Identification and characterization of microRNAs from peanut (Arachis hypogaea L.) by high-throughput sequencing.

Abstract

MicroRNAs (miRNAs) are noncoding RNAs of approximately 21 nt that regulate gene expression in plants post-transcriptionally by endonucleolytic cleavage or translational inhibition. miRNAs play essential roles in numerous developmental and physiological processes and many of them are conserved across species. Extensive studies of miRNAs have been done in a few model plants; however, less is known about the diversity of these regulatory RNAs in peanut (Arachis hypogaea L.), one of the most important oilseed crops cultivated worldwide.
A library of small RNA from peanut was constructed for deep sequencing. In addition to 126 known miRNAs from 33 families, 25 novel peanut miRNAs were identified. The miRNA* sequences of four novel miRNAs were discovered, providing additional evidence for the existence of miRNAs. Twenty of the novel miRNAs were considered to be species-specific because no homolog has been found for other plant species. qRT-PCR was used to analyze the expression of seven miRNAs in different tissues and in seed at different developmental stages and some showed tissue- and/or growth stage-specific expression. Furthermore, potential targets of these putative miRNAs were predicted on the basis of the sequence homology search.
We have identified large numbers of miRNAs and their related target genes through deep sequencing of a small RNA library. This study of the identification and characterization of miRNAs in peanut can initiate further study on peanut miRNA regulation mechanisms, and help toward a greater understanding of the important roles of miRNAs in peanut.

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Identification and Characterization of microRNAs from
Peanut (Arachis hypogaea L.) by High-Throughput
Sequencing
Xiaoyuan Chi1., Qingli Yang1., Xiaoping Chen2, Jinyan Wang1,3, Lijuan Pan1, Mingna Chen1, Zhen
Yang1, Yanan He1, Xuanqiang Liang2, Shanlin Yu1*
1Shandong Peanut Research Institute, Qingdao, People’s Republic of China, 2Crops Research Institute, Guangdong Academy of Agricultural Sciences, People’s Republic
of China, 3State Key Laboratory of Crop Genetics and Germplasm Enhancement, College of Horticulture, Nanjing Agricultural University, Nanjing, People’s Republic of
China
Abstract
Background: MicroRNAs (miRNAs) are noncoding RNAs of approximately 21 nt that regulate gene expression in plants
post-transcriptionally by endonucleolytic cleavage or translational inhibition. miRNAs play essential roles in numerous
developmental and physiological processes and many of them are conserved across species. Extensive studies of miRNAs
have been done in a few model plants; however, less is known about the diversity of these regulatory RNAs in peanut
(Arachis hypogaea L.), one of the most important oilseed crops cultivated worldwide.
Results: A library of small RNA from peanut was constructed for deep sequencing. In addition to 126 known miRNAs from
33 families, 25 novel peanut miRNAs were identified. The miRNA* sequences of four novel miRNAs were discovered,
providing additional evidence for the existence of miRNAs. Twenty of the novel miRNAs were considered to be species-
specific because no homolog has been found for other plant species. qRT-PCR was used to analyze the expression of seven
miRNAs in different tissues and in seed at different developmental stages and some showed tissue- and/or growth stage-
specific expression. Furthermore, potential targets of these putative miRNAs were predicted on the basis of the sequence
homology search.
Conclusions: We have identified large numbers of miRNAs and their related target genes through deep sequencing of a
small RNA library. This study of the identification and characterization of miRNAs in peanut can initiate further study on
peanut miRNA regulation mechanisms, and help toward a greater understanding of the important roles of miRNAs in
peanut.
Citation: Chi X, Yang Q, Chen X, Wang J, Pan L, et al. (2011) Identification and Characterization of microRNAs from Peanut (Arachis hypogaea L.) by High-
Throughput Sequencing. PLoS ONE 6(11): e27530. doi:10.1371/journal.pone.0027530
Editor: Lin Zhang, University of Pennsylvania School of Medicine, United States of America
Received September 5, 2011; Accepted October 18, 2011; Published November 16, 2011
Copyright: ? 2011 Chi et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted
use, distribution, and reproduction in any medium, provided the original author and source are credited.
Funding: This study was supported by grants from the China Agriculture Research System (CARS-14), the National Natural Science Foundation of China
(31000728; 31100205), the Natural Science Fund of Shangdong Province (ZR2009DQ004; ZR2011CQ036), the Promotive Research Fund for Young and Middle-
aged Scientists of Shandong Province (BS2010NY023), Qingdao Municipal Science and Technology Plan Project (11-2-4-9-(3)-jch; 11-2-3-26-nsh). The funders had
no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Competing Interests: The authors have declared that no competing interests exist.
* E-mail: yshanlin1956@163.com
. These authors contributed equally to this work.
Introduction
MicroRNAs (miRNA) are endogenous tiny RNAs (,21 nt in
length) that can play important regulatory roles in animals and
plants by targeting mRNAs for cleavage or translational repres-
sion. Since the discovery of the first miRNA, lin 4 in Caenor-
habditis elegans [1], thousands of miRNAs have been identified in
various multi-cellular eukaryotes, including humans, flies, nema-
todes and plants, and are deposited in the miRBase data-
base (http://www.mirbase.org/, Release 16.0, September 2010)
[2,3,4]. There is increasing evidence that miRNAs play significant
roles in various biological processes, including developmental
transition and patterning, response to the environment and
maintaining genome stability as well as defense against viruses
and bacteria in eukaryotes. Although interest in miRNAs has
attracted the attention of many scientists, and hundreds of plant
miRNAs and their targets have been identified by experimental or
computational approaches, the majority of studies are focused on
two model plant species: Arabidopsis thaliana and rice (Oryza
sativa) [3,5]. To further understand the function of plant miRNAs,
more efforts should be made to include plant species with specific
developmental features, which might contain miRNAs that are
specific to these features [6].
miRNAs are characterized by their precursor stem-loop
secondary structures and are conserved across species [7,8]. The
biogenesis of plant miRNAs is a complex multi-step enzymatic
process [7,9,10]. miRNAs are initially transcribed by RNA
polymerase II in the cell nucleus as long primary miRNAs that
are cleaved into miRNA:miRNA* duplexes by the enzyme Dicer-
like 1 (DCL1). Export of the duplexes into the cell cytoplasm is
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mediated by the protein HASTY. After methyl groups are added
to the 39 ends of the duplexes catalyzed by the protein HEN1, one
strand of the duplexes is selectively incorporated into the RNA-
induced silencing complex (RISC) to form the mature miRNAs,
whereas the other strand, designated miRNA*, is typically
degraded. Guided by miRNAs, the RISC recognizes the
complementary sites on the target mRNAs and causes transcript
cleavage [7,11] or translational arrest [12,13]. Recently, DCL4
has been shown to play a role in the biogenesis of a few miRNAs
with long hairpin precursors [14].
Three major approaches are used for identifying miRNAs in
plants: forward genetics, bioinformatic prediction and direct
cloning and sequencing. Only a few miRNAs have been identified
by forward genetic studies [12,15] and predicting species-specific
miRNAs by the bioinformatics method is difficult. Direct cloning
and sequencing is the most effective method available for the
discovery of plant miRNAs. Many groups have used this approach
to clone and identify miRNAs in A. thaliana, O. sativa, cotton
wood (Populus tricbocarpa), wheat (Triticum aestivum) and oilseed
rape (Brassica napus) [16,17,18,19,20]. The development of high-
throughput sequencing methods, such as the 454 Technology and
the Solexa platform, has greatly improved this approach, which
can identify low-abundance or tissue-specific miRNAs. However,
there are some differences between these novel sequencing
technologies. It is reported that the longest reads are obtained
using the 454 Technology, whereas the Solexa platform can yield
a higher number of reads [6], and is suitable for sequencing
shorter reads (up to 35 bp) [21]. Because the miRNA sequences
are only ,21 nt in length, the Solexa platform appears to be
preferred for miRNA discovery [22].
Peanut (also known as groundnut, Arachis hypogaea L.), an
allotetraploid species (2n=4x=40; AABB), is one of the five most
important oilseed crops cultivated worldwide [23]. Peanut seed
contains ,50% oil, of which ,80% consists of oleic acids (36–
67%) and linoleic acids (15–43%) [24]. The studies of the small
RNAs in peanut have been reported [25,26] but, compared with
the number of miRNAs that have been identified in A. thaliana,
O. sativa and P. trichocarpa, more miRNAs can be mined out
from peanut. We describe the deep sequencing and analysis of
small RNA transcriptomes from peanut using the high-throughput
Solexa technology; 126 known miRNAs and 25 novel miRNAs
were identified on the basis of either sequence similarity or the
secondary structure of their precursors. We used quantitative real-
time RT-PCR (qRT-PCR) to analyze the expression patterns of
seven identified peanut miRNAs in different peanut tissues (root,
stem, leaf, flower and seed) and different developmental stages of
the seed. Furthermore, we made a deep analysis of peanut miRNA
target functions using the GO database and KEGG pathway.
Results
Sequence analysis of short RNAs
We used high-throughput sequencing of small RNA libraries to
identify low-abundance candidate miRNAs in peanut. In all,
25,686,617 reads were obtained from the Solexa sequencing
machine for the small RNA library (mixed tissues of leaf, stem,
root and seed of the cultivated peanut). After removing the
adaptor/acceptor sequences, filtering the low-quality tags and
cleaning up the contamination formed by the adaptor–adaptor
ligation, 23,394,602 (91.08%) clean reads were obtained, repre-
senting 8,453,305 unique sequences. Among the total reads,
2,228,153 were found to be similar to miRNAs. The rest of the
sequences were found to be other types of RNA, including non-
coding RNA, tRNA, rRNA, snRNA or snoRNA. The numbers
and proportions of different categories of small RNAs are given in
Table 1.
The composition of different categories of small RNAs often
reflects the roles in a particular tissue or species and associated
biogenetic machines. The majority of small RNAs from the
libraries were 24 nt long (Figure 1) and accounted for 48% of the
total sequence number, followed by 21 nt (20.9%), 23 nt (7.7%)
and 22 nt (7.2%). This result was consistent with those reported
for other plant species, including A. thaliana, Medicago truncatula, O.
sativa, Populus spp. and Citrus trifoliate, where 24 nt sRNAs
dominated the sRNA transcriptome [6,14,21,22,27,28]. Such a
high percentage of 24 nt small RNAs could reflect the complexity
of the peanut genome because 24 nt siRNAs are known to be
involved in heterochromatin modification, especially for a genome
with a high content of repetitive sequences [29,30].
Identification of known miRNAs in peanut
There are 17,341 miRNAs from 133 species deposited in the
miRBase database (Release 16.0, September 2010) and 2,846
miRNAs from 33 plant species belong to 632 different families.
After removing repeat sequences, the 1,565 unique miRNA
sequences were used as queries to search the potential miRNAs in
peanut.
On the basis of sequence similarity, our analysis revealed that
126 known miRNAs were identified, which belong to 33 miRNA
families with an average of about 4 miRNA members per family
(Table S1). As expected, most of the miRNAs identified in peanut
were highly conserved in diverse plant species [31], suggesting that
the ancient regulatory pathways mediated by evolutionarily
conserved miRNAs are present in legumes. We analyzed the
miRNA members of known families and found significant
divergence among them. The miR165 family was the largest
identified, with 26 members that were distinguished by internal
nucleotide differences. miR166 (17 members), miR167 (8 mem-
bers) and miR169 (7 members) were the second, third and fourth
miRNA families, respectively. Of the remaining 29 miRNA
families, 13 contained 2–6 members, and 16 miRNA families were
each represented by a single member.
The sequencing frequencies for miRNAs in the library can be
used as an index for estimating the relative abundance of miRNAs.
Solexa sequencing produced a large number of miRNA sequences,
allowing us to determine the relative abundance of miRNAs in
peanut; the frequencies of miRNA families varied from 1
(miR2914) to 1,330,176 (miR156), indicating that expression
varies significantly among different miRNA families. Counting
redundant miRNA reads revealed that 11 out of 33 conserved
Table 1. Distribution of small RNAs among different
categories in peanut.
Category Unique RNAs Percent (%) Total RNAs Percent (%)
Total small RNAs8,453,305 10023,394,602100
miRNA 22,7670.272,228,1539.52
rRNA 38,3070.45955,143 4.08
siRNA264,353 3.13 2,069,8958.85
snRNA 1,440 0.024,7260.02
snoRNA8520.012,735 0.01
tRNA8,2130.10 742,5433.17
Unannotated8,117,37396.0317,391,40774.34
doi:10.1371/journal.pone.0027530.t001
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miRNA families were represented by more than 1,000 reads in the
peanut dataset. The miR156 (1,330,176 reads), miR166 (215,652
reads) and miR167 (62,193 reads) families were the most frequent
in the library. The majority of peanut miRNA families were
sequenced less than 1,000 reads, and miR393, miR403, miR1507
and miR2914 were detected less than 10 reads. Sequence analysis
indicated that the relative abundance of certain members within
the miRNA families varied greatly in peanut, suggesting functional
divergence within the family. For example, abundance of the
miR156 family varied from 1 read (miR156f) to 474,415 reads
(miR157) in the deep sequencing, similar to the case for some
other miRNA families, such as miR166 (1–120,435 reads) and
miR167 (2–57,060 reads). These results indicate that different
members have clearly different expression levels in one miRNA
family, probably because the expression is tissue- or developmental
stage-specific.
Novel miRNAs in peanut
For the identification of novel peanut miRNAs, we rely on
peanut EST sequences as miRNA surrounding sequences in
prediction, because details of the peanut genome sequence are
limited. A total of 25 small RNAs met our criteria as established
according to Allen et al. (2005) [32] and were considered putative
novel peanut miRNAs (Table 2; Figure S1). Of these miRNAs, 4
candidates contained both miRNA and miRNA* sequences. We
believe that the detection of miRNA*s is a strong clue, albeit not
infallible, for the formation of precursor hairpin structures and
added weight to the authenticity of the predicted candidates
[27,31]. However, the evolution and function of antisense
miRNAs remains unclear. We propose that these miRNAs might
differ from their sense partners by acting on different mRNA
targets [33]. These novel candidates displayed a concentrated
length distribution between 20 nt and 23 nt, with a peak at
,21 nt. Precursors of these novel miRNAs had negative folding
free energies ranging from 287.2 to 221.9 kcal mol21, with an
average of about 250.01 kcal mol21according to Mfold, which
was similar to the free energy values of other plant miRNA
precursors (259.5 kcal mol21in A. thaliana and 271.0 kcal mol21
in O. sativa). These values were much lower than the reported
folding free energies of tRNA (227.5 kcal mol21) or rRNA
(233 kcal mol21) [34]. The predicted hairpin structures for the
precursors of these miRNAs required 75–343 nt, with a majority
of the identified miRNA precursors (88%) requiring 75–188 nt,
similar to what had been observed in A. thaliana and O. sativa [5].
We searched the nucleotide databases for homologs to
determine whether these novel miRNAs are conserved among
other plant species. This analysis indicated that miR2, miR4,
miR9, miR10 and miR11 are conserved in other dicotyledonous
plants (dicots), such as Glycine max, Vicia faba, Vitis vinifera, M.
truncatula and Trifolium pratense. These findings indicate that these
five miRNAs are conserved in dicots but not in rice or barley
(Hordeum vulgare), suggesting that these are dicot-specific miRNAs.
The predicted novel miRNAs exhibited much lower expression
levels, consistent to the notion that non-conserved miRNAs are
often expressed at a lower level than conserved miRNAs. Only one
member was identified in each novel miRNA family and 5 out of
25 novel miRNA families were sequenced more than 1,000 reads.
The miR3 (5,716 reads) and miR9 (5,280 reads) families were the
most frequent in the library. The majority of peanut miRNA
families were sequenced less than 100 reads, and 9 families were
detected less than 10 reads. The low abundance of novel miRNAs
might suggest a specific role for these miRNAs under various
growth conditions, in specific tissues, or during developmental
stages. Whether these low-abundant miRNAs are expressed at
higher levels in other tissues and organs, such as flowers,
gynophores, or pods, or whether they are regulated by biotic or
abiotic stress, remains to be investigated [25].
Expression patterns of known and novel microRNAs
identified in peanut
Knowledge of the expression patterns of miRNAs could provide
clues about their functions [20]. To gain insight into the possible
Figure 1. Length distribution and abundance of the sequences.
doi:10.1371/journal.pone.0027530.g001
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developmental stage- or tissue/organ-dependent roles of miRNAs
in peanut, we examined the expression patterns of miRNAs in
different tissues and in seed at different developmental stages.
The expression levels of 3 novel miRNAs (miR3, miR7, miR16
and miR16*) and 4 conserved miRNAs (miR156, miR166, miR396
and miR3508) were examined by stem-loop RT-PCR (Figure 2;
Table 3). The expression patterns of miR3 and miR7 were similar:
high expression in leaf, flower and root, and low expression in seed
and stem. miR16 and miR16* had similar expression patterns: they
were expressed abundantly in root and leaf, moderately in flower
and seed and weakly in stem. This was reasonable because both
mature miRNA and corresponding miRNA* were produced from
the same precursors. Expression of miR156 was higher in leaf, root
and stem, and lower in flower and seed. miR166 was expressed
predominantly in flower followed by leaf and root, and weakly in
seed. miR396 appeared to be highly expressed in leaf, root and
flower but was detected only rarely in stem and seed. miR3508
expression appeared to be restricted to root and leaf. These
observations suggested that most miRNAs in peanut are expressed
preferentiallyinoneortwotissues,and onlysomeofthemarehighly
expressed in multiple tissues examined.
The expression patterns of the seven miRNAs across six
developmental stages of seed are shown in Figure 2. The
expression of miR3 was relatively high at the initial four stages,
but showed a dramatic decrease in abundance during later stages;
whereas miR7 reached a maximum level of expression at the
initial stage and had a downward trend thereafter. miR16 and
miR16* accumulated differently over six developmental stages,
suggesting differential regulation of mature miRNAs and miR-
NA*s. miR166 showed gradual increases in abundance during
earlier stages with highest expression at 40 days after pegging
(DAP) and a gradual decrease thereafter. miR396 had the highest
expression level at 40 DAP and much lower levels at other stages.
The expressions of miR156 and miR3508 had no obvious pattern
during seed development with a peak of RNA accumulation at 50
and 40 DAP, respectively.
Prediction of miRNA targets in peanut
In order to better understand the biological functions of the
newly identified as well as the known peanut miRNAs, we
searched for putative target genes using the psRNATarget
program with default parameters (http://plantgrn.noble.org/
Table 2. Novel miRNAs predicted from peanut.
Name Count Sequence
Length
(nt) EST no*
Precursor
length (nt)
Folding energy
(kcal mol21)
miR1 1099GAGAUCAGAGAUGCACACAUUU22C20R5_004_B09 109
247.3
miR1*81 AUGUGUGGGUUUCUGGUCUCCA22C20R5_004_B09109
247.3
miR2 235GAGAUCAGAUCAUGUGGCAGU 21 C20R5_011_B12 92
233.6
miR35716 UUCCAUACAUCAUCUAUCUAAC22C20R5_011_C07 116
253.2
miR49 GGUUCUAGAUCGACGGUGGCA 21C20R5_013_G0676
221.9
miR512UUGGUAGCGGCGAAGCAGGA 20C20R5_029_G05 100
243.2
miR637 CAGGACCGGUGGAGUGUUAUGC22 C20R5_054_H07 84
248.4
miR71481UUAUUGUCGGACUAAGGUGUCU22 HS018_G07114
249.1
miR7*1 ACACUUAGUCUUGCGAUAACU21HS018_G07 114
249.1
miR8 157GACUAAUCUGUCGCGGAUCU 20HS019_H02 270
287.2
miR9 5280GCUCAAGAAAGCUGUGGGAGA 21HS049_E06 143
251.2
miR109GGGUUCUAGAUCGACGGUGGC 21HS273_C01 246
268.4
miR115UGUAUGGUGGAUGUAGGCAUU 21HS274_G03 161
256.3
miR12 16AAAGAUAACAUAUAACUCUGC 21 TFL_001_D10117
221.9
miR138CAUACGAGUUGUAAGAAGAAU 21TFL_007_A09107
232.2
miR14 25GAGGAAGAGGAGGAUGAAGGCC 22TFR6_007_H02 152
267.7
miR159AGAGCUCUCAACUACCGGAGA 21TFR7_024_G09 106
247.4
miR161100 AGAGAUCAGAGAUGCACACAUU22ES717218 131
250.3
miR16*82UGUGUGGGUUUCUGGUCUCCA 21ES717218 131
250.3
miR175UUGUUUGCGAGUUGGGAUUUU 21ES721379 139
258.1
miR18 37 UCGCAGGACCGGUGGAGUGUUA22ES718804 123
258.8
miR198CAAGUGGUCUGCUACUAAAUU 21GO257540 146
232.2
miR20 13UGAAUACCUCAUUCGGCCUCU 21GO259494 343
274.6
miR219CACUGUUAUCAAUGGGUGUAUCU 23GO266079 97
223.12
miR225GCUUGGAAGGAUGUUAGAGUA21GO26846475
224
miR23 21UGACUGAAGUAGGAGGGAAAU 21GO267334141
263.4
miR2428GGAGUGAAACUGAGAACACAAA22GO324297151
259.8
miR2526UAGGCUUAUGACCUCUUUCCA21GO325945188
265.1
miR25* 6GAAAGAGUUUAUAAGCCUACU21GO325945188
265.1
doi:10.1371/journal.pone.0027530.t002
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psRNATarget/). Under the strict criteria described in Materials
and Methods, we found 381 potential targets with an average of
2.5 targets per miRNA molecule. The target genes were found for
32 known and 23 novel peanut miRNA families. Detailed
annotation results of the best BLASTX hits against the NR
protein database are given in Tables S2 and S3.
The putative target genes appeared to be involved in a broad
range of biological processes and most of them were classified as
transcription factors and functional proteins in plant metabolism
and environmental stress response. These results are similar to
those of earlier studies [25]. The predicted targets include
homologs of known targets for conserved miRNAs and novel
targets. As expected, many conserved miRNAs targeted transcrip-
tion factors similar to those predicted in A. thaliana or soybean (G.
max) [35,36], such as those encoding the squamosa promoter-
binding protein (SBP, ahy-miR156), the NAC domain protein
(ahy-miR164), the auxin response factor (ARF, ahy-miR167),
nuclear transcription factor Y (ahy-miR169), APETALA2 (AP2,
ahy-miR172) and growth regulating factor (GRF, ahy-miR396),
reinforcing the idea that conserved plant miRNAs are involved in
essential biological processes. Other predicted targets included
proteins such as transport inhibitor response 1 and auxin signaling
F-box protein for ahy-miR393, resveratrol synthase and laccase for
ahy-miR397, serine hydroxymethyltransferase for ahy-miR398,
basic blue protein for ahy-miR408, polyphenol oxidase for ahy-
miR3508 and transcripts that coded for unknown proteins. These
observations suggested that the function of some well-conserved
miRNAs had drifted during long periods of plant evolution.
Although miRNAs are well conserved over long evolutionary
timescales, some of their sequences have changed and display
variations in a few nucleotide positions [37], which provides the
chance for some miRNAs to base pair with other target mRNAs,
exhibiting a species-specific regulatory pattern [38].
Unlike conserved miRNAs, the targets of novel peanut miRNAs
were not enriched in transcription factors. Their target genes
included those encoding the chlorophyll a/b-binding protein, heat
shock protein, glycosyltransferase, diacylglycerol kinase family
protein, aldo/keto reductase, formin-like protein, UDP-glucuro-
nate 4-epimerase, cold-induced plasma membrane protein,
aspartate carbamoyltransferase and nitrate transporter, implying
that the corresponding novel miRNAs participate in some specific
developmental processes in peanut. We predicted many genes with
unknown function and hypothetical genes for miRNA targeting
and careful analysis of these potential targets will contribute to our
understanding of the role of miRNAs in legumes.
All targets regulated by peanut annotated miRNAs and novel
miRNAs identified in this study were subjected to GO analysis to
investigate gene ontology [39]. We found that 109 genes were
involved in 68 different molecular functions, 87 genes took part in
52 biological processes and 46 genes participated in 21 cellular
components (Figure 3; Table S4). Our GO biological process
demonstrated that a total of 34 miRNA families could be involved
in 52 different biological processes, such as response to stress,
oxidation reduction, fatty acid biosynthesis, carbohydrate metab-
olism etc. Following GO analysis, we used KEGG to construct a
pathway enrichment of predicted miRNA target genes (Table S4).
Many metabolism networks were found to be involved, including
plant–pathogen interaction, lipid metabolism, amino acid metab-
olism, carbohydrate metabolism, energy metabolism, nitrogen
metabolism, signal transduction etc.
Discussion
Although miRNAs have been studied extensively in the past
several years, no systematic study has been reported for peanut,
one of the most important oilseed crops cultivated worldwide.
Recently, some miRNAs from peanut were identified by
computational and direct cloning approaches [25,26], but the
identity and function of most peanut miRNAs are still largely
unknown. Using high-throughput Solexa technology, we found
evidence for the existence of 33 known miRNA families as well as
25 novel miRNA families in peanut. Five of these new miRNAs
were found to be conserved in other dicots, including G. max, V.
faba and M. truncatula, suggesting that they are dicot-specific.
However, we did not find homologs of the remaining 20 miRNAs
in other plants, and these might represent peanut-specific
miRNAs. By deep sequencing of peanut small RNAs (6,009,541
reads), Zhao et al. (2010) identified 22 conserved miRNA families
(miR156–miR894) and 14 novel miRNAs (miRn1–miRn14) [25].
Twenty-two of the conserved miRNA families and one of the
novel miRNAs (miRn1 or miR3508) were identified as miRNAs in
this study. Differences in tissue sampling and sequencing depth are
likely to account for most of the differences of miRNAs identified
between the two studies.
We used stem-loop RT-PCR to validate the predicted known
and novel miRNAs and the results demonstrated that most of the
tested miRNAs were expressed with tissue-, and/or growth stage-
specific characteristics. Our qRT-PCR analysis validated the
miRNA prediction in peanut and their preferential expression can
provide important clues about where these miRNAs function. Our
future work will focus on the demonstration of the role of these
peanut miRNAs in control of peanut growth and development.
The expression analysis of ahy-miR156 revealed a tissue-specific
expression pattern similar to that found in A. thaliana. ahy-miR156
showed higher expression levels in leaf, root and stem, and lower
levels in seed and flower. In A. thaliana, miR156 was strongly
expressed during seedling development and showed weak
expression in mature tissues [40]. miR156 promoted juvenile
development by repressing members of the SBP family of
transcription factors. O. sativa miR156 showed an expression
profile similar to those found in A. thaliana and peanut [41].
ahy-miR166 was expressed predominantly in flower followed by
leaf and root, and weakly in seed, which was closely related to its
functions. It has been proven that the miR166/165 group and its
Table 3. qRT-PCR-validated miRNAs and their sequences.
miRNASequence
miR3UUCCAUACAUCAUCUAUCUAAC
miR7UUAUUGUCGGACUAAGGUGUCU
miR16AGAGAUCAGAGAUGCACACAUU
miR16*UGUGUGGGUUUCUGGUCUCCA
miR3508UAGAGGGUCCCCAUGUUCUCA
miR156UGACAGAAGAGAGUGAGCAC
miR166UCGGACCAGGCUUCAUUCCCC
miR396 UUCCACAGCUUUCUUGAACUG
doi:10.1371/journal.pone.0027530.t003
Figure 2. qRT-PCR validation and expression analysis of miRNAs in peanut.
doi:10.1371/journal.pone.0027530.g002
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target genes regulate diverse aspects of plant development,
including shoot apical and lateral meristem formation, leaf
polarity, floral and root development, and vascular development
[42,43]. The A. thaliana miR166/165 group targets five members
of the HD-ZIP III transcription factor genes and functions by
cleaving target mRNAs through complementary base pairing
[15,44,45]. Plants expressing 35S-MIR166g, which targeted
members of the HD-ZIP transcription factor family, had
radialized leaves, fasciated apical meristems and female sterility
[15].
miR396, which is predicted to target GRF genes in A. thaliana,
plays vital roles in plant growth, development and resistance to
stress [46]. It has been reported that miR396-targeted AtGRF
transcription factors are required for coordination of cell division
and differentiation during leaf development in A. thaliana [47]. In
addition, A. thaliana miR396 mediated the development of leaves
and flowers in transgenic tobacco; over-expression of ath-miR396
in tobacco resulted in a small, narrow leaf phenotype and defects
in the four whorls of floral organs [48]. Transgenic O. sativa and A.
thaliana plants constitutively over-expressing osa-mir396c showed
reduced salt and alkali stress tolerance compared to that of wild-
type plants [49]. In this study, ahy-miR396 was preferentially
expressed in leaf, root and flower, which is in good agreement with
the research results discussed above.
Expression of miR3508, a legume-specific miRNA, appeared to
be restricted to root and leaf. In peanut, it was predicted to target
polyphenol oxidase (PPO) genes. PPO catalyzing the oxygen-
dependent oxidation of phenols to quinones, which is ubiquitous
among angiosperms, is localized on the thylakoids of chloroplasts
and in vesicles or other bodies in non-green plastid types [50,51].
This enzyme is responsible for the typical browning of plant
extracts and damaged tissues and is assumed to be involved in
plant defense against pests and pathogens [52]. Further study of
the relationship between PPO and miR3508 should reveal the
function of this pair in the regulation of peanut growth and
development.
The expression of miR16*, the only miRNA* analyzed in this
study, was closely related to that of mature miR16. It is reported
that the relations between the accumulation abundance of mature
miRNAs and miRNA*s are varied: some miRNAs and miRNA*s
accumulate in the same tissue(s), whereas some accumulate in
different tissues. In addition, for some miRNAs, the expression
level of miRNA* was even higher than that of the corresponding
miRNA [53]. Differential accumulation of miRNAs and miRNA*s
could be due to the different activities of proteins involved directly
in sRNA biogenesis [54].
A further step of target identification is necessary to assess and
define a putative function for a miRNA in plant. Our analysis
revealed that some of the predicted targets of conserved miRNAs
in peanut had a conserved function with miRNA targets in A.
thaliana and these miRNA target sequences were also highly
conserved among a wide variety of plant species, as reported by
Floyd and Bowman (2004) [55]. Consistent with earlier reports,
some of these targets in peanut are plant-specific transcription
factors, such as SBP, AP2, NAC, GRF and the ARF family.
Moreover, some targets, especially for novel miRNAs, were
distinct from A. thaliana and O. sativa genes, indicating that these
targets might be involved in peanut-specific processes [20]. It will
be interesting to identify the functions of these predicted target
genes in peanut.
Cultivated peanuts are important oilseed crops worldwide,
containing significant amounts of lipid and protein [23]. In this
study, we searched for miRNAs that might play a function in
regulating biological processes related to the biosynthesis of lipid
and protein. Our results demonstrated that 4 miRNA families
(miR156, miR159, miR171 and miR14) had a total of 4 targets,
which were involved in amino acid metabolism, fatty acid
metabolism and lipid metabolism. These results suggest that
miRNAs might have an important role in lipid and protein
accumulation in peanut.
In summary, we have identified large numbers of miRNAs from
peanut, analyzed their expression and predicted the putative
targets of these miRNAs. It will be very important to experimen-
tally characterize these miRNAs and their downstream targets, as
this will lead to better understanding of the function relationship
and mechanism of miRNAs in the regulation network. Addition-
Figure 3. Gene categories and distribution of miRNA targets in peanut.
doi:10.1371/journal.pone.0027530.g003
microRNAs in Peanut
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Page 8

ally, the deep sequencing approach to microRNA discovery
suggests that a significant number of novel microRNAs remain to
be discovered and characterized.
Materials and Methods
Ethics statement
No specific permits were required for the described field studies.
No specific permissions were required for these locations and
activities. The location is not privately-owned or protected in any
way and the field studies did not involve endangered or protected
species.
Plant material
Peanuts (A. hypogaea L. cultivar Huayu19) were grown in a
growth chamber with a 16 h light at 26uC/8 h dark at 22uC cycle.
Leaf, stem, and root from 12 days old seedlings and immature
peanut seeds were collected, immediately frozen in liquid nitrogen
and stored at 280uC.
Small RNA library development and sequencing
Mixed peanut tissues (leaf, stem, root and seed) were used. To
identify as many tissue- or developmental stage-specific miRNAs
as possible, we pooled the total RNAs from leaf, stem, root and
seed samples in an equal fraction ratio. Total RNA was isolated
using TRIzolH (Invitrogen, USA) then subjected to 15% (w/v)
denaturing PAGE (polyacrylamide gel electrophoresis), after which
the small RNA fragments of 18–28 nt were isolated from the gel
and purified. Next, the small RNA molecules were ligated to a 59
adaptor and a 39 adaptor sequentially and then converted to DNA
by RT-PCR. Finally, ,20 mg products of RT-PCR were
sequenced directly using a Solexa 1G genome analyzer according
to the manufacturer’s protocols (Beijing Genomics Institute,
China) [56]. The sequenced short reads data have been deposited
to the Short Read Archive section at NCBI under accession
numbers SRA045592.
Small RNA analysis
The raw data were first processed by the Fastx-toolkit pipeline
to remove poor-quality reads and clip adapter sequences and
sequences longer than 17 nt were used for further analysis. We
searched against the Rfam database (http://www.sanger.ac.uk/
software/Rfam) [57] and the GenBank noncoding RNA database
(http://www.ncbi.nlm.nih.gov/)
matched sequences, which might be noncoding RNAs (e.g. tRNA,
rRNA, snRNA and snoRNA) or degradation fragments of
mRNAs. In addition, the unique small RNA sequences were used
to do a BLASTn search against the miRNA database, miRBase
16.0 [2], in order to identify known miRNAs in peanut. Only
perfectly matched sequences were considered to be known
miRNAs. All small RNA fragments and the identified orthologs
of known miRNAs from miRBase were screened from expressed
sequence tag (EST) sequences using the SOAP 2.0 program [58].
toremovethese perfectly
Prediction of novel miRNA
Small RNA sequences were aligned to peanut ESTs to obtain
precursor sequences in order to identify novel miRNAs in peanut.
Contexts of perfectly matched hits were extracted as 6150 bp.
The Mireap program developed by the Beijing Genome Institute
(BGI) was used to analyze structural features of miRNA precursors
to identify novel miRNA candidates. The resulting structures were
retained as novel miRNA candidates only if they met the criteria
described by Allen et al. (2005) [32]. The secondary structures of
filtered pre-miRNA sequences were checked using Mfold [59]. In
each case, only the structure with the lowest-energy was selected
for manual inspection.
Validation and expression analysis of peanut miRNAs
using stem-loop RT-PCR
Samples of peanut leaf, stem, root, mature flower and seed from
10–60 DAP were collected from plants growing in the Shandong
Peanut Research Institute. Each tissue type was collected in
triplicate, immediately frozen in liquid nitrogen and stored at
280uC. Total RNA was extracted from each tissue sample using
the mirVana miRNA isolation kit (Ambion, Austin, TX) according
to the manufacturer’s protocol. Total RNA was quantified and
assessed for quality using a Nanodrop ND-1000 spectrophotom-
eter (Nanodrop Technologies, Wilmington, DE). RNA samples
were stored at 280uC until further analysis. Applied Biosystems
TaqMan MicroRNA Assays were used to detect and quantify
peanut miRNAs according to the manufacturer’s protocol. Briefly,
1 mg of RNA from each tissue sample was used to generate a
single-stranded miRNA cDNA by reverse transcription using the
Applied Biosystems TaqMan MicroRNA Reverse Transcription
Kit and miRNA-specific stem-loop primers provided with the kit.
Next, the expression levels of seven peanut miRNAs were analyzed
in five tissues and six seed developmental stages using qRT-PCR
and miRNA-specific primers on a LightCycler 480 instrument
system (Roche, Germany). 5.8S rRNA was used as the reference
gene in qRT-PCR detection of peanut miRNAs. We analyzed
changes in the expression levels of seven identified peanut
miRNAs, which included 4 known miRNAs (miR156, miR166,
miR396 and miR3508) and 3 peanut-specific miRNAs (miR3,
miR7, miR16 and miR16*).
Prediction of miRNA targets
The putative target sites of miRNA candidates were identified
by aligning the miRNA sequences with the assembled ESTs of
peanut using the psRNATarget program with default parameters
(http://plantgrn.noble.org/psRNATarget/) [60].
All predicted target genes were evaluated by scoring, and the
criteria used were: each G:U wobble pairing was assigned
0.5 point; each indel was assigned 2.0 points; and all other
noncanonicalWatson–Crick pairings
1.0 point. The total score for an alignment was calculated on
the basis of 20 nt; when the query was longer than 20 nt, scores for
all possible consecutive 20 nt subsequences were computed, and
the minimum score was considered to be the total score for the
query–subject alignment. Because targets complementary to the
miRNA 59 end appear to be critical, mismatches other than
G:U18 wobbles at positions 2–7 at the 59 end were further
penalized by 0.5 point in the final score [61]. Sequences were
considered to be miRNA targets if the total score was less than
3.0 points [25].
were eachassigned
Analysis of GO and the KEGG pathway
In order to better understand miRNA target function and
classification as well as the metabolic regulatory networks
associated with peanut miRNAs and their targets, BLASTX
was done with the target sequence and the NCBI database. All
predicted target proteins with an E value of 1e-30 were identified
by BLASTX searching against the Interpro and KEGG database
and the best hits were used to validate the target gene function
and metabolic pathway regulated by miRNAs. We obtained
biological process, cellular component and molecular function of
target genes the same as in the GO database by using the
Interpro database.
microRNAs in Peanut
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Supporting Information
Figure S1
tures of miRNAs in peanuts. Red colored letter: mature
miRNA sequence; blue colored letter: miRNA* sequence.
(DOC)
Examples of the predicated secondary struc-
Table S1
(DOC)
Conserved miRNAs from peanut.
Table
peanut.
(DOC)
S2
Identified targets of known miRNAs in
Table S3
(DOC)
Identified targets of new miRNAs in peanut.
Table S4
of targets.
(XLS)
GO classification and KEGG pathway analysis
Author Contributions
Conceived and designed the experiments: SLY. Performed the experi-
ments: XYC QLY. Analyzed the data: XPC JYW LJP MNC. Contributed
reagents/materials/analysis tools: ZY YNH XQL. Wrote the paper: XYC
XPC.
References
1. Lee RC, Feinbaum RL, Ambros V (1993) The C. elegans heterochronic gene
lin-4 encodes small RNAs with antisense complementarity to lin-14. Cell 75:
843–854.
2. Griffiths-Jones S, Saini HK, van Dongen S, Enright AJ (2008) miRBase: tools for
microRNA genomics. Nucleic Acids Res 36: D154–158.
3. Griffiths-Jones S (2006) miRBase: the microRNA sequence database. Methods
Mol Biol 342: 129–138.
4. Griffiths-Jones S, Grocock RJ, van Dongen S, Bateman A, Enright AJ (2006)
miRBase: microRNA sequences, targets and gene nomenclature. Nucleic Acids
Res 34: D140–144.
5. Zhang B, Pan X, Cannon CH, Cobb GP, Anderson TA (2006) Conservation
and divergence of plant microRNA genes. Plant J 46: 243–259.
6. Moxon S, Jing R, Szittya G, Schwach F, Rusholme Pilcher RL, et al. (2008)
Deep sequencing of tomato short RNAs identifies microRNAs targeting genes
involved in fruit ripening. Genome Res 18: 1602–1609.
7. Bartel DP (2004) MicroRNAs: genomics, biogenesis, mechanism, and function.
Cell 116: 281–297.
8. Carrington JC, Ambros V (2003) Role of microRNAs in plant and animal
development. Science 301: 336–338.
9. Chen X (2008) MicroRNA metabolism in plants. Curr Top Microbiol Immunol
320: 117–136.
10. Ramachandran V, Chen X (2008) Small RNA metabolism in Arabidopsis.
Trends Plant Sci 13: 368–374.
11. Baulcombe D (2004) RNA silencing in plants. Nature 431: 356–363.
12. Aukerman MJ, Sakai H (2003) Regulation of flowering time and floral organ
identity by a MicroRNA and its APETALA2-like target genes. Plant Cell 15:
2730–2741.
13. Chen X (2004) A microRNA as a translational repressor of APETALA2 in
Arabidopsis flower development. Science 303: 2022–2025.
14. Rajagopalan R, Vaucheret H, Trejo J, Bartel DP (2006) A diverse and
evolutionarily fluid set of microRNAs in Arabidopsis thaliana. Genes Dev 20:
3407–3425.
15. Williams L, Grigg SP, Xie M, Christensen S, Fletcher JC (2005) Regula-
tion of Arabidopsis shoot apical meristem and lateral organ formation by
microRNA miR166g and its AtHD-ZIP target genes. Development 132:
3657–3668.
16. Lu S, Sun YH, Shi R, Clark C, Li L, et al. (2005) Novel and mechanical stress-
responsive MicroRNAs in Populus trichocarpa that are absent from Arabidopsis.
Plant Cell 17: 2186–2203.
17. Sunkar R, Girke T, Jain PK, Zhu JK (2005) Cloning and characterization of
microRNAs from rice. Plant Cell 17: 1397–1411.
18. Sunkar R, Zhu JK (2004) Novel and stress-regulated microRNAs and other
small RNAs from Arabidopsis. Plant Cell 16: 2001–2019.
19. Wang L, Wang MB, Tu JX, Helliwell CA, Waterhouse PM, et al. (2007)
Cloning and characterization of microRNAs from Brassica napus. FEBS Lett
581: 3848–3856.
20. Yao Y, Guo G, Ni Z, Sunkar R, Du J, et al. (2007) Cloning and characterization
of microRNAs from wheat (Triticum aestivum L.). Genome Biol 8: R96.
21. Szittya G, Moxon S, Santos DM, Jing R, Fevereiro MP, et al. (2008) High-
throughput sequencing of Medicago truncatula short RNAs identifies eight new
miRNA families. BMC Genomics 9: 593.
22. Song C, Wang C, Zhang C, Korir NK, Yu H, et al. (2010) Deep sequencing
discovery of novel and conserved microRNAs in trifoliate orange (Citrus
trifoliata). BMC Genomics 11: 431.
23. Chen ZB, Wang ML, Barkley NA, Pittman RN (2010) A simple allele-specific
PCR assay for detecting FAD2 alleles in both A and B genomes of the cultivated
peanut for high-oleate trait selection. Plant Mol Biol Rep 28: 542–548.
24. Moore KM, Knauft DA (1989) The inheritance of high-oleic acid in peanut.
J Hered 80: 252–253.
25. Zhao CZ, Xia H, Frazier TP, Yao YY, Bi YP, et al. (2010) Deep sequencing
identifies novel and conserved microRNAs in peanuts (Arachis hypogaea L.).
BMC Plant Biol 10: 3.
26. Sunkar R, Jagadeeswaran G (2008) In silico identification of conserved
microRNAs in large number of diverse plant species. BMC Plant Biol 8: 37.
27. Fahlgren N, Howell MD, Kasschau KD, Chapman EJ, Sullivan CM, et al.
(2007) High-throughput sequencing of Arabidopsis microRNAs: evidence for
frequent birth and death of MIRNA genes. PLoS One 2: e219.
28. Morin RD, Aksay G, Dolgosheina E, Ebhardt HA, Magrini V, et al. (2008)
Comparative analysis of the small RNA transcriptomes of Pinus contorta and
Oryza sativa. Genome Res 18: 571–584.
29. Herr AJ (2005) Pathways through the small RNA world of plants. FEBS Lett
579: 5879–5888.
30. Vazquez F (2006) Arabidopsis endogenous small RNAs: highways and byways.
Trends Plant Sci 11: 460–468.
31. Sunkar R, Zhou X, Zheng Y, Zhang W, Zhu JK (2008) Identification of novel
and candidate miRNAs in rice by high throughput sequencing. BMC Plant Biol
8: 25.
32. Allen E, Xie Z, Gustafson AM, Carrington JC (2005) microRNA-directed
phasing during trans-acting siRNA biogenesis in plants. Cell 121: 207–221.
33. Zhang B, Pan X, Stellwag EJ (2008) Identification of soybean microRNAs and
their targets. Planta 229: 161–182.
34. Bonnet E, Wuyts J, Rouze P, Van de Peer Y (2004) Evidence that microRNA
precursors, unlike other non-coding RNAs, have lower folding free energies than
random sequences. Bioinformatics 20: 2911–2917.
35. Chuck G, Candela H, Hake S (2009) Big impacts by small RNAs in plant
development. Curr Opin Plant Biol 12: 81–86.
36. Song QX, Liu YF, Hu XF, Zhang WK, Ma B, et al. (2011) Identification of
miRNAs and their target genes in developing soybean seeds by deep sequencing.
BMC Plant Biology 11: 5.
37. Axtell MJ, Bowman JL (2008) Evolution of plant microRNAs and their targets.
Trends Plant Sci 13: 343–349.
38. Yin ZJ, Shen FF (2010) Identification and characterization of conserved
microRNAs and their target genes in wheat (Triticum aestivum). Genet Mol Res
9: 1186–1196.
39. Du Z, Zhou X, Ling Y, Zhang Z, Su Z (2010) agriGO: a GO analysis toolkit for
the agricultural community. Nucleic Acids Res 38: W64–70.
40. Axtell MJ, Bartel DP (2005) Antiquity of microRNAs and their targets in land
plants. Plant Cell 17: 1658–1673.
41. Xie K, Wu C, Xiong L (2006) Genomic organization, differential expression,
and interaction of SQUAMOSA promoter-binding-like transcription factors and
microRNA156 in rice. Plant Physiol 142: 280–293.
42. Boualem A, Laporte P, Jovanovic M, Laffont C, Plet J, et al. (2008)
MicroRNA166 controls root and nodule development in Medicago truncatula.
Plant J 54: 876–887.
43. Prigge MJ, Otsuga D, Alonso JM, Ecker JR, Drews GN, et al. (2005) Class III
homeodomain-leucine zipper gene family members have overlapping, antago-
nistic, and distinct roles in Arabidopsis development. Plant Cell 17: 61–76.
44. Byrne ME (2006) Shoot meristem function and leaf polarity: the role of class III
HD-ZIP genes. PLoS Genet 2: e89.
45. Kim J, Jung JH, Reyes JL, Kim YS, Kim SY, et al. (2005) microRNA-directed
cleavage of ATHB15 mRNA regulates vascular development in Arabidopsis
inflorescence stems. Plant J 42: 84–94.
46. Rodriguez RE, Mecchia MA, Debernardi JM, Schommer C, Weigel D, et al.
(2010) Control of cell proliferation in Arabidopsis thaliana by microRNA
miR396. Development 137: 103–112.
47. Wang C, Wang X, Kibet NK, Song C, Zhang C, et al. (2011) Deep sequencing
of grapevine flower and berry short RNA library for discovery of novel
microRNAs and validation of precise sequences of grapevine microRNAs
deposited in miRBase. Physiol Plant 143: 64–81.
48. Yang FX, Liang G, Liu DM, Yu DQ (2009) Arabidopsis miR396 mediates the
development of leaves and flowers in transgenic tobacco. J Plant Biol 52:
475–481.
49. Gao P, Bai X, Yang L, Lv D, Li Y, et al. (2010) Over-expression of osa-
MIR396c decreases salt and alkali stress tolerance. Planta 231: 991–1001.
50. Vaughn KC, Lax AR, Duke SO (1998) Polyphenol oxidase: The chloroplast
oxidase with no established function. Physiologia Plantarum 72: 659–665.
51. Li L, Steffens JC (2002) Overexpression of polyphenol oxidase in transgenic
tomato plants results in enhanced bacterial disease resistance. Planta 215:
239–247.
microRNAs in Peanut
PLoS ONE | www.plosone.org9November 2011 | Volume 6 | Issue 11 | e27530

Page 10

52. Constabel CP, Yip L, Patton JJ, Christopher ME (2000) Polyphenol oxidase
from hybrid poplar. Cloning and expression in response to wounding and
herbivory. Plant Physiol 124: 285–295.
53. Xue LJ, Zhang JJ, Xue HW (2009) Characterization and expression profiles of
miRNAs in rice seeds. Nucleic Acids Res 37: 916–930.
54. Mi S, Cai T, Hu Y, Chen Y, Hodges E, et al. (2008) Sorting of small RNAs into
Arabidopsis argonaute complexes is directed by the 59 terminal nucleotide. Cell
133: 116–127.
55. Floyd SK, Bowman JL (2004) Gene regulation: ancient microRNA target
sequences in plants. Nature 428: 485–486.
56. Liang C, Zhang X, Zou J, Xu D, Su F, et al. (2010) Identification of miRNA
from Porphyra yezoensis by high-throughput sequencing and bioinformatics
analysis. PLoS One 5: e10698.
57. Griffiths-Jones S, Moxon S, Marshall M, Khanna A, Eddy SR, et al. (2005)
Rfam: annotating non-coding RNAs in complete genomes. Nucleic Acids Res
33: D121–124.
58. Li R, Yu C, Li Y, Lam TW, Yiu SM, et al. (2009) SOAP2: an improved ultrafast
tool for short read alignment. Bioinformatics 25: 1966–1967.
59. Zuker M (2003) Mfold web server for nucleic acid folding and hybridization
prediction. Nucleic Acids Res 31: 3406–3415.
60. Dai X, Zhao PX (2011) psRNATarget: a plant small RNA target analysis server.
Nucl Acids Res. pp 1–5.
61. Zhang YJ (2005) MiRU: an automated plant miRNA target prediction server.
Nucl Acids Res 33: W701–W704.
microRNAs in Peanut
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