MicroRNAs (miRNAs) are short non-coding RNAs that regulate gene expression and biological processes through binding to messenger RNAs. Predicting the relationship between miRNAs and their targets is crucial for research and clinical applications. Many tools have been developed to predict miRNA–target interactions, but variable results among the different prediction tools have caused confusion for users. To solve this problem, we developed miRgo, an application that integrates many of these tools. To train the prediction model, extreme values and median values from four different data combinations, which were obtained via an energy distribution function, were used to find the most representative dataset. Support vector machines were used to integrate 11 prediction tools, and numerous feature types used in these tools were classified into six categories—binding energy, scoring function, evolution evidence, binding type, sequence property, and structure—to simplify feature selection. In addition, a novel evaluation indicator, the Chu-Hsieh-Liang (CHL) index, was developed to improve the prediction power in positive data for feature selection. miRgo achieved better results than all other prediction tools in evaluation by an independent testing set and by its subset of functionally important genes. The tool is available at http://predictor.nchu.edu.tw/miRgo.
MicroRNAs (miRNAs) are short non-coding RNAs (~21 nucleotides) that have important roles in cell biology. miRNAs are involved in the control of a variety of physiological processes including development, cell proliferation, apoptosis, tissue differentiation and metabolism, by binding to and then silencing translation of target mRNAs1,2,3,4. The function of miRNAs in regulation of gene expression was first described by researchers studying C. elegans; they found that miRNA lin-4 was able to suppress the expression of the lin-14 target gene5. In animals, the mechanism by which miRNAs silence gene expression can be described in three steps. In the first step, a hairpin-shaped transcript of the DNA encoding the miRNA, referred to as the primary miRNA, is trimmed by Drosha and Pasha into a loop-shaped structure ~70 nucleotides in length, resulting in the pre-miRNA. The pre-miRNA is then transported into the cytoplasm by exportin-5 and then is processed by Dicer to cleave the hairpin structure into two single strands. One of the strands becomes the mature miRNA and then binds with Argonaute protein to form an RNA-induced silencing complex, which blocks mRNA translation or induces mRNA degradation6.
To understand the function of a miRNA, one must first determine its target genes and binding sites, but this task can be challenging because miRNA–mRNA binding is often incomplete in vivo7, and the mechanism by which miRNAs are targeted to specific genes is mostly unknown. Most miRNAs bind to the 3′-untranslated region (UTR) of the target mRNA at a 2- to 8-nucleotide region near the 5′ end, the “seed region”8. Because there are often mismatches, gaps, and G:C wobble outside the seed region, it may be possible to deduce the binding site by identifying the seed region based on these features. However, G:C wobble often occurs in the seed region, so this approach is unreliable9. To study the miRNA–mRNA relationship, numerous laboratory methods, such as western blotting, luciferase reporter assay, green fluorescent protein (GFP) reporter assay, reverse transcription polymerase chain reaction (RT-PCR), pulsed stable isotope labeling by amino acids in cell culture (pulsed SILAC or pSILAC), microarray analysis, branched DNA probe assay, and northern blotting, have been used by researchers, but these laboratory methods often require considerable resources10. To save time and resources, many researchers have developed tools that predict miRNA–mRNA binding sites and the gene regulatory effect of miRNAs11.
Previously published miRNA target site prediction tools can be classified into five categories: sequence-based tools such as TargetScan12, miRanda13, PITA14, and PACCMIT-CDS15; energy-based tools such as PicTar16, RNAhybrid17, RNAduplex18, and microT-CDS19; machine learning–based tools such as MBSTAR20, MiRTDL21, TarPmiR22, and miRDB23; statistics-based tools such as RNA2224; and database-based tools such as StarMirDB25. The results generated by different tools are not always consistent. In other research fields that have problems in result variability among different tools, researchers often combine multiple tools into integrated systems. This approach has proven successful for predicting protein interactions26, protein subcellular location27, miRNA in transcripts28, and protein stability changes29. In this study, we have integrated existing tools to develop a novel prediction system, miRgo, which is free for use by researchers worldwide. This system integrates 11 prediction tools and was trained using miRTarBase30, which is a curated database of miRNA–mRNA interactions with 360,000 laboratory data entries obtained from western blotting, luciferase reporter assays, microarray analyses, and next-generation sequencing.
miRgo was developed with the support vector machine (SVM) algorithm31 and the minimum redundancy–maximum relevance (mRMR) feature selection method32. To reduce the size and dimension of the training set, three smaller datasets were obtained by filtering according to extreme values and middle values of binding energy, which was calculated with an energy distribution function. Another dataset, which was randomly selected from the database, was included for comparison. To reduce the number of features, the prediction results from different prediction tools were classified into six categories—binding energy, scoring function, evolution evidence, binding type, sequence property, and structure—to simplify the feature selection process. miRgo shows superior accuracy compared with other tools in 10-fold cross-validation33 of the top 30% of features of the training set. For testing with an independent testing set, because of the limited performance of Matthews Correlation Coefficient (MCC), Accuracy (Acc), and the F1 scores in interpretation of test results, an evaluation function, the CHL index, was designed and defined as the normalized harmonic mean of MCC, Acc, and the F1 score. This index prevents the accuracy paradox34 problem and emphasizes prediction of positive data. When evaluated by the CHL index and the F1 score, miRgo performed better than all other tools in the independent testing set and in its subsets of functionally important genes. A website tool based on miRgo was built, and prediction data generated using miRgo are reported for future use by researchers.
Material and Methods
Data collection and positive and negative set construction
There were 2,588 human mature miRNA sequences in miRBase35 version V21. We acquired 322,352 records describing the relationship between the 2,588 human miRNAs and 14,886 targets from miRTarBase release 7.030. To reduce the amount and dimension of the data, the CD-HIT-EST36 clustering tool from the CD-HIT toolkit was used under a sequence identity threshold of 0.8. After removal of sequence redundancy, 292,686 records related to the 2,588 miRNAs were obtained and defined as the total positive dataset. The total negative dataset was generated by the permutation method described by Zhang et al.37.
To train the models, four training sets—trA, trB, trC, and trR—were used. The first three training sets (trA, trB, and trC) were selected from the aforementioned records using the binding energy distribution function (Fig. 1) obtained by RNAhybrid17 and RNAduplex18. The proportion of positive and negative data in the training sets was adjusted by a positive/negative (P/N) ratio analysis from the energy distribution function. The trA dataset, with 5,176 total records, consisted of the positive subset trA_P, which contained 2,588 records from the most stable miRNA–target pairs to the 2,588 selected miRNAs, and the negative subset trA_N, which contained 2,588 records from the most unstable pairs. The trB dataset, with 10,352 total records, consisted of 5,176 records from the most stable and second-most stable pairs (the trB_P subset) and 5,176 records from the most unstable pairs and second-most unstable pairs (the trB_N subset). The trC dataset, with 10,352 total records, consisted of pairs with extreme and mid-range binding energy. The last training set, trR, consisted of 10,532 randomly selected records. To test the models, 1,877 data records related to 38 miRNAs and 1,258 genes retrieved from MiRTDL21 (originally from TarBase v7.038) were used. After removal of data that was duplicated in the training sets, the testing data included 1,248 positive records and 241 negative reports. In addition, the genes with the Gene Ontology39 annotation in the aforementioned testing set were selected for evaluation of the accuracy of the models.
Since 2005, a number of computational tools for predicting miRNA–target interactions have been published (Table 1). To build the miRgo prediction system, a meta-predictor was developed via integration by SVM of 11 of the 14 prediction tools: RNA22, RNAhybrid, TargetScan, PITA, miRanda, RNAduplex, microT-CDS, StarMirDB, PACCMIT-CDS, MBSTAR, and TarPmiR. PicTar was excluded from integration because of its outdated database, miRDB was excluded because of the lack of information it provided, and information from MiRTDL was not integrated because it was used as a testing set for miRgo.
Feature extraction and encoding
To integrate the results of the prediction tools, the feature encoding system must first be integrated. There are differences in feature encoding among the results of the prediction tools. The results of some prediction tools are encoded as 1 and 0 to represent a binding pair and a non-binding pair, respectively. The prediction results of miRanda13 are encoded into four categories: good mirSVR13 score, conserved miRNA (miRanda_S_C); good mirSVR score, non-conserved miRNA (miRanda_S_0); non-good mirSVR score, conserved miRNA (miRanda_0_C); and non-good mirSVR score, non-conserved miRNA (miRanda_0_0). The prediction results of STarMirDB25 are encoded into six categories: 3′ UTR-seed sites (STMDB_3US), 3′ UTR-seedless sites (STMDB_3ULS), CDS-seed sites (STMDB_CS), CDS-seedless sites (STMDB_CLS), 5′ UTR-seed sites (STMDB_5US), and 5′ UTR-seedless sites (STMDB_5ULS). To develop miRgo, 32 feature types from the various tool results were selected for encoding and integrated into six categories: energy, scoring function, evolution evidence, binding type, sequence property, and structure. The feature types selected are listed in Table 2 and are explained below. All feature encoding systems included are listed in Supplementary Table S1.
The seed type of the miRNA–gene binding based on the results of the prediction tools STarMirDB25, PITA14, MBSTAR20, and TargetScan12 was taken as an encoded feature. For encoding of the seven canonical seed types used in these tools, a seven-dimension vector was constructed. If a particular seed type was present in a miRNA–target pair, the value of that seed in the vector was set to 1; otherwise the value was set to 0. The feature codes are shown in Supplementary Table S2.
The dataset in TargetScan includes binding position and range, which can be encoded into the nucleotide composition of the binding site sequence. In addition, each record from the prediction results of miRanda, RNAduplex, and StarMirDB, which includes information about the starting and ending positions of a binding site, can also be converted into the nucleotide composition of the binding site sequence. No range information is included in PITA and MBSTAR, so only the binding position was obtained and encoded as data from these tools. For miRNA–target pairs with no prediction result, the value for the position and nucleotide proportion was set to 0.
Feature selection and model construction
After constructing the training sets and integrating the prediction methods and feature encoding system (as described above, we selected the dataset used for training of the classifiers based on 10-fold cross-validation. To further improve the accuracy of the classifiers, a number of features selected by the incremental feature selection (IFS) method40 followed by the mutual information quotient (MIQ) scheme of the mRMR method32 were included in the model. The SVM classifier and learning method were selected by comparing seven classifiers from the Weka toolkit41—baye, function, lazy, meta, misc, rule, and tree—and 47 learning methods with LIBSVM31. Because of the difficulty in obtaining consistent results using current evaluation indicators to evaluate the performance of miRgo and other tools, a novel evaluation indicator, the CHL index, was developed and is described in next paragraph. The miRgo development flowchart is shown in Fig. 2.
Classifier performance evaluation
To evaluate the performance of classifiers, four values are commonly measured: (1) true positive rate (TP), the proportion of miRNA-target pairs that bind to each other and are correctly predicted by the classifier as binding pairs; (2) false negative rate (FN), the proportion of pairs that bind to each other but are falsely predicted by the classifier as non-binding pairs; (3) false positive rate (FP), the proportion of pairs that do not bind to each other but are falsely predicted by the classifier as binding pairs; and (4) true negative rate (TN), the proportion of pairs that do not bind to each other and are correctly predicted by the classifier as non-binding pairs. Several evaluation metrics—accuracy (Acc), sensitivity (Sn), specificity (Sp), precision, the F1 score, and Matthews correlation coefficient (MCC)—can be obtained from TP, FN, FP, and TN. These metrics are shown in Formulas 1–6.
Acc, an indicator of overall prediction accuracy, is calculated as shown in Formula 1.
Sn (also called recall) is an indicator of the power for detecting positives and is shown in Formula 2.
Sp is an indicator of the power for detecting negatives and is shown in Formula 3.
Precision is an indicator of the accuracy of predicting positives, as shown in Formula 4.
The F1 score, or the F-measure, is a weighted arithmetic mean of precision and Sn. The range of this score is from 0 to 1. It indicates the prediction accuracy for positive data. The F1 score is shown in Formula 5.
The MCC is an objective indicator that is used to evaluate prediction power on positives or negatives. By balancing the effect of positive and negative prediction accuracy, it is generally more reliable than Sn, Sp, or precision. The range of MCC is from −1 to 1. If MCC is equal to 1, the prediction is totally correct, and if MCC is equal to −1, the prediction is totally incorrect. All-positive or all-negative prediction will yield a MCC of 0. MCC is shown in Formula 6.
Among the prediction metrics described above, Acc is seemingly a useful indicator for accuracy, but its usefulness is actually limited because of the accuracy paradox, which also affects the F1 score42. Using MCC avoids the accuracy paradox, but because the tools that are focused solely on negative data prediction may still achieve a high MCC score, it is unreliable when positive data prediction is important. During construction of miRgo, we found that Acc, F1-score, and MCC were inconsistent when various models were compared. To avoid the pitfalls of these three metrics and to resolve these inconsistencies, we developed a metric, the CHL index, that represents the harmonic mean of the Acc, the F1 score, and MCC′ (a normalized MCC that has a value range of 0–1). The effect of positive and negative prediction data on the CHL index is between that of the F1 score and MCC, so positive prediction data will have more weight on the CHL index than in the MCC, and negative prediction data will have more weight on the CHL index than in the F1 score. The calculations for MCC′ and the CHL index are shown in Formulas 7 and 8.
The positive-negative ratio optimization for training data
An imbalance between positive and negative data may cause bias in machine learning. To search for an optimal P/N ratio for the training set, we designed models trained with four different P/N ratios. Figure 3 shows that when evaluated by the CHL index, a P/N ratio of 1:1 achieved the best result. The positive data were based on the 2,588 miRNA–target pairs. The negative data were generated by permutation and combination. The models were trained by 10 consecutive runs of SVM with randomly selected data sets.
After selecting the best training sets with a P/N ratio of 1:1, various classifiers were tested with the selected sets for accuracy. Five classifiers from the Weka toolkit, including baye, function, lazy, meta, and tree, and seven algorithms were tested and compared with LIBSVM. The results are shown in Supplementary Table S3.
Training data for cross-validation
To select the best data set for model construction, data sets trA, trB, trC, and trR were tested with 10-fold cross-validation with a selected subset as the training set and other subsets as the validation or testing set. The results of trA, the best-performing data set, are shown in Table 3. The results of the other data sets are shown in Supplementary Tables S4–S6.
When tested with trA, the prediction model miRgo–trA achieved the best results for all metrics among 18 tools (Table 3). Of the metrics tested, Sn showed the most variation among tools, and Sp showed the least variation. MCC also showed marked variation. The variation in the CHL index among the tools was less than that for MCC but was greater than that for Sp. When tested with trB, the prediction model miRgo–trB achieved the best results for all metrics among 18 tools, but it was less accurate than miRgo–trA (Supplementary Table S4). Its worse performance might be because some miRNAs have just one target (i.e. these miRNAs don’t have the second-most stable pair), so other miRNAs’ the third-most stable binding pair, which may have weaker binding energy, will be used instead, or because some second-most stable pairs had weaker binding energy. When tested with trC, the prediction model miRgo–trC performed better than most tools but showed a worse Acc, F1-score, and MCC than did TargetScan and TarPmiR, the two tools that may be more focused on mid-range data (Supplementary Table S5). When tested with trR, the prediction model miRgo–trR achieved the best results for all metrics among 18 tools but was still worse than miRgo–trA (Supplementary Table S6).
The best data set, trA, was used for final model training. To assess the characteristics of accuracy metrics, correlation between Acc, the F1 score, MCC′, and the CHL index was measured by testing with trA and various tools (Supplementary Fig. S1). Acc was closely correlated with MCC′ and was markedly different than the F1 score; the CHL index was closely correlated with the F1 score but was markedly different than Acc and MCC′. Thus, the CHL index may give more weight to negative data prediction power.
To select a suitable feature selection method, we first compared the performance of six feature selection methods from the Weka41 toolkit—CVAttributeEval, GainRatioAttributeEval, InfoGainAttributeEval, OneRAttributeEval, CorrelationAttributeEval, and SymmetricalUncertAttributeEval—on model construction by trA. Because there was no meaningful performance difference, CVAttributeEval was arbitrarily selected to represent the Weka method. CVAttributeEval was then compared with the mRMR feature selection method for performance based on the incremental feature selection (IFS) procedure40. The performance of the method without feature selection, miRgo_trA, and the two feature selection methods, miRgo_trA_FS-mRMR and miRgo_trA_FS-CVAE, is shown in Table 4.
The model trained by the trA set without feature selection scored 0.9990, 0.9973, and 0.9986 on Sn, MCC, and the CHL index, respectively. After the CVAttributeEval feature selection was conducted, Sn, MCC, and the CHL index increased to 1.0000, 0.9977, and 0.9988, respectively. After the mRMR feature selection was conducted, Sn, MCC, and the CHL index increased to 1.0000, 0.9981, and 0.9990, respectively. Because of better performance, the MIQ scheme of the mRMR method was chosen for model construction. Based on the ranked features evaluated by the mRMR method, the IFS procedure was then used to determine the optimal number of features. During the IFS procedure, features in the ranked feature list are added one by one from higher to lower rank, and then 184 different feature subsets are obtained. An IFS curve, revealing the relation between the CHL index and the feature subset, is plotted in Fig. 4, which shows that several subsets with no more than eleven most important features would make the CHL index to reach maximum. We then chose the eleven features from six tools, including minimal free energy, predicted binding position, and p-value from RNAhybrid; context score and seed type from TargetScan; nucleotide proportion from miRanda(A,C) and RNAduplex; endpoints of the predicted binding site from StarMirDB, p-value from RNA22, and miTG score from DIANA-microT for model construction. These features cover energy, scoring function, binding type, and sequence property.
To examine whether overfitting occurred, independent testing was conducted on models built before and after feature selection. The test showed that miRgo_trA_FS-CVAE scored better than miRgo_trA_FS-mRMR for Sn, A, and the F1 score but scored worse than miRgo_trA_FS-mRMR for MCC and the CHL index, the two metrics that consider both Sn and Sp. miRgo_trA_FS-mRMR was superior if positive and negative data were concerned, and the CHL index enhanced the importance of positive data while retaining the accuracy paradox−solving ability of MCC (Table 5).
Model evaluation by independent testing dataset
To compare the performance of miRgo_trA_FS-mRMR with other prediction tools, testing was conducted with an independent dataset. miRgo_trA_FS-mRMR performed better than all other tools when measured by Acc, the F1 score, and the CHL index. The only metric for which miRgo_trA_FS-mRMR performed worse than any tool was MCC, where MBSTAR yielded a score of 0.2807 and miRgo_trA_FS-mRMR yielded a score of 0.1810. The reason for the high MCC score of MBSTAR may be caused by low sensitivity, with only 451 records generated when tested with 1,525 records of independent data. By missing 833 records of positive data and 241 records of negative data, measuring MCC with MBSTAR may have falsely overestimated the accuracy based on negative data. Because most predictions of miRNA–target interactions focus on positive data, the better measure would be the CHL index, which avoids the accuracy paradox while still focusing on positive data; miRgo_trA_FS-mRMR and MBSTAR scored 0.7316 and 0.5273, respectively. The independent testing results are shown in Table 6.
Performance evaluation with functionally important genes
The Gene Ontology resource (GO; http://geneontology.org) collects current scientific knowledge concerning the functions of genes and provides functional annotation of gene products39. All the knowledge regarding the functions of genes is supported by the scientific literature43. Therefore, genes with the GO annotation indicates that the functions of these genes have been investigated to some extent and imply that these genes might be interesting or functionally important. We are interested in the performance of miRgo in predicting the miRNA-target relationships of these genes with the GO annotation. The analysis was done by testing according to three types of functional data in the Gene Ontology database: biological process, molecular function, and cellular component. Independent testing data were categorized into three types, and all tools were tested based each category. miRgo performed better than all other tools in all three types of functional data, scoring 0.6841, 0.6899, and 0.6945 in biological process, molecular function, and cellular component, respectively, when evaluated by the CHL index. The results are shown in Fig. 5.
Prediction of miRNA–target relationships is important in biology because prediction of binding pairs may save time and material for experimental biologists. Here we described the integrated tool miRgo, which combines 11 features covering binding energy, scoring function, binding type, and sequence characteristics from six different prediction tools. The training set used for development, trA, was obtained by selecting the most-stable and least-stable binding pairs via an energy filter distribution function. The resulting classifiers showed high accuracy in prediction of both positive and negative data without overfitting. Compared with the integration of 11 tools, the integration of six tools and 11 features was superior in speed and accuracy.
Regarding miRNA–target interactions, the prediction of positive data is more important than that of negative data. To address this specific need, we developed a novel metric, the CHL index, which focuses more on Sp than the F1 score and focuses more on Sn than MCC. For example, STMDB_3US and MBSTAR have similar Sn values but very different Sp values (0.7303 and 1.0000) (Table 6). These two tools show a difference of 0.3 in the F1 score, but 0.6 in the CHL index, demonstrating that the CHL index is more Sp focused than is the F1 score. miRNADA_S_0 and MBSTAR have similar Sp values but very different Sn values (0.0093 and 0.3512). These two tools show a difference of 0.2421 for MCC but 0.4789 for the CHL index, demonstrating that the CHL index is more Sn focused than is MCC. Therefore the CHL index may have more discrimination power for examination of miRNA–target prediction models.
Compared with the previous prediction tools, miRgo was trained using the newest data from miRBase, containing 2,588 miRNAs. When tested with functional data from the Gene Ontology database and evaluated by the CHL index, it performed better than all other tools. A website (http://predictor.nchu.edu.tw/miRgo) has been built for users to assess miRgo. This tool takes gene name, gene ensemble ID, Refseq ID, gene sequence, and miRNA sequence as input and generates a prediction of binding status in addition to possible binding miRNAs.
Carrington, J. C. & Ambros, V. Role of microRNAs in plant and animal development. Sci. 301, 336–338, https://doi.org/10.1126/science.1085242 (2003).
Bartel, D. P. MicroRNAs: genomics, biogenesis, mechanism, and function. Cell 116, 281–297, https://doi.org/10.1016/s0092-8674(04)00045-5 (2004).
Carthew, R. W. & Sontheimer, E. J. Origins and Mechanisms of miRNAs and siRNAs. Cell 136, 642–655, https://doi.org/10.1016/j.cell.2009.01.035 (2009).
Vidigal, J. A. & Ventura, A. The biological functions of miRNAs: lessons from in vivo studies. Trends Cell Biol. 25, 137–147, https://doi.org/10.1016/j.tcb.2014.11.004 (2015).
Lee, R. C., Feinbaum, R. L. & Ambros, V. The C. elegans heterochronic gene lin-4 encodes small RNAs with antisense complementarity to lin-14. Cell 75, 843–854 (1993).
Lund, E. & Dahlberg, J. E. Substrate selectivity of exportin 5 and Dicer in the biogenesis of microRNAs. Cold Spring Harb. Symp. Quant. Biol. 71, 59–66, https://doi.org/10.1101/sqb.2006.71.050 (2006).
Bartel, D. P. MicroRNAs: target recognition and regulatory functions. Cell 136, 215–233, https://doi.org/10.1016/j.cell.2009.01.002 (2009).
Lewis, B. P., Burge, C. B. & Bartel, D. P. Conserved seed pairing, often flanked by adenosines, indicates that thousands of human genes are microRNA targets. Cell 120, 15–20, https://doi.org/10.1016/j.cell.2004.12.035 (2005).
Garcia, D. M. et al. Weak seed-pairing stability and high target-site abundance decrease the proficiency of lsy-6 and other microRNAs. Nat. Struct. Mol. Biol. 18, 1139–1146, https://doi.org/10.1038/nsmb.2115 (2011).
Thomson, D. W., Bracken, C. P. & Goodall, G. J. Experimental strategies for microRNA target identification. Nucleic Acids Res. 39, 6845–6853, https://doi.org/10.1093/nar/gkr330 (2011).
Wei, L. Y., Huang, Y., Qu, Y. Y., Jiang, Y. & Zou, Q. Computational Analysis of miRNA Target Identification. Curr. Bioinform 7, 512–525, https://doi.org/10.2174/157489312803900974 (2012).
Agarwal, V., Bell, G. W., Nam, J. W. & Bartel, D. P. Predicting effective microRNA target sites in mammalian mRNAs. Elife 4, https://doi.org/10.7554/eLife.05005 (2015).
Betel, D., Koppal, A., Agius, P., Sander, C. & Leslie, C. Comprehensive modeling of microRNA targets predicts functional non-conserved and non-canonical sites. Genome Biol. 11, R90, https://doi.org/10.1186/gb-2010-11-8-r90 (2010).
Kertesz, M., Iovino, N., Unnerstall, U., Gaul, U. & Segal, E. The role of site accessibility in microRNA target recognition. Nat. Genet. 39, 1278–1284, https://doi.org/10.1038/ng2135 (2007).
Sulc, M., Marin, R. M., Robins, H. S. & Vanicek, J. PACCMIT/PACCMIT-CDS: identifying microRNA targets in 3’ UTRs and coding sequences. Nucleic Acids Res. 43, W474–479, https://doi.org/10.1093/nar/gkv457 (2015).
Krek, A. et al. Combinatorial microRNA target predictions. Nat. Genet. 37, 495–500, https://doi.org/10.1038/ng1536 (2005).
Kruger, J. & Rehmsmeier, M. RNAhybrid: microRNA target prediction easy, fast and flexible. Nucleic Acids Res. 34, W451–454, https://doi.org/10.1093/nar/gkl243 (2006).
Lorenz, R. et al. ViennaRNA Package 2.0. Algorithms Mol. Biol. 6, 26, https://doi.org/10.1186/1748-7188-6-26 (2011).
Reczko, M., Maragkakis, M., Alexiou, P., Grosse, I. & Hatzigeorgiou, A. G. Functional microRNA targets in protein coding sequences. Bioinforma. 28, 771–776, https://doi.org/10.1093/bioinformatics/bts043 (2012).
Bandyopadhyay, S., Ghosh, D., Mitra, R. & Zhao, Z. MBSTAR: multiple instance learning for predicting specific functional binding sites in microRNA targets. Sci. Rep. 5, 8004, https://doi.org/10.1038/srep08004 (2015).
Shuang, C. et al. MiRTDL: A Deep Learning Approach for miRNA Target Prediction. IEEE/ACM Trans. Comput. Biol. Bioinform 13, 1161–1169, https://doi.org/10.1109/TCBB.2015.2510002 (2016).
Ding, J., Li, X. & Hu, H. TarPmiR: a new approach for microRNA target site prediction. Bioinforma. 32, 2768–2775, https://doi.org/10.1093/bioinformatics/btw318 (2016).
Wong, N. & Wang, X. miRDB: an online resource for microRNA target prediction and functional annotations. Nucleic Acids Res. 43, D146–152, https://doi.org/10.1093/nar/gku1104 (2015).
Miranda, K. C. et al. A pattern-based method for the identification of microRNA binding sites and their corresponding heteroduplexes. Cell 126, 1203–1217, https://doi.org/10.1016/j.cell.2006.07.031 (2006).
Rennie, W. et al. STarMirDB: A database of microRNA binding sites. RNA Biol. 13, 554–560, https://doi.org/10.1080/15476286.2016.1182279 (2016).
Xia, J. F., Zhao, X. M. & Huang, D. S. Predicting protein-protein interactions from protein sequences using meta predictor. Amino Acids 39, 1595–1599, https://doi.org/10.1007/s00726-010-0588-1 (2010).
Liu, J., Kang, S., Tang, C., Ellis, L. B. & Li, T. Meta-prediction of protein subcellular localization with reduced voting. Nucleic Acids Res. 35, e96, https://doi.org/10.1093/nar/gkm562 (2007).
Xue, B., Lipps, D. & Devineni, S. Integrated Strategy Improves the Prediction Accuracy of miRNA in Large Dataset. PLoS One 11, e0168392, https://doi.org/10.1371/journal.pone.0168392 (2016).
Chen, C. W., Lin, J. & Chu, Y. W. IStable: off-the-shelf predictor integration for predicting protein stability changes. BMC Bioinforma. 14(Suppl 2), S5, https://doi.org/10.1186/1471-2105-14-S2-S5 (2013).
Chou, C. H. et al. miRTarBase update 2018: a resource for experimentally validated microRNA-target interactions. Nucleic Acids Res. 46, D296–D302, https://doi.org/10.1093/nar/gkx1067 (2018).
Chang, C. C. & Lin, C. J. LIBSVM: A Library for Support Vector Machines. Acm T Intel Syst Tec 2, https://doi.org/10.1145/1961189.1961199 (2011).
Peng, H., Long, F. & Ding, C. Feature selection based on mutual information: criteria of max-dependency, max-relevance, and min-redundancy. IEEE Trans. Pattern Anal. Mach. Intell. 27, 1226–1238, https://doi.org/10.1109/TPAMI.2005.159 (2005).
Refaeilzadeh, P., Tang, L. & Liu, H. In Encyclopedia of Database Systems (eds. Ling Liu & M. Tamer ÖZsu) 532–538 (Springer US, 2009).
Valverde-Albacete, F. J. & Pelaez-Moreno, C. 100% classification accuracy considered harmful: the normalized information transfer factor explains the accuracy paradox. PLoS One 9, e84217, https://doi.org/10.1371/journal.pone.0084217 (2014).
Kozomara, A., Birgaoanu, M. & Griffiths-Jones, S. miRBase: from microRNA sequences to function. Nucleic Acids Res. 47, D155–D162, https://doi.org/10.1093/nar/gky1141 (2019).
Fu, L., Niu, B., Zhu, Z., Wu, S. & Li, W. CD-HIT: accelerated for clustering the next-generation sequencing data. Bioinforma. 28, 3150–3152, https://doi.org/10.1093/bioinformatics/bts565 (2012).
Zhang, Y. & Verbeek, F. J. Comparison and integration of target prediction algorithms for microRNA studies. J Integr Bioinform 7, https://doi.org/10.2390/biecoll-jib-2010-127 (2010).
Vlachos, I. S. et al. DIANA-TarBase v7.0: indexing more than half a million experimentally supported miRNA:mRNA interactions. Nucleic Acids Res. 43, D153–159, https://doi.org/10.1093/nar/gku1215 (2015).
The Gene Ontology, C. The Gene Ontology Resource. 20 years and still GOing strong. Nucleic Acids Res. 47, D330–D338, https://doi.org/10.1093/nar/gky1055 (2019).
Liu, H. A. & Setiono, R. Incremental feature selection. Appl. Intell. 9, 217–230, https://doi.org/10.1023/A:1008363719778 (1998).
Frank, E., Hall, M., Trigg, L., Holmes, G. & Witten, I. H. Data mining in bioinformatics using Weka. Bioinforma. 20, 2479–2481, https://doi.org/10.1093/bioinformatics/bth261 (2004).
Zhu, X. & Davidson, I. Knowledge discovery and data mining: challenges and realities. (Information Science Reference Hershey, PA, 2007).
Thomas, P. D. The Gene Ontology and the Meaning of Biological Function. Methods Mol. Biol. 1446, 15–24, https://doi.org/10.1007/978-1-4939-3743-1_2 (2017).
This work was financially supported by the Advanced Plant Biotechnology Center from The Featured Areas Research Center Program within the framework of the Higher Education Sprout Project by the Ministry of Education (MOE) in Taiwan, and by the grants from the Ministry of Science and Technology in Taiwan (108-2321-B-005-008/108-2634-F-005-002 to Y.-W.C. and 103-2221-E-005-075/106-2311-B-005-004-MY3 to L.-C.H.) and from National Chung Hsing University/Chung Shan Medical University Joint Research Program (NCHU-CSMU-10811 to Y.-W.C.)
The authors declare no competing interests.
Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.
About this article
Cite this article
Chu, YW., Chang, KP., Chen, CW. et al. miRgo: integrating various off-the-shelf tools for identification of microRNA–target interactions by heterogeneous features and a novel evaluation indicator. Sci Rep 10, 1466 (2020). https://doi.org/10.1038/s41598-020-58336-5
By submitting a comment you agree to abide by our Terms and Community Guidelines. If you find something abusive or that does not comply with our terms or guidelines please flag it as inappropriate.