Abstract
miRNAs are small non-coding RNAs that regulate gene expression by
binding to the 3′-UTR of genes. Many recent studies have reported that
miRNAs play important biological roles by regulating specific mRNAs or
genes. Many sequence-based target prediction algorithms have been
developed to predict miRNA targets. However, these methods are not
designed for condition-specific target predictions and produce many
false positives; thus, expression-based target prediction algorithms
have been developed for condition-specific target predictions. A
typical strategy to utilize expression data is to leverage the negative
control roles of miRNAs on genes. To control false positives, a
stringent cutoff value is typically set, but in this case, these
methods tend to reject many true target relationships, i.e., false
negatives. To overcome these limitations, additional information should
be utilized. The literature is probably the best resource that we can
utilize. Recent literature mining systems compile millions of articles
with experiments designed for specific biological questions, and the
systems provide a function to search for specific information. To
utilize the literature information, we used a literature mining system,
BEST, that automatically extracts information from the literature in
PubMed and that allows the user to perform searches of the literature
with any English words. By integrating omics data analysis methods and
BEST, we developed Context-MMIA, a miRNA-mRNA target prediction method
that combines expression data analysis results and the literature
information extracted based on the user-specified context. In the
pathway enrichment analysis using genes included in the top 200
miRNA-targets, Context-MMIA outperformed the four existing target
prediction methods that we tested. In another test on whether
prediction methods can re-produce experimentally validated target
relationships, Context-MMIA outperformed the four existing target
prediction methods. In summary, Context-MMIA allows the user to specify
a context of the experimental data to predict miRNA targets, and we
believe that Context-MMIA is very useful for predicting
condition-specific miRNA targets.
Introduction
MicroRNAs (miRNAs) are small non-coding RNAs that are 19-24 nucleotides
in length. These RNAs regulate gene expression at the
post-transcriptional level by binding to the 3′-UTR of mRNAs [[36]1,
[37]2]; thus, miRNAs are functionally important. There are numerous
scientific findings on the functional roles of miRNAs by regulating
specific genes. For example, it is reported that miR-15 and miR-16-1
bind to BCL2 [[38]3] and that apoptosis is induced. Another example is
that miR-125b, miR-145, miR-21 and miR-155 are dysregulated in breast
cancer cells, and different expression levels of these miRNAs have
significant correlations with breast cancer phenotypes, such as tumor
stages and status of estrogen and progesterone receptors [[39]4].
Moreover, it is well known that miRNAs are related to proliferation,
differentiation, and cell death [[40]5].
The functional roles of miRNAs differ in different contexts. In other
words, the relationship between miRNA and target genes is dynamic in
different conditions. Thus, it is very important to identify which
genes are targeted by miRNAs in a given context. There are more than
1000 miRNAs, and approximately 60% of protein-coding genes are
regulated by miRNAs [[41]6]. Since it is not possible to perform
biological experiments for such a large number of miRNAs and genes,
computational prediction is very important, and numerous computational
methods have been developed for predicting targets of miRNAs. The first
generation of computational tools leverage sequence complementary
information and binding energy potentials. These prediction methods
include TargetScan [[42]7], PITA [[43]8], mirSVR [[44]9], miRanda
[[45]10] and PicTar [[46]11]. These tools generally come with
corresponding databases that compile miRNA-target information. In
addition to sequence complementary information, there are different
approaches used in each of these methods. miRanda estimates the energy
on sequence matching of miRNA and mRNA pairs to predict targets
[[47]10]. PicTar first finds candidate 3′-UTR sites and uses a hidden
Markov model (HMM) to filter out target sites [[48]11]. TargetScan
considers a conservation seed match and then considers regions outside
seed matches [[49]7]. The mirSVR algorithm uses a support vector
regression method to compute scores on candidate target sites that are
identified by miRanda [[50]9]. PITA uses the accessibility of target
sites as a main feature to predict targets [[51]8].
Target prediction methods based on the sequence similarity score rely
on the existence of target sites, and these methods are accompanied by
target databases. However, such target information is not condition
specific without considering which miRNAs and which genes are
expressed; thus, there are many false positives even if the target
information is accurate, which is not the case since many target
databases do not agree on the miRNA-target relationship. To make the
target information condition specific, many expression-based target
prediction methods have been developed. These methods take miRNA-mRNA
expression data and several sequence-based target databases as input
data and filter out miRNA-mRNA targets using statistical significance
or computational algorithms. We briefly summarize the previous
expression-based algorithms. GenMiR++ used a Bayesian model and
expectation maximization algorithm to predict the posterior probability
of a miRNA target for mRNA [[52]12]. MMIA employs a two-step method,
where the first step is to select differentially expressed miRNA, and
the second step is to select negatively correlated differentially
expressed mRNA [[53]13] only for the differentially expressed miRNAs.
MMIA also supports sequence data analysis on a cloud environment, which
enables the user to utilize both microarray data and NGS data [[54]14].
MAGIA2 is a web-based tool that considers the correlation among miRNA
and mRNA and transcription factor (TF) regulation [[55]15]. CoSMic
extracts the significant target mRNA cluster for each miRNA [[56]16].
CoSMic employs methods similar to gene set enrichment analysis (GSEA)
to identify miRNA targets [[57]17]. miRNAmRNA is a target prediction
algorithm based on the global test of a linear regression model
[[58]18]. To extract condition-specific miRNA activity, identifying
causal relationships using intervention calculus when the DAG is absent
was proposed [[59]19]. A recent tool, PlantMirnaT, was designed as a
plant-specific miRNA-mRNA sequencing data analysis algorithm [[60]20].
The unique feature of PlantMirnaT is using the expression quantity
information from sequencing data and employing a split ratio model to
identify the relationship of target pairs.
Motivation
There are approximately 1,500 known miRNAs in the human genome. The
number of possible miRNA-gene pairs exceeds 30 million when more than
20,000 protein-coding genes are considered. Among these pairs, only a
fraction of the relationships are significant in terms of biological
functions, e.g., phenotypes or cancer subtypes. Computational methods
for predicting the miRNA target employ various techniques to identify
phenotype-specific miRNA targets. Because this is a typical prediction
problem, the challenges can be summarized in terms of false positives
and false negatives.
* Target databases have high false positive rates: Sequence-based
target prediction algorithms, such as TargetScan, mirSVR, and PITA,
and their corresponding databases generally produce high false
positives. There are two major reasons for these high false
positives. First, these databases contain all known targets; thus,
the target information is not condition specific. For this reason,
when transcriptome data measured in a specific condition are
analyzed, many targets are false positives. Second, sequence-based
prediction methods do not consider the regulatory role of miRNA,
which generally results in a negative correlation between miRNA and
the target gene. In addition, sequence-based prediction methods do
not consider sample-specific sequence information. For example,
sequence variations in the target regions can affect the target
relationship, but the current algorithms do not consider minor but
subtle sequence variations.
* Expression-based methods may have false negative rates:
Expression-based methods utilize negative correlation information
between miRNA and targets or similar approaches. For these methods,
there is always an issue of establishing a cutoff threshold value,
e.g., for a negative correlation. If the cutoff value is not
stringent, then there are too many miRNA-target relationships.
Thus, in general, it is a common practice to set a quite stringent
cutoff value. In this case, many true miRNA-target relationships
can be rejected, i.e., the false negative issue.
Addressing the false positive and false negative issues is a very
challenging problem unless we fully understand how miRNAs regulate
target genes. Using sequence pairing information and gene expression
information is very useful because such methods have already produced
many biologically meaningful results. However, one important
information source, the literature, is not utilized in current methods.
The scientific literature is currently growing exponentially. As shown
in [61]Fig 1, more than 100,000 papers related to ‘cancer’ are
published every year. Thus, if we combine sequence pairing information
and gene expression information with the literature information, we can
certainly make a good improvement in predicting miRNA targets, reducing
both false positives and false negatives. In particular, as with the
use of gene expression information, the use of the literature
information should be condition specific. The main issues are how to
handle the vast amount of studies in the literature, how to allow the
user to specify the experimental conditions, and finally, how to
combine sequence pairing information, gene expression information and
the literature information in a single computational framework.
Fig 1. The number of published papers related to the keyword ‘cancer’ since
2010.
[62]Fig 1
[63]Open in a new tab
More than 100,000 papers have been published every year.
Toward this goal, two research groups are working together to design
and implement a novel human-specific miRNA-target prediction method.
First, we compute the omics score by utilizing sequence pairing
information and gene expression information to produce candidate
miRNA-target pairs. Then, we compute the literature-based context score
to evaluate each candidate miRNA-target pair using the Biomedical
Entity Search Tool (BEST) [[64]21]. Using BEST, the user can specify
the experimental condition using a set of any keywords, which will
automatically be translated to a set of genes and related miRNAs.
Subsequently, the two scores, the omics score and the context score,
are combined into a single score in a conditional probabilistic form.
The remainder of this paper is organized as follow. In the Methods
section, we explain how to compute the omics score based on the
expression data and miRNA-gene relationship and the context score from
the literature according to user-provided keywords. In the Results
section, we show how our proposed method performs compared with four
existing methods in experiments with omics datasets in the public
domain.
Methods
In this section, we explain how our method, Context-MMIA, predicts
human miRNA targets by combining the literature information and gene
expression data. Context-MMIA takes two-class (control vs. treated)
human miRNA-mRNA expression data as input. Then, with user-specified
keywords as the context of the experiment, it computes the
probabilities of miRNA-gene pairs relevant to the phenotype differences
by combining gene/miRNA expression data and the literature data.
[65]Fig 2 illustrates the workflow of Context-MMIA. First,
differentially expressed miRNAs (DEmiRNAs) and differentially expressed
mRNAs or genes (DEmRNAs) are determined with a cutoff value at the
relaxed level such that most of the true positives can be retained in
this step. Note that we use negative correlation information and the
literature information to filter out and re-weight candidates for
interaction pairs in the following steps. In the second step of
processing omics data, human miRNA-mRNA pairs are predicted using miRNA
target databases such as TargetScan, mirSVR, and PITA. These miRNA-mRNA
pairs are further screened by negative correlation information between
miRNA and mRNA. In the third step, for each pair of miRNA and mRNA,
Context-MMIA calculates the omics score based on expression data and
the context score based on the literature information compiled based on
the user-provided keywords. Finally, target pairs are ranked by
combining the omics score and context score. For each miRNA-mRNA pair,
Context-MMIA computes alignments of human miRNA and the 3′-UTR of mRNA
and generates the visualization of the miRNA-mRNA alignment on the
website.
Fig 2. Schematic workflow for Context-MMIA.
[66]Fig 2
[67]Open in a new tab
The system accepts expression information of miRNA and mRNA as inputs.
In the MMIA step, DEmiRNAs and DEmRNAs are extracted based on their
expression level difference, and their negative correlation is
computed. In the Context-MMIA step, the system computes omics and
context scores based on user-provided keywords by utilizing the BEST
system. Finally, the system ranks miRNA-mRNA pairs using the scores.
Identifying genes and miRNAs based on the user-provided context
Context-MMIA takes a set of keywords from the user to specify the
context of the experiment. Currently, the most widely used biomedical
literature database, PubMed, contains over 26 millions records. When we
perform a search with the keyword ‘cancer’, over 3 million records are
retrieved. Thus, we believe that this literature database contains
enough articles to rank miRNA-gene pairs in terms of the user-provided
context. However, there are two major issues in ranking miRNA-gene
pairs: given the keywords, relevant papers should be identified and
relevant gene names and miRNA names should also be identified. Since
not all papers contain the user-provided keywords, it is necessary to
infer the relevance of the words to extract genes and miRNAs in the
relevant articles. To address this issue, we use BEST to identify
relevant words and genes/miRNAs [[68]21]. BEST has predefined
biomedical entities for each category, such as drug, pathway, gene, and
disease, and then it identifies relevant entities extracted from PubMed
articles from the user query. For example, it returns entities such as
‘ERBB2’, ‘wnt signaling pathway’, and ‘tamoxifen’ with the keyword
‘breast cancer’ as an input. BEST has its own scoring system for
entities, which is very useful in ranking gene-miRNA pairs with respect
to the user-provided keywords. For example, there are keywords ‘breast
cancer’ and entities ‘cell cycle’, ‘mir-200c’, ‘BRCA1’, and ‘ESR’. At
the beginning, BEST compiles PubMed articles containing ‘breast cancer’
and the four entities in the abstract. Then, it measures the score and
the rank for each entity and lists entities ordered by score. After
compiling articles containing ‘BRCA1’ and ‘breast cancer’, BEST
calculates a document score for each article and sums the score to
measure the entity score, which is denoted as BEST(BreastCancer,
BRCA1). In this paper, we use BEST to measure the relevance of each
miRNA and mRNA for a given user query.
Omics score
The omics score (OS) is the probability of a gene-miRNA contributing to
the class difference when expression data are analyzed. The OS is based
on the general principle that differentially expressed miRNA targets
genes differentially, resulting in negative correlations between genes
and miRNA; then, differentially expressed gene explains the phenotype
differences. Context-MMIA computes the omics score based on a strategy
similar to MMIA. It measures miRNA differential scores, mRNA
differential scores, and then correlation scores. The DEmiRNAs and
DEmRNAs can be determined by MMIA. After the DEmRNAs and DEmiRNAs are
determined, the probability of miRNA-mRNA contributing to the class
difference is calculated. Let the p-values of miRNA and mRNA be
[MATH:
pmi :MATH]
and
[MATH:
pgj :MATH]
, respectively. For miRNA m[i], m[i]’s differential score diff(m[i]) is
defined by [69]Eq 1, and its normalization diff[n](m[i]) is defined by
[70]Eq 2.
[MATH: diff(mi)=-
log2(pmi)
:MATH]
(1)
[MATH: diffn(mi
)=diff(mi<
/mi>)-min(diff)max(diff)-min(diff)
:MATH]
(2)
The calculation of diff[n] for mRNA is similar to that of miRNA. The
range of diff[n] is between 0 and 1 by [71]Eq 2. If miRNA is
significantly differentially expressed in a given condition, then the
value of diff[n] will be close to 1.
Correlation score is defined by measuring the Pearson’s correlation
coefficient of the miRNA-mRNA pair’s logarithmic expression as in
[[72]22]. Context-MMIA considers only negatively correlated miRNA-mRNA
pairs; thus, a negative value of the coefficient is defined as the
correlation score as in [73]Eq 3.
[MATH:
corr(mi,gj
)=-pearson_correlatio
n(mi,gj) :MATH]
(3)
The omics score of miRNA-mRNA OS(m[i], g[j]) is defined in [74]Eq 4.
[MATH:
OS(mi,gj)=diffn(mi
)*corr(mi,gj)*diffn(gj
) :MATH]
(4)
By definition, OS(m[i], g[j]) ∈ [0, 1]; thus, a value of OS close to 1
means that the miRNA and mRNA are both significantly differentially
expressed and anticorrelated. Thus, we predict that the pair is related
to the phenotype difference with a high confidence in terms of
expression data.
Context score
We defined the context score (CS) to measure the probability of a
miRNA-mRNA pair contributing to the phenotype difference in terms of
the literature information. As described in the previous section, BEST
estimates a score between predefined entities and keywords. We denoted
the user-input keyword as k, which is context specified by the user
(e.g., disease, gene, pathway, and so forth). As shown in [75]Eq 5,
CS(m[i], g[j]|k) measures the significance of the m[i]-g[j] pair for k
in terms of the literature information.
[MATH:
CS(mi,gj|k
)=P(mi<
/mi>|k)*P(gj|k)
:MATH]
(5)
To compute P(m[i]|k), we used Bayes’ rule and transformed P(m[i]|k)
into [76]Eq 6 because BEST only measures the score for predefined
entities and does not support undefined keywords (e.g., broad keyword,
new drug or pathway, and so on) [[77]23].
[MATH:
P(mi|<
/mo>k)=P<
mi>n(k|mi)*Pn(mi)∑l=1pPn(k|ml<
/msub>)*Pn(ml) :MATH]
(6)
By converting P(m[i]|k) using Bayes’ rule, our method provides the user
with a freeform keyword environment, which allows the user to easily
utilize our system even when the user is not familiar with biological
terms.
[MATH:
P(k|mi)=lo
g2(BEST(k,mi<
/msub>)+1)
:MATH]
(7)
The literature significance of miRNA (m[i]) for a given keyword k,
P(k|m[i]), is computed as shown in [78]Eq 7. BEST(k, m[i]) is the score
of m[i] for k computed by BEST, and we converted the scale of the score
by taking the logarithm of the BEST score. For example, assume that the
keyword ‘immune system’ and the miRNA ‘miR-155’ are used in an
analysis. If the relation between ‘miR-155’ and ‘immune system’ is well
studied, then P(immune system | miR155) and BEST(immune system, miR155)
will have a high score.
[MATH:
P(mi)<
/mo>=log2(BEST(<
/mo>mi,mi)+1)
:MATH]
(8)
[79]Eq 8 describes how to compute P(m[i]), which denotes how much
literature information exists for m[i]; the more that papers report
m[i], the higher the value it will have. After computing P(m[i]) and
P(k|m[i]), normalization terms P[n](m[i]) and P[n](k|m[i]) are defined
by the min-max normalization.
P(m[i]|k) is computed using Bayes’ rule and specifies the significance
of m[i] given the literature domain k, and the value of P(m[i]|k) has a
correlation with the amount of studies, i.e., the number of papers
about m[i] in domain k. For mRNA g[j], P(g[j]|k) is computed in a
similar way, and we measured the significance of the m[i]-g[j] pair in
k by computing CS(m[i], g[j]|k) using P(m[i]|k) and P(g[j]|k).
Pair score
The pair score of m[i], g[j] and k is denoted as Score(m[i], g[j], k),
which is a confidence value of target prediction in terms of both
expression and literature data.
[MATH:
Score(mi,gj<
/msub>,k)=OS(mi,
gj)*CS(mi,gj|k) :MATH]
(9)
[80]Eq 9 can be interpreted as a weighted omics score, where the weight
is determined by a probability of a m[i], g[j] pair being true in terms
of the user-provided context given keywords k.
Results
To evaluate Context-MMIA, we performed three experiments in comparison
with four existing tools: MMIA, MAGIA2, CoSMic and GenMiR++.
The three experiments were pathway analysis, reproducibility of
validated miRNA targets in human, and sensitivity tests when different
keywords were used for specifying the experimental context. We used
2-class microarray datasets containing miRNA and mRNA expression
profiles in humans. [81]GSE21411 [[82]24], [83]GSE40059 [[84]25], and
[85]GSE53482 [[86]26] from human disease studies were used. Each study
reports experimentally validated miRNA and the correlated target mRNA
pair, which was used to evaluate the miRNA target prediction methods in
this section. A detailed description of each dataset is listed in
[87]Table 1.
Table 1. Dataset summary.
Each GEO study comes with an experimentally validated miRNA-mRNA target
(the second column) to affect their disease domain (the third column).
Disease information was used to test performances when different
contexts are specified.
Data Experimentally validated target Disease
[88]GSE21411 hsa-miR-23a—NEDD4L Interstitial Lung Diseases
[89]GSE40059 hsa-miR-200c—CFL2 Breast Cancer
[90]GSE53482 hsa-miR-155—JARID2 Primary Myelofibrosis
[91]Open in a new tab
[92]Table 1 summarizes the validated target pair and the domain of the
experimental design in each dataset. In the interstitial lung diseases
(ILD) study, it was reported that ZEB-1 affects the persistence of
disease in ILD through suppression of NEDD4L by miR-23a. In the
[93]GSE40059 breast cancer study, the authors investigated differences
between aggressive breast cancer cell lines and less-aggressive cell
lines and reported that CFL2 was up-regulated by miR-200c. The authors
also reported that CFL2 expression was correlated with tumor grade. In
the primary myelofibrosis (PMF) study, the authors revealed that
overexpressed miR-155-5p regulates JARID2, and they suggested that
regulated JARID2 may be related to MK hyperplasia in PMF. Disease
information was used to test performances when different contexts are
specified for Context-MMIA. It is necessary to choose keywords to
specify contexts. ‘Interstitial lung disease’ and ‘primary
myelofibrosis’ are too specific to use literature data; thus, we used
the more general words ‘lung disease’ and ‘myelofibrosis’ as the
keywords for Context-MMIA.
Pathway analysis
To evaluate the effectiveness of the approach used in Context-MMIA, we
compared it with four expression-based methods: MMIA, MAGIA2, GenMiR++,
and CoSMic. GenMiR++ computes probabilities for target pairs using an
EM algorithm. MMIA extracts DEmiRNA to reduce the search space by a
user-defined cutoff and finds negatively expressed target DEmRNAs.
MAGIA2 provides several methods for the integrated analysis, and we
chose Pearson’s correlation method from among these methods. After
measuring the correlation, MAGIA2 calculates the false discovery rate
(FDR) for each target. CoSMic extracts an mRNA cluster for each miRNA
and computes the significance of a cluster using permutation tests.
Likewise, each algorithm uses a different strategy to predict the miRNA
target and to reduce the search space. We used these four algorithms to
compare performances in terms of the predictive power. The methods
compute confidence values for the predicted miRNA and mRNA targets,
typically probability or p-value. We ranked the prediction results in
terms of the confidence values. In the experiments, we used a p-value
cutoff of 0.1 for Context-MMIA. For MMIA, a p-value of 0.05 was used
for both DEmiRNA and DEmRNA selection.
For the performance evaluation, we used the top 200 predicted
miRNA-mRNA pairs predicted by each method. Then, we mapped genes
included in the interacting pairs to human pathways using DAVID
[[94]27, [95]28] to determine which pathways were significantly
enriched. Among these pathways, we carefully selected pathways that are
most likely related to the disease through the literature study as
shown in [96]Table 1. We set evaluation criteria as how these
literature-guided pathways were predicted by each method. [97]Table 2
shows the ratios of the number of genes that are mapped to
significantly enriched pathways to the number of genes included in the
top 200 miRNA-target edges. The number of genes is less than 200
because the same gene was multiply targeted, e.g., miR-200c-BRCA1 and
miR-23a-BRCA1.
Table 2. The ratio of the mapped genes and the number of the genes in the top
200 miRNA-target pairs.
From each method, we extracted the top 200 target pairs using each
method and performed pathway analysis using DAVID. The numerator is the
number of genes mapped to the enriched pathways, and the denominator is
the genes in the top 200 edges. The ratio of Context-MMIA is the
largest for each dataset.
Methods [98]GSE21411 [99]GSE40059 [100]GSE53482
Context-MMIA 37 / 79 45 / 157 42 / 127
MMIA 12 / 157 20 / 179 11 / 124
GenMiR++ 0 / 194 18 / 197 26 / 200
MAGIA2 18 / 182 12 / 191 19 / 193
CoSMic 24 / 196 9 / 195 X
[101]Open in a new tab
As shown in [102]Table 2, the number of genes mapped to the
significantly enriched pathways is quite different for each method even
though the number of genes does not considerably differ for each
method. In terms of the ratio of mapped genes to predicted genes,
Context-MMIA outperforms the existing methods 2 to 4 times. A gene set
in a pathway means that genes have similar biological functions in
terms of regulating molecular processes. Thus, the ratios in [103]Table
2 indicate that Context-MMIA produces more functionally coherent gene
sets.
[104]Table 3 lists pathways related to ‘breast cancer’ and enriched
pathways predicted by each method for the [105]GSE40059 dataset. The
enriched pathway analysis for the data from all three experiments is
presented in [106]S1 File. The circles in [107]Table 3 mean an enriched
pathway when DAVID pathway analysis was performed by using genes in the
top 200 edges. For example, if the ECM-receptor interaction is enriched
in the Context-MMIA and GenMiR++ results, circles are marked in the
context column and the second column for the corresponding tools. As
shown in [108]Table 3, more pathways related to ‘breast cancer’ were
enriched in the gene sets produced by Context-MMIA than in the gene
sets produced by the competing methods. In addition, several important
pathways were enriched only in Context-MMIA. For example, it is well
known that approximately half of breast tumors have stronger MAP kinase
activity than the surrounding benign tissues [[109]32]. Inflammation
plays a pivotal role in tumor initiation, promotion, angiogenesis and
metastasis. Cytokines are important in all the phenomena, and it has
been reported that cytokines participate in regulating both induction
and protection in breast cancer [[110]33]. In addition, many studies
have reported that TGF-beta signaling is critically important in the
regulation of breast cancer [[111]38]. High focal adhesion kinase
expression is known to be related to aggressive breast cancer
phenotypes [[112]47]. Furthermore, cell adhesion molecules (CAMs) have
a strong relationship with the process of metastasis, which is an
important feature in predicting breast cancer prognosis [[113]42].
Moreover, a study revealed that activated leukocyte cell adhesion
molecule (ALCAM) expression has a correlation with clinical outcomes
such as grade, TNM stage, and NPI [[114]48].
Table 3. Enriched pathway analysis on [115]GSE40059 breast cancer data.
Breast-cancer-related pathways are selected by the literature search. A
circle in a cell means that the pathway is enriched by the gene set
predicted by each method (A: Context-MMIA, B: MMIA, C: GenMiR++, D:
MAGIA2, and E: CoSMic). More pathways are enriched by the gene set in
the Context-MMIA result.
Breast-Cancer-Related Pathway A B C D E
Purine metabolism [[116]29] O
Pyrimidine metabolism [[117]30] O
ABC transporters [[118]31] O
MAPK signaling pathway [[119]32] O
Cytokine-cytokine receptor interaction [[120]33] O
Neuroactive ligand-receptor interaction [[121]34] O
p53 signaling pathway [[122]35] O O
Apoptosis [[123]36] O
Notch signaling pathway [[124]37] O
TGF-beta signaling pathway [[125]38] O
Axon guidance [[126]39] O
Focal adhesion [[127]40] O O O
ECM-receptor interaction [[128]41] O
Cell adhesion molecules (CAMs) [[129]42] O O
Adherens junction [[130]43] O
Regulation of actin cytoskeleton [[131]44] O
Glioma [[132]45] O
Melanoma [[133]46] O
[134]Open in a new tab
Reproducibility of validated targets in humans
[135]Table 4 shows the rankings of experimentally validated targets
among the targets predicted by each method. Because Context-MMIA
computes the context score using the literature data for given
keywords, there is a possibility that the original papers of the
datasets can affect the context score. Thus, we penalized the validated
targets to compute P(k|m[i]) by excluding each paper when the BEST tool
measures a score BEST(k, m[i]).
Table 4. Reproducibility of validated targets.
This table contains the rankings of validated target pairs in three
datasets. The validated targets are listed in the second column of
Table I. Context-MMIA outperformed existing tools in predicting the
validated targets. MAGIA2 and CoSMic failed to reproduce the validated
targets.
Data [136]GSE21411 [137]GSE40059 [138]GSE53482
Context-MMIA 481 338 21
MMIA 1411 387 1465
GenMiR++ 8625 1673 95492
MAGIA2 X X X
CoSMic X X X (Not Work)
[139]Open in a new tab
As shown in [140]Table 4, Context-MMIA outperformed the other
expression-based methods even though the penalized score is used. MMIA
took the second place in reproducing the validated targets, but it
ranked validated targets much lower than Context-MMIA. Although not
rejecting the validated targets, GenmiR++ ranked validated targets very
low. This result shows that GenmiR++ produced too many false positives
for the three datasets. MAGIA2 failed to identify the validated targets
as positive target pairs in any datasets because none of the validated
target pairs satisfied the statistical cutoff. CoSMic also failed to
identify the validated target pairs for two datasets, [141]GSE21411 and
[142]GSE40059. In addition, CoSMic did not run successfully for dataset
[143]GSE53482 due to an input error issue. Many tools were not
successful in reproducing validated targets, which can be an indication
of false negatives.
To further confirm the reproducibility of our algorithm, we
investigated how many experimentally verified targets in humans are
detected in the top 200 miRNA-mRNA pairs by each of the methods.
Experimentally validated human miRNA-mRNA pairs were extracted from
miRTarBase [[144]49], which curated experimentally validated
miRNA-target interactions (MTI) by reporter assay, western blot,
microarray, and next-generation sequencing experiments. We used human
functional MTIs with strong evidence for functionality in humans as
true interacting pairs. [145]Table 5 summarizes the number of validated
targets in the top 200 miRNA-mRNA pairs predicted by each method.
Table 5. Detection of human-specific validated targets.
This table contains the number of validated target pairs in three
datasets. The validated targets are extracted from miRTarBase target
pairs filtered by human functional miRNA target interaction (MTI).
Data [146]GSE21411 [147]GSE40059 [148]GSE53482
Context-MMIA 27 38 24
MMIA 5 4 12
GenMiR++ 3 4 3
MAGIA2 0 0 0
CoSMic 7 0 X (Not Work)
[149]Open in a new tab
As shown in 5, Context-MMIA predicted two to five times more validated
targets compared to the existing methods. Context-MMIA predicted more
than 10% of the experimentally validated MTIs in humans, with is a
considerably higher prediction accuracy than existing methods; thus, we
believe that Context-MMIA suggests good candidates for further
experimental validation.
Sensitivity tests when different keywords are used
The performance of Context-MMIA depends on how the keywords to specify
context are related to the goal of the experiment. In addition to
disease-related keywords, we performed experiments using less-relevant
keywords such as insulin resistance, influenzas, HIV and hepatocellular
carcinoma. The results of Context-MMIA using less-relevant keywords are
presented in [150]Table 6. The relevant keywords for the three datasets
are listed in the third column of [151]Table 1. As shown in [152]Table
6, the rankings of the validated pairs were considerably higher when
the keywords that reflect experimental designs were used. This result
indicates that our method is able to reflect the degree of relevance to
the experimental design and capture the different miRNA-mRNA pairs when
different keywords were used. In summary, the experiments with
irrelevant keywords showed that our method can capture the miRNA-mRNA
pairs, reflecting the user-specified biological context.
Table 6. Sensitivity tests when different keywords are used.
Rankings of validated targets are shown when different keywords are
used. The validated targets had high ranks when disease-related
keywords were used.
Keyword [153]GSE21411 [154]GSE40059 [155]GSE53482
Correct keyword 481 338 21
Insulin resistance 12479 2036 4250
Influenzas 6826 1169 1623
HIV 5865 4002 3238
Hepatocellular carcinoma 5278 3265 7180
[156]Open in a new tab
Conclusion
We presented Context-MMIA, a human-specific miRNA-mRNA target pair
prediction system that utilizes both expression profiles and the
literature information from the user-specified experimental design
goals. A major contribution of our system is that we handled the false
positives and false negatives, which are an inherent issue in
expression-based prediction tools, by incorporating the user-specified
context information from the literature. Analyses on three independent
human datasets showed that Context-MMIA can capture the true positive
miRNA-mRNA target pairs that are specific to a biological context.
Context-MMIA outperformed existing tools in a series of experiments,
such as pathway analysis, validated target ranking, and irrelevant
keyword experiments.
We emphasize that computational predictions of miRNA-mRNA target pairs
should be further validated in biological experiments and that our
system is intended to provide good candidates for experimental
validation. Context-MMIA is available at
[157]http://biohealth.snu.ac.kr/software/contextMMIA
Supporting information
S1 File. Pathway analysis results.
S1 File contains pathway results for the other two datasets.
(PDF)
[158]Click here for additional data file.^ (128.6KB, pdf)
Data Availability
GSE21411, GSE40059 and GSE53482 are available from the GEO
database(accession numbers GSE21411, GSE40059, GSE53482).
Funding Statement
This work was supported by grant numbers 2012M3A9D1054622,
2014M3C9A3063541, and 2012M3C4A7033341, National Research Foundation of
Korea (URL:
[159]http://www.nrf.re.kr/nrf_tot_cms/index.jsp?pmi-sso-return2=none).
The authors who received the funding are: Minsik, Sungmin, Ji Hwan,
Heejoon, Sunwon, Jaewoo, Sun. The funders had no role in study design,
data collection and analysis, decision to publish, or preparation of
the manuscript.
References