOLD program was used to check for
possible miRNA secondary structures. In this context, the microHarvester should be mentioned as it is a useful
web service designed to detect miRNA homologues in any set of sequences,
given an miRNA precursor [ 47]. The microHarvester is filter based and uses the conservation patterns of the microRNAs combined with
BLAST [ 48], Smith-Waterman [ 49], and RNAfold [ 50]. Wang et al presented a new computational tool in
2005 designed to search for homologues and paralogues of
known miRNAs; miRAlign [ 51]. It is claimed that
miRAlign outperforms all earlier programs of this kind, due to a
less strict conservation search, the ability to take more
structural properties into account, as well as its capability to
create structural alignments based on a single miRNA. It should be
noted that miRAlign is tested primarily on animal data. It was
able to detect 59 miRNA candidates in Anopheles gambiae of which 37 has later been reported in the MicroRNA registry [ 27, 28]. COMPUTATIONAL DETECTION OF miRNAs IN VIRAL GENOMES The first miRNAs detected in a viral genome were reported in Science 2004
[ 35]. Pfeffer and colleagues recorded the small
RNA profile of Epstein-Barr virus (EBV) positive cells. They detected
several expressed miRNA genes in EBV, and given the function of miRNAs they
concluded that they had identified regulators of host and/or viral gene
expression. The detection of these 5 novel miRNAs was made by cloning of
small RNAs from EBV-infected cells. 4% of the small RNAs originated from
EBV. The 5 novel EBV miRNAs were detected by Northern blotting. One miRNA
was found in the 5′ UTR, one in the coding sequence, and one in the 3′ UTR
of the same gene, BHRF-1. The last two miRNAs are from a cluster in the intronic regions of the BART gene. The miRANDA algorithm was used in their prediction of mRNA targets, a method developed for detecting miRNA targets in Drosophila [ 52]. Several host and/or EBV mRNA targets were found for
every miRNA. The majority of the target mRNAs have more then one miRNA
binding site. In 2005 Pfeffer et al reported on the identification of several
miRNAs in the herpesvirus family [ 33]. Their study combined a new computational method for miRNA prediction with a cloning
approach similar to the one used in their initial discovery of
viral miRNAs [ 35]. They were able to predict miRNAs in many large DNA viruses, but they were unable to predict or
experimentally identify miRNAs in small RNA viruses or
retroviruses. Another important finding in this study was that the
EBV miRNAs neither have any significant sequence similarity with
host miRNAs, nor do they seem to be conserved in the herpesvirus
family. This observation indicated that methods depending on
cross-species sequence conservation such as MiRscan and miRseeker, described above, are not well suited for prediction of viral miRNAs. The computational approach developed by Pfeffer and colleagues was based on defining a set of properties of known
miRNA precursor stems and subsequently training a support vector
machine (SVM) to separate known pre-miRNAs from stem-loops
unlikely to code for miRNAs. The SVM was then applied on the set
of all genomic regions potentially forming a stem-loop secondary
structure. The SVM reported predictions based on a chosen
threshold that resulted in the detection of 71% of the true
pre-miRNAs from the training set with only 3% false positives.
Their program also had a method for ranking the candidates with a
score above the threshold; this method is independent of the SVM
threshold score. Disregarding the direction of transcription,
Pfeffer et al made 23 unique predictions of which 14 (61%)
were experimentally verified. One should keep in mind that some of
the predicted miRNAs can be very hard to detect as they may be
expressed only under rare conditions. Further studying the expression of the EBV BHRF-1 gene
and its miRNAs, Pfeffer and colleagues suggest that viruses are
able to simultaneously transcribe both miRNAs and mRNA from the
same region. Pfeffer et al also suggest that their conclusions
support the view of independent miRNA evolution in viruses, as
viral miRNAs seem to lack sequence conservation. In addition, most
miRNAs are transcribed by pol II [ 53], while viral miRNAs may also be transcribed by pol III [ 25, 33]. Almost at the same time as Pfeffer et al published their results [ 33], Cai et al published a paper on the detection of
miRNAs in the human pathogenic Kaposi's sarcoma-associated
herpesvirus (KSHV) [ 54]. They reported the detection of 11 distinct miRNAs, of which all were expressed in latent KSHV
infected cells. These 11 miRNAs were detected by cloning small
RNAs followed by RT-PCR and Northern blot analyses. MirBase
(release 7.1, October 2005) [ 27, 28] lists 12 KSHV miRNAs, of which 10 were identified in both studies, while both Pfeffer et al and Cai et al report one additional unique miRNA. Grey et al developed a computational method based on pre-miRNA stem-loop
properties and combined it with stem-loop conservation [ 36], despite the findings by Pfeffer et al about lack of sequence conservation for viral miRNAs, but in line with the findings in primates [ 55]. Grey and colleagues studied the closely related human and chimpanzee cytomegaloviruses (HCMV and CCMV). First, all conserved stem-loop structures scoring better than a 60% similarity threshold were detected. The resulting 110 highly conserved stem-loop sequences were then run through the MiRscan program [ 31]. MiRscan then suggested 13 high-scoring candidates. Northern blot analysis was used on total RNA harvested at different time points for transcription verification. Five of the 13 candidates were expressed during infection, and three of these were among the ones detected by Pfeffer et al. All but one of the miRNAs found in the study by Pfeffer et al but not identified in the study by Grey et al were conserved in
CCMV and had a MiRscan score above the threshold. The reason they were not
detected was the initial stem-loop finder algorithm. The miRNAs of the simian virus 40 (SV40) has also been studied
[ 19]. Sullivan et al created a computer program called VirMir that identifies miRNA precursor candidates in small genomes
(max 300 kbp). The VirMir program utilizes the RNAfold package
[ 50]. Sullivan and colleagues ended up with two candidates out of which one region produced a suitably sized pre-miRNA that
was detected by a Northern blot. The detected miRNA precursor was
found to be a member of a seemingly small fraction of the miRNA
precursor family, namely, those producing one mature miRNA from
each stem of the precursor hairpin. Interestingly, they also
discovered that both of these miRNAs are acting on the same target
mRNA. Bennasser et al argue that there are 5 likely miRNA candidates in the human
immunodeficiency virus (HIV-1) [ 56]. Attempts to validate the candidates were in progress, but all of the miRNA candidates were found
to have several cellular mRNA targets by a rule based target finder
algorithm. As small-interfering RNAs (siRNAs) are somewhat related to miRNAs
due to the fact that their pathways partially overlap and both become part
of a RISC complex [ 21, 24], it is worth mentioning that the HIV-1 genome encodes an siRNA [ 57]. So there is evidence that viruses can encode both miRNAs and siRNA. The existence of both viral miRNAs and siRNAs was also suggested by Lu and Cullan in their paper on the adenovirus VA1 [ 58]. A COMPUTATIONAL SEARCH FOR EBV miRNA PRECURSORS In 2004 we investigated the challenges in computational detection
of miRNAs encoded in the EBV genome. The EBV genome sequence
( NC_001345) was retrieved from NCBI, and then the sRNAloop
program [ 43] (parameters:
hairpin structure no more than 75 nt, loop longer than 3 nt, score threshold 22) was used
to scan the entire genome for potential miRNA precursors. A total
of 148 candidates were found, including all the five known EBV
miRNAs. We kept only one copy of the candidates appearing more
than once in the genome, narrowing down the number of candidates
to 70. Potential miRNA precursors inside coding regions were not
excluded. We then used mfold [ 37]
to estimate the free energy of the entire precursors, using the web service
( http://bioweb.pasteur.fr/seqanal/interfaces/mfold-simple.html).
The free energy estimates for the five known EBV miRNAs ranged
from −25 kcal/mol to −33.8 kcal/mol. We kept
approximately 40 candidates having a free energy less than
−24.5 kcal/mol, which is about the same threshold as used in
the study by Lai et al [ 32]. We then ranked the candidates as follows: the candidates from
nonrepeat noncoding regions or hypothetical protein coding regions
were ranked first, followed by candidates in known protein coding
regions, and finally the remaining candidates. All of the five
known pre-miRNAs were among the top ten candidates. Based on these
criteria we selected the top 14 candidates for further studies,
including the 5 known miRNAs. This leaves 9 novel predictions, as
shown in Table 2, the according secondary structure
predictions can be found in . Attempts to
experimentally verify either the 5 known miRNAs or the 9
new candidates were unsuccessful. Several possible human and EBV
target mRNAs were predicted for the 9 novel pre-miRNA candidates
(data not shown) using a ParAlign [ 59] sequence similarity search with the predicted stem sequences and a set of rules
similar to the ones used by the miRANDA algorithm [ 52]. A schematic view of our approach can be found in . | Table 2Computationally predicted miRNA precursor candidates from
the Epstein-Barr virus (this study), ranked according to our
criteria. The free-energy estimates were computed by the online
mfold [37] version of
December 2004. All predicted secondary structures (more ...) |
It is important to assess the significance of viral miRNA-induced
posttranscriptional gene regulation in an infected cell. In C elegans, miRNAs play vital roles during development [ 3, 4], while such a critical role for miRNAs has not yet been discovered in viruses. Sullivan et al argue that the importance of the EBV miRNAs in viral mRNA regulation is uncertain, while claiming a more important role of the SV40 miRNA, which they have proven to reduce the cytotoxic T-lymphocyte susceptibility and also reduce local cytokine release [ 19]. The homology findings of Grey et al indicate that the viral miRNAs have not
evolved independently [ 36], suggesting a more
significant role than implied by theories of independent evolution. The importance of further miRNA knowledge is illustrated by the
successful use of miRNA expression profiles to classify human
cancers [ 60], as well as data indicating that many human miRNAs are located in regions frequently associated with cancer [ 61]. Our study clearly indicates that predicting pre-miRNA structures
seems reasonably easy apart from deciding on a score threshold for
candidates. The most challenging task is to predict the accurate
position of the mature miRNA within the precursor. The most
promising strategy for predicting novel miRNAs in viruses appears
to be a combination of the conserved stem-loop search by Grey et al and the precursor miRNA feature searches used in the Grey and Pfeffer studies. Grey et al suggest a refinement of the stem-loop finder to improve the search results as it excluded true
positives that would have been accepted by the later stages of the
algorithm. A broader search for stem-loop structures is also
anticipated by the reports by Wang et al [ 44] of much longer loops (20–75 nt) in A thaliana than in the
loops in the known HMCV miRNAs (4–12 nt) [ 33, 36]. Algorithms might also be improved by exploiting the findings of
Berezikov et al [ 55]; while miRNAs stems show strong conservation and the loops vary in their degree of conservation,
the miRNA precursors' flanking regions show a striking drop in
conservation. This conservation profile can be used for
phylogenetic shadowing [ 62], a technique for sequence comparison between closely related species. This approach was used
to predict and identify several primate miRNAs [ 55]. Introducing a search for miRNA targets [ 29, 52, 63– 67] at an earlier stage of the algorithm could also improve the results. In most miRNA detection approaches this is often a final
separate part [ 44, 45]. We suggest that including an miRNA regulatory module (MRM) [ 68] search at an early stage could
be a valuable improvement. Concerning experimental approaches and verification it should be noted that
miRNA candidates found to originate from within exons are often regarded as
cloning artefacts and therefore discarded. However, as stated by Berezikov et al, there is no experimental evidence excluding miRNAs candidates in these
regions [ 55]. Furthermore, there is evidence indicating that a region coding for both an miRNA and a protein can be used almost simultaneously for miRNA and protein production [ 54]. A large portion of the currently known miRNAs have emerged as a result of cloning, but cloning approaches are likely to be biased towards abundant miRNAs [ 43]. Current computational methods are useful tools for identifying miRNA candidates,
however before better methods have been developed, we still need to verify
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