- Open Access
MiRenSVM: towards better prediction of microRNA precursors using an ensemble SVM classifier with multi-loop features
© Zhou et al; licensee BioMed Central Ltd. 2010
- Published: 14 December 2010
MicroRNAs (simply miRNAs) are derived from larger hairpin RNA precursors and play essential regular roles in both animals and plants. A number of computational methods for miRNA genes finding have been proposed in the past decade, yet the problem is far from being tackled, especially when considering the imbalance issue of known miRNAs and unidentified miRNAs, and the pre-miRNAs with multi-loops or higher minimum free energy (MFE). This paper presents a new computational approach, miRenSVM, for finding miRNA genes. Aiming at better prediction performance, an ensemble support vector machine (SVM) classifier is established to deal with the imbalance issue, and multi-loop features are included for identifying those pre-miRNAs with multi-loops.
We collected a representative dataset, which contains 697 real miRNA precursors identified by experimental procedure and other computational methods, and 5428 pseudo ones from several datasets. Experiments showed that our miRenSVM achieved a 96.5% specificity and a 93.05% sensitivity on the dataset. Compared with the state-of-the-art approaches, miRenSVM obtained better prediction results. We also applied our method to predict 14 Homo sapiens pre-miRNAs and 13 Anopheles gambiae pre-miRNAs that first appeared in miRBase13.0, MiRenSVM got a 100% prediction rate. Furthermore, performance evaluation was conducted over 27 additional species in miRBase13.0, and 92.84% (4863/5238) animal pre-miRNAs were correctly identified by miRenSVM.
MiRenSVM is an ensemble support vector machine (SVM) classification system for better detecting miRNA genes, especially those with multi-loop secondary structure.
- Support Vector Machine
- Majority Vote
- Support Vector Machine Classifier
- Feature Subset
- miRNA Gene
MicroRNAs (miRNAs)  are single-stranded, endogenous ~22nt small non-coding RNAs (sncRNA) that can play important regular roles in animals and plants by targeting mRNA for cleavage or post-translation repression . Mature miRNAs are derived from longer precursors (pre-miRNAs), each of which can fold into a hairpin structure that contains one or two mature miRNAs in either or both its arms. Accordingly, miRNA biogenesis is highly regulated, controlled at both transcriptional and post-transcriptional levels , and overexpression and underexpression of miRNAs are linked to various human diseases, particularly cancers [4, 5].
MiRNAs are always located in the introns of protein-coding genes , introns and exons of non-coding genes . In mammalian genomes, it is also possible to find miRNAs in repetitive regions, and some studies suggest that transposable elements may be involved in the creation of new miRNAs . MiRNA biogenesis in animals contains two steps . In the first step, the primary miRNA (pri-miRNA), which is several hundred nucleotides long, is processed in the nucleus by a multiprotein complex containing an enzyme called Drosha to produce the ~70nt long miRNA stem-loop precursor (pre-miRNA), which is then exported to the cytoplasm. In the cytoplasm, the second step takes place where the pre-miRNA matures into a ~22nt long miRNA:miRNA* duplex, with each strand originating from opposite arms of the stem-loop . Then, the miRNA strand of the miRNA:miRNA* duplex is loaded into a ribonucleoprotein complex known as the miRNA-induced silencing complex (miRISC). Until recently, the miRNA* was thought to be peeled away and degraded. However, some studies indicate that miRNA* is also sorted into Argonauts and might have a regular function in Drosophila melanogaster [10, 11].
Identification of miRNA genes is an eminent and challenging problem towards the understanding of post-transcriptional gene regulation. The short length of miRNAs and their ability to act redundantly or to have only a subtle phonotypical impact impose a limitation to the use of mutagenesis and other conventional genetics techniques . Direct cloning is the initial choice, but only abundant miRNA genes can be easily detected. Since not all miRNAs are well expressed in many tissues, miRNAs that have very low expression levels or that are expressed tissue-specifically possibly can not be detected, and recently research suggests that lowly expressed Human miRNA genes evolve rapidly . This situation is partially mitigated by the deep-sequencing techniques that nevertheless require extensive computational analysis to distinguish miRNAs from other small non-coding RNAs of the same size . Therefore, computational approaches are essential for miRNA gene finding in sequenced genomes.
In these years, large-scale computational approaches have been developed, such as filter-based approaches [6, 15], homology-based research [16, 17], mixed approaches [14, 18], and machine learning methods. Filter-based approaches (e.g. MirScan, mirSeeker), focusing on identifying high-quality sets of conserved miRNA candidates, are able to recover a substantial part of the known miRNAs. However, they are critically dependent on conservation criteria to obtain reasonable specificity. Homology-based approaches (e.g. ERPIN, MiRAlign) rely exclusively either on sequence conservation or structure conservation so that lineage- or species-specific miRNA genes may escape the detection. In fact, many miRNA gene prediction approaches incorporate a homology search as part of their protocols, in addition to the ordinary search for orthologous. Mixed approaches (e.g. PalGrade, miRDeep) combine experimental with computational procedures in order to identify a wider range of miRNAs. As mentioned above, experimental approaches cannot easily detect low-expression or tissue-specific miRNAs.
The most popular computational miRNA gene finding methods are machine learning based approaches. Most of them share the same overall strategy but use different approaches to identify good stem-loop candidates, since they all try to generalize a positive set of already known miRNAs and a negative set of stem-loops that are not pre-miRNAs . Several machine learning methods have been proposed to tackle the problem of identifying miRNA genes. SVM is a popular framework used to learn the distinctive characteristics of miRNAs. There are other machine learning methods that employ techniques such as HMM (Hidden Markov Model) [20, 21], Random Forests , Naïve Bayes classifier , and Random walk algorithm  etc. Most approaches use sets of features including sequence conservation [25–27], topological properties [26, 28], thermodynamic stability [26, 27], and some other properties like entropy measures .
However, there are two major drawbacks with the existing machine learning based miRNAs identification approaches. One drawback is raised by the imbalance of positive and negative examples used. Since the real number of miRNAs in any given genome is still an open problem, it is assumed that there is a very few miRNA precursors in any randomly chosen stem-loop extracted from the genome. Positive examples are usually selected from miRNAs identified by experimental procedures or other computational methods. And the number of positive examples we can obtain is substantially smaller than that of negative examples. The imbalance issue between positive and negative examples can greatly degrade the performance of current machine learning approaches. Certainly, with a growing number of miRNAs being identified, we can expect an increasingly better performance from these methods. The other drawback lies in the fact that most existing machine learning based methods [23–25] make a few structural assumptions concerning stem length, loop size and number, as well as minimum free energy (MFE). Therefore, sequences with multi-branched loops secondary structure or MFE higher than -16 kal/mol possibly can not be predicted by those methods, which subsequently degrade the prediction performance. We have investigated Homo sapiens miRNAs in miRBase , and found that there are an increasing number of pre-miRNAs, which do not satisfy the above-mentioned assumptions (see Table S1 and S2 in the Additional file 1 for detail).
In this paper, we still treat the miRNA gene finding problem as a classification problem, and develop a powerful classification system, named miRenSVM, to overcome the two drawbacks mentioned above. On one hand, miRenSVM uses ensemble learning to deal with the imbalance issue; On the other hand, in addition to the features exploited by the existing methods, miRenSVM further includes the multi-loop features in its classifiers, and F-score is used to select final classification features. As a result, miRenSVM can achieve better performance than the existing methods.
In summary, miRenSVM distinct itself from the existing methods at least in three aspects: (1) Lower expression and tissue-specific miRNAs can be easily identified since different types of features are use. (2) Due to using ensemble SVM classifiers, both positive and negative examples can be exploited as many as possible. (3) No structural assumption for miRNA candidates is made. Particularly, multi-loop features are considered.
Results of different features sets
We used 65 local and global features that are subsumed into three groups, which capture miRNA's sequence, secondary structure and thermodynamic properties respectively. In this section, we used single SVM classifier to check how different feature sets impact classification performance.
Classification results obtained by outer 3-fold cross validation with different feature groups and feature selection
Selected by F-score
Second, all the 65 features were used to train a single SVM classifier with the whole training dataset, and the performance was also evaluated by the outer 3-fold cross validation method. The results are SE (87.50%) and Gm (92.99%), which are a little better than the best results of using any individual features group. This indicates that the combination of different kinds of features can improve classification performance. The next step is to improve the prediction speed without degrading the accuracy rate. We thus considered feature selection method to select the intrinsic ones from all the 65 features. Feature selection is often applied to high dimensional data prior to classification learning. This procedure can reduce not only the cost of recognition by reducing the number of features that need to be collected, but in some cases it can also provide a better classification accuracy due to the finite sample size effect . Here, we used F-score to select the best feature subset for our miRenSVM. This procedure is implemented by the libsvm's feature selection tool. We evaluated the effectiveness of the feature subset selected by F-score method by training a single SVM classifier on the entire training set, and studying the sensitivity and the number of correctly predicted miRNAs. All the results of these experiments are summarized in Table 1. As shown in Table 1, after feature selection, the classification performance becomes better.
32 features selected by F-score
A(((, A…, U(((, U(.(, U…, G(((, C(((, C(.(
dP, dP/n_loops, Avg_bp_stem, diversity, |A-U|/L,|G-C|/L, %(A-U)/n_loops, %(G-C)/n_loops
NEFE, MFEI 1 , MFEI 2 , MFEI 3 , MFEI 4 , dG, Diff, Freq, Tm, dH/L, dS/L, Tm/L, p-value_MFE, p-value_EFE, z-score_MFE, z-score_EFE
Results of SVM ensembles
Results of classifier ensembles with different aggregation methods
As shown in Table 3, both majority vote and mean distance get a better performance than using a single SVM classifier developed with the 32 selected features (Gm =93.16%). Compared with mean distance method, majority vote always archives higher sensitivity (SE), but its specificity (SP) is much lower, which impacts its overall accuracy (Acc). If this type of classifier is used for real-life prediction, due to its lower specificity, the chance of incorrectly predicting random sequences with stem-loop like secondary structure would be quite high. Therefore, we choose the best classifier developed under the mean distance method as the final miRenSVM classifier. The mean distance method obtains the best classification results on our dataset, that is, the highest Gm (94.76%) with SE=93.05% and SP=96.5%, and an acceptable Acc (96.1%). There is another reason to choose mean distance, that is efficiency. The ensemble SVM classifier predicts each test sample only one time while each test sample has to be predicted k times under majority vote.
We then validated our miRenSVM on the testing dataset. This set contains 14 Homo sapiens and 13 Anopheles gambiae miRNA precursor sequences newly published in miRBase13.0. The result shows that miRenSVM obtains 100% accuracy. Particularly, 4 sequences (MI0009983, MI0009988, MI0010486, and MI0010488) in the testing set whose MFE is higher than -13.70 kal/mol are all predicted correctly by our miRenSVM. In order to further demonstarte the advantage of the miRenSVM approach, we tested our miRenSVM on the miRBase13.0 and achieved a high sensitivity. MiRBase13.0 contains 27 animal genomes, including 5238 miRNA precursor sequences (not including hsa and aga pre-miRNAs). MiRenSVM correctly classified 92.84% (4863/5238) pre-miRNAs.
Results of comparison with existing methods
triplet-SVM was proposed by Xue et al. to recognize pre-miRNAs based on the triplet element structure-sequence features. The method is trained on known human pre-miRNAs and obtains a high accuracy (~90%) when applied to several other species. Unlike miRenSVM, triplet-SVM uses only structure-sequence information, and therefore can predict miRNAs quickly. However, this method is not designed to detect miRNAs with multi-loop secondary structure or miRNAs with high MFE. triplet-SVM predicts only 518 (235 real and 283 pseudo) sequences. Although it has an acceptable sensitivity (84.68%), its specificity (77.74%) is not comparable to ours (96.5%).
BayesMiRNAfind was developed by Yousef et al., which uses a different machine learning method, naïve Bayes classifier, to predict miRNAs conserved between human and mouse. Yousef et al. applied their method to the forward strand of the mouse genome sequence and present results for different score cut offs. BayesMiRNAfind is trained with cross-species dataset, which contains 13 different organisms. Results show that our miRenSVM detects more already known pre-miRNAs than BayesMiRNAfind : of the total 250 real pre-miRNAs, BayesMiRNAfind correctly predicts 220, while miRenSVM correctly predicts 233. Most of the negative training samples (~92%) used in our miRenSVM are also used to train BayesMiRNAfind. BayesMiRNAfind detects 1695 out of 1810 sequences in 3'-UTRdb and Rfam, while miRenSVM finds 1746 of the same 1810 sequences, thus miRenSVM achieves a much higher specificity.
MicroPred is an SVM-based method designed recently by Rukshan and Vasile to detect human miRNA gene . Like miRenSVM, microPred uses 29 different features for SVM classification, and employs SMOTE to deal with the class imbalance problem. Although the features used in microPred is a little different from that in miRenSVM, they also cover the sequence, structure and thermodynamics aspects of miRNA precursors. Also trying to improve performance with an imbalance learning method, microPred achieves a sensitivity of little higher than our method: out of the 250 known miRNAs in miRbase12.0, microPred detects 236 and we detect 233. However, microPred predicts 516% more miRNA candidates than miRenSVM (394 compared to 64). Thus, miRenSVM has a much higher specificity than microPred, although microPred specificity is estimated high. The better performance of miRenSVM is possibly due to the features used in the classification system. Considering that a large number of pseudo stem-loop sequences have secondary structure with multi-loops, microPred uses only one multi-loop relevant feature (MFEI 4 ), while miRenSVM uses four (MFEI 4 , dP/n_loops, %(A-U)/n_loops, %(G-C)/n_loops).
The miRenSVM was first trained on Homo sapiens and Anopheles gambiae genomes, and got 93.05% sensitivity, 96.5% specificity and 96.1% accuracy via outer 3-fold cross validation method. We then applied it to detect new miRNAs of hsa and aga genome in miRBase13.0. All 27 new pre-miRNAs were correctly detected. To further demonstrate the advantage of our approach, we tested miRenSVM on 27 additional animal genomes registered in miRBase13. Out of the 5238 animal pre-miRNAs across the 27 other species, miRenSVM correctly identified 4863, i.e, the recognition rate is 92.84%. The approach outperformed another recently published method  in detecting miRNA precursors with multi-branched loops, and obtained higher and more reliable results than the existing methods [23, 25, 32], while there is a little overlap among sets of miRNA candidates predicted by the different methods.
Since the number of possible candidate hairpins within the whole genome is very large and the number of real pre-miRNA is still small for some species, current specificity is still not satisfactory for multi-genomes applications and some false positive predictions can be produced. Finding more information to reduce the false positive rate should be further investigated. However, latest reports suggested that some human miRNA precursors have Box H/ACA snoRNA features . It might be necessary for us to reconsider those previously regarded as false-positive predictions, since our dataset contains a certain amount of hsa and aga snoRNAs.
In this study, we presented miRenSVM, a SVM-based computational approach that detects real miRNA precursors from pseudo ones with their intrinsic features. MiRenSVM uses both global and local intrinsic features of known miRNAs as its input. Several machine learning technologies including feature selection, imbalance learning and multi-classification were applied. Our approach is more general than the existing methods, since it is not sensitive to pre-miRNA's structure and thermodynamic characteristics. And it can achieve better prediction performance than the existing methods.
692 hsa and 52 aga pre-miRNA sequences in miRBase12.0 were chosen to serve as the positive set.
9225 hsa and 92 aga 3’UTR sequences in 3’-UTRdb (release 22.0) whose length ranges from 70nt and 150nt were chosen to form one part of the negative set.
For hsa, an ncRNA dataset was already collected by Griffiths-Jones  that was used in  lately, but none sncRNA dataset of aga is available now. We selected all 256 aga ncRNA sequences in Rfam9.1, in which 68 sequences that were redundant or longer than 150nt were removed. These sequences form another part of the negative set, which are listed in the additional file 2.
14 hsa and 14 aga new hairpin sequences in miRBase13.0 were used to evaluate our miRenSVM system.
In this step, sequences with the similarity score higher than 0.90 were removed by CD-HIT program  from the training set and testing set respectively. The 27 selected testing sequences were summarized in Table S3 of the supplementary file.
21 sequences from 3’-UTRdb, whose second structure could not be predicted by RNAfold or UNAfold were removed. Finally, we constructed a training set with 697 true pre-miRNA sequences, 5428 pseudo pre-miRNA sequences, and a testing set with 27 bran-new real pre-miRNA sequences. After predicting the secondary structure, nearly 84% of the 5428 pseudo miRNA precursors have the secondary structure with multi-loop.
27 animal genomes (not including has and aga) in miRBase13.0 contain 5238 pre-miRNA sequences. We collected these sequences and used them to further evaluate the proposed approach miRenSVM.
The extraction of an appropriate set of features with which a classifier is trained is one of the most challenging issues in machine learning-based classifier development. In our study, both hairpin secondary structure and multi-loop structure features were considered. Concretely, we characterized a miRNA precursor by 65 local and global features that capture its sequence, secondary structure and thermodynamic properties. These features were subsumed into three groups as follows.
32 triplet elements
Sequence and structure properties are characterized by triplet structure-sequence elements proposed in . In the predicted secondary structure, there are only two states for each nucleotide, paired or unpaired, indicated by brackets (‘(’ or‘)’) and dots (‘.’), respectively. We do not distinguish these two situations in this work and use ‘(’ for both situations, and GU wobble pair is allowed here. For any 3 adjacent nucleotides, there are 8 possible structure compositions: ‘(((’, ‘((.’, ‘(..’, ‘(.(’, ‘.((’, ‘.(.’, ‘..(’ and ‘…’. Considering the middle nucleotide among the 3, there are 32 (8*4) possible structure-sequence combinations, which are denoted as “U(((”, “A((.”, etc.
15 base pair features
Some secondary structure relevant features are already introduced by existing pre-miRNA classification methods [27, 32]. In this paper, we included 11 secondary structure features (G/C ratio, %C+G, dP, Avg_BP_Stem, Diversity, |A-U|/L, |G-C|/L, |G-U|/L, (A-U)/n_stems, (G-C)/n_stems, (G-U)/n_stems) in our miRenSVM. Furthermore, for identifying real miRNA precursors with multi- loop, we used four new features related to the loop number in the predicted secondary structure. They are:
♦ dP/n_loops, where n_loops is the number of loops in secondary structure.
♦ %(A-U)/n_loops, %(G-C)/n_loops, %(G-U)/n_loops, where %(X-Y) is the ratio of X-Y base pairs in the secondary structure.
These features were extracted using the RNAfold program contained in Vienna RNA package (1.8.3)  with default parameters.
18 thermodynamic features
It has been proved that using only secondary structure is not enough to effectively predict miRNA . Since miRNA precursors usually have lower MFE than other small ncRNAs and random short sequences, thus MFE related features were introduced, such as (dG, MFEI 1 , MFEI 2 , MFEI 3 , MFEI 4 , Freq). Other 8 global thermodynamics features (NEFE, Diff, dH, dS, Tm, dH/L, dS/L, Tm/L), and 4 statistically significant features (p-value_MFE, p-value_EFE, z-score_MFE, z-score_EFE) were chosen from previous research [23, 24, 36]. When evaluating those statistically significant features related with MFE and ensemble free energy (EFE), for each original sequence, 300 random sequences were generated by Sean Eddy's squid program . dH, dS, Tm, dH/L, dS/L, Tm/L were calculated by UNAfold 3.7. More detail of all the 65 features are provided in additional file 1.
where are the average values of the i th features of the whole, positive, and negative data sets, respectively; is the i th feature of the k th positive instance, and is the i th feature of the k th negative instance. Larger F-scores indicate better discrimination . All the 65 local and global candidate features were ranked by F-score in order to determine which features will be used in the final model.
The miRenSVM approach
Support vector machine
The internal of miRenSVM is Support Vector Machine, a supervised classification technique derived from the statistical learning theory of structural risk minimization principle . A support vector machine constructs a hyperplane or set of hyperplanes in a high-dimensional space, which can be used for classification, regression or other tasks. SVM has been adopted extensively as an effective discriminative machine learning tool to address the miRNA prediction problem [25, 27, 43]. The model selection for SVMs involves the selection of a kernel function and its parameters that yield the optimal classification performance for a given dataset . In our study, we used radial basic function (RBF) due to its higher reliability in finding optimal classification solutions in most situations. The SVM algorithm was implemented by C++ interface libsvm (version 2.89) package , and the training process of miRenSVM follows the guidelines described in .
SVM classifiers ensemble
One major factor that will influence the performance of a machine learning system is class imbalance, that is, the examples of some classes heavily outnumber the examples of the other classes . Training a classifier system with an imbalance dataset will result in poor classification performance, especially for the rare classes . And a classifier should have good and balanced performance over all classes for it to be useful in real-world applications.
For miRNA gene detection, the imbalance issue was widely recognized . Existing machine learning based methods either employ random under-sampling to choose a portion of representative examples or just ignore it. It has already shown that both random over-sampling and random under-sampling have some drawbacks. The former does not add any information in addition to incurring large amount of compitation cost, and the later actually misses information and thus leads to poor performance. There remains a challenge: for a given dataset, how to select an appropriate sampling proportion?
In this work, the training dataset contains 697 positive (real pre-miRNA) samples and 5428 negative (pseudo pre-miRNA) samples, the ratio of negative to positive is 7.79:1. To address the drawbacks of over-sampling and under-sampling, we employed a SVM ensemble scheme. We tried to generate training sets with a desired distribution such that neither removing any training sample nor increasing the training time. An ensemble SVM classifier has several advantages over the ordinary classifiers. First, an ensemble SVM classifier exploits the information of the entire dataset, while random under-sampling uses only part of the dataset; On the other hand, it consumes less computation compared to random over-sampling. Second, an ensemble SVM classifier is able to overcome some drawbacks of a single classifier. With multi sub SVM classifiers, miRenSVM is more robust and expected to learn the exact parameters for a global optimum .
Performance evaluation method and metrics
Outer 3-fold cross validation
We used the libsvm 2.89 package to establish the miRenSVM classification system. Here, the complete training dataset is randomly divided into three equally sized partitions, while each partition has the same ratio of positive samples to negative samples. Then, any two partitions are merged together as the training dataset to train an SVM classifier. Following that, the resulting model is tested over the third data partition. This procedure is repeated three times with different combinations of training (two partitions) and testing (the remaining partition) datasets in an outer 3-fold cross validation style, and the classification result is gotten by averaging the results of the three tests above.
where TP, FP, TN and FN are the numbers of true positive predictions, false positive predictions, true negative predictions and false negative predictions, respectively.
The authors appreciate Prof. Malik Yousef and Mr. Manohara Rukshan Batuwita for predicting the test sequences.
This article has been published as part of BMC Bioinformatics Volume 11 Supplement 11, 2010: Proceedings of the 21st International Conference on Genome Informatics (GIW2010). The full contents of the supplement are available online at http://www.biomedcentral.com/1471-2105/11?issue=S11.
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