Page 1

The Sequence Structures of Human MicroRNA Molecules

and Their Implications

Zhide Fang1, Ruofei Du1, Andrea Edwards2, Erik K. Flemington3*, Kun Zhang2*

1Biostatistics Program, School of Public Health, Louisiana State University Health Sciences Center, New Orleans, Louisiana, United States of America, 2Department of

Computer Science, Xavier University of Louisiana, New Orleans, Louisiana, United States of America, 3Department of Pathology, Tulane University School of Medicine,

New Orleans, Louisiana, United States of America

Abstract

The count of the nucleotides in a cloned, short genomic sequence has become an important criterion to annotate such

a sequence as a miRNA molecule. While the majority of human mature miRNA sequences consist of 22 nucleotides, there

exists discrepancy in the characteristic lengths of the miRNA sequences. There is also a lack of systematic studies on such

length distribution and on the biological factors that are related to or may affect this length. In this paper, we intend to fill

this gap by investigating the sequence structure of human miRNA molecules using statistics tools. We demonstrate that the

traditional discrete probability distributions do not model the length distribution of the human mature miRNAs well, and we

obtain the statistical distribution model with a decent fit. We observe that the four nucleotide bases in a miRNA sequence

are not randomly distributed, implying that possible structural patterns such as dinucleotide (trinucleotide or higher order)

may exist. Furthermore, we study the relationships of this length distribution to multiple important factors such as

evolutionary conservation, tumorigenesis, the length of precursor loop structures, and the number of predicted targets. The

association between the miRNA sequence length and the distributions of target site counts in corresponding predicted

genes is also presented. This study results in several novel findings worthy of further investigation that include: (1) rapid

evolution introduces variation to the miRNA sequence length distribution; (2) miRNAs with extreme sequence lengths are

unlikely to be cancer-related; and (3) the miRNA sequence length is positively correlated to the precursor length and the

number of predicted target genes.

Citation: Fang Z, Du R, Edwards A, Flemington EK, Zhang K (2013) The Sequence Structures of Human MicroRNA Molecules and Their Implications. PLoS ONE 8(1):

e54215. doi:10.1371/journal.pone.0054215

Editor: Yan Gong, College of Pharmacy, University of Florida, United States of America

Received July 7, 2012; Accepted December 10, 2012; Published January 18, 2013

Copyright: ? 2013 Fang 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: Research reported in this publication was supported by National Institutes of Health grants (NIGMS P20GM103424, NCRR-RCMI 5G12RR026260-04), an

US Department of Army grant (W911NF-12-1-0066) and an NSF grant (EPS-1006891). 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: eflemin@tulane.edu (EKF); kzhang@xula.edu (KZ)

Introduction

MicroRNAs (miRNAs) have been identified as a group of small

endogenous non-coding RNAs that negatively regulate protein-

coding messenger RNAs (mRNAs) at the post-transcriptional level.

ThederivedprocessandthemainactivityofamiRNAareclearand

welldescribedintheliterature.MaturemiRNAsaresingle-stranded

RNAsconsistingofabout22nucleotidesandarederivedfromlonger

non-codingprimarymiRNAs(pri-miRNAs)andthenfromprecursor

miRNAs (pre-miRNAs) by the sequential actions of the Drosha and

DicerRNAcleavingenzymes[1–3].ThemainfunctionofmiRNAsis

to step in and intervene in the translation of mRNAs or to induce

degradation of the mRNAs. In mammals, mature miRNAs are

incorporatedintoanRNA-inducingsilencingcomplex(RISC).The

activated RISC permits the miRNAs to bind to the 39 untranslated

regions(39UTR)ofspecifictargetmRNAstosuppresstranslationand

cause their degradation by mRNA decay [4–6]. There may not be

a one-to-one correspondence between the miRNAs and targeted

mRNAs. A single miRNA may have multiple mRNA targets. It is

a challenging task to predict the targeted mRNAs of a miRNA,

thoughtheprecisepredictionisessentialtostudyitsfunctionalactivity

and its association with diseases. The process of deriving a miRNA

molecule and its mainactivity isdepicted inFigure 1.

To annotate a cloned sequence as a miRNA, the most

important criteria include the characteristic length (approximately

22-nucleotide) of the sequence and a corresponding compact pre-

miRNA loop structure, with a median of 83 nucleotides [7]. The

association between the biological significance and the sequence

length heterogeneity has been recently recognized for a mature

miRNA in Arabidopsis thaliana [8]. This study shows the importance

and necessity to study the distributional structure of the sequence

lengths of mature miRNA molecules in the genome, and the

factors that may affect this length heterogeneity. With the

development of profiling technology and the advances of

bioinformatics/computational tools, the number of miRNAs

identified has increased dramatically. Since the first miRNA was

discovered in 1993 [9] and the biological functions of miRNAs

were recognized to be conserved in different species in 2000

[10,11], the number of mature human miRNAs jumped from 152

in August 2004 to 1732 in April 2011, according to miRBase,

a database of published miRNA sequences and annotation [12–

16]. In this paper, we systematically investigate the length

distribution of miRNAs, anticipating that the nature of non-

uniformity of this distribution can reveal the complexity of the

miRNA molecular structures and have implications for genetic

research.

PLOS ONE | www.plosone.org1January 2013 | Volume 8 | Issue 1 | e54215

Page 2

Materials and Methods

Materials

The sequences of 1732 human mature miRNAs and the

corresponding precursor miRNAs were downloaded from the

public database miRBase (Release 17, April 2011). All the

calculations were carried out using the R language.

Statistical Methods

A random variable is defined to have an asymmetric Laplace

distribution if it has density

ph,k,s(x)~Ifx§hg(x)exp {

ffiffiffi

s

ffiffiffi

2

p

k

(x{h)

!

zIfxvhg(x)exp

2

p

sk(x{h)

!

,

where h,k,s are three unknown parameters. It reduces to

symmetric Laplace distribution when k~1: The function I is

the indicator function. The maximum likelihood estimates,^h h,^ k k,^ s s,

of these parameters are available in [17]. With these estimates, the

fitted discrete asymmetric Laplace distribution, DALaplace, has

the probability masses defined by,

p(k)~

p^h h,^ k k,^ s s(k)

P

allk

p^h h,^ k k,^ s s(k),

where k ranges from 16 to 27 (the range of the sequence lengths of

human mature miRNAs). The discrete symmetric Laplace

distribution (DLaplace) is defined in the same fashion.

A zero-inflated Poisson model is defined as

fp,l(x)~(1{p)Ifx~0g(x)zplx

x!exp({l),

where x is non-negative integer, and p [½0,1?

parameters. This is a mixture model and it reduces to Poisson

distribution with mean l when p~1, or a single point distribution

ðÞ, l are unknown

putting its all mass at zero when p~0: We fitted this model to the

absolute differences of mature miRNA sequence lengths and their

median, and obtained the maximum likelihood estimates [18]:

^ p p,^l l: Then the discrete, symmetric zero-inflated model (DSZero-

Inf) is defined as,

p(k)~ Ifk~mg(k)z1

2Ifk=mg(k)

??

f^ p p,^l l(k),

where k ranges from 16 to 27, and m is the median of the observed

sequence lengths of human mature miRNAs.

The tPoisson distribution is defined as

p(k)~clkexp({l)

k!

,

where k ranges from 16 to 27, l is the average sequence length of

all human mature miRNAs, and c is a constant such that the sum

of all probabilities is one.

Results and Discussion

The Distribution of the Sizes of Human Mature miRNA

Molecules

The number of nucleotides in a human mature miRNA is

a discrete random variable, which ranges from 16 to 27 and has

a mode and a median of 22. A histogram of lengths of all human

mature miRNA molecules is presented in Figure 2(a). Though

the Poisson distribution is the traditional model for fitting the

count data, it does not fit the length distribution of mature miRNA

molecules well. Figure 2(b) is the Poissonness plot of the data

[19]. It is created by plotting flog(xk)zlog(k!)g against fkg,

where k is the count, xkis the corresponding observed frequency

and k! represents the factorial of k. It is clear that the plotted points

do not fall onto a straight line, with the points at the middle above

the line and the points at both ends below the line. This suggests

non-Poisson distribution should be employed to fit the length

distribution of mature miRNAs.

A unique feature of Poisson distribution is the equality of its

mean and variance. This is not the case for the lengths of human

Figure 1. Biogenesis of mature miRNAs and their functional activity.

doi:10.1371/journal.pone.0054215.g001

Sequence Structures of miRNAs and the Implications

PLOS ONE | www.plosone.org2January 2013 | Volume 8 | Issue 1 | e54215

Page 3

mature miRNA molecules because the sample mean (21.52) is

much larger than the sample variance (2.51). This fact also implies

that negative binomial distribution, another popular distribution

to model the count data and handle the over-dispersion problem

in counts, could not fit the human mature miRNA lengths well.

We show in Figure 3 the schematic fitting results of three

discrete distributions to the sequence lengths of human mature

miRNA molecules. These include a discrete analogue of the

asymmetric Laplace distribution (denoted as DALaplace), a dis-

crete, symmetric distribution induced from the zero-inflated model

(denoted as DSZero-Inf) and a truncated Poisson distribution

(denoted as tPoisson). Details of DALaplace, DSZero-inf and

tPoisson are discussed in the Materials and Methods. Interested

readers are referred to [17] for the definition of the asymmetric

Laplace distribution and methods for parameter estimation, and to

[19] for the definition and applications of the zero-inflated model.

It is clear from the plot that DALaplace performs best while

tPoisson is the worst. The sum of squares of the residuals

(differences between observed percentages and corresponding

fitted values) are 0.0047, 0.01, 0.175 for DALaplace, DSZero-inf

and tPoisson, respectively, further illustrating the performance of

these models. We also calculated AIC (Akaike information

criterion) to evaluate the relative goodness of fit of these non-

nested models. AICs for DALaplace, DSZero-inf and tPoisson are

5893.396, 6117.659, and 7970.977 respectively. The order of

these values confirms our selection of the model.

The Randomness of Bases in Mature Human miRNA

Molecules

Another question of interest to biologists is whether there is any

structural pattern in a human mature miRNA; in other words,

whether the proportion of one nucleotide base is significantly

higher or lower than those of other bases. We intend to address

this problem in this subsection. Given the length of a mature

miRNA sequence, the vector of counts, nA, nC, nG, nU,of the

bases, A, C, G, U, follows a multinomial distribution. By the

likelihood ratio method for the test of proportional homogeneity,

we conclude that the proportions of the four bases in every

sequence are significantly different (p-value < 0). We further find

that at the significance level of 0.05, that there are 341 (about

20%) mature miRNA sequences showing inequality of base

probabilities. The sample proportions of four bases in all miRNA

sequences are presented in Figure 4(a), with the 95% simulta-

neous confidence interval [20] at the top of corresponding bar.

These intervals clearly indicate that the four bases are not equally

probable in all the sequences. All these findings imply that there

may exist structural patterns in the sequences of certain mature

miRNAs.

However, as demonstrated in Figure 4(b), the content of GC

(50.8%) is very close to that of AU (49.2%). The 99.9% confidence

interval for the GC content is (0.499, 0.516), which is narrow and

covers 0.5. We comment that the hypothesis of the GC content

being 50% holds as long as the significance level is set to be greater

than 0.0028. This is due to the facts that the sample size N (the

total number of bases in all mature miRNA sequences) is large and

that in the hypothesis testing of a proportion, the significant

probability goes to zero as N approaches infinity.

The Relationship to Evolutionary Conservation

Highly conserved DNA sequences are thought to have

functional value. The genetic conservation across evolution has

been an important benchmark for detecting functionally important

nucleic acid sequences, and for studying gene interactions in

a group of co-regulated genes [21–24]. Hirsh and Fraser [25]

revealed a negative and highly significant relationship between the

importance of a gene and the evolutionary rate. Similar relation-

ship for miRNAs was also studied in the literature. Zhang et al.

[26] reported the rapid evolution of some miRNA clusters. In this

subsection, we present our findings on the correlation between

evolutionary conservation and the length of mature miRNA

molecules. To our knowledge, this is the first study exploring this

relationship.

All human mature miRNAs are divided into two classes,

conserved and human-specific, by using the procedure documen-

ted in [27]. Out of 1732 mature miRNAs, there are 914 (about

52.8%) miRNAs labeled as conserved and 818 (about 47.2%) as

human-specific. These two ratios are significantly different (one-

sided p-value is 0.01). Figure 5 shows the length distributions of

the sequences in these two groups. We can see that the sequence

lengths of conserved miRNAs are symmetrically distributed

around 22. Both the discrete, symmetric, zero-inflated distribution

(DSZero-inf) and the discrete, symmetric Laplace (DLaplace) can

model the distribution decently and there is little difference

between these two models. On the contrary, the sequence lengths

of human-specific miRNAs seem to be bi-modally distributed with

modes of 16 and 22. One may need a mixture of two distributions

to model this variable well. The percentage (7.3%) of the short

human-specific miRNAs that have length of 16 or 17 is about ten-

fold of that (0.77%) of the short conserved miRNAs (a Z-test for

equality of two percentages gives a p-value close to 0).

Figure 2. Histogram and corresponding Poissonness plot of

the sequence lengths (sizes) of human mature miRNA mole-

cules.

doi:10.1371/journal.pone.0054215.g002

Figure 3. Histogram of sequence lengths of human mature

miRNA molecules and four fitted models.

doi:10.1371/journal.pone.0054215.g003

Sequence Structures of miRNAs and the Implications

PLOS ONE | www.plosone.org 3January 2013 | Volume 8 | Issue 1 | e54215

Page 4

All these results indicate that rapid evolution seems to increase

the variation in the sequence lengths of human mature miRNA

molecules, and thus complicate the distribution of the length

variable.

The Characteristic Size of Human miRNA Oncogenes and

Tumor Suppressors

It has become evident that miRNAs control the expression

levels of gene products that are important in cancer progression. A

number of studies have shown that many miRNAs reside within

chromosomal fragile sites in the human genome and that many

miRNAs have been linked to the initiation, progression, and

metastasis of human malignancies, with the earlier reports

associating miRNAs with cancers being published in [28,29].

Some miRNAs are able to target oncogenes – those with capacity

to induce tumor migration and invasion, or tumor suppressor

genes – those with capacity to suppress cancer and metastasis [30–

33]. The essence of the miRNA’s regulatory mechanism in cancer

lies in that increased expression of certain miRNAs can result in

down-regulation of tumor suppressor genes, while decreased

expression of other miRNAs can lead to increased expression of

oncogenes. Examples include hsa-miR-10B [34] and hsa-miR-21

[35] in breast cancer, and hsa-miR-155 [36] in human B cell

lymphomas as oncogenes; and hsa-let-7 [37] in lung cancer, and

hsa-miR-15 and hsa-miR-16 [28] in chronic lymphocytic leukemia

as tumor suppressors.

To investigate the distributions of the sequence lengths of the

mature miRNA molecules that are associated with cancer, we

generate a class of miRNAs regulating either oncogenes or tumor

suppressor genes. For a miRNA to be included, there must be at

least one publication indicating the causal relationship between the

miRNA and the related oncogene or tumor suppressor gene. We

include those miRNAs that play opposite roles in different cancers

due to the fact that one miRNA may regulate multiple targets, and

the same miRNA may play opposite roles in cancer progression in

that it acts as a tumor suppressor in certain cancers and as an

oncogene in others [38]. This makes our selection slightly different

from that in [16]. If no such a causal relationship exists, a miRNA

is selected as an oncogene if it is up-regulated in at least three

publications, or as a tumor suppressor if it is down-regulated in at

least another three papers. We exclude the miRNAs which show

conflicted roles in the same cancer. We obtained 173 cancer

related miRNAs listed in Table 1, where the function of a miRNA

is marked ‘‘mixed’’ if it regulates some oncogenes in a certain

cancers and other tumor suppressor genes in different types of

cancers. We find that the characteristic sequence lengths of these

miRNAs are very stable, with 60.7% of human miRNAs having

sequences of 22 nucleotides, 96.5% of human miRNAs having

Figure 4. The sample proportions of nucleotide bases and GC, AU contents.

doi:10.1371/journal.pone.0054215.g004

Figure 5. Histograms and fitted distributions of the sequence lengths of mature conserved and human – specific miRNAs.

doi:10.1371/journal.pone.0054215.g005

Sequence Structures of miRNAs and the Implications

PLOS ONE | www.plosone.org4 January 2013 | Volume 8 | Issue 1 | e54215

Page 5

sequences of 22+1 nucleotides, and 99.4% of human miRNAs

having sequences of 22+2 nucleotides. The only miRNA whose

sequence is of 18 nucleotides, outside of the interval 22+2, is has-

miR-516a-3p. This miRNA has connection to human breast

cancer progression [39]. The length distribution for the miRNAs

exclusively regulating oncogenes (or tumor suppressors) is very

similar to that of all cancer-related miRNAs. These observations

suggest that an extremely long or short miRNA is unlikely cancer-

Table 1. All human mature miRNAs associated with cancer and their functions.

miRNA FunctionmiRNA functionmiRNA FunctionmiRNA Function

let-7asupp miR-148bsupp miR-21 onco miR-34bsupp

let-7a-2*supp miR-150 mixedmiR-210mixed miR-34c-5psupp

let-7bsupp miR-152supp miR-214 suppmiR-370supp

let-7c supp miR-153supp miR-215suppmiR-372 onco

let-7dsupp miR-155 oncomiR-216bsupp miR-373*onco

let-7esupp miR-15asupp miR-218suppmiR-373 onco

let-7fsupp miR-15bsupp miR-219-1-3p onco miR-374aonco

let-7f-1*onco miR-16mixed miR-22supp miR-375mixed

let-7gsuppmiR-16-1* mixedmiR-221onco miR-376asupp

let-7isupp miR-17oncomiR-222oncomiR-376bsupp

miR-1suppmiR-181amixed miR-223mixed miR-377supp

miR-100suppmiR-181a-2* oncomiR-224onco miR-424supp

miR-101suppmiR-181bsuppmiR-23amixed miR-429supp

miR-106aonco miR-181csuppmiR-23bsuppmiR-432supp

miR-106boncomiR-182oncomiR-24-1* oncomiR-449asupp

miR-107mixedmiR-182*oncomiR-24oncomiR-451 supp

miR-10aoncomiR-183suppmiR-24-2*oncomiR-485-5psupp

miR-10b oncomiR-184oncomiR-25oncomiR-486-5psupp

miR-122 supp miR-185suppmiR-26amixedmiR-494onco

miR-124supp miR-18aoncomiR-26b mixed miR-495supp

miR-125a-5psuppmiR-18a* suppmiR-27aoncomiR-497supp

miR-125bmixedmiR-191onco miR-27bsuppmiR-498 onco

miR-125b-1*suppmiR-192suppmiR-296-5poncomiR-503onco

miR-125b-2*suppmiR-193a-3psuppmiR-29asuppmiR-510 onco

miR-126* mixedmiR-193bsuppmiR-29bsuppmiR-516a-3ponco

miR-126mixedmiR-194suppmiR-29b-2*suppmiR-519a supp

miR-127-3psuppmiR-195 suppmiR-29c suppmiR-520c-3p onco

miR-128suppmiR-196a mixedmiR-30amixedmiR-520hsupp

miR-129-5psuppmiR-196a* oncomiR-30a*suppmiR-521onco

miR-130b oncomiR-197oncomiR-30e suppmiR-532-5ponco

miR-133asupp miR-199b-5psupp miR-31suppmiR-661supp

miR-133b supp miR-19aonco miR-32 oncomiR-675onco

miR-134suppmiR-19b oncomiR-320asuppmiR-7supp

miR-135asuppmiR-19b-2*oncomiR-324-5psuppmiR-9mixed

miR-137mixed miR-200asuppmiR-326 suppmiR-9*onco

miR-138supp miR-200bsupp miR-328 supp miR-92aonco

miR-139-3p suppmiR-200cmixedmiR-330-3psuppmiR-93onco

miR-140-5suppmiR-203suppmiR-335supp miR-95 supp

miR-141suppmiR-204mixedmiR-337-3psuppmiR-96onco

miR-143mixedmiR-205supp miR-340oncomiR-98onco

miR-145suppmiR-206suppmiR-342-5psuppmiR-99asupp

miR-146amixedmiR-20amixedmiR-345onco

miR-146b-5psuppmiR-20a*oncomiR-346onco

miR-148asuppmiR-20boncomiR-34a supp

doi:10.1371/journal.pone.0054215.t001

Sequence Structures of miRNAs and the Implications

PLOS ONE | www.plosone.org5 January 2013 | Volume 8 | Issue 1 | e54215

Page 6

related. But for a cancer-related miRNA, the sequence length does

not affect its classification to oncogenes or tumor suppressors.

The Relationship to the Size of the pre-miRNA

The stem-loop structure of the precursor miRNA is developed

prior to the corresponding mature miRNAs. Thus, the association

between the biogenesis of a miRNA gene and the sequence

features of its stem-loop precursor is also important. Firstly, we

study the distribution of the sequence lengths of pre-miRNAs. As

presented in Figure 6, this distribution has a median (and mode)

of 83 nucleotides, but it is skewed to the right (Figure 6(a)). A

normal model with mean 4.41 (log(83)) and standard deviation 0.16

fits the logarithm of the sequence lengths very well (the red curve

in Figure 6(b) is the fitted model). This indicates that a log-normal

distribution can be employed to model the length distribution of

the human pre-miRNAs. A good feature is that the log-normal

distribution maximizes the entropy probability among distribu-

tions whose logarithms have fixed mean and variance [40].

Next we study the relationship between the sequence lengths of

human pre-miRNAs and the corresponding mature miRNAs. The

sequence length of pre-miRNAs varies from 41 to 180 and there

are multiple pre-miRNA lengths corresponding to each mature

miRNA length. We first calculate the average sequence lengths of

the precursors corresponding to the same mature miRNA length,

and then fit the regression model of the mature miRNA length to

the average precursor length. As shown in Figure 7, there is

a positive, significant relationship between the human mature and

precursor miRNA sequence lengths (the slope of the red line is

0.388, with p value of 6:7|10{5). The multiple R2of the

regression is 81%. The improvement due to the quadratic

polynomial model fit (the blue curve) is not significant, with the

p value of the quadratic term equal to 0.539. We also obtained the

maximal information coefficient (MIC) and related statistical

indexes proposed by Reshef et al. [41]: MIC=0.65, MIC-r2(a

measure of non-linearity, r is the Pearson correlation coeffi-

cient)=20.15, MAS (the maximum asymmetry score for non -

monotonicity)=0 and MCN (minimum cell number for complex-

ity)=2. By comparing these indexes with those in Table S1 in

[41], we can conclude a linear association between these two

variables – the sequence lengths of human precursor miRNA and

mature miRNA.

Lastly, we look into where the human mature miRNA resides

within its stem-loop precursor – in the 59 arm or 39 arm. We

scrutinize 1732 mature miRNAs in total, with has-miR-378d

excluded from the analysis because it locates in both the 39 arm of

the stem-loop precursor hsa-mir-378d-1 and the 59 arm of the stem-

loop precursor hsa-mir-378d-2. There is no significant difference

between the percentages of miRNAs in the 59 arm (49.4%) and 39

arm (50.6%), and both sequence length distributions are symmet-

ric around approximately 22 (nucleotides). However, as Figure 8

indicates, difference exists between the sequence length distribu-

tions of miRNAs resided in the two arms of the precursors. Longer

mature miRNAs (with more than 22 nucleotides) more often

locate in the 59 arm than in the 39 arm of the precursors. Among

the miRNAs located in the 59 arm and 39 arm separately, the

percentages of the miRNAs with exactly 22 nucleotides are

significantly different (46.2% (in the 59 arm) versus 50.3% (in the

39 arm), with p value being 0.044).

The Association of miRNA Sequence Length and the

Number of Predicted Target Genes

A miRNA is a non-coding, functional RNA molecule. Its role in

post-transcriptional gene regulation is carried out by binding to

the target mRNA and then destabilizing the mRNA or suppressing

its translation. In most cases, one miRNA does not bind to a single

mRNA but instead binds to multiple targets. The number of

predicted target genes varies dramatically from one miRNA to

another. Whether this number is associated with the sequence

length of the miRNA molecule may have some implication in

genetic research and is worthwhile to be studied. We retrieved all

of the predicted targets from a web tool miRDB [42,43]. Four

mature miRNAs (hsa-miR-3124-5p, hsa-miR-3647-3p, hsa-miR-

3647-5p, hsa-miR-3648) were excluded from the analysis because

each of them has no predicted target gene according to this tool.

Many miRNAs have the same sequence length. Each of these

miRNAs binds to at least one gene. Figure 9 presents the average

numbers of predicted target genes versus the mature miRNA

sequence length. Generally speaking, the plot shows that the

average number of the target genes is positively correlated to the

sequence length of the miRNA, and this relationship is statistically

significant when considering only the miRNAs with sequence

lengths between 17 and 25 (the regression line (red) has a positive

Figure 6. Distributions of the sequence lengths of human pre-miRNAs.

doi:10.1371/journal.pone.0054215.g006

Sequence Structures of miRNAs and the Implications

PLOS ONE | www.plosone.org6January 2013 | Volume 8 | Issue 1 | e54215

Page 7

slope, 18, with the p value being 0.003),indicating that longer

miRNAs tend to regulate more genes. We comment that there are

only 17 miRNAs with sequence length of 16, 26 or 27.

Nevertheless, this positive correlation is an interesting observation

whose biological significance warrants further investigation. In-

creased miRNA length raises the possibility that the extra

nucleotides are involved in facilitating protein complex formation.

This could take the form of aiding more stable interactions with

core RISC complexes or through interactions with additional

regulatory co-factors. An interesting model for the latter is that

these additional factors regulate targeting to different subsets of 39

UTRs depending on the cell phenotype. In this scenario, a broader

target repertoire can exist but tissue specific co-factors dictate the

subset of targets in a particular tissue.

The Relationship between miRNA Sequence Length and

the Distribution of Target Binding Sites

In this subsection, we look into the relationship between the

lengths and the seed sequences of miRNAs. The motivation for

this analysis is that the miRNA target prediction heavily depends

on the binding between the seed sequence of the miRNA and the

39 UTR sequences of the targeted mRNAs [44]. Meanwhile, the

non-seed sequence may play some roles in processing of precursor

and/or the association with RICS proteins. We downloaded the

summary counts, including the numbers of (non-)conserved 8mer

sites, 7mer-m8 sites and 7mer-1A sites in the targeted transcripts,

from a public domain TargetScan Human Release 6.1 [45–48].

This database contains 1524 mature miRNAs. As specified by the

Figure 7. Scatter-plot of the average sequence length of pre-miRNAs versus the sequence length of miRNAs: the red line is the

linear regression and the blue curve is the quadratic polynomial regression.

doi:10.1371/journal.pone.0054215.g007

Figure 8. Bar charts of mature miRNAs in the 59 and 39 arms of the precursors respectively.

doi:10.1371/journal.pone.0054215.g008

Sequence Structures of miRNAs and the Implications

PLOS ONE | www.plosone.org7 January 2013 | Volume 8 | Issue 1 | e54215

Page 8

website, 8mer refers to a perfect match to nucleotides 2–8 of the

mature miRNA (the seed+nucleotide 8) and an A residue at the

first nucleotide position of the mature miRNA; 7mer-m8 refers to

a perfect match to nucleotides 2–8; and 7mer-1A refers to a perfect

match to nucleotides 2–7 plus an A residue at the first nucleotide

position. If multiple genes are targeted by a miRNA, we calculate

the sum of site counts from each gene and used it as the count of

target sites for this miRNA. By doing so, we obtained six numbers,

corresponding to (non-)conserved 8mer, 7mer-m8 and 7mer-1A

sites, for each miRNA. The distributions of these count numbers

are presented in Figure 10. The left panel shows the combined

counts of conserved and non-conserved sites and the right panel

shows the counts of conserved sites only. Both panels indicate the

same pattern of count distributions. The distributions of target site

counts are very similar for miRNAs with sequence lengths from 17

to 26 (more alike across the sequence lengths from 21 to 23), while

the distribution of counts from miRNAs with the shortest or

longest sequence shows some discrepancy. Interestingly, for

combined counts of conserved and non-conserved sites, the counts

of 8-mer sites are much lower than those of 7mer-m8 sites and

7mer-1A sites which have the same medians. This phenomenon

changes when only the conserved sites are considered. The median

counts of 7mer-1A sites are lowest for miRNAs with sequence

lengths from 16 to 26, but highest for miRNAs with sequence

length 27.

Figure 9. Scatter-plot of the average numbers of predicted target genes versus the mature miRNA sequence length.

doi:10.1371/journal.pone.0054215.g009

Figure 10. Histograms of target site counts of the transcripts predicted by miRNAs with the same sequence lengths.

doi:10.1371/journal.pone.0054215.g010

Sequence Structures of miRNAs and the Implications

PLOS ONE | www.plosone.org8 January 2013 | Volume 8 | Issue 1 | e54215

Page 9

Conclusion

The length has become one of the important criteria to annotate

a clone sequence as a miRNA [7]. Though it is a common

understanding that a human mature miRNA has about 22

nucleotides, the statistical characteristics of the length distribution

of the miRNA molecules are not trivial, and have been less

studied. Based on graphics methods and the model selection

criteria, we demonstrated that, compared with conventionally used

Poisson distribution, the discrete analogue of a asymmetric

Laplace distribution can nicely model the length distribution of

human mature miRNA molecules. It has lower residual sum of

squares and smaller AIC. The association study revealed that the

sequence length heterogeneity is related to some biological factors

such as evolution conservation, miRNA’s regulatory mechanism,

etc. We found that highly conserved miRNA sequences are of

lengths concentrated at 22 nucleotides while human-specific

miRNAs show large variation in the length. Furthermore, the

miRNAs that regulate oncogenes/tumor suppressors also show

stable lengths of 22 nucleotides, and longer miRNAs tend to

regulate more genes. These findings may have some implications

on (cancer) genetics research and warrant additional follow-up

studies.

Author Contributions

Helped with the experimental design and justification, provided editorial

comments, and participated in figure preparation: AE KZ. Participated in

the experimental design and justification, provided editorial comments and

biological interpretation: EKF. Conceived and designed the experiments:

ZF KZ. Performed the experiments: ZF RD. Analyzed the data: ZF RD.

Wrote the paper: ZF KZ.

References

1. Kim YK, Kim VN (2007) Processing of intronic microRNAs. EMBO J 26: 775–

783.

2. Lee Y, Ahn C, Han J, Choi H, Kim J, et al. (2003) The nuclear RNase III

Drosha initiates microRNA processing. Nature 425: 415–419.

3. Du T, Zamore PD (2005) microPrimer: the biogenesis and function of

microRNA. Development 132: 4645–4652.

4. Carthew RW (2006) Gene regulation by microRNA. Curr Opin Genet Dev 16:

203–208.

5. Filipowicz W (2005) RNAi: The nuts and bolts of the RISC machine. Cell 122:

17–20.

6. Ambros V (2004) The functions of animal microRNAs. Nature 431: 350–355.

7. Ambros V, Bartel B, Bartel DP, Burge CB, Carrington JC, et al. (2003) A

uniform system for microRNA annotation. RNA 9: 277–279.

8. Vaucheret H (2009) AGO1 homeostasis involves differential production of 21-nt

and 22-nt miR168 species by MIR168a and MIR168b. PLoS ONE 4: e6442.

9. Lee RC, Ambros V (1993) The C. elegans heterochronic gene lin-4 encodes

small RNAs with antisense complementarity to lin-14. Cell 75: 843–854.

10. Reinhart BJ, Slack FJ, Basson N, Pasquinelli AE, Bettinger JC, et al. (2000) The

21-nucleotide let-7 RNA regulates developmental timing in Caenorhabditis

elegans. Nature 403: 901–906.

11. Pasquinelli AE, Reinhart BJ, Slack F, Martindale MQ, Kuroda MI, et al. (2000)

Conservation of the sequence and temporal expression of let-7 heterochronic

regulatory RNA. Nature 408: 86–89.

12. Kozomara A, Griffiths-Jones S (2011) miRBase: integrating microRNA

annotation and deep-sequencing data. Nucleic Acids Res 39: D152–D157.

13. Griffiths-Jones S, Saini HK, van Dongen S, Enright AJ (2007) miRBase: tools for

microRNA genomics. Nucleic Acids Res 36: D154–D158.

14. 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–D144.

15. Griffiths-Jones S (2003) The microRNA Registry. Nucleic Acids Res 32: D109–

D111.

16. Ambros V, Bartel B, Bartel DP, Burge CB, Carrington JC, et al. (2003) A

uniform system for microRNA annotation. RNA 9: 277–279.

17. Kotz S, Kozubowski TJ, Podgorski K (2002) Maximum likelihhod estimation of

asymmetric laplace parameters. Ann Inst Stat Math 54: 816–826.

18. Lambert D (1992) Zero-inflated Poisson regression, with application to defects in

manufacturing. Technometrics 34: 1–14.

19. Hoaglin DC (1980) A Poissonness Plot. Am Stat 34: 146–149.

20. Quesenberry CP, Hurst DC (1964) Large sample simultaneous confidence

intervals for multinomial proportions. Technometrics 5: 191–195.

21. Stuart JM, Segal E, Koller D, Kim SK (2003) A Gene coexpression network for

global discovery of conserved genetic modules. Science 302: 249–255.

22. Pal C, Papp B, Lercher MJ (2006) An integrated view of protein evolution. Nat

Rev Genet 7: 337–348.

23. Vaquerizas JM, Kummerfeld SK, Teichmann SA, Luscombe NM (2009) A

census of human transcription factors: function, expression and evolution. Nat

Rev Genet 10: 252–263.

24. Wang D, Qiu C, Zhang H, Wang J, Cui Q, et al. (2010) Human microRNA

oncogenes and tumor suppressors show significantly different biological patterns:

from function to targets. PLoS ONE 5: e13067.

25. Hirsh AE, Fraser HB (2001) Protein dispensability and rate of revolution. Nature

411: 1046–1049.

26. Zhang R, Peng Y, Wang W, Su B (2007) Rapid evolution of an X-linked

microRNA cluster in primates. Genome Res 17: 612–617.

27. Zhang Q, Lu M, Cui Q (2008) SNP analysis reveals an evolutionary acceleration

of the human-specific microRNAs. Nature Proceedings. Available: http://

precedings.nature.com/documents/2127/version/1/files/npre20082127-1.pdf.

Accessed 02 June 2012.

28. Calin GA, Dumitru CD, Shimizu M, Bichi R, Zupo S, et al. (2002) Frequent

deletions and downregulation of micro-RNA genes miR15 and miR16 at 13q14

in chronic lymphocytic leukaemia. Proc Natl Acad Sci U S A 99: 15524–15529.

29. Calin GA, Sevignani C, Dumitru CD, Hyslop T, Noch E, et al. (2004) Human

microRNA genes are frequently located at fragile sites and genomic regions

involved in cancers. Proc Natl Acad Sci U S A 101: 2999–3004.

30. Cimmino A, Calin GA, Fabbri M, Lorio MV, Ferracin M, et al. (2005) miR-15

and miR-16 induce apoptosis by targeting BCL2. Proc Natl Acad Sci U S A 102:

13944–13949.

31. Calin GA, Croce CM (2006) MicroRNA signatures in human cancers. Nat Rev

Cancer 6: 857–866.

32. Caldas C, Brenton JD (2005) Sizing up miRNAs as cancer genes. Nat Med 11:

712–714.

33. Calin GA, Liu CG, Ferracin M, Hyslop T, Spizzo R, et al. (2007)

Ultraconserved regions encoding ncRNAs are altered in human leukemias

and carcinomas. Cancer Cell 12: 215–229.

34. Ma L, Teruya-Feldstein J, Weinberg RA (2007) Tumor invasion and metastasis

initiated by microRNA-10b in breast cancer. Nature 449: 682–688.

35. Si ML, Zhu S, Wu H, Lu Z, Wu F, et al. (2007) miR-21-mediated tumor growth.

Oncogene, 26: 2799–2803.

36. Eis PS, Tam W, Sun L, Chadburn A, Li Z, et al. (2005) Accumulation of miR-

155 and BIC RNA in human B cell lymphomas. Proc Natl Acad Sci U S A 102:

3627–3632.

37. Johnson SM, Grosshans H, Shingara J, Byrom M, Jarvis R, et al. (2005) RAS is

regulated by the let-7 microRNA family. Cell 120: 635–647.

38. Yu SL, Chen HY, Chang GC, Chen CY, Chen HW, et al. (2008) MicroRNA

Signature Predicts Survival and Relapse in Lung Cancer. Cancer Cell 13: 48–

57.

39. Foekens JA, Sieuwerts AM, Smid M, Look MP, Weerd V, et al. (2008) Four

miRNAs associated with aggressiveness of lymph node-negative, estrogen

receptor-positive human breast cancer. Proc Natl Acad Sci U S A 105:

13021–13026.

40. Park SY, Bera AK (2009) Maximum entropy autoregressive conditional

heteroskedasticity model. J Econometrics 150: 219–230.

41. Reshef DN, Reshef YA, Finucane HK, Grossman SR, McVean G, et al. (2011)

Detecting novel associations in large data sets. Science 334: 1518–1524.

42. Wang X, EI Naga IM (2008) Prediction of both conserved and nonconserved

microRNA targets in animals. Bioinformatics 24: 325–332.

43. Wang X (2008) miRDB: a microRNA target prediction and functional

annotation database with a wiki interface. RNA 14: 1012–1017.

44. Ellwanger DC, Buttner FA, Mewes HW, Stumpflen V (2011) The sufficient

minimal set of miRNA seed types. Bioinformatics 27: 1346–1350.

45. Lewis BP, Burge CB, Bartel DP (2005) Conserved seed pairing, often flanked by

adenosines, indicates that thousands of human genes are microRNA targets. Cell

120: 15–20.

46. Friedman RC, Farh KKH, Burge CB, Bartel DP (2009) Most mammalian

mRNAs are conserved targets of microRNAs. Genome Res 19: 92–105.

47. Grimson A, Farh KKH, Johnston WK, Garrett-Engele P, Lim LP, et al. (2007)

microRNA targeting specificity in mammals: determinants beyond seed pairing.

Mol cell 27: 91–105.

48. Garcia DM, Baek D, Shin C, Bell GW, Grimson A, et al. (2011) Week seed-

pairing stability and high target-site abundance decrease the proficiency of lsy-6

and other miRNAs. Nat Struct Mol Biol 18: 1139–1146.

Sequence Structures of miRNAs and the Implications

PLOS ONE | www.plosone.org9 January 2013 | Volume 8 | Issue 1 | e54215