Mature DliRNA identification via the use of a Naive Bayes classifier

Katerina Gkirtzou, Panagiotis Tsakalides and Panayiota Poirazi

Abstract-MicroRNAs (miRNAs) are small single strandedRNAs, on average 22nt long, generated from endogenoushairpin-shaped transcripts with post-transcriptional activity.Although many computational methods are currently availablefor identifying miRNA genes in the genomes of various species,very few algorithms can accurately predict the functional partof the miRNA gene, namely the mature miRNA. We introducea computational method that uses a Naive Bayes classifier toidentify mature miRNA candidates based on sequence andsecondary structure information of the miRNA precursor.Specifically, for each mature miRNA, we generate a set ofnegative examples of equal length on the respective precursor(s).The true and negative sets are then used to estimate probabilitydistributions for sequence composition and secondary structureon each position along the RNA. The distance between thesedistributions is estimated using the symmetric Kullback-Leiblermetric. The positions at which the two distributions differsignificantly and consistently over a to-fold cross-validationprocedure are used as features for training the Naive Bayesclassifier. A total of t5 classifiers were trained with true positiveand negative examples from human and mouse. A performanceof76% sensitivity and 65% specificity was achieved using a con­sensus averaging over a to-fold cross-validation procedure. Ourfindings suggest that position specific sequence and structureinformation combined with a simple Bayes classifier achieve agood performance on the challenging task of mature miRNAidentification.

I. INTRODUCTION

MIcroRNAs (miRNAs) are small, non-coding RNAsthat play an important role in regulating the ex­

pression of numerous genes across several species [1]. Asregulatory molecules, they influence the output of manyprotein-coding genes by targeting mRNAs for cleavage ortranslational repression [2].

Although miRNAs are functionally similar to short inter­fering RNAs (siRNAs), they are unique in terms of theirbiogenesis. Most of the miRNA genes are transcribed byRNA Polymerase II. The primary transcripts of miRNAs(pri-miRNAs) are then processed into hairpin intermediates(precursor miRNAs or pre-miRNAs) by the microproces­sor complex (the enzyme Drosha and the binding proteinDGCR8/Pasha). The pre-miRNAs are then exported to the

This work was supported by FORTH-ICS and the EMBO Young Inves­tigator Program

K. Gkirtzou is with the Department of Computer Science, Univer­sity of Crete and the Institute of Computer Science (ICS), Founda­tion of Research and Technology, Hellas (FORTH), Heraklion, Greecegkirtzou@csd.uoc.gr

P. Tsakalides is with the Department of Computer Science, Univer­sity of Crete and the Institute of Computer Science (lCS), Founda­tion of Research and Technology, Hellas (FORTH), Heraklion, Greecetsakalid@csd.uoc.gr

P. Poirazi is with the Institute of Molecular Biology and Biotechnology(IMBB), Foundation of Research and Technology, Hellas (FORTH), Herak­lion, Greece poirazi@imbb. forth. gr

cytoplasm by RanGTP and Exportin-5. In the cytoplasm,the pre-miRNAs are processed by Dicer into short RNAduplexes termed miRNA duplexes. The mature miRNA fromeach miRNA duplex then binds to an Argonaute protein,forming the miRNP complex. The miRNAs base-pair withtheir mRNA targets, leading either to mRNA cleavage, ifthere is sufficient complementarity between miRNA and thetarget mRNA, or to translational repression [3].

Several computational methods have been developed andare currently used in parallel with experimental techniquesin order to facilitate the discovery of new miRNAs. Mostcomputational methods focus on the discovery of either novelmiRNA genes in the genomes of various species or possiblemRNA targets of the known miRNAs. On the contrary, fewattempts have been made to computationally predict thefunctional part of the miRNA precursor, namely the maturemiRNA. A number of studies ([4], [5], [6]) combine miRNAgene prediction with the identification of a possible startposition for the mature. To our knowledge, only one study [7]focuses exclusively on mature miRNA prediction, utilizingthermodynamic and structural information of the precursorRNA.

In this work, we introduce a computational method thatuses a Naive Bayes classifier to identify mature miRNAcandidates based on sequence and secondary structure in­formation of the miRNA precursor.

II. METHOD

A. Naive Bayes Classifier

Naive Bayes is a simple probabilistic classifier whichis based on the application of the Bayesian theorem withstrong (naive) independence assumptions. According to theBayesian classifier, a new sample x described by the featurevector x == (Xl, X2, ... ,xn ) will be assigned to the class thatminimizes the overall risk using the following formula:

e

a(x) == argminaiEA L A(ai IWj )P(Wj Ix)j=I

where:

• WI, ... , We is a finite set of classes,• A == {aI, ... ,ae } is a finite set of actions, where ai

means selecting class Wi,• A(ai IWj) is the loss associated with deciding Wi, when

the true state of nature is W j and• P(Wj Ix) is the posterior probability of Wj being the true

state of nature given x.The posterior probability P(Wj Ix) can be computed by

the Bayes' formula (see section 2.9 of [8]):

where

• P(xlwj) is the state--conditional probability for x con­ditioned on W j being the true class,

• P(Wj) is the prior probability that nature is in state Wjand

• P(x) == 2::;=1 P(xlWj )P(Wj) is the evidence for x.

The Naive Bayes classifier is based on the simplifyingassumption that the input features among samples of anygiven class are conditionally independent given the class [9].In other words, given the class of a sample, the probabilityof observing the conjuction Xl, X2, ... , Xn is just the productof the probabilities for the individual features of this sample:

n

P(Xl' X2,"" xnlcj) == II P(xiICj)'

In our case, an observation for classification (i.e. a sample)is a mature miRNA candidate and the possible classes aretwo: the Positive class, which contains true mature miRNAs(denoted WI) and the Negative class, which contains falsemature miRNAs (denoted W-l)' Suppose we have a maturemiRNA candidate with features x == (Xl, X2, ... ,xn) andwe want to classify it to the class that minimizes theclassification error. The simplest case is to consider that allerrors have the same cost, so the loss function of interest isthe zero--one loss function and the Bayes Decision Rule isconverted to the following:

Decide WI if P(wllx) > P(w-llx);otherwise decide W-l

(see section 2.3 of [8]). Since P(x) is only a normalizationfactor, it can be omitted in order to minimize calculationtime. Moreover, since there is no information about theprobabilities of each class we can assume that the priorprobability that nature is in state Wj, P(Wj), is 50% forboth positive and negative data. This assumption prevents usfrom favoring a particular class. Under these assumptions,the Bayes Decision Rule is given by the following simplifiedformula:

Decide WI if P(XIWl) > P(XIW-l);othewise decide W-l

In this work, we use the aforementioned formula to buildmultiple classifiers that are trained to discriminate betweenrandomly selected sets of positive and negative miRNAsamples as detailed below.

B. The Negative class

Given that known miRNA precursors do not producemultiple overlapping mature miRNAs from the same armof the foldback precursor [10], we generate a set of negative

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Feature Values

Fig. 1. Class conditional probabilities of a feature with combined sequenceand structural information for position 0 within mature miRNA candidatesof both the positive (black color) and the negative class (white color). Thex axis shows the possible values of the feature, where: I ---+ A match, 2---+ A mismatch, 3 ---+ C match, 4 ---+ C mismatch, 5 ---+ G match, 6 ---+ Gmismatch, 7 ---+ U match and 8 ---+ U mismatch. The y axis shows the classconditional probability for this feature.

examples in the following way: for each true mature miRNA,we use a same-size sliding window and select all possible"negative" matures which can be created by sliding 1 basepair towards either direction from the mature, excludingany hairpin loops. This procedure results in a very largenegative set, where each true mature has a variable number ofrespective "negatives", depending on the length and numberof precursors. To avoid overfitting the classifiers to thenegative data, we only use a randomly selected subset of10 negative examples for each true mature.

C. Input Features

The miRNA precursors form irregular hairpin structures,containing various mismatches, internal loops and bulges. Inour method, a mature miRNA is represented as a sequence ofpositions, where each position contains sequence information(A, C, U, G) or structural information (match or mismatch),derived from the respective precursor(s). Apart from thefeatures that lie in positions within the mature miRNA, wealso consider features that lie within a flanking region ofvariable size (0, 5, 7, 10 or 12nt) that extends symmetricallyalong both sides of the mature sample. These features arealso positions on the precursor RNA and contain sequence orstructural information just like the features located within themature miRNA. Since certain positions within the flankingregion may be located outside the precursor, we use a special'novalue' flag to indicate the lack of information at thesepositions and do not take them into account when estimatingthe Kullback-Leibler divergence between the two classes(see below).

D. Feature Selection

Classifier's Description Sensitivity Specificity MCC

Combination of 12 Features,Ont flanking region 67.10% 55.10% 0.0850

Combination of 16 Features,5nt flanking region 76.04% 53.34% 0.1074

Combination of 31 Features,7nt flanking region 75.96% 53.20% 0.1071

Combination of 19 Features,10nt flanking region 79.15% 47.01% 0.0960

Combination of 35 Features,12nt flanking region 74.30% 51.33% 0.0945

TABLE I

BAYES CLASSIAER TRAINED WITH FEATURES CONTAINING SEQUENCE

INFORMATION.

III. RESULTS

We evaluated our method using a dataset of experimentallyverified human and mouse miRNAs from miRBase (version10.0, [13], [14], [15]). The human dataset consists of 533precursors and 722 mature miRNAs, while the mouse datasetconsists of 442 precursors and 579 mature miRNAs. Weused 500 of the human and 347 of the mouse precursors-which generate 692 and 440 mature miRNAs, respectively­to train and validate our classifiers utilizing a leave-la-outcross validation procedure.

For each of the mature miRNAs in the training set, anegative set was generated as described in section II-B. Atotal of 15 classifiers were trained with different featuresets and the classification performance was assessed usingconsensus averaging over aID-fold cross validation. Toensure a realistic estimation of classification accuracy, thevalidation sets consisted of true miRNA precursors, whosemature miRNAs were left out from the training sets inthe cross validation procedure. Classification accuracy wasestimated using a fixed 22nt size sliding window. All possiblematures which could be created by sliding 1 base pair in bothstem arms of the precursor, apart from the hairpin loop(s),were assigned to either class based on the averaged outcomeof the 15 trained classifiers. It is important to note thatclassification accuracy was estimated based on exact matchof the starting position of the predicted compared to thereal mature miRNA. Even Int deviations were consideredas negative examples.

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Feature Values

. P(i)DKL(PIIQ) =L P (z)log2 Q(i)

1,

Unfortunately, the KL divergence is not a true metric sinceit is not symmetric. To overcome this problem we usedthe symmetric and nonnegative Kullback-Leibler divergence[12], which is defined as:

12(DKL (PIIQ) + DKL(QIIP))

Fig. 2. Class conditional probabilities of a feature with combined sequenceand structural information for position 3 within mature miRNA candidatesof both the positive (black color) and the negative class (white color). Thex axis shows the possible values of the feature, where: 1 ---+ A match, 2---+ A mismatch, 3 ---+ C match, 4 ---+ C mismatch, 5 ---+ G match, 6 ---+ Gmismatch, 7 ---+ U match and 8 ---+ U mismatch, while the y axis shows theclass conditional probability for this feature.

The aforementioned location-specific information is usedto select a set of features, namely those positions on theprecursor that contain discriminatory information betweentrue matures and negative samples. The discriminatory powerof each position is estimated using the symmetric Kullback­Leibler divergence between the distributions of positive andnegative data.

The Kullback-Leibler divergence (K-L divergence) is ameasure of the difference between two probability distribu­tions [11]. For Probability Mass Functions (PMFs) P and Qof a discrete random variable, the K-L divergence of Q fromP is defined as:

and is commonly used in classification problems. Figures1 and 2 show the class conditional probability distributionsfor two features with combined sequence and structureinformation for positions a and 3 within the mature miRNAcandidates of the negative and positive data used to trainour classifiers (see below). These features have the highestKullback-Leibler divergence among all features that liewithin the mature miRNA. As evident from both figures thetwo class distributions are very similar making discriminationa very challenging task.

Tables I, II and III show the top scoring classifiers, basedon Matthews Correlation Coefficient (MCC), for differentinput features. We use three types of classifiers, each utilizinglocation-specific information about the sequence (Table I),the structure (Table II), or both the sequence and structure(Table III) of the training examples. Each table shows thesensitivity, specificity and Matthews Correlation Coefficient(MCC) [16] achieved with different numbers of such featuresand with different sizes of flanking regions around the

TABLE II

BAYES CLASSIFIER TRAINED WITH FEATURES CONTAINING STRUCTURE

INFORMATION.

Classifier's Description Sensitivity Specificity MCC

Combination of 10 Features,Ont flanking region 65.70% 54.30% 0.0730

Combination of 26 Features,5nt flanking region 76.34% 52.64% 0.1056

Combination of 23 Features,7nt flanking region 77.85% 54.29% 0.1186

Combination of 39 Features,1Dnt flanking region 81.01 % 56.63% 0.1373

Combination of 38 Features.12nt flanking region 79.89% 55.51% 0.1300

mature miRNA. The pOSItions along the precursor whichserved as input features were selected based on the K-Ldivergence metric and they were located either within themature miRNA, or inside a flanking region around it.

We found that as the size of the flanking region increased,the sensitivity of the classifiers tended to improve, while thespecificity remained relatively unaffected, independently ofthe type of features used. This improvement seemed to reacha maximum for a flanking region of about 10nt. For classi­fiers with flanking regions of 12nt utilizing either sequence orstructure information (Tables I and II respectively), the extrafeatures did not further improve the accuracy, suggesting thatthey probably add more noise than useful information.

TABLE III

BAYES CLASSIFIER TRAINED WITH FEATURES CONTAINING BOTH

SEQUENCE AND STRUCTURE INFORMATION.

Classifier's Description Sensitivity Specificity MCC

Combination of 20 Features,Ont flanking region 68.50% 62.50% 0.1250

Combination of 29 Features,5nt flanking region 71.32% 65.34% 0.1394

Combination of 36 Features,7nt flanking region 74.26% 66.46% 0.1562

Combination of 42 Features,10nt flanking region 76.50% 65.61% 0.1606

Combination of 39 Features,12nt flanking region 77.81% 64.14% 0.1590

Moreover, the classifiers utilizing features with combinedinformation for both sequence and structure achieved anoverall better performance -in terms of improved specificityand MCC- than the ones using sequence or structure infor-

mation alone. Note that a high specificity score is particularlyimportant in this task, since the number of negative examplesis much larger than the number of positive ones. Finally, allclassifiers achieved a much higher sensitivity than specificityscore, most likely because of the very high similarity betweennegative and positive examples as well as the requirementfor exact start position match between true and predictedmiRNAs.

IV. CONCLUSIONS

In this work, we presented a computational approachthat identifies mature miRNAs based on the secondarystructure and sequence characteristics of the precursor. Weused experimentally verified miRNAs to train and evaluatethe performance of a Naive Bayes classifier in terms ofsensitivity and specificity.

Unlike the method presented here, most of the computa­tional tools that can be used to predict the functional part ofthe miRNA precursor estimate their performance accuracyin terms of true positive rate alone (sensitivity), ignoring thefalse positive rate ([4], [6], [7]). It is a matter of semantics aswell as a great challenge to define a true negative examplewhen it comes to mature miRNAs. However, a major issuein such a classification task is not only to maximize theidentification of true positives but also to minimize the falsepositive rate. Our method tries to combine both of thesecriteria. Since minimizing the false positive rate is verydifficult, we plan to develop and incorporate a number offiltering criteria that will help eliminate some of the falsepositive examples. Moreover, we plan to use our methodto predict the mature miRNAs on six novel miRNA geneswhich were recently identified by Hidden Markov Modelsin our lab and were experimentally shown to produce shortRNAs [17]. This combined computational and experimentalapproach will result in a more realistic evaluation of ourmethod's performance accuracy.

In conclusion, our findings suggest that position specificsequence and structure information combined with a simpleBayes classifier achieve a good performance on the challeng­ing task of mature miRNA identification.

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