Distinguishing between MicroRNA Targets from Diverse Species

using Sequence Motifs and K-mers

Malik Yousef

1,*

, Waleed Khalifa

, İlhan Erkin Acar

and Jens Allmer

4,5,*

Community Information Systems, Zefat Academic College, Zefat, 13206, Israel

Computer Science, The College of Sakhnin, Sakhnin, 30810, Israel

Biotechnology, Izmir Institute of Technology, 35430 Urla, Izmir, Turkey

Molecular Biology and Genetics, Izmir Institute of Technology, 35430 Urla, Izmir, Turkey

Bionia Incorporated, IZTEKGEB A8, 35430 Urla, Izmir, Turkey

Keywords: MicroRNA, Target Prediction, Motif, Machine Learning.

Abstract: A disease phenotype is often due to dysregulation of gene expression. Post-translational regulation of protein

abundance by microRNAs (miRNAs) is, therefore, of high importance in, for example, cancer studies.

MicroRNAs provide a complementary sequence to their target messenger RNA (mRNA) as part of a complex

molecular machinery. Known miRNAs and targets are listed in miRTarBase for a variety of organisms. The

experimental detection of such pairs is convoluted and, therefore, their computational detection is desired

which is complicated by missing negative data. For machine learning, many features for parameterization of

the miRNA targets are available and k-mers and sequence motifs have previously been used. Unrelated

organisms like intracellular pathogens and their hosts may communicate via miRNAs and, therefore, we

investigated whether miRNA targets from one species can be differentiated from miRNA targets of another.

To achieve this end, we employed target information of one species as positive and the other as negative

training and testing data. Models of species with higher evolutionary distance generally achieved better results

of up to 97% average accuracy (mouse versus Caenorhabditis elegans) while more closely related species did

not lead to successful models (human versus mouse; 60%). In the future, when more targeting data becomes

available, models can be established which will be able to more precisely determine miRNA targets in host-

pathogen systems using this approach.

1 INTRODUCTION

Proteins have a large influence on the phenotype and,

therefore, their abundance can be fine-tuned on

several levels while their dysregulation may often

lead to disease. The most direct regulators of protein

abundance are microRNAs (miRNAs) which are

involved in post-transcriptional gene regulation

(Erson-Bensan, 2014). They modulate protein

abundance via interacting with messenger RNA

(mRNA) thereby fine-tuning translation rates (Saçar

and Allmer, 2013). To achieve this, a short stretch of

nucleotides (mature miRNA; ~20 nt) serves as a

recognition sequence within the RNA induced

silencing complex (RISC). Post-transcriptional

regulation via miRNAs is found in a wide range of

species ranging from viruses (Grey, 2015) to plants

(Yousef et al., 2015). Experimentally determined

mature miRNAs and pre-miRNAs (their sources) are

stored in miRBase (Griffiths-Jones, 2010) and its

release 21 contains about 28,000 mature miRNAs

(~2,600 for human), but it has been estimated that

more miRNAs may exist (Londin et al., 2015).

Unfortunately, the experimental detection of

miRNAs is difficult since they can only be analyzed

when co-expressed with their target mRNAs which is

impossible to achieve for all miRNA-mRNA pairs at

the moment (Saçar and Allmer, 2013). Therefore,

computational prediction of pre-miRNAs is

employed and most approaches are based on machine

learning using two-class classification (Allmer, 2014;

M. Saçar and Allmer, 2014). Such ab initio models

have been established for metazoan (Allmer and

Yousef, 2012) and we have shown that similar models

can be trained for plants (Yousef et al., 2015).

Machine learning for pre-miRNAs depends on

parameterization of the biological structure and many

features are available (Sacar and Allmer, 2013). We

Yousef M., Khalifa W., Acar Ä

r. and Allmer J.

Distinguishing between MicroRNA Targets from Diverse Species using Sequence Motifs and K-mers.

DOI: 10.5220/0006137901330139

In Proceedings of the 10th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2017), pages 133-139

ISBN: 978-989-758-214-1

133

have recently also added sequence motifs as

additional features for describing pre-miRNAs

(Yousef et al., 2016a). Parameterization is important

to train classifiers which, based on a feature-value set,

can learn to differentiate between the positive

(miRNA) and the negative class. Many machine

learning approaches like support vector machines

(Ding, Zhou, & Guan, 2010) and random forest (Jiang

et al., 2007) have been used but in general two-class

classifications suffers from the lack of bona fide

negative pre-miRNA examples (Khalifa et al., 2016).

The same is true, if not worse, for negative examples

of the targets of miRNAs. Such targets are short

stretches of nucleotides complementary to the mature

sequences incorporated into RISC. Experimentally

supported, so called miRNA-mRNA duplexes, are

available in miRTarBase (Hsu et al., 2011) and

TarBase (Sethupathy et al., 2006), but there is no

dataset for which it is clear that it does not contain

target sites for even selected miRNAs. This is

especially complicating the computational prediction

of miRNA targets (Hamzeiy et al., 2014). Therefore,

one-class classification has been used for miRNA

target prediction (Yousef et al., 2016b).

Here we employ two class classification, but

avoid the problem of missing negative data since

instead of trying to determine miRNA targets we

investigate the difference among miRNA targets

among species. Thus, it is our aim to differentiate

between miRNA targets of one species by using

another species as negative training data employing

only sequence-based features, which means that

positive and negative classes derived from known

miRNA targets. Our approach is further supported by

the finding that miRNA targets are not highly

conserved within vertebrate, fly, and nematode

3’UTRs (Chen and Rajewsky, 2006). For family

classification of pre-miRNAs Ding et al. used n-

grams (Ding et al., 2011) which is somewhat related

to the problem investigated here. Ding et al. aimed to

assign a miRNA to a family of miRNAs while we are

determining to which species a miRNA target

belongs. We further aimed to establish the

evolutionary distance which allows differentiation

between targets of different species. We observed a

slight trend to better differentiation for species that

are further apart evolutionarily, but especially mouse

and rat examples present unexpected outliers which

may be due to low quality data and low relative

amount of data available for rat. Facilitating the

differentiation of miRNA targets among species may

in the future allow the investigation of

communication between host and parasite (Saçar et

al., 2014; Saçar Demirci et al., 2016).

2 MATERIALS & METHODS

2.1 Datasets

We downloaded all microRNAs’ targets for all

species available on miRTarbase with about 500

targets or more. Data for Homo sapiens (has),

Caenorhabditis elegans (Cel), Mus musculus (Mmu),

Rattus norvegicus (Rno), and Bos taurus (Bta) were

downloaded from miRTarBase (Release 6.0: Sept. 15,

2015); for details see Table 1.

The miRNA-mRNA duplexes, representing

miRNA targets were filtered according to sequence

similarity using USEARCH (Edgar, 2010) on the

sequences of each species and also on a per species

basis to ensure that there is no bias due to multiple

identical target sequences. We only found 74 similar

sequences between Hsa and Mmu, which were

removed.

Table 1: List of the species whose known miRNA mRNA

duplexes were used in this study and their amounts

available on miRTarBase.

Species

Number of

target sites

After

Cleaning

Average

Duplex

Length

Cel 4,029 2,233 23.2

Mmu 54,951 9,278 29.0

Hsa 317,542 89,752 25.2

Rno 658 532 22.5

Bta 489 393 24.1

2.2 Parameterization of MicroRNA

Targets

2.2.1 K-mers

These are short stretches of nucleotides of length k

which are also termed n-grams or words. Such

sequence-based features were used for ab initio pre-

miRNA detection, and may also be useful for target

prediction (Yousef et al., 2016b). Formally, a k-mer

is one element of the relevant alphabet, here {A, U,

C, G}. A 2-mer can generate 16 different elements:

AA, AC, …, UU. Higher k have also been used (Çakır

and Allmer, 2010), but here we limited k to 1 ≤ k ≤ 3

leading to 84 features. As features k-mer frequencies

were calculated from the target sequences divided by

the k-mers in the sequence given by len(sequence) - k

+ 1.

2.2.2 Motif Features Describing MicroRNA

Targets

Instead of describing exact sub sequences, motifs

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allow for approximate matches including some

degree of error tolerance. The MEME (Multiple

Expectation Maximization for Motif Elicitation)

Suite (Bailey, T. L. et al., 2009) was used to establish

motifs which are short stretches of nucleotides that

occur more frequently than expected by chance

within the given set of sequences. MEME is based on

(Bailey and Elkan, 1994) which repeatedly searches

for ungapped sequence motifs within the input

sequences which explains its long runtime. MEME

provides regular expressions and sequence profiles to

represent the motifs. Profiles are more informative

than regular expressions, which is why, different from

our previous works (Yousef et al., 2016a, 2015), we

decided to use profiles for feature creation. For each

species we discovered 100 motifs serving either as

positive or negative data thus 200 motifs were

available for each experiment in addition to 84 k-

mers. To calculate feature scores, profiles were

aligned with the target sequence and shifted along

until the end of the profile reached the end of the

sequence or vice versa in case the profile was longer

than the sequence. At each position, a score was

calculated by adding up the frequencies in the profile

for matching nucleotides at their respective positions.

The motif position leading to the highest score is

reported as the final score for that input sequence.

2.2.3 Feature Vector and Feature Selection

For each experiment 284 features were available, but,

not all features are equally effective to train a machine

learning classifier and therefore, we used KNIME

(Berthold et al., 2008) to calculate information gain

(Yang and Pedersen, 1997) on a per experiment basis

and accepted the 100 features with highest

information gain. This feature set was used during

model establishment to select from the possible

features in this study: A … U (k=1), AA … UU (k=2),

AAA … UUU (k=3), Motif

, Motif

, …,

Motif

; where n=200.

2.3 Classification Approach

Random Forest (RF) was used for classification in

this study since it outperformed support vector

machines (Vapnik, 1995), decision trees (DT), and

Naive Bayes (NB) in tests preceding the study. The

classification approach was setup using the data

analytics platform KNIME (Berthold et al., 2008).

Figure 1: Workflow for model establishment. Data was transformed into a feature vector and the best 100 were selected.

During the 100-fold MCCV training and testing scheme 80% randomly selected examples were used to train the classifier

and 20% were used for testing. All performance measures for testing and holdout data were collected during CV and reported

at the end of the workflow.

Distinguishing between MicroRNA Targets from Diverse Species using Sequence Motifs and K-mers

135

Models were trained and tested using 100 fold Monte

Carlo Cross Validation (Xu and Liang, 2001) and in

each fold of the cross validation (CV) the data were

split into 80% training and 20% testing. During

random selection, negative and positive examples

were sampled in equal amounts since we showed that

this approach is beneficial for model establishment in

pre-miRNA detection (Sacar and Allmer, 2013). For

each of the 100-fold Monte Carlo cross validation

(MCCV) the performance was recorded (Figure 1).

Table 2: Number of motifs and k-mers among the top 100

features during training and testing according to the training

scheme in Figure 1.

# of motifs # of

-mers

Mmu vs Cel 23 77

Rno vs 24 76

Hsa vs Cel 22 78

Rno vs Hsa 24 76

Bta vs mmu 26 74

Bta vs Hsa 26 74

Bta vs Cel 71 29

Rno vs Cel 65 35

Rno vs Bta 33 67

Hsa vs Mmu 63 37

2.3.1 Model Performance Evaluation

For each established model we calculated a number

of performance measures for the evaluation of the

classifier such as sensitivity, specificity and accuracy

according to the following formulations (with TP:

true positive, FP: false positive, TN: true negative,

and FN referring to false negative classifications):

Sensitivity = TP / (TP + FN); (SE, Recall)

Specificity = TN / (TN + FP); (SP)

Precision = TP / (TP + FP)

F-Measure = 2 (precision * recall) / (precision +

recall)

Accuracy = (TP + TN) / (TP + TN + FP + FN);

(ACC)

MCC =



TP TNFP FN







TPFP



TPFN



TNFN



TNFP



; Matthews

Correlation Coefficient (Matthews, 1975).

All reported performance measures refer to the

average of 100-fold Monte Carlo Cross Validation

(MCCV).

3 RESULTS AND DISCUSSION

The random forest classifier was used to establish

machine learned models using an 80/20 split from

random sampled and stratified training and testing

data during 100-fold MCCV (Figure 1). During

feature selection generally few motifs (22-33%) were

selected, but for Bta vs Cel, Rno vs Cel, and Has vs

Mmu 63-71% were selected (Table 2).

In general, about 25% of the informative features

were motifs which, given the low amount of examples

available for some species (Table 2), was to be

expected. The number of features that should

optimally be used for classification was tested (Figure

2). For many tests even low number of features lead

to relatively good results. To select the most suitable

number of features we used species combinations

which lead to slightly above 70% average accuracy

since lower and higher accuracies may be biased.

Therefore, we selected 100 features since for Bta vs

Cel and for Rno vs Cel this number of features led to

the best average accuracy (Figure 2).

The feature sets consisting of 100 parameters

were then used to establish models to differentiate

between miRNA targets from one versus the other

species (Table 3).

Table 3 indicates that distantly related species

(Figure 3) are easier to differentiate using the trained

models. Examples are Mmu vs Cel, Hsa vs Cel, Bta

vs Cel, and Rno vs Cel. However, Rno vs Mmu which

are the perhaps most closely related species (Figure

3) in this study achieved an unexpectedly high

accuracy whereas Hsa vs Mmu and Rno vs Bta were

according to expectations. Table 3 provides the

average accuracy and other model performance

measures. To confirm that the 100 fold model training

and testing is of low variance, accuracy was recorded

at each step (Figure 4). The distribution was best for

Mmu vs. Cel and worst for Rno vs. Bta judged by the

interquartile distance. Interestingly, all tests

involving Bta contain large interquartile ranges.

According to the results in Table 3 both Rno and

Mmu may contain foreign examples in their datasets

such that they 1) become different from each other

and 2) do not fit to the general expectation. For Mmu

we previously discovered that filtering their pre-

miRNAs by a very simple measure (RPM > 100)

leads to a 10% increase in average model accuracy for

pre-miRNA detection (Saçar Demirci and Allmer,

manuscript in preparation). It seems likely, that the

effect of this may be even more pronounced in

dependent datasets like miRNA targets since pre-

miRNAs that are unlikely true lead to targets which

are impossibly true. This seems to strongly affect

classification accuracy in this case.

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Figure 2: Average accuracy in respect to number of selected features.

Figure 3: Phylogenetic relationship among organisms and groups used in this study was established using phyloT

(http://phylot.biobyte.de). Itol (http://itol2.embl.de/) was used to create this graph (Letunic & Bork, 2011).

Figure 4: Accuracy distribution over 100 fold MCCV for models establishment to differentiate miRNA targets between

selected species.

0,50

0,60

0,70

0,80

0,90

1,00

284 100 50 40 30 20 10 5 2 1

Mmu vs Cel

Rno vs Mmu

Hsa vs Cel

Rno vs Hsa

Bta vs Mmu

Bta vs Hsa

Bta vs Cel

Rno vs Cel

Rno vs Bta

Hsa vs Mmu

Distinguishing between MicroRNA Targets from Diverse Species using Sequence Motifs and K-mers

137

4 CONCLUSIONS

Machine learning has become an important tool for

miRNA and miRNA target detection; however,

missing negative data poses an obstacle (Allmer and

Yousef, 2012). The general aim for miRNA target

prediction is to determine the targets in the 3’UTRs

of known genes. In this work we intended to study

whether it is possible to establish machine models

that can differentiate between miRNA targets from

different species. A somewhat related approach

previously categorized miRNAs into families,

thereby, showing that miRNAs can be related (Ding

et al., 2011). Contradicting this approach is that

miRNAs can evolve rapidly (Liang and Li, 2009).

Our aim is further supported by the finding that

3’UTRs (the most abundant targets for miRNAs) are

not highly conserved (Chen and Rajewsky, 2006).

Machine learning was performed using an 80/20 100-

fold MCCV approach and it was shown that 100

selected features and among them generally about

30% motifs was a successful mixture for model

establishment. While in general the results reflected

our expectations and we can conclude that given

proper examples miRNA targets can be differentiated

if the phylogenetic distance is high and that it is not

possible to distinguish between miRNA targets of

closely related species. Additionally, we were able to

show that it seems likely that among rat and mouse

examples in miRTarBase there seem to be many

incorrect target assignments. Nonetheless, it is our

contention that miRNA targets can be distinguished

between unrelated species which will be especially

useful for the detection of targets in host-pathogen

systems (Saçar et al., 2014; Saçar Demirci et al.,

2016).

ACKNOWLEDGEMENTS

The work was supported by the Scientific and

Technological Research Council of Turkey [grant

number 113E326] to JA. The work was supported by

the Zefat academic college for MY. MY

acknowledges Anas Yousef’s help in this research.

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