A Review Of Databases Predicting The Effects Of SNPs In MiRNA Genes Or MiRNA-Binding Sites

A review of databases predicting the effects of SNPs in
miRNA genes or miRNA-binding sites
Tobias Fehlmann,* Shashwat Sahay,* Andreas Keller†
and Christina Backes†
Corresponding Author: Andreas Keller, Chair for Clinical Bioinformatics, Saarland University, 66123 Saarbrücken, Germany. Tel. þ49 174 1684638;
E-mail: andreas.keller@ccb.uni-saarland.de
*These authors contributed equally to this work.
†
These authors contributed equally to this work.
Abstract
Modern precision medicine comprises the knowledge and understanding of individual differences in the genomic sequence of
patients to provide tailor-made treatments. Regularly, such variants are considered in coding regions only, and their effects
are predicted based on their impact on the amino acid sequence of expressed proteins. However, assessing the effects of vari-
ants in noncoding elements, in particular microRNAs (miRNAs) and their binding sites, is important as well, as a single miRNA
can influence the expression patterns of many genes at the same time. To analyze the effects of variants in miRNAs and their
target sites, several databases storing variant impact predictions have been published. In this review, we will compare the
core functionalities and features of these databases and discuss the importance of up-to-date data resources in the context of
web applications. Finally, we will outline some recommendations for future developments in the field.
Key words: miRNAs; SNPs; databases; target sites
Introduction
With the advent of next-generation sequencing, the amount of
available biological data sets is continuously increasing [1, 2].
Having these high-throughput technologies, the discovery of sin-
gle-nucleotide polymorphisms (SNPs) or single-nucleotide vari-
ants (SNVs) has been greatly facilitated. It is therefore not
surprising that during the past decade, the number of known
variants has increased exponentially. The largest resource as of
today storing human genetic variations is NCBI’s dbSNP [3],
which in its current version (build 150) encompasses over 100
million validated variants, resulting in one variant every 30 bases.
Importantly, SNPs have been used as markers for a large panel of
diseases, such as cystic fibrosis [4], various cancers [5–7] and neu-
rodegenerative diseases [8, 9]. Indeed, variants in coding regions
might directly affect protein formation and expression and are
therefore still in the main focus of current variant analysis appli-
cations. The effects of variants located in noncoding regions,
however, are more difficult to elucidate.
In recent years, increasing attention has been paid to the
noncoding regions of the human genome. In fact, noncoding re-
gions make up over 98% of the genome [10]. Many regulatory
RNA classes have been discovered in these so far, such as long
noncoding RNAs, or microRNAs (miRNAs). The latter are en-
dogenous small noncoding RNA molecules that play a central
role in posttranscriptional gene regulation [11]. They are evolu-
tionary conserved and expected to regulate a large part of the
human protein coding genes and a majority of pathways [12].
Therefore, especially blood-borne miRNAs have been investi-
gated as noninvasive biomarkers for an early detection of
multiple diseases [13–15], highlighting their potential for preci-
sion medicine. Regarding their general mechanism of action,
Tobias Fehlmann is a PhD student at the Chair for Clinical Bioinformatics, Saarland University, Germany. He has been working in the field of miRNAs in
Bioinformatics since 2014.
Shashwat Sahay is a Master student at the Chair for Clinical Bioinformatics, Saarland University, Germany. He has been working in the field of miRNAs in
Andreas Keller is a Professor and head of the Chair for Clinical Bioinformatics at Saarland University. He has been working in the field of miRNAs in
Christina Backes is a Postdoc at the Chair for Clinical Bioinformatics at Saarland University. She has been working in the field of miRNAs in Bioinformatics
since 2009.
Submitted: 7 July 2017; Received (in revised form): 23 October 2017
V
C The Author 2017. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com
, 20(3), 2019, 1011–1020
doi: 10.1093/bib/bbx155
Advance Access Publication Date: 27 November 2017
Software Review
Briefings in Bioinformatics
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miRNAs bind to the 30
untranslated region (UTR) of mRNAs,
which leads either to mRNA cleavage or translational inhibition
[16]. Recent studies revealed that miRNAs can sometimes bind
to the 50
UTR as well, leading to an increased mRNA expression
[17–20]. Multiple biogenesis pathways have been reported for
miRNAs in mammals [21]; however, most miRNAs seem to fol-
low a single pathway. Their maturation process starts with the
transcription of a longer primary miRNA (pri-miRNA) molecule
containing a local stem-loop structure. This molecule is pro-
cessed by the Microprocessor complex composed of Drosha and
DGCR8, which cleaves the stem-loop to release a small hairpin,
the precursor miRNA (pre-miRNA), with a 2 nucleotide (nt) long
30
overhang [22]. The hairpin is then exported into the cytosol,
where its loop is cleaved by Dicer resulting in a small RNA du-
plex [23]. The latter is then loaded onto an Argonaute protein to
form the RNA-induced silencing complex (RISC) [24] and subse-
quently one of the two strands is degraded. The remaining
strand then guides the RISC to its mRNA target.
The complete binding mechanism of miRNAs is not yet fully
understood, though the complementarity of the seed region
consisting of six to eight nts starting at the second position
from the 50
end of the miRNA to the 30
UTR is playing a crucial
role for mRNA target selection [25]. As perfect, or nearly perfect
complementarity to such binding sites, also called miRNA re-
sponse elements (MREs), is required, it is evident that SNVs in
these sites or inside the miRNA seed sequence can have a sub-
stantial impact on the overall regulation network. Thereby,
SNVs in MREs can lead to a loss of binding ability of certain
miRNAs, but at the same time increase the binding ability of
other miRNAs. Further, SNVs outside of MREs might result in
the creation of new MREs. In the same vein, SNVs in miRNAs
might lead to the loss of regulation ability of a target gene, but
also to a gain of regulation of another target gene. Furthermore,
SNVs in pri- or pre-miRNAs could have a large regulatory effect
as well, as they could, in rare cases, lead to changes in the sec-
ondary structure of the pri-miRNA and thus to reduced cleaving
efficiency by Drosha and Dicer [26].
Even though the seed regions of miRNAs are evolutionarily
conserved [27] and the occurrence of SNVs in these regions are
rare, a multitude of diseases has been found to be associated
with such [28–30]. Among the prominent examples are mental
disorders like schizophrenia and autism [31], multitudinous
cancers and nonsyndromic progressive hearing loss [32].
Similarly, SNVs in 30
UTRs have been correlated with multiple
cancers [33–36] and neurodegenerative diseases [37]. These ex-
amples highlight the need for a better understanding of the
presence and effects of SNVs in miRNAs and UTRs, as they can
lead to completely different phenotypes.
For analyzing the effects of SNVs on miRNA–target relations
on a system-wide basis, several miRNA-target SNP databases
such as miRdSNP [38], MirSNP [39], PolymiRTS database [40] and
miRNASNP [41] have been developed. However, as the proced-
ure of target prediction is computationally expensive and the
number of known SNVs has been increasing substantially over
the past years, most of these resources are outdated. In this re-
view, we will compare several state-of-the-art databases that
are available and evaluate apparent information gaps to the
content of up-to-date resources.
Overview of SNP effect prediction web servers
In this section, we present four databases helping to predict and
assess the effects of SNVs in human miRNAs and their respect-
ive target genes. Table 1 presents a compact overview of these
databases and their provided features.
miRdSNP
Published in 2012, miRdSNP [38] provides information about the
distance between MREs and SNPs from dbSNP (build 130) [3]. It
incorporates experimentally validated MREs from TarBase [42],
miRTarBase [43], miRecords [44] and miR2disease [45], as well
as the MRE predictions yielded by PicTar [46] and TargetScan 5.1
[47] on the wild type 30
UTR sequences. Further, a manually
curated set of disease-causing SNPs (dSNPs) is available, allow-
ing to post-filter the provided predictions accordingly. In add-
ition, new MREs induced by dSNPs that were predicted using
miRanda [48] are listed.
Table 1. Overview of the features provided by the evaluated databases
miRdSNP MirSNP PolymiRTS Database 3.0 MiRNASNP v2.0
Year of publication 2012 2012 2013 2015
Search miRNAs/genes/SNPs Yes Yes Yes Yes
Batch search No Yes Yes No
Search-linked SNPs No Yes No No
Browse Yes No Yes Yes
Binding site locus visualization Yes No No No
Binding site miRNA/mRNA visualization Partiala
Yes No Yes
MRE gain/loss Partialb
Yes Yes Yes
Binding affinity increase/decrease No Yes No No
SNP distance Yes, in 30
UTR No No Yes, in miRNA flanks
Filter by MAF No Yes No Yes, in pre-miRNAs
Conservation information No Yes Yes No
Filter by experimental support Yes No Yes Yes
Filter by disease/traits/GO Yes No Yes No
Contains INDELs No No Yes No
Contains miRNA/gene expression No No No Yes
SNPs in miRNAs No No Yes Yes
SNPS in 30
UTR Yes Yes Yes Yes
a
Depends on the target prediction algorithm used.
b
Only gains were predicted for dSNPs.
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The website provides three tabs for browsing through the
collected interactions. On the first tab, a searchable table con-
taining all MREs and dSNPs is printed. Users can search using
SNPs by their dbSNP ID, miRNAs by their miRBase name and
genes by their symbols. In addition, the table can be filtered ac-
cording to the SNP distance to a binding site and associated dis-
eases. For each entry in the table, the corresponding binding
site of the miRNA can be displayed, as well as the location of
the associated SNP. In addition, for each SNP linkage disequilib-
rium (LD) frequency information is provided. On the second tab,
density plots visualizing MREs, SNPs and dSNPs are shown
through an interactive plot, allowing to further inspect regions
in the UCSC genome browser. In a third tab, a table showing the
entire gene set and its associated MREs and SNPs are displayed.
This table is however not filterable. For each gene, all binding
sites and SNPs can be queried.
In conclusion, miRdSNP is focusing on the spatial relation-
ship of SNPs, and thus, its major strength is the distance infor-
mation provided for all SNPs relative to known MREs. Another
major strength is the collection of manually curated dSNPs and
the ability to filter interactions accordingly. Its major weak-
nesses are on one hand the missing information on MRE losses
and on the other that SNPs in miRNAs are not considered at all.
The database is available at http://mirdsnp.ccr.buffalo.edu/.
MirSNP
Published in 2012, MirSNP [39] provides a collection of human
SNPs in potential MREs located in 30
UTRs, as predicted by
miRanda. SNPs were collected from dbSNP build 135. For each
reported interaction, the effect induced by the corresponding
SNP is reported, i.e. the creation/deletion of an MRE or the in-
crease/decrease of binding affinity. Besides, information on the
minor allele frequency (MAF) and LD are provided, if available.
Finally, it implements the ability to filter predictions with lists
of SNPs from, e.g., GWAS and eQTL studies, including optionally
linked SNPs from multiple populations as provided by HapMap
[49].
The website provides three search forms allowing to perform
single searches, batch searches or searches with a list of disease
or trait associated SNPs. Via these forms, the user can query the
database using SNPs by their dbSNP ID, genes by their symbols
or RefSeq mRNA ID or miRNAs by their miRBase name. While
individual searches can be restricted by MAF, linked SNPs can
be requested as well.
The search functionality was unavailable during our review
process; therefore, we can only describe the search results ac-
cording to the help page of the database. After submitting a re-
quest, the user is redirected to a separate page containing a
table with predicted effects on MREs, i.e. gains and losses or
changes to the binding affinity. This particular table displays in-
formation for both the reference and alternative alleles. In add-
ition to the basic effects, multiple measures are listed: mirSVR
score, MAF, miRanda score, binding energy, conservation infor-
mation of phastCons 46way vertebrates from UCSC and a visu-
alization of the miRNA/mRNA binding site.
In summary, MirSNP is focusing on MREs predicted by
miRanda on 30
UTRs having known SNPs. Its major strength
consists in the integration of MAF annotations and conserva-
tion scores of miRNA seed motifs. In contrast, major weak-
nesses are missing information on experimentally validated
MREs and missing support of considering SNPs in miRNAs.
The database is available at http://bioinfo.bjmu.edu.cn/
mirsnp/search/.
PolymiRTS database 3.0
The PolymiRTS Database was originally released in 2007 [50]
and has now reached its third version [40], published in 2013. It
is the most comprehensive database available to-date. In add-
ition to SNPs, it also offers support for considering small inser-
tions or deletions (INDELs) in the genomic regions of miRNAs
and their target sites. In its third version, SNPs and INDELs were
collected from dbSNP build 137. The predictions of creations or
deletions of MREs were performed via TargetScan 6.2 [51].
Following, their likelihood is assessed using their TargetScan
contextþ score difference to the reference target site. In add-
ition, experimental support information was incorporated from
miRecords, TarBase, miRTarBase and multiple studies, and
added to the predicted MREs, if available. Furthermore, target
sites identified by CLASH (cross linking, ligation and sequencing
of hybrids) experiments [52], which allow to directly identify the
location of pairs comprising a target site and its binding miRNA,
are also provided. Likewise, the database links polymorphisms
in MREs with possibly impacted gene pathways from the KEGG
database [53], and with various human diseases and traits based
on data in the NHGRI GWAS catalog [54], dbGaP [55] and eQTLs
from GTEx eQTL browser [56].
The website provides four tabs for browsing and searching
interactions with either one or multiple terms and according to
the chromosomal location. Users can search SNPs by their
dbSNP ID, miRNAs by their miRBase name and genes by their
symbol, description or RefSeq mRNA ID. Further, users can start
a query by providing traits, such as ‘Metabolic syndrome’ or
gene ontology [57] terms. All search results can be filtered to
show only gains or losses of MREs and to show only effects hav-
ing a particular experimental support. PolymiRTS also allows to
filter search results by minimum occurrence of miRNA sites in
other vertebrate genomes. After submitting, a list of genes ful-
filling the requested criteria is shown to the user. By selecting
one of these genes, the user is then redirected to a new tab,
where all interactions related to this gene are presented. These
are split into multiple tables, separating CLASH data, SNPs in
miRNA target sites and MRE gains/losses caused by SNPs in
miRNA seeds. If available, information on associations with
human diseases and pathways is provided.
In conclusion, the PolymiRTS Database 3.0 provides a large
panoply of features going from simple MRE gains and losses up
to pathways and diseases. The major strengths of this database
are on one side the variety of features and, importantly, on the
other the multitude of experimental evidence integrated, in par-
ticular CLASH experiments. Weaknesses of PolymiRTS are miss-
ing visualizations of binding sites in the context of the entire
30
UTR, and the alignment of a miRNA-mRNA duplex, which is
only partly shown. Furthermore, the display of all relevant in-
formation is suboptimal, as it is mixed with a lot of other con-
tent and therefore confusing.
The database is available at http://compbio.uthsc.edu/
miRSNP/.
miRNASNP v2.0
It was initially published in 2012 [58] and updated in 2015 [41]. It
provides information on the gain or loss of potential MREs
caused by SNPs in 30
UTRs or miRNA seed regions. MREs were
predicted by miRanda and TargetScan 6.2, and SNPs collected
from dbSNP build 137. Experimental validated targets were
retrieved from TarBase, starBase [59], miRecords, miRTarBase
and miR2disease, providing the ability to filter the predictions
according to these annotations. In addition to its base
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functionality, the database allows to evaluate the effects of
SNPs in pre-miRNAs on their folding energy, and supplies lists
of SNPs in flanking regions. To estimate the effect on the ex-
pression levels, correlation of miRNAs and target genes expres-
sion data from The Cancer Genome Atlas (TCGA) [60] was
integrated. Similar to PolymiRTS, miRNASNP incorporates infor-
mation about SNPs in GWAS-identified trait-associated regions
from the NHGRI GWAS Catalog and about LD blocks for multiple
populations. Finally, it also provides the possibility to predict
the effects of novel SNVs on miRNA target binding, i.e. gain or
loss of MREs, as well as the impact on the structure of the pre-
miRNA.
The website allows to browse different subsections of the
database by providing respective links on the homepage. The
search functionality is incorporated directly in the browse inter-
face and can also be used from another tab. A search tab allows
users to search for MRE gains or losses caused by SNPs in
miRNA seed regions or in 30
UTRs. Search requests can be per-
formed via dbSNP ID, miRNA miRBase name or gene symbol.
Once selected, the user is redirected to a distinct browse inter-
face, where post-filtering according to miRNA or gene expres-
sion is possible, as well as to SNPs in LD regions. Lost MREs can
be filtered based on the available experimental validation and
negative correlation of miRNA expression with gene expression.
A resulting table prints information on the expression of
miRNAs and genes in different tissues covered by the integrated
TCGA samples. Moreover, the miRNA- or mRNA-binding site is
displayed and the binding energy changes of the miRNA/mRNA
duplexes are shown. Other search functions are provided as
well, so that users can search for specific miRNA precursors, the
effects of SNPs in flanking regions, seed regions or pre-miRNA
except seed regions. As a result of these queries, a list of SNPs
and their distances to the pre-miRNA and a list of SNPs with
their predicted effect on the mature miRNA expression, as well
as the introduced energy changes, are displayed. In addition, for
each pre-miRNA, a detailed overview helps to retrieve the loca-
tion and expression of mature miRNAs, the list of SNPs found in
it and their effect on the secondary structure. In particular, for
these SNPs, LD regions are shown in different populations and
linked diseases or traits are reported. miRNASNP v2.0 offers a
tab where users can input custom-mutated UTR sequences or
miRNA sequences in addition to mutated pre-miRNA sequences
to predict the resulting MRE gains/losses or the effect on the
secondary structure.
In summary, miRNASNP v2.0 is based on MRE predictions of
miRanda and TargetScan and provides many additional fea-
tures. Its major strengths are the incorporation of expression
data, the ability to assess the impact of SNPs on the secondary
structure of pre-miRNAs including the potential effects on the
mature miRNA expression and the possibility to evaluate novel
data. A major weakness is the missing batch search for known
and novel data sets.
The database is available at http://bioinfo.life.hust.edu.cn/
miRNASNP2/index.php.
Comparison of SNP effect prediction Web
servers
In our study, we compared the above described databases based
on four criteria: their data sources, target prediction methods,
database functionality and integration of experimental infor-
mation. After the comparison, we assessed their individual
performance based on a benchmark on 16 reported and vali-
dated SNP effects.
Data sources
The release versions of the used data sets for each of the data-
bases were retrieved from their respective publications. In cases
where release versions for data sources were not specified, they
were determined using the year of publication of the respective
database. As shown in Table 2, the latest published databases
miRNASNP v2.0 and PolymiRTS are the most up-to-date services
among other published applications. Among these databases,
only miRdSNP uses the provided wild-type MRE predictions
from TargetScan and PicTar, which are available on their tool
websites. Despite the fact that this approach helps in cutting
down the processing time for predictions, this may lead to
missed predicted normal interactions, as PicTar used an old as-
sembly of the human genome (hg17) as reference genome for
the MRE predictions, which thus needed to be lifted to the sub-
sequent version (hg18), used by all other resources of miRdSNP.
With the exception of PolymiRTS and miRNASNP, none of
the other databases have been updated after their initial publi-
cation. Even PolymiRTS and miRNASNP have not been updated
since 3 and 2 years, respectively. The current version of dbSNP
(build 150) contains 98 million more validated SNPs, i.e. 3.6-fold
more SNPs, than the most recent dbSNP version (build 137) used
by the here described databases. Furthermore, all of them use
outdated miRBase versions. In Table 3, we report the number of
considered miRNAs, UTRs and SNPs and the reported inter-
actions of each database accordingly. In addition, we computed
the latest numbers based on dbSNP build 149, miRBase version
21 and common predictions of miRanda, and the seed matching
step of TargetScan 7.1. As it can be seen in Table 3, the number
of 30
UTRs has not changed much over the years.
Interestingly, MiRNASNP v2.0 considers all reported 30
UTRs,
in contrast to the other databases that only consider the longest
ones per gene, which explains the observed difference. When
comparing the latest statistics with those of PolymiRTS 3.0 and
miRNASNP v2.0, which are using the most recent dbSNP build
among the four databases, we observe that the number of SNPs
in miRNA target sites has increased by 3–5-fold, and the number
of SNPs in miRNA seed regions has even grown 9–11-fold. These
numbers are also reflected in the total of reported interactions,
which have grown 8–12-fold. Of course, these numbers do not
only depend on the tools at hand but also on the prediction
algorithms.
To compute the latest numbers, we have taken a similar ap-
proach to MiRNASNP v2.0, except that we did not restrict the
predicted sites by TargetScan to the conserved ones. Therefore,
our method reports less than PolymiRTS (reports all TargetScan
hits) or MiRSNP (reports all miRanda hits), but more than
MiRNASNP v2.0, if applied on current data. In Figure 1, all pre-
dicted interactions caused by polymorphisms in the 30
UTR of
all considered databases are shown. To this end, we collected
all interactions from the database files available on their re-
spective websites and converted all miRNA names to their latest
in miRBase. To avoid counting identical effects found in differ-
ent UTRs but for the same gene multiple times, we compared
the reported miRNA–gene symbol–SNP triplets of each database
instead of their UTR RefSeq identifier. In addition, we excluded
the predicted increases or decreases in binding affinity of
MirSNP. We can see that 76% (133) of MRE gains covered by
miRdSNP are also found in all other databases. On the other
hand, 573 442 interactions are also covered by all the other
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databases, representing between 27 and 60% of the total num-
ber of interactions reported by them. Especially, PolymiRTS dif-
fers from the others, which is not surprising because of its
unfiltered use of TargetScan and reporting of interactions for
both allelles. Regarding MirSNP and miRNASNP v2.0, 20%
(192 240 and 191 021) of their interactions are unexplained by
any other database, hence highlighting their heterogeneity.
Prediction methods
In Table 4, we summarized the prediction software used by the
different tools. In general, all databases use either TargetScan
or miRanda. A recent review by Riffo-Campos et al. [61] com-
pares these algorithms in their latest versions. Interestingly,
miRdSNP uses miRanda for the prediction of new MREs induced
by SNPs, but uses data sets pre-predicted by TargetScan 5.2 and
PicTar for normal interaction. This might be surprising for
users, as one would expect the same prediction software used
throughout the entire project. MiRNASNP v2.0 uses both
miRanda and TargetScan for the prediction of gains and losses
of MREs.
In the majority of the databases in this review, SNP-induced
UTR sequences were preprocessed to remove redundancy of
target sites that are not affected by SNPs and to reduce the
computational burden. MiRdSNP, miRNASNP and MirSNP con-
sider only 25, 25 and 20 nts upstream and downstream of the
SNP, respectively. However, this might exclude target sites with
gaps or bulges, as miRNAs do not require perfect complemen-
tarity to the UTR sequences to form a miRNA-mRNA complex.
We compared the prediction results of miRanda3.3a for short-
ened UTR sequences (30 nts) to the original complete UTR se-
quence and noticed that some target sites were not predicted by
the algorithm when applied using the shorter sequences. On re-
peated testing, we found that a length of at least 80 nts (up-
stream and downstream) was required for the miRanda
prediction algorithm to yield the same results as for the whole
UTR region as input. Therefore, we chose this threshold for the
predictions we made using miRanda as presented in Table 3.
For PolymiRTS, we found no information regarding the con-
sidered nucleotides upstream and downstream. To assess the
impact of shortening the UTR sequences to 25 nts, we computed
the number of gains and losses predicted by miRanda with the
shortened length and with the full UTR length on our collected
data. Using the full UTR length, 11 433 628 gains and 11 953 935
losses were predicted by miRanda, whereas with shortened UTR
length, more gains and losses could be found (11 553 901 and
12 074 380). To emphasize, the impact is noncritical (1%), but ex-
istent and potential correct predictions could be missed.
Table 2. Overview of the integrated resources and build versions of the databases in this review
miRdSNP MirSNP PolymiRTS Database 3.0 MiRNASNP v2.0 Latest
Genome Assembly hg18 hg19 hg19 hg19 hg38
dbSNP 130 135 137 137 150
HapMap 27 28a
– – 28
TargetScan 5.1 – – – 7.1
PicTar N.A. – – – N.A.
miRBase 17a
18 20 19 21
miRTarBase 2.5a
– 4.3þ
4.5a
6.1
MiRecords N.A. – N.A. N.A. N.A.
miR2disease N.A. – – N.A. N.A.
TarBase 6.0þ
– 6.0þ
7.0a
7.0
starBase – – – N.A. N.A.
CLASH experiments – – N.A. – N.A.
TGCA – – – N.A. N.A.
N.A. Version not available.
– Data source not used.
a
Data source derived from date of publication.
Table 3. Overview number of SNPs, miRNAs and interactions supported by the databases
miRdSNP MirSNP PolymiRTS Database 3.0 miRNASNP v2.0 Latesta
Number of miRNA (human) N.A. 1921 2578 2042 2588
Number of UTRs 19 834 29 273 18 678b
37 348 19 107
Number of SNPs in miRNA target sites (human) 175 351 414 510 358 874 566 176 1 810 468
Experimentally validated miRNA targets (human) N.A. N.A. 2070 393 936 322 160
Number of SNPs in miRNA seed regions (human) N.A. N.A. 271 227 2525
Number of total interactions 174c
1 562 149c
2 089 646c,d
1 271 259c
15 739 025
Gains of MREs because of polymorphism in miRNA N.A. N.A. N.A. 162 441c
5 559 979
Losses of MREs because of polymorphism in miRNA N.A. N.A. N.A. 153 290c
5 540 025
Gains of MREs because of polymorphism in UTR 174c
497 895c
906 703c
509 791c
1 940 014
Losses of MREs because of polymorphism in UTR N.A. 480 046c
923 082c
445 737c
2 699 007
N.A. Not available.
a
According to dbSNP 149 and miRBase 21 and common predictions of miRanda and the seed matching step of TargetScan 7.1.
b
Data from PolymiRTS 2.0.
c
Effects for same gene only counted once.
d
Without MRE effects because of polymorphisms in miRNAs.
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Database functionality
All databases offer the possibility to search for miRNA names,
gene symbols or dbSNP identifiers to identify MRE gains or
losses. Furthermore, all except miRdSNP and MirSNP consider
SNPs and their effects in miRNAs. MirSNP and the PolymiRTS
also offer the possibility to query multiple entries at the same
time by providing a corresponding list of queries. In addition, all
databases, except MirSNP, include experimental evidences col-
lected from multiple miRNA target catalogs. Besides these fea-
tures, only miRNASNP v2.0 allows to evaluate the effects of
novel SNVs. However, miRNASNP accepts queries on the effects
of SNP-induced UTR or miRNA sequences only one by one,
whereas the possibility of uploading a VCF file would be more
convenient, thereby allowing to easily study the effects of de-
tected SNVs by variant-calling pipelines.
Querying for SNPs linked to diseases is only implemented in
miRdSNP. Even though PolymiRTS provides disease-related in-
formation, it allows querying for traits only.
Although the majority of databases considers only single
point mutations, it is important to note that PolymiRTS 3.0 also
considers small INDELs, which form a large part of genomic
variations. In addition, PolymiRTS is the only tool that inte-
grates information from the KEGG pathway database, making it
easier for the user to study the effects of genomic variations on
biological pathways. Another important feature provided by
PolymiRTS and MiRSNP is the annotation of evolutionary con-
servation of target sites.
Integration of experimental information
Another important factor to consider comprises the inclusion of
experimental information for the predicted miRNA-target pairs
as supported by the individual databases. As shown in Table 2,
all databases except MirSNP integrate information from
miRNA–target catalogs, allowing to filter for only experimen-
tally validated miRNA–target relationships. It should however
be noted that even if miRNA–target relationships are validated,
often the exact location of the binding site is not known.
Therefore, there is a higher risk of MRE losses of validated inter-
actions being false positives because the miRNA originally
never bound at the specified location. MiRdSNP is the only data-
base containing manually curated disease-associated SNPs. In
comparison, PolymiRTS associates genes with polymorphisms
in 30
UTRs with diseases and traits by considering the results
from genome-wide association studies (GWASs) and expression
quantitative trait loci studies (eQTLs). MiRNASNP v2.0 considers
GWAS as well and reports LD regions. The traits associated to
the LD regions seem however to be only accessible when query-
ing for a particular pre-miRNA.
As miRNAs usually upregulate or downregulate their target
genes, it makes sense to consider their expression. MiRNASNP
takes this into account by including miRNA and mRNA expres-
sion data (retrieved from TCGA) and reports their specific correl-
ation. The PolymiRTS database misses this feature; however, it
provides annotations from CLASH experiments, which provide
explicit evidence for miRNA-mRNA target relationships in the
context of SNPs or INDELs. This is an advantage as compared
Figure 1. Total number of interactions shared by all evaluated databases.
Table 4. Versions of target prediction algorithms used in the con-
sidered databases
miRdSNP MirSNP PolymiRTS
Database 3.0
MiRNASNP
v2.0
TargetScan – – 6.2 6.2
miRanda 3.3a 3.3a – 3.3a
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with the information content provided by other databases,
where miRNA-mRNA target relationships are only reported
based on their original sequences.
Benchmark on 16 reported and validated SNP effects
We evaluated the ability of the databases to recover 16 experi-
mentally validated effects of 13 variants in the binding sites of
miRNAs in 30
UTRs, which were reported by multiple studies
[37, 62–64] and summarized in a recent review [65]. We excluded
miRSNP from the evaluation process; since at the time of run-
ning the benchmark, the search functionality was not available.
The results for the remaining three databases are presented in
Table 5.
First of all, we noticed a usability problem when searching
SNPs in 30
UTRs using miRNASNP. The results for the same
query were different when using the entry search page located
at http://bioinfo.life.hust.edu.cn/miRNASNP2/search.php, or the
results search page that is available after querying the database
using the entry search page. An example is rs4245739, for which
six target gains were reported when using the entry search
page, whereas no target gains were reported if searching via the
results search page. The reason for this might lie within the re-
sults search page, as there only miRNAs with any annotated ex-
pression data are shown. Additionally, the database provides
the possibility to show results for miRNAs with a minimum
average expression. However, the default option, which is to
show interactions for all, does not show all miRNAs but filters
out every miRNA for which no expression data are available, re-
sulting in the observed difference above.
As shown in Table 5, none of the queried effects were found
in miRdSNP. This is because of the fact that MRE gains are only
reported for their specifically determined subset of disease-
associated SNPs and that MRE losses are not reported. Of the 16
evaluated effects, 13 were found in PolymiRTS and 12 in
miRNASNP. We found that MRE gains or losses were often in-
verted in comparison with the reported effects. The reason for
this is that the PolymiRTS database considers as reference nu-
cleotide the ancestral allele, whereas miRNASNP considers the
nucleotide in the reference genome. An example of an MRE loss
not found in any database is induced by rs550067317. It is not
reported because it is present in dbSNP only since build 142,
which is newer than any builds used by the other databases. A
further example of an MRE gain reported by no database is
caused by rs2735383. This can be explained by the unusual
binding of hsa-miR-629-5p, which has no complementary base
pairing at the first position of the seed, and is therefore dis-
carded by TargetScan. Another SNP rs35592567 for which
PolymiRTS did not report any interaction forms a new binding
site found by TargetScan because the first base of the seed can
then bind, resulting in a perfect 8mer match. We could not de-
termine why PolymiRTS is not reporting this interaction, as it is
using TargetScan. MiRNASNP reports this interaction because
not only TargetScan but also miRanda predicts it.
The MRE gain of hsa-miR-522-3p with PLIN4 caused by
rs8887 is reported by PolymiRTS but not by miRNASNP. This is
because of the fact that TargetScan predicts the binding site
with the A allele because of an 8mer seed match. MiRanda does
predict the binding site as well, however, only with a binding
energy of 8.96 kCal/Mol, which is filtered out by miRNASNP.
Table 5. Results of querying the databases with 16 reported and validated SNP effects
mirRdSNP PolymiRTS Database
3.0
miRNASNP v2.0
Interaction
Gains hsa-miR-191-5p, hsa-miR-887-3p with
MDM4 because of rs4245739
Not a dSNP, so no
gain information
Reported Reported, gain/loss inverted
hsa-miR-140-5p with TP63 because of
rs35592567
Not a dSNP, so no
gain information
Not reported Reported, gain/loss inverted
hsa-miR-214-5p, hsa-miR-550a-5p
with HNF1B because of rs2229295
Not a dSNP, so no
gain information
Reported, gain/loss
inverted
Reported
hsa-miR-4271 with APOC3 because of
rs4225
SNP not found Reported Reported
hsa-miR-522-3p with PLIN4 because of
rs8887
SNP not found Reported/wo gain/
loss information
Not reported
hsa-miR-124-3p with FXN because of
rs11145043
SNP not found Reported, gain/loss
inverted
Reported, gain/loss inverted
hsa-miR-629-5p with NBS1 because of
rs2735383
SNP not found Not reported Not reported
Losses hsa-miR-34b-3p with SCNA because of
rs10024743
– Reported Reported
hsa-miR-96-5p and hsa-miR-182-5p
with PALLD because of rs1071738
– Reported Reported, but only 182-5p
hsa-miR-137 with EFNB2 because of
rs550067317
– SNP not found SNP not found
hsa-miR-510-5p with HTR3E because
of rs56109847
hsa-miR-433-3p with FGF20 because of
rs12720208
hsa-miR-155-5p with AGTR1 because
of rs5186
Reported
interactions
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Therefore, as both tools need to predict the MRE gain in
miRNASNP, nothing is reported. The last different prediction of
PolymiRTS and MiRNASNP is for the MRE loss of hsa-miR-96-5p
with PALLD caused by rs1071738, which is only reported by
PolymiRTS. This can again be explained by the different predic-
tions of TargetScan and miRanda. TargetScan predicts in the
original sequence a binding site with a 6mer followed by an A,
which is disrupted by the SNP at the third base with a change of
C to G. This MRE loss is not detected when using miRanda be-
cause it predicts different target sites in this gene, which are not
affected by the SNP.
When evaluating the binding sites of the predicted inter-
actions, we noticed that the MRE gain in the 30
UTR of HNF1B be-
cause of rs2229295 for hsa-miR-214-5p and for hsa-miR-500a-5p
would not be detected anymore in hg38 by conventional predic-
tion algorithms, as the nucleotide adjacent to the annotated
SNP changed from G to A between hg19 and hg38, prohibiting
any binding, as illustrated in Figure 2. This example also high-
lights the importance of the reference genomes used by these
databases.
In summary, PolymiRTS and miRNASNP cover our set of ex-
perimentally validated MRE gains and losses well, whereas
miRdSNP did not find any MRE because of its restrictions to
dSNPs. PolymiRTS performs minimally better than miRNASNP,
as it covers one effect more, which is why we see it as a close
winner. Of course, the size of this benchmark compared with
the complete set of predictions is small, and therefore, a much
larger benchmark comprising thousands of interactions would
be required to evaluate the performance in a fair manner.
However, as the number of experimentally validated effects of
SNPs in MREs is small, we preferred to set the focus of the
benchmark on a high-confidence set instead of including
potential false positives.
Which database to choose?
As shown in the previous sections, all databases have some ex-
clusive features. However, the search functionality of MirSNP
was not available at the time of creating this review, which is
why we cannot recommend it currently. The PolymiRTS
Database 3.0 covers nearly all features of other databases and
should therefore be the database of choice per default. If expres-
sion correlations of miRNAs and their mRNA targets are import-
ant or effects of SNPs on the pre-miRNAs, miRNASNP v2.0 is the
database of choice. Furthermore, if users are studying novel
SNVs, they should consider miRNASNP v2.0 as well, after reduc-
ing their SNVs to a small subset.
We provide the up-to-date data we collected for this review
in a database called miRSNPdb, which is reachable under www.
ccb.uni-saarland.de/mirsnp. Users relying on more recent SNPs
or having their own novel SNVs can use it to retrieve MRE gains
and losses.
Future challenges
The substantial increase in annotated miRNAs and SNPs has
made it extremely computationally intensive to predict novel
target sites. We found that the first step performed by
TargetScan took 15 h for 2588 miRNAs from miRBase v21 and
all 19 107 30
UTRs from Ensembl 85 [66]. As the number of SNPs
in miRNA target sites is nearly 100-fold higher than the number
of 30
UTRs, even when reducing the predictions to the con-
sidered regions, the number of miRNA-mRNA target pairs rises
substantially. Therefore, the runtime of target prediction pro-
grams and/or the algorithms assessing the impact of SNPs need
to be improved to be able to keep up with the increasing amount
of available data.
All presented databases focus exclusively on the impact of
SNPs in miRNAs and 30
UTRs. However, it has been shown that
miRNAs can also bind to 50
UTRs or even to coding regions. The
extent of these interactions is still unclear; therefore, including
them into relevant databases could promote their investigation.
More specifically, focusing on already validated interactions
should be considered as the first step, as the effects in 50
UTRs
are expected to be less frequent, and therefore, including all
predictions would also include a large set of false positives.
Until now, all available databases focus on single SNPs or
INDELs in either the 30
UTR sequences or in miRNAs. However,
it is not unlikely that multiple variants occur at the same time
and induce other effects. The inherent exponential increase in
variant combinations is a major challenge in this regard.
With the recent advances of high-throughput miRNA-mRNA
mapping via CLASH experiments, new miRNA target prediction
tools, such as TarPmiR [67], have been developed. The progresses
in the target prediction field will allow to improve the predictions
of gains and losses induced by variants. Combined with the
continuous increase in SNV data, keeping databases up-to-date
with the latest software and reference data is important.
We believe that because of the ever-growing amount of
annotated SNPs and the thereby resulting substantial growth of
predicted MRE, gains and losses a larger focus should be put on
the curation of high-confidence sets. These could be narrowed
down at first by considering the common predictions of more
target prediction tools. The resulting predictions could then fur-
ther be refined by experimental evidence, stemming, for ex-
ample, from CLASH experiments. In addition, annotations for
explicitly experimentally validated MRE gains or losses, such as
Figure 2. Difference in the sequence of human genome in build hg19 and hg38 leading to undetected binding sites for hsa-miR-214-5p and hsa-miR-500a-5p.
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the ones collected by Moszynska et al. [65], would highly im-
prove the quality of such sets to eventually form a reliable gold
standard. Overall, we think that such high-confidence data
would increase the usefulness of such databases for precision
medicine to a reasonable extent.
Key Points
• A substantial increase has been observed in the number
of reported SNPs and the thereby induced MREs over
the past years.
• All currently available databases are based on outdated
resources.
• PolymiRTS is the most complete available database fol-
lowed by miRNASNP v2.0.
• Users studying novel SNVs should consider miRNASNP
v2.0.
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