Get 20M+ Full-Text Papers For Less Than $1.50/day. Start a 14-Day Trial for You or Your Team.

Learn More →

mSRFR: a machine learning model using microalgal signature features for ncRNA classification

mSRFR: a machine learning model using microalgal signature features for ncRNA classification kmutt.ac.th Bioinformatics and Systems Biology This work presents mSRFR (microalgae SMOTE Random Forest Relief model), a Program, School of Bioresources classification tool for noncoding RNAs (ncRNAs) in microalgae, including green algae, and Technology, King Mongkut’s University of Technology Thonburi diatoms, golden algae, and cyanobacteria. First, the SMOTE technique was applied to (KMUTT), Bangkok 10150, Thailand address the challenge of imbalanced data due to the different numbers of Biotechnology program, School of microalgae ncRNAs from different species in the EBI RNA-central database. Then the Bioresources and Technology, KMUTT, Bang Khun Thian, Bangkok top 20 significant features from a total of 106 features, including sequence-based, 10150, Thailand secondary structure, base-pair, and triplet sequence-structure features, were selected Full list of author information is using the Relief feature selection method. Next, ten-fold cross-validation was applied available at the end of the article to choose a classifier algorithm with the highest performance among Support Vector Machine, Random Forest, Decision Tree, Naïve Bayes, K-nearest Neighbor, and Neural Network, based on the receiver operating characteristic (ROC) area. The results showed that the Random Forest classifier achieved the highest ROC area of 0.992. Then, the Random Forest algorithm was selected and compared with other tools, including RNAcon, CPC, CPC2, CNCI, and CPPred. Our model achieved a high accuracy of about 97% and a low false-positive rate of about 2% in predicting the test dataset of microalgae. Furthermore, the top features from Relief revealed that the %GA dinucleotide is a signature feature of microalgal ncRNAs when compared to Escherichia coli, Saccharomyces cerevisiae, Arabidopsis thaliana, and Homo sapiens. Keywords: Microalgae, Machine learning, Non-coding RNAs, Random Forest, Signature feature, SMOTE Introduction Microalgae are a large and diverse group of organisms, with eukaryotic and prokaryotic species found in freshwater, marine, and terrestrial habitats [1–3]. They possess a broad range of biochemical compounds that potentially impact public health, the econ- omy, foods, pharmaceuticals, medicine, bioenergy, environment, and waste treatment [4–7]. Research has sought to understand the mechanisms of beneficial compound production and ways to apply and commercialize them by exploring gene manipulation © The Author(s). 2022 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 licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence 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 licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data. Anuntakarun et al. BioData Mining (2022) 15:8 Page 2 of 11 and regulation, metabolic pathways, omics, and several advanced technologies [8]. Many publications have reported that noncoding RNAs (ncRNAs) play an important role in regulating gene expression, including mRNA destruction, inhibition of transla- tion, post-transcriptional regulation, and control of chromosome dynamics [9–11]. Moreover, many ncRNAs can be found in various organisms, such as mammals, plants, bacteria, and viruses. A system of RNA interference in the post-transcriptional modifi- cation process was first found in unicellular green algae [12]. There are many reports about ncRNAs and their functions in plants [13]. However, identifying ncRNAs from laboratory experiments can be time-consuming and costly. Over the last decade, ma- chine learning algorithms have been used in many research fields and for the identifica- tion of ncRNAs. In addition, they have facilitated the comparisons of ncRNAs and the identification of homologous structures in databases using BLAST [14]. These conven- tional approaches rely on the similarity of sequences and only allow for sequences with a high percentage of identity and coverage with previously reported ncRNAs. In con- trast, supervised machine learning integrates many informative features and data learn- ing to create models for the identification of candidate ncRNAs. A number of ncRNA identification tools based on computational prediction are available [15–19]. Neverthe- less, these tools were not designed for a specific group of microalgae and might not have discriminative features for microalgae ncRNA identification. Presently, there are many transcriptome data from next-generation sequencing for microalgae. In addition, RNA sequencing has enabled the discovery of many coding transcripts and ncRNAs in particular conditions. This research intends to develop an ncRNA identification tool that can discriminate ncRNAs from partial coding or coding sequences (CDS) in microalgae, including diatoms, golden algae, green algae, and cyanobacteria, using classifier algorithms in a machine learning approach. We applied the synthetic minority oversampling technique (SMOTE), which uses the k-nearest neighbor to synthesize new data from a minority class, to address the challenge of data imbalance due to the different numbers of samples in each data class. The significant features for ncRNA identification were selected to improve performance, increase ac- curacy and reduce false positives. The proposed tool will facilitate discovering and clas- sifying novel ncRNAs from the huge amounts of data from transcriptome studies enabled by next-generation sequencing technologies. Material and methods Datasets Noncoding RNAs (ncRNAs) and partial/coding sequences of microalgae were retrieved from the European Bioinformatics Institute (EBI) RNA-central database (ftp://ftp.ebi.ac. uk/pub/databases/RNAcentral) and the NCBI database, respectively. The microalgae in- cluded diatoms, golden algae, green algae, and cyanobacteria. In addition, we removed partial or coding sequences that are longer than 800 nt according to the size limitation of the secondary structure folding tool. The retrieved sequences were randomly divided into two parts; 80% of the data was used as a training dataset and the remaining 20% as a test dataset. The data was, unfortunately, highly unbalanced. For example, in the training dataset, there was a much higher number of cyanobacterial ncRNAs (13,116 sequences) compared to those from eukaryotes (3375 sequences). To address the Anuntakarun et al. BioData Mining (2022) 15:8 Page 3 of 11 problem, we applied random-sampling and over-sampling techniques to the original training dataset to create a balanced training dataset. Since the numbers of ncRNA and CDS sequences from golden algae and CDS from diatoms were much fewer than 1125, we applied the SMOTE technique to the datasets. SMOTE was implemented in Weka [20] using the default parameter. It works by recre- ating new instances from two or more neighbor instances of the same class in the fea- ture space. On the other hand, the over-represented samples, such as the 13,116 prokaryotic ncRNAs and 5448 prokaryotic CDS, were each reduced to 3375 sequences by random selection. The balanced dataset includes 3375 cyanobacterial ncRNAs, 3375 eukaryotic ncRNAs (1125 sequences from each of the three eukaryotic species), 3375 cyanobacterial CDS sequences, and 3375 eukaryotic CDS sequences (1125 sequences from each of the three eukaryotic species) (Table 1). Features Four categories of the total 106 features, namely sequence-based, secondary structure, base-pair, and triplet sequence-structure features (Table 1s), were collected from the HLRF tool [21, 22]. Other features in HLRF such as the tetra-nucleotide (4-mer) and the group of structural robustness features, were removed because the performance of 4-mer features was similar to that of 3-mer and 2-mer features combined with second- ary structure features [14] and the group of structural robustness features that are suit- able for the identification of precursor miRNAs, respectively. Descriptions of the features are provided in Supplementary Material 1s – 4s. Feature selection Student’s t-test, Wilcoxon rank-sum test, Information gain [23], OneR [24], and Relief [25] were used to select the top 20 features that potentially discriminate between ncRNAs and coding sequences of the four groups of microalgae. For Student's t-test and Wilcoxon rank-sum test, the p-values according to the statistical testing were uti- lized to rank features. Information gain ranks features using the entropy score. OneR uses a rule-based classification algorithm to calculate feature importance. Lastly, the Relief feature selection technique ranks features using the distance to the nearest neighbors. Table 1 Datasets of ncRNAs and CDS of microalgae Group of Types of Training Training dataset after Test microalgae sequences dataset balancing dataset Diatom ncRNAs 1234 1125 308 CDS 356 1125* 88 Golden algae ncRNAs 168 1125* 41 CDS 60 1125* 15 Green algae ncRNAs 1973 1125 493 CDS 6818 1125 1704 Cyanobacteria ncRNAs 13,116 3375 3280 CDS 5448 3375 1363 Data generated by random selection; * Data generated by SMOTE Anuntakarun et al. BioData Mining (2022) 15:8 Page 4 of 11 Performance measurement We used the accuracy (ACC), sensitivity (Sn), specificity (Sp), and false-positive rate (FPR) to evaluate the performance of the classifier model prediction. The performance indices were calculated as ACC = (TP + TN)/(TP + TN + FP + FN), Sn = TP/(TP + FN), Sp = TN/(TN + FP) and FPR = FP/(TN + FP), where TP, TN, FP, and FN are the num- bers of true positives, true negatives, false positives, and false negatives, respectively. For algorithm selection, six classifier algorithms, namely Naïve Bayes (NB), Random Forest (RF), Neural Networks (NN), K-nearest neighbor (KNN), Decision Tree (DT), and Support Vector Machine (SVM), were evaluated. They were trained, tested, and compared using default parameters on the open-source data mining software Weka [20]. For the Random Forest model, in particular, the number of trees was set to 100, and the maximum depth of the tree was set to unlimited. Model building In total, samples in the training dataset include 6750 ncRNAs (positive dataset) and 6750 partial coding sequences (negative dataset) from eukaryotic and prokaryotic algae. A total of 106 features were extracted from the sample data. Then the best classifier algorithm among NB, DT, RF, NN, KNN, and SVM was chosen based on their performance in classifying the positive and negative datasets using all 106 features. Subsequently, the selected classifier al- gorithm was used to classify the positive and negative datasets again, but with only the top features extracted by the five feature selection methods (Student’st-test, Wilcoxon rank- sum test, Information gain, OneR, and Relief). Top features that yielded the highest per- formance were used in the next step for 10-fold cross-validation with the six classifier algo- rithms. Finally, the best-performing classifier algorithm was selected as the final model and was further evaluated with the test dataset (20% of all data shown in Table 1). The overall workflow of the present work is shown in Fig. 1. Fig. 1 The overall workflow of this work. Feature selection was performed, and machine learning models were applied. Finally, the models were evaluated by 10-fold cross-validation, and the best model was tested with a test set Anuntakarun et al. BioData Mining (2022) 15:8 Page 5 of 11 Identification of unique features of ncRNAs of microalgae The unique and common top features extracted by the feature selection methods were analyzed and illustrated with a Venn diagram. Results Feature selection model for microalgal ncRNA classification Firstly, a suitable classifier was chosen by comparing the performance of six different classifier algorithms, NB, DT, RF, NN, KNN, and SVM, using all 106 features. Results of the 10-fold cross-validation of each classifier algorithm are shown in Table 2s. RF gave the highest ROC area, while NN had slightly higher accuracy, sensitivity, specifi- city, and a lower false-positive rate. Nevertheless, RF is less computationally intensive regarding training and less prone to overfitting compared to NN [26], and its built-in feature importance graphical plots are easier to interprete and comprehend [27]. More- over, RF has been widely used in many classification tools for ncRNAs prediction [21, 22]; therefore, we selected it as a classifier to compare the performance of a set of top features from different feature selection methods in the following step. Various feature selection methods, namely Information gain, OneR, Relief, t-test, and Wilcoxon rank-sum test, were used to identify features that can potentially distinguish microalgae ncRNA sequences from coding sequences. The top features identified by each feature selection method are shown in Table 3s. They comprise 5 sequence-based features, 12 secondary structure features, and 3 base-pair features by Information gain, 15 sequence-based features, 3 secondary structure features, and 2 base-pair features by OneR, 13 sequence-based features, 4 secondary structure features, 2 base-pair features, and 1 triplet sequence-structure feature by Relief, 4 sequence-based features, 14 secondary structure features, 10 base-pair features and 4 triplet sequence-structure features by Stu- dent’s t-test, and 4 sequence-based features, 16 secondary structure features, 13 base-pair features, and 5 triplet sequence-structure features by Wilcoxon rank-sum test. Results of the 10-fold cross-validation of the top features from each feature selection method using the Random Forest algorithm are shown in Table 2. The Relief method yielded the best performance in terms of the accuracy, sensitivity, specificity, and false-positive rate. This may be because Relief employs filtering algorithms, and weights computed for individual features can guide downstream machine learning. In addition, this technique does not re- move feature correlations or feature redundancies [28]. We used a Venn diagram to visualize the top features (Fig. 1s) and compare the fea- ture selection methods (Table 4 s). Only four features, namely TmL, Prob, CM, and Table 2 Performance of different feature selection methods with Random Forest algorithms using 10-fold cross validation Feature selection Performance measurement methods ACC (%) Sn (%) Sp (%) FPR ROC area Infogain 98.7 98.7 98.7 0.013 0.999 OneR 98.9 98.9 98.9 0.011 0.999 Relief 98.9 99.0 99.0 0.01 0.999 t-test 98.3 97.7 98.9 0.011 0.999 Wilcoxon 98.6 98.4 98.9 0.011 0.999 Anuntakarun et al. BioData Mining (2022) 15:8 Page 6 of 11 GA, were commonly selected by all methods. To prove the importance of these features for the identification of ncRNAs in microalgae, we constructed a Random Forest model using only the four common top features (F4model) and then compared its perform- ance to that of the original model (F20model), which was based on the 20 features (Table 5 s). The F4 model achieved a 95% accuracy compared to 97% for the F20 model, indicating that the four features contributed majorly to the identification of ncRNAs in microalgae. In addition, three of the features (TmL, Prob, CM) are second- ary structure features, a probable indication of the relevance of secondary structure fea- tures. For instance, the structural entropy-derived dS feature, also found in the top 20 significant features, represents the fold stability of RNA sequences and can be used to classify precursor miRNAs from pseudo precursor miRNA sequences [29]. In addition, the TmL feature, which represents the melting energy of structure normalized by the sequence length, has been reported to discriminate between ncRNAs and coding RNAs [30]. In fact, secondary structure features have been used in a wide range of ncRNA identification tools such as HeteroMirPred [21] and HLRF [22], and to identify mature miRNAs from precursor miRNAs [31]. Therefore, the top 20 features selected by the Relief method (F20 model) were used for further analysis. Unique ncRNA features of microalgae We compared the microalgal ncRNA top features with those of other organisms, in- cluding bacteria, yeast, plants, and humans. Venn diagrams illustrating the top 20 fea- tures selected by the different feature selection methods, including Information gain, OneR, Relief, t-test, and Wilcoxon rank-sum test, are presented in Figs. 2s–5s. The top features that were commonly selected by all methods are as follows: TmL, pairprob7, AG, CM, and GG for bacteria (Escherichia coli); dF and TmL for yeast (Saccharomyces cerevisiae); div, pairprob8, pairprob4, dH, pairprob7, TmL, pairprob9, CM, efe, dS, pair- prob3, mfe and Tm/Loop for plants (Arabidopsis thaliana); and dH, TmL, pairprob9, pairprob2, diff, Tm/Loop, dS and mfe5 for humans (Homo sapiens). Interestingly, GA and Prob features were unique to microalgae, i.e., they were not among the top features for bacteria, yeast, plants, or humans. Comparison of GA and Prob values of ncRNAs from microalgae to those from other organisms We compared the two unique features of the microalgal ncRNAs, namely the average GA and Prob values, to the values of ncRNAs from other organisms (Fig. 2A). The Prob feature value of ncRNAs from microalgae was significantly smaller than that of plant and human ncRNAs but was comparable to that of yeast and bacterial ncRNAs. Moreover, the %GA dinucleotide (GA) in microalgal ncRNAs was significantly higher (p-value < 0.01) than in the bacterial, yeast, plant, and human ncRNAs (Fig. 2B), whereas the %GA dinucleotide of coding sequences in microalgae was significantly less (p-value < 0.01) in comparison (Fig. 2C). From these results, it can be concluded that the %GA dinucleotide is potentially a signature feature of microalgal ncRNAs consider- ing its abundance. Anuntakarun et al. BioData Mining (2022) 15:8 Page 7 of 11 Fig. 2 Comparison of the features from microalgae to bacteria, yeast, plants and humans: average Prob values (A), average %GA values in ncRNAs (B), and average %GA values in partial coding sequences (C) Anuntakarun et al. BioData Mining (2022) 15:8 Page 8 of 11 Comparison of performance of different classifier algorithms Results of the performance evaluation of the six classifier algorithms, namely Decision Tree (DT), K-nearest neighbor (KNN), Naïve Bayes (NB), Neural Network (NN), Ran- dom Forest (RF), and Support Vector Machine (SVM), using 10-fold cross-validation with the top 20 features selected by the Relief method are shown in Table 6 s. The clas- sifier algorithms mostly showed comparable performance. However, the Random Forest algorithm had the highest accuracy, specificity, ROC area, and the lowest false positive rate and was thus chosen, as the false positive rate is particularly important for identify- ing microalgal ncRNAs. Random Forest, as an ensemble of multiple decision trees, could be useful for the identification of noncoding RNAs in microalgae, just as it has been used for a broad range of organisms [21]. Benchmarking of our mSRFR model performance with other tools We evaluated the performance of the trained microalgal Random Forest model using the top 20 features from the Relief feature selection method (mSRFR model) to identify ncRNAs from the test dataset. We benchmarked our model against other classification tools, namely RNAcon [14], CPC [15], CPC2 [16], CNCI [17], CPPred [18], and a Ran- dom Forest model using the top 20 features from the Relief feature selection but with- out applying the SMOTE technique to balance the dataset (mRFR model). As shown in Table 3 and Fig. 3, the results are divided into four groups: cyanobacteria, diatoms, gold algae, and green algae. The accuracy of our mRFR was high and similar to mSRFR. However, for golden algae, the accuracy of mSRFR was higher by around 2%. In addition, mSRFR showed the lowest false positive rate when compared with the other tools. Table 3 Performance comparison of our mSRFR model to others (RNAcon, CPC, CPC2, CNCI, and CPPred) used in discriminating ncRNAs and CDS of microalgae RNAcon CPC CPC2 CNCI CPPred mRFR mSRFR Cyanobacteria ACC (%) 72 93 71 55 70 99 99 Sn (%) 94 90 99 70 100 99 99 Sp (%) 19 99 2 20 0 99 99 FPR 0.81 0.01 0.98 0.8 1 0.01 0.01 Diatom ACC (%) 77 94 85 73 78 99 99 Sn (%) 76 96 100 71 100 99 99 Sp (%) 80 85 34 78 0 99 99 FPR 0.20 0.15 0.66 0.22 1 0.01 0.01 Golden Algae ACC (%) 87 81 80 86 73 93 95 Sn (%) 95 83 100 97 100 92 93 Sp (%) 67 85 26 60 0 93 99 FPR 0.33 0.15 0.74 0.40 1 0.07 0.01 Green Algae ACC (%) 65 72 73 89 22 99 99 Sn (%) 70 99 100 67 100 99 99 Sp (%) 63 64 66 96 0 99 99 FPR 0.37 0.36 0.34 0.04 1 0.01 0.01 Anuntakarun et al. BioData Mining (2022) 15:8 Page 9 of 11 Fig. 3 Performance comparison of our model, with or without applying the SMOTE technique and other tools (RNAcon, CPC, CPC2, CNCI, and CPPred) to discriminate between ncRNAs and CDS in the test dataset Discussion To the best of our knowledge, this tool is the first ncRNAs classification tool trained specifically for microalgae, the most promising biofuel candidates rich in high-value bioactive compounds. Identifying ncRNAs in microalgae might help improve our under- standing of their regulatory functions in gene manipulation. Moreover, ncRNA investi- gation in microalgae may provide new insights into metabolic regulation and engineering for product improvement. The main contributions are threefold: First, we developed an ncRNA identification tool for microalgae-based on a Random Forest model. Random Forest, an ensemble machine learning method, has been used for a broad range of organisms for its robustness and minimal overfitting problems. Second, to address the problem of imbalanced datasets of different groups of microal- gae, for example, where the numbers of both ncRNAs and coding sequences from golden algae were far fewer than those of the other groups, we applied the SMOTE technique to balance the datasets. Interestingly, the SMOTE (mSRFR) model performed better than the model without SMOTE (mRFR) in discriminating ncRNAs from CDS in golden algae, suggesting that the SMOTE model can handle imbalanced datasets better. Third, a group of features specific to ncRNAs in microalgae were identified. More- over, our study also revealed four features (TmL, Prob, CM, and GA) that contributed majorly to the performance of the model. The RF classifier model utilizing only these four features, instead of all top 20 features, achieved a reasonably high accuracy of 95%. Our analysis further shows that of the four features, Prob and GA are unique features in discriminating ncRNAs from coding sequences in microalgae. They were not among the top features, by any of the methods, for the bacterial, yeast, plant, or human ncRNAs discrimination. We propose the GA dinucleotide as one of the microalgal ncRNAs signature features, given its higher abundance in microalgal ncRNAs com- pared to other species. Conversely, the coding sequences of microalgae contained fewer GA dinucleotides in comparison to those of the other organisms. The GA dinucleotide or GA motif is highly conserved in T box and S box sequences, which are transcription Anuntakarun et al. BioData Mining (2022) 15:8 Page 10 of 11 termination control systems. The T box functions in regulating amino acid transporter genes, aminoacyl-tRNA synthetase, and amino acid biosynthesis, while the S box is related to methionine synthesis [32]. The high GA dinucleotide composition of microalgal ncRNAs could be relevant for transcription termination control systems as microalgal photosynthesis is highly regulated on the level of transcriptional control [33]. Conclusions In this study, mSRFR (m for microalgae; S for SMOTE; RF for Random Forest; R for the Relief method) was used for ncRNA identification in microalgae-based on a Ran- dom Forest model using the top 20 features from a total of 106 features selected by the Relief feature selection method. The tool achieved a high accuracy of about 97% and a low false-positive rate of 2% in discriminating microalgal ncRNAs from coding se- quences in a test dataset containing 7292 sequences. Currently, next-generation se- quencing technologies, such as RNA-seq, have become popular for the study of gene expression in many organisms. For future research, we aim to extend our tool to sup- port input formats of raw reads from next-generation sequencing data, such as fastq, and to develop a web-based computational pipeline that will enable users to identify potential ncRNAs in microalgae from next-generation sequencing data. Authors’ contributions Conceptualization: MR, SL, TL. Data curation: SA. Formal analysis: SA. Funding acquisition: MR, WW Methodology: SA, SL. Writing - original draft: SA. Writing - review & editing: MR, SL, TL, WW. The authors read and approved the final manuscript. Funding This work was supported by National Center for Genetic Engineering and Biotechnology (BIOTEC) for Songtham Anuntakarun’ scholarship and National Research University (NRU) Project-year-2557, King Mongkut’s University of Tech- nology Thonburi, Thailand. Availability of data and materials mSRFR: A machine learning model using microalgal signature features for ncRNA classification is an open-source col- laborative initiative available in the GitHub repository (https://github.com/anun001/smRFR-smote-microalgae-Random- Forest-Relief-model). Declaration Competing interests The authors declare that there is no conflict of interest regarding the publication of this article. Author details Bioinformatics and Systems Biology Program, School of Bioresources and Technology, King Mongkut’s University of Technology Thonburi (KMUTT), Bangkok 10150, Thailand. School of Information Technology, KMUTT, Bang Mod, Thung Khru, Bangkok 10140, Thailand. Biochemical Engineering and Systems Biology Research Group, National Center for Genetic Engineering and Biotechnology (BIOTEC), National Science and Technology Development Agency at King Mongkut’s University of Technology Thonburi, Bang Khun Thian, Bangkok 10150, Thailand. Department of Mathematics, Faculty of Science, KMUTT, Bangkok 10140, Thailand. Biotechnology program, School of Bioresources and Technology, KMUTT, Bang Khun Thian, Bangkok 10150, Thailand. Algal Biotechnology Research Group, Pilot Plant Development and Training Institute (PDTI), KMUTT, Bang Khun Thian, Bangkok 10150, Thailand. Received: 31 October 2021 Accepted: 6 February 2022 References 1. Hoffmann L. Algae of terrestrial habitats. Bot Rev. 1989;55(2):77–105. https://doi.org/10.1007/BF02858529. 2. John DM, Whitton BA, Brook AJ. The freshwater algal flora of the British Isles: an identification guide to freshwater and terrestrial algae, vol. I. Cambridge: Cambridge University Press; 2002. 3. Geider RJ, La Roche J. Redfield revisited: variability of C:N:P in marine microalgae and its biochemical basis. Eur J Phycol. 2002;37(1):1–17. https://doi.org/10.1017/S0967026201003456. 4. Delhi N. Functional ingredients and algae for foods and nutraceuticals. Burlington: Elsevier Science; 2013. 5. Wan Ngah WS, Hanafiah MAKM. Removal of heavy metal ions from wastewater by chemically modified plant wastes as adsorbents: a review. Bioresour Technol. 2008;99(10):3935–48. https://doi.org/10.1016/j.biortech.2007.06.011. Anuntakarun et al. BioData Mining (2022) 15:8 Page 11 of 11 6. Schenk PM, Thomas-Hall SR, Stephens E, Marx UC, Mussgnug JH, Posten C, et al. Second generation biofuels: high-efficiency microalgae for biodiesel production. BioEnergy Res. 2008;1(1):20–43. https://doi.org/10.1007/s12155-008-9008-8. 7. Thillairajasekar K, Duraipandiyan V, Perumal P, Ignacimuthu S. Antimicrobial activity of Trichodesmium erythraeum (Ehr) (microalga) from south east coast of Tamil Nadu. India Int J Integr Biol. 2009;5:167–70. 8. Lauritano C, Ferrante MI, Rogato A. Marine natural products from microalgae: an -omics overview. Mar Drugs. 2019;17(5): 269. https://doi.org/10.3390/md17050269. 9. Mattick JS, Makunin IV. Non-coding RNA. Hum Mol Genet. 2006;15 spec (1):R17–29. 10. Beermann J, Piccoli MT, Viereck J, Thum T. Non-coding RNAs in development and disease: background, mechanisms, and therapeutic approaches. Physiol Rev. 2016;96(4):1297–325. https://doi.org/10.1152/physrev.00041.2015. 11. Serghiou S, Kyriakopoulou A, Ioannidis JPA. Long noncoding RNAs as novel predictors of survival in human cancer: a systematic review and meta-analysis. Mol Cancer. 2016;15(1):50. https://doi.org/10.1186/s12943-016-0535-1. 12. Molnár A, Schwach F, Studholme DJ, Thuenemann EC, Baulcombe DC. miRNAs control gene expression in the single- cell alga Chlamydomonas reinhardtii. Nature. 2007;447(7148):1126–9. https://doi.org/10.1038/nature05903. 13. Yu Y, Zhang Y, Chen X, Chen Y. Plant noncoding RNAs: hidden players in development and stress responses. Annu Rev Cell Dev Biol. 2019;35(1):407–31. https://doi.org/10.1146/annurev-cellbio-100818-125218. 14. Panwar B, Arora A, Raghava GPS. Prediction and classification of ncRNAs using structural information. BMC Genomics. 2014;15(1):127. https://doi.org/10.1186/1471-2164-15-127. 15. Kong L, Zhang Y, Ye ZQ, Liu XQ, Zhao SQ, Wei L, et al. CPC: assess the protein-coding potential of transcripts using sequence features and support vector machine. Nucleic Acids Res. 2007;35:W345–9. https://doi.org/10.1093/nar/gkm391. 16. Kang YJ, Yang DC, Kong L, Hou M, Meng YQ, Wei L, et al. CPC2: a fast and accurate coding potential calculator based on sequence intrinsic features. Nucleic Acids Res. 2017;45(W1):W12–6. https://doi.org/10.1093/nar/gkx428. 17. Sun L, Luo H, Bu D, Zhao G, Yu K, Zhang C, et al. Utilizing sequence intrinsic composition to classify protein-coding and long noncoding transcripts. Nucleic Acids Res. 2013;41(17):e166. https://doi.org/10.1093/nar/gkt646. 18. Tong X, Liu S. CPPred: Coding potential prediction based on the global description of RNA sequence. Nucleic Acids Res. 2019;47(8):e43. https://doi.org/10.1093/nar/gkz087. 19. Bao M, Cervantes Cervantes M, Zhong L, Wang JTL. Searching for noncoding RNAs in genomic sequences using ncRNAscout. Genom Proteom Bioinform. 2012;10(2):114–21. https://doi.org/10.1016/j.gpb.2012.05.004. 20. Hall M, Frank E, Holmes G, Pfahringer B, Reutemann P, Witten IH. The WEKA data mining software: an update. ACM SIGK DD Explor Newsl. 2009;11(1):10–8. https://doi.org/10.1145/1656274.1656278. 21. Lertampaiporn S, Thammarongtham C, Nukoolkit C, Kaewkamnerdpong B, Ruengjitchatchawalya M. Heterogeneous ensemble approach with discriminative features and modified-SMOTEbagging for pre-miRNA classification. Nucleic Acids Res. 2013;41(1):e21. https://doi.org/10.1093/nar/gks878. 22. Lertampaiporn S, Thammarongtham C, Nukoolkit C, Kaewkamnerdpong B, Ruengjitchatchawalya M. Identification of noncoding RNAs with a new composite feature in the hybrid random Forest Ensemble algorithm. Nucleic Acids Res. 2014;42(11):e93. https://doi.org/10.1093/nar/gku325. 23. Kent JT. Information Gain and a General Measure of Correlation. Biometrika. 1983;70(1):163-73. http://www.jstor.org/sta ble/2335954 Accessed 06 Oct 2016. 24. Holte RC. Very simple classification rules perform well on Most commonly used datasets. Mach Learn. 1993;11(1):63–91. https://doi.org/10.1023/A:1022631118932. 25. Robnik-Šikonja M, Kononenko I. An adaptation of Relief for attribute estimation in regression. Mach Learning Proc Fourteenth Int Conf. 1997;5:296–304. 26. Ahmad MW, Mourshed M, Rezgui Y. Trees vs neurons: comparison between random forest and ANN for high-resolution prediction of building energy consumption. Energy Build. 2017;147:77–89. https://doi.org/10.1016/j.enbuild.2017.04.038. 27. Wehenkel M, Sutera A, Bastin C, Geurts P, Phillips C. Random forests based group importance scores and their statistical interpretation: application for Alzheimer’s disease. Front Neurosci. 2018;12:1–19. https://doi.org/10.3389/fnins.2018.00411. 28. Urbanowicz RJ, Olson RS, Schmitt P, Meeker M, Moore JH. Benchmarking relief-based feature selection methods for bioinformatics data mining. J Biomed Inform. 2018;85:168–88. https://doi.org/10.1016/j.jbi.2018.07.015. 29. Shaw TI, Manzour A, Wang Y, Malmberg RL, Cai L. Analyzing modular RNA structure reveals low global structural entropy in microRNA sequence. J Bioinform Comput Biol. 2011;9(2):283–98. https://doi.org/10.1142/S0219720011005495. 30. Wan Y, Qu K, Ouyang Z, Kertesz M, Li J, Tibshirani R, et al. Genome-wide measurement of RNA folding energies. Mol Cell. 2012;48(2):169–81. https://doi.org/10.1016/j.molcel.2012.08.008. 31. Leclercq M, Diallo AB, Blanchette M. Computational prediction of the localization of microRNAs within their pre-miRNA. Nucleic Acids Res. 2013;41(15):7200–11. https://doi.org/10.1093/nar/gkt466. 32. Winkler WC, Grundy FJ, Murphy BA, Henkin TM. The GA motif: an RNA element common to bacterial antitermination systems, rRNA, and eukaryotic RNAs. RNA. 2001;7(8):1165–72. https://doi.org/10.1017/S1355838201002370. 33. Wilde A, Hihara Y. Transcriptional and posttranscriptional regulation of cyanobacterial photosynthesis. Biochim Biophys Acta. 2016;1857(3):296–308. https://doi.org/10.1016/j.bbabio.2015.11.002. Publisher’sNote Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png BioData Mining Springer Journals

mSRFR: a machine learning model using microalgal signature features for ncRNA classification

Download PDF
11 pages

Loading next page...
 
/lp/springer-journals/msrfr-a-machine-learning-model-using-microalgal-signature-features-for-ZjLXB6NxYe

References (55)

Publisher
Springer Journals
Copyright
Copyright © The Author(s) 2022
eISSN
1756-0381
DOI
10.1186/s13040-022-00291-0
Publisher site
See Article on Publisher Site

Abstract

kmutt.ac.th Bioinformatics and Systems Biology This work presents mSRFR (microalgae SMOTE Random Forest Relief model), a Program, School of Bioresources classification tool for noncoding RNAs (ncRNAs) in microalgae, including green algae, and Technology, King Mongkut’s University of Technology Thonburi diatoms, golden algae, and cyanobacteria. First, the SMOTE technique was applied to (KMUTT), Bangkok 10150, Thailand address the challenge of imbalanced data due to the different numbers of Biotechnology program, School of microalgae ncRNAs from different species in the EBI RNA-central database. Then the Bioresources and Technology, KMUTT, Bang Khun Thian, Bangkok top 20 significant features from a total of 106 features, including sequence-based, 10150, Thailand secondary structure, base-pair, and triplet sequence-structure features, were selected Full list of author information is using the Relief feature selection method. Next, ten-fold cross-validation was applied available at the end of the article to choose a classifier algorithm with the highest performance among Support Vector Machine, Random Forest, Decision Tree, Naïve Bayes, K-nearest Neighbor, and Neural Network, based on the receiver operating characteristic (ROC) area. The results showed that the Random Forest classifier achieved the highest ROC area of 0.992. Then, the Random Forest algorithm was selected and compared with other tools, including RNAcon, CPC, CPC2, CNCI, and CPPred. Our model achieved a high accuracy of about 97% and a low false-positive rate of about 2% in predicting the test dataset of microalgae. Furthermore, the top features from Relief revealed that the %GA dinucleotide is a signature feature of microalgal ncRNAs when compared to Escherichia coli, Saccharomyces cerevisiae, Arabidopsis thaliana, and Homo sapiens. Keywords: Microalgae, Machine learning, Non-coding RNAs, Random Forest, Signature feature, SMOTE Introduction Microalgae are a large and diverse group of organisms, with eukaryotic and prokaryotic species found in freshwater, marine, and terrestrial habitats [1–3]. They possess a broad range of biochemical compounds that potentially impact public health, the econ- omy, foods, pharmaceuticals, medicine, bioenergy, environment, and waste treatment [4–7]. Research has sought to understand the mechanisms of beneficial compound production and ways to apply and commercialize them by exploring gene manipulation © The Author(s). 2022 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 licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence 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 licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data. Anuntakarun et al. BioData Mining (2022) 15:8 Page 2 of 11 and regulation, metabolic pathways, omics, and several advanced technologies [8]. Many publications have reported that noncoding RNAs (ncRNAs) play an important role in regulating gene expression, including mRNA destruction, inhibition of transla- tion, post-transcriptional regulation, and control of chromosome dynamics [9–11]. Moreover, many ncRNAs can be found in various organisms, such as mammals, plants, bacteria, and viruses. A system of RNA interference in the post-transcriptional modifi- cation process was first found in unicellular green algae [12]. There are many reports about ncRNAs and their functions in plants [13]. However, identifying ncRNAs from laboratory experiments can be time-consuming and costly. Over the last decade, ma- chine learning algorithms have been used in many research fields and for the identifica- tion of ncRNAs. In addition, they have facilitated the comparisons of ncRNAs and the identification of homologous structures in databases using BLAST [14]. These conven- tional approaches rely on the similarity of sequences and only allow for sequences with a high percentage of identity and coverage with previously reported ncRNAs. In con- trast, supervised machine learning integrates many informative features and data learn- ing to create models for the identification of candidate ncRNAs. A number of ncRNA identification tools based on computational prediction are available [15–19]. Neverthe- less, these tools were not designed for a specific group of microalgae and might not have discriminative features for microalgae ncRNA identification. Presently, there are many transcriptome data from next-generation sequencing for microalgae. In addition, RNA sequencing has enabled the discovery of many coding transcripts and ncRNAs in particular conditions. This research intends to develop an ncRNA identification tool that can discriminate ncRNAs from partial coding or coding sequences (CDS) in microalgae, including diatoms, golden algae, green algae, and cyanobacteria, using classifier algorithms in a machine learning approach. We applied the synthetic minority oversampling technique (SMOTE), which uses the k-nearest neighbor to synthesize new data from a minority class, to address the challenge of data imbalance due to the different numbers of samples in each data class. The significant features for ncRNA identification were selected to improve performance, increase ac- curacy and reduce false positives. The proposed tool will facilitate discovering and clas- sifying novel ncRNAs from the huge amounts of data from transcriptome studies enabled by next-generation sequencing technologies. Material and methods Datasets Noncoding RNAs (ncRNAs) and partial/coding sequences of microalgae were retrieved from the European Bioinformatics Institute (EBI) RNA-central database (ftp://ftp.ebi.ac. uk/pub/databases/RNAcentral) and the NCBI database, respectively. The microalgae in- cluded diatoms, golden algae, green algae, and cyanobacteria. In addition, we removed partial or coding sequences that are longer than 800 nt according to the size limitation of the secondary structure folding tool. The retrieved sequences were randomly divided into two parts; 80% of the data was used as a training dataset and the remaining 20% as a test dataset. The data was, unfortunately, highly unbalanced. For example, in the training dataset, there was a much higher number of cyanobacterial ncRNAs (13,116 sequences) compared to those from eukaryotes (3375 sequences). To address the Anuntakarun et al. BioData Mining (2022) 15:8 Page 3 of 11 problem, we applied random-sampling and over-sampling techniques to the original training dataset to create a balanced training dataset. Since the numbers of ncRNA and CDS sequences from golden algae and CDS from diatoms were much fewer than 1125, we applied the SMOTE technique to the datasets. SMOTE was implemented in Weka [20] using the default parameter. It works by recre- ating new instances from two or more neighbor instances of the same class in the fea- ture space. On the other hand, the over-represented samples, such as the 13,116 prokaryotic ncRNAs and 5448 prokaryotic CDS, were each reduced to 3375 sequences by random selection. The balanced dataset includes 3375 cyanobacterial ncRNAs, 3375 eukaryotic ncRNAs (1125 sequences from each of the three eukaryotic species), 3375 cyanobacterial CDS sequences, and 3375 eukaryotic CDS sequences (1125 sequences from each of the three eukaryotic species) (Table 1). Features Four categories of the total 106 features, namely sequence-based, secondary structure, base-pair, and triplet sequence-structure features (Table 1s), were collected from the HLRF tool [21, 22]. Other features in HLRF such as the tetra-nucleotide (4-mer) and the group of structural robustness features, were removed because the performance of 4-mer features was similar to that of 3-mer and 2-mer features combined with second- ary structure features [14] and the group of structural robustness features that are suit- able for the identification of precursor miRNAs, respectively. Descriptions of the features are provided in Supplementary Material 1s – 4s. Feature selection Student’s t-test, Wilcoxon rank-sum test, Information gain [23], OneR [24], and Relief [25] were used to select the top 20 features that potentially discriminate between ncRNAs and coding sequences of the four groups of microalgae. For Student's t-test and Wilcoxon rank-sum test, the p-values according to the statistical testing were uti- lized to rank features. Information gain ranks features using the entropy score. OneR uses a rule-based classification algorithm to calculate feature importance. Lastly, the Relief feature selection technique ranks features using the distance to the nearest neighbors. Table 1 Datasets of ncRNAs and CDS of microalgae Group of Types of Training Training dataset after Test microalgae sequences dataset balancing dataset Diatom ncRNAs 1234 1125 308 CDS 356 1125* 88 Golden algae ncRNAs 168 1125* 41 CDS 60 1125* 15 Green algae ncRNAs 1973 1125 493 CDS 6818 1125 1704 Cyanobacteria ncRNAs 13,116 3375 3280 CDS 5448 3375 1363 Data generated by random selection; * Data generated by SMOTE Anuntakarun et al. BioData Mining (2022) 15:8 Page 4 of 11 Performance measurement We used the accuracy (ACC), sensitivity (Sn), specificity (Sp), and false-positive rate (FPR) to evaluate the performance of the classifier model prediction. The performance indices were calculated as ACC = (TP + TN)/(TP + TN + FP + FN), Sn = TP/(TP + FN), Sp = TN/(TN + FP) and FPR = FP/(TN + FP), where TP, TN, FP, and FN are the num- bers of true positives, true negatives, false positives, and false negatives, respectively. For algorithm selection, six classifier algorithms, namely Naïve Bayes (NB), Random Forest (RF), Neural Networks (NN), K-nearest neighbor (KNN), Decision Tree (DT), and Support Vector Machine (SVM), were evaluated. They were trained, tested, and compared using default parameters on the open-source data mining software Weka [20]. For the Random Forest model, in particular, the number of trees was set to 100, and the maximum depth of the tree was set to unlimited. Model building In total, samples in the training dataset include 6750 ncRNAs (positive dataset) and 6750 partial coding sequences (negative dataset) from eukaryotic and prokaryotic algae. A total of 106 features were extracted from the sample data. Then the best classifier algorithm among NB, DT, RF, NN, KNN, and SVM was chosen based on their performance in classifying the positive and negative datasets using all 106 features. Subsequently, the selected classifier al- gorithm was used to classify the positive and negative datasets again, but with only the top features extracted by the five feature selection methods (Student’st-test, Wilcoxon rank- sum test, Information gain, OneR, and Relief). Top features that yielded the highest per- formance were used in the next step for 10-fold cross-validation with the six classifier algo- rithms. Finally, the best-performing classifier algorithm was selected as the final model and was further evaluated with the test dataset (20% of all data shown in Table 1). The overall workflow of the present work is shown in Fig. 1. Fig. 1 The overall workflow of this work. Feature selection was performed, and machine learning models were applied. Finally, the models were evaluated by 10-fold cross-validation, and the best model was tested with a test set Anuntakarun et al. BioData Mining (2022) 15:8 Page 5 of 11 Identification of unique features of ncRNAs of microalgae The unique and common top features extracted by the feature selection methods were analyzed and illustrated with a Venn diagram. Results Feature selection model for microalgal ncRNA classification Firstly, a suitable classifier was chosen by comparing the performance of six different classifier algorithms, NB, DT, RF, NN, KNN, and SVM, using all 106 features. Results of the 10-fold cross-validation of each classifier algorithm are shown in Table 2s. RF gave the highest ROC area, while NN had slightly higher accuracy, sensitivity, specifi- city, and a lower false-positive rate. Nevertheless, RF is less computationally intensive regarding training and less prone to overfitting compared to NN [26], and its built-in feature importance graphical plots are easier to interprete and comprehend [27]. More- over, RF has been widely used in many classification tools for ncRNAs prediction [21, 22]; therefore, we selected it as a classifier to compare the performance of a set of top features from different feature selection methods in the following step. Various feature selection methods, namely Information gain, OneR, Relief, t-test, and Wilcoxon rank-sum test, were used to identify features that can potentially distinguish microalgae ncRNA sequences from coding sequences. The top features identified by each feature selection method are shown in Table 3s. They comprise 5 sequence-based features, 12 secondary structure features, and 3 base-pair features by Information gain, 15 sequence-based features, 3 secondary structure features, and 2 base-pair features by OneR, 13 sequence-based features, 4 secondary structure features, 2 base-pair features, and 1 triplet sequence-structure feature by Relief, 4 sequence-based features, 14 secondary structure features, 10 base-pair features and 4 triplet sequence-structure features by Stu- dent’s t-test, and 4 sequence-based features, 16 secondary structure features, 13 base-pair features, and 5 triplet sequence-structure features by Wilcoxon rank-sum test. Results of the 10-fold cross-validation of the top features from each feature selection method using the Random Forest algorithm are shown in Table 2. The Relief method yielded the best performance in terms of the accuracy, sensitivity, specificity, and false-positive rate. This may be because Relief employs filtering algorithms, and weights computed for individual features can guide downstream machine learning. In addition, this technique does not re- move feature correlations or feature redundancies [28]. We used a Venn diagram to visualize the top features (Fig. 1s) and compare the fea- ture selection methods (Table 4 s). Only four features, namely TmL, Prob, CM, and Table 2 Performance of different feature selection methods with Random Forest algorithms using 10-fold cross validation Feature selection Performance measurement methods ACC (%) Sn (%) Sp (%) FPR ROC area Infogain 98.7 98.7 98.7 0.013 0.999 OneR 98.9 98.9 98.9 0.011 0.999 Relief 98.9 99.0 99.0 0.01 0.999 t-test 98.3 97.7 98.9 0.011 0.999 Wilcoxon 98.6 98.4 98.9 0.011 0.999 Anuntakarun et al. BioData Mining (2022) 15:8 Page 6 of 11 GA, were commonly selected by all methods. To prove the importance of these features for the identification of ncRNAs in microalgae, we constructed a Random Forest model using only the four common top features (F4model) and then compared its perform- ance to that of the original model (F20model), which was based on the 20 features (Table 5 s). The F4 model achieved a 95% accuracy compared to 97% for the F20 model, indicating that the four features contributed majorly to the identification of ncRNAs in microalgae. In addition, three of the features (TmL, Prob, CM) are second- ary structure features, a probable indication of the relevance of secondary structure fea- tures. For instance, the structural entropy-derived dS feature, also found in the top 20 significant features, represents the fold stability of RNA sequences and can be used to classify precursor miRNAs from pseudo precursor miRNA sequences [29]. In addition, the TmL feature, which represents the melting energy of structure normalized by the sequence length, has been reported to discriminate between ncRNAs and coding RNAs [30]. In fact, secondary structure features have been used in a wide range of ncRNA identification tools such as HeteroMirPred [21] and HLRF [22], and to identify mature miRNAs from precursor miRNAs [31]. Therefore, the top 20 features selected by the Relief method (F20 model) were used for further analysis. Unique ncRNA features of microalgae We compared the microalgal ncRNA top features with those of other organisms, in- cluding bacteria, yeast, plants, and humans. Venn diagrams illustrating the top 20 fea- tures selected by the different feature selection methods, including Information gain, OneR, Relief, t-test, and Wilcoxon rank-sum test, are presented in Figs. 2s–5s. The top features that were commonly selected by all methods are as follows: TmL, pairprob7, AG, CM, and GG for bacteria (Escherichia coli); dF and TmL for yeast (Saccharomyces cerevisiae); div, pairprob8, pairprob4, dH, pairprob7, TmL, pairprob9, CM, efe, dS, pair- prob3, mfe and Tm/Loop for plants (Arabidopsis thaliana); and dH, TmL, pairprob9, pairprob2, diff, Tm/Loop, dS and mfe5 for humans (Homo sapiens). Interestingly, GA and Prob features were unique to microalgae, i.e., they were not among the top features for bacteria, yeast, plants, or humans. Comparison of GA and Prob values of ncRNAs from microalgae to those from other organisms We compared the two unique features of the microalgal ncRNAs, namely the average GA and Prob values, to the values of ncRNAs from other organisms (Fig. 2A). The Prob feature value of ncRNAs from microalgae was significantly smaller than that of plant and human ncRNAs but was comparable to that of yeast and bacterial ncRNAs. Moreover, the %GA dinucleotide (GA) in microalgal ncRNAs was significantly higher (p-value < 0.01) than in the bacterial, yeast, plant, and human ncRNAs (Fig. 2B), whereas the %GA dinucleotide of coding sequences in microalgae was significantly less (p-value < 0.01) in comparison (Fig. 2C). From these results, it can be concluded that the %GA dinucleotide is potentially a signature feature of microalgal ncRNAs consider- ing its abundance. Anuntakarun et al. BioData Mining (2022) 15:8 Page 7 of 11 Fig. 2 Comparison of the features from microalgae to bacteria, yeast, plants and humans: average Prob values (A), average %GA values in ncRNAs (B), and average %GA values in partial coding sequences (C) Anuntakarun et al. BioData Mining (2022) 15:8 Page 8 of 11 Comparison of performance of different classifier algorithms Results of the performance evaluation of the six classifier algorithms, namely Decision Tree (DT), K-nearest neighbor (KNN), Naïve Bayes (NB), Neural Network (NN), Ran- dom Forest (RF), and Support Vector Machine (SVM), using 10-fold cross-validation with the top 20 features selected by the Relief method are shown in Table 6 s. The clas- sifier algorithms mostly showed comparable performance. However, the Random Forest algorithm had the highest accuracy, specificity, ROC area, and the lowest false positive rate and was thus chosen, as the false positive rate is particularly important for identify- ing microalgal ncRNAs. Random Forest, as an ensemble of multiple decision trees, could be useful for the identification of noncoding RNAs in microalgae, just as it has been used for a broad range of organisms [21]. Benchmarking of our mSRFR model performance with other tools We evaluated the performance of the trained microalgal Random Forest model using the top 20 features from the Relief feature selection method (mSRFR model) to identify ncRNAs from the test dataset. We benchmarked our model against other classification tools, namely RNAcon [14], CPC [15], CPC2 [16], CNCI [17], CPPred [18], and a Ran- dom Forest model using the top 20 features from the Relief feature selection but with- out applying the SMOTE technique to balance the dataset (mRFR model). As shown in Table 3 and Fig. 3, the results are divided into four groups: cyanobacteria, diatoms, gold algae, and green algae. The accuracy of our mRFR was high and similar to mSRFR. However, for golden algae, the accuracy of mSRFR was higher by around 2%. In addition, mSRFR showed the lowest false positive rate when compared with the other tools. Table 3 Performance comparison of our mSRFR model to others (RNAcon, CPC, CPC2, CNCI, and CPPred) used in discriminating ncRNAs and CDS of microalgae RNAcon CPC CPC2 CNCI CPPred mRFR mSRFR Cyanobacteria ACC (%) 72 93 71 55 70 99 99 Sn (%) 94 90 99 70 100 99 99 Sp (%) 19 99 2 20 0 99 99 FPR 0.81 0.01 0.98 0.8 1 0.01 0.01 Diatom ACC (%) 77 94 85 73 78 99 99 Sn (%) 76 96 100 71 100 99 99 Sp (%) 80 85 34 78 0 99 99 FPR 0.20 0.15 0.66 0.22 1 0.01 0.01 Golden Algae ACC (%) 87 81 80 86 73 93 95 Sn (%) 95 83 100 97 100 92 93 Sp (%) 67 85 26 60 0 93 99 FPR 0.33 0.15 0.74 0.40 1 0.07 0.01 Green Algae ACC (%) 65 72 73 89 22 99 99 Sn (%) 70 99 100 67 100 99 99 Sp (%) 63 64 66 96 0 99 99 FPR 0.37 0.36 0.34 0.04 1 0.01 0.01 Anuntakarun et al. BioData Mining (2022) 15:8 Page 9 of 11 Fig. 3 Performance comparison of our model, with or without applying the SMOTE technique and other tools (RNAcon, CPC, CPC2, CNCI, and CPPred) to discriminate between ncRNAs and CDS in the test dataset Discussion To the best of our knowledge, this tool is the first ncRNAs classification tool trained specifically for microalgae, the most promising biofuel candidates rich in high-value bioactive compounds. Identifying ncRNAs in microalgae might help improve our under- standing of their regulatory functions in gene manipulation. Moreover, ncRNA investi- gation in microalgae may provide new insights into metabolic regulation and engineering for product improvement. The main contributions are threefold: First, we developed an ncRNA identification tool for microalgae-based on a Random Forest model. Random Forest, an ensemble machine learning method, has been used for a broad range of organisms for its robustness and minimal overfitting problems. Second, to address the problem of imbalanced datasets of different groups of microal- gae, for example, where the numbers of both ncRNAs and coding sequences from golden algae were far fewer than those of the other groups, we applied the SMOTE technique to balance the datasets. Interestingly, the SMOTE (mSRFR) model performed better than the model without SMOTE (mRFR) in discriminating ncRNAs from CDS in golden algae, suggesting that the SMOTE model can handle imbalanced datasets better. Third, a group of features specific to ncRNAs in microalgae were identified. More- over, our study also revealed four features (TmL, Prob, CM, and GA) that contributed majorly to the performance of the model. The RF classifier model utilizing only these four features, instead of all top 20 features, achieved a reasonably high accuracy of 95%. Our analysis further shows that of the four features, Prob and GA are unique features in discriminating ncRNAs from coding sequences in microalgae. They were not among the top features, by any of the methods, for the bacterial, yeast, plant, or human ncRNAs discrimination. We propose the GA dinucleotide as one of the microalgal ncRNAs signature features, given its higher abundance in microalgal ncRNAs com- pared to other species. Conversely, the coding sequences of microalgae contained fewer GA dinucleotides in comparison to those of the other organisms. The GA dinucleotide or GA motif is highly conserved in T box and S box sequences, which are transcription Anuntakarun et al. BioData Mining (2022) 15:8 Page 10 of 11 termination control systems. The T box functions in regulating amino acid transporter genes, aminoacyl-tRNA synthetase, and amino acid biosynthesis, while the S box is related to methionine synthesis [32]. The high GA dinucleotide composition of microalgal ncRNAs could be relevant for transcription termination control systems as microalgal photosynthesis is highly regulated on the level of transcriptional control [33]. Conclusions In this study, mSRFR (m for microalgae; S for SMOTE; RF for Random Forest; R for the Relief method) was used for ncRNA identification in microalgae-based on a Ran- dom Forest model using the top 20 features from a total of 106 features selected by the Relief feature selection method. The tool achieved a high accuracy of about 97% and a low false-positive rate of 2% in discriminating microalgal ncRNAs from coding se- quences in a test dataset containing 7292 sequences. Currently, next-generation se- quencing technologies, such as RNA-seq, have become popular for the study of gene expression in many organisms. For future research, we aim to extend our tool to sup- port input formats of raw reads from next-generation sequencing data, such as fastq, and to develop a web-based computational pipeline that will enable users to identify potential ncRNAs in microalgae from next-generation sequencing data. Authors’ contributions Conceptualization: MR, SL, TL. Data curation: SA. Formal analysis: SA. Funding acquisition: MR, WW Methodology: SA, SL. Writing - original draft: SA. Writing - review & editing: MR, SL, TL, WW. The authors read and approved the final manuscript. Funding This work was supported by National Center for Genetic Engineering and Biotechnology (BIOTEC) for Songtham Anuntakarun’ scholarship and National Research University (NRU) Project-year-2557, King Mongkut’s University of Tech- nology Thonburi, Thailand. Availability of data and materials mSRFR: A machine learning model using microalgal signature features for ncRNA classification is an open-source col- laborative initiative available in the GitHub repository (https://github.com/anun001/smRFR-smote-microalgae-Random- Forest-Relief-model). Declaration Competing interests The authors declare that there is no conflict of interest regarding the publication of this article. Author details Bioinformatics and Systems Biology Program, School of Bioresources and Technology, King Mongkut’s University of Technology Thonburi (KMUTT), Bangkok 10150, Thailand. School of Information Technology, KMUTT, Bang Mod, Thung Khru, Bangkok 10140, Thailand. Biochemical Engineering and Systems Biology Research Group, National Center for Genetic Engineering and Biotechnology (BIOTEC), National Science and Technology Development Agency at King Mongkut’s University of Technology Thonburi, Bang Khun Thian, Bangkok 10150, Thailand. Department of Mathematics, Faculty of Science, KMUTT, Bangkok 10140, Thailand. Biotechnology program, School of Bioresources and Technology, KMUTT, Bang Khun Thian, Bangkok 10150, Thailand. Algal Biotechnology Research Group, Pilot Plant Development and Training Institute (PDTI), KMUTT, Bang Khun Thian, Bangkok 10150, Thailand. Received: 31 October 2021 Accepted: 6 February 2022 References 1. Hoffmann L. Algae of terrestrial habitats. Bot Rev. 1989;55(2):77–105. https://doi.org/10.1007/BF02858529. 2. John DM, Whitton BA, Brook AJ. The freshwater algal flora of the British Isles: an identification guide to freshwater and terrestrial algae, vol. I. Cambridge: Cambridge University Press; 2002. 3. Geider RJ, La Roche J. Redfield revisited: variability of C:N:P in marine microalgae and its biochemical basis. Eur J Phycol. 2002;37(1):1–17. https://doi.org/10.1017/S0967026201003456. 4. Delhi N. Functional ingredients and algae for foods and nutraceuticals. Burlington: Elsevier Science; 2013. 5. Wan Ngah WS, Hanafiah MAKM. Removal of heavy metal ions from wastewater by chemically modified plant wastes as adsorbents: a review. Bioresour Technol. 2008;99(10):3935–48. https://doi.org/10.1016/j.biortech.2007.06.011. Anuntakarun et al. BioData Mining (2022) 15:8 Page 11 of 11 6. Schenk PM, Thomas-Hall SR, Stephens E, Marx UC, Mussgnug JH, Posten C, et al. Second generation biofuels: high-efficiency microalgae for biodiesel production. BioEnergy Res. 2008;1(1):20–43. https://doi.org/10.1007/s12155-008-9008-8. 7. Thillairajasekar K, Duraipandiyan V, Perumal P, Ignacimuthu S. Antimicrobial activity of Trichodesmium erythraeum (Ehr) (microalga) from south east coast of Tamil Nadu. India Int J Integr Biol. 2009;5:167–70. 8. Lauritano C, Ferrante MI, Rogato A. Marine natural products from microalgae: an -omics overview. Mar Drugs. 2019;17(5): 269. https://doi.org/10.3390/md17050269. 9. Mattick JS, Makunin IV. Non-coding RNA. Hum Mol Genet. 2006;15 spec (1):R17–29. 10. Beermann J, Piccoli MT, Viereck J, Thum T. Non-coding RNAs in development and disease: background, mechanisms, and therapeutic approaches. Physiol Rev. 2016;96(4):1297–325. https://doi.org/10.1152/physrev.00041.2015. 11. Serghiou S, Kyriakopoulou A, Ioannidis JPA. Long noncoding RNAs as novel predictors of survival in human cancer: a systematic review and meta-analysis. Mol Cancer. 2016;15(1):50. https://doi.org/10.1186/s12943-016-0535-1. 12. Molnár A, Schwach F, Studholme DJ, Thuenemann EC, Baulcombe DC. miRNAs control gene expression in the single- cell alga Chlamydomonas reinhardtii. Nature. 2007;447(7148):1126–9. https://doi.org/10.1038/nature05903. 13. Yu Y, Zhang Y, Chen X, Chen Y. Plant noncoding RNAs: hidden players in development and stress responses. Annu Rev Cell Dev Biol. 2019;35(1):407–31. https://doi.org/10.1146/annurev-cellbio-100818-125218. 14. Panwar B, Arora A, Raghava GPS. Prediction and classification of ncRNAs using structural information. BMC Genomics. 2014;15(1):127. https://doi.org/10.1186/1471-2164-15-127. 15. Kong L, Zhang Y, Ye ZQ, Liu XQ, Zhao SQ, Wei L, et al. CPC: assess the protein-coding potential of transcripts using sequence features and support vector machine. Nucleic Acids Res. 2007;35:W345–9. https://doi.org/10.1093/nar/gkm391. 16. Kang YJ, Yang DC, Kong L, Hou M, Meng YQ, Wei L, et al. CPC2: a fast and accurate coding potential calculator based on sequence intrinsic features. Nucleic Acids Res. 2017;45(W1):W12–6. https://doi.org/10.1093/nar/gkx428. 17. Sun L, Luo H, Bu D, Zhao G, Yu K, Zhang C, et al. Utilizing sequence intrinsic composition to classify protein-coding and long noncoding transcripts. Nucleic Acids Res. 2013;41(17):e166. https://doi.org/10.1093/nar/gkt646. 18. Tong X, Liu S. CPPred: Coding potential prediction based on the global description of RNA sequence. Nucleic Acids Res. 2019;47(8):e43. https://doi.org/10.1093/nar/gkz087. 19. Bao M, Cervantes Cervantes M, Zhong L, Wang JTL. Searching for noncoding RNAs in genomic sequences using ncRNAscout. Genom Proteom Bioinform. 2012;10(2):114–21. https://doi.org/10.1016/j.gpb.2012.05.004. 20. Hall M, Frank E, Holmes G, Pfahringer B, Reutemann P, Witten IH. The WEKA data mining software: an update. ACM SIGK DD Explor Newsl. 2009;11(1):10–8. https://doi.org/10.1145/1656274.1656278. 21. Lertampaiporn S, Thammarongtham C, Nukoolkit C, Kaewkamnerdpong B, Ruengjitchatchawalya M. Heterogeneous ensemble approach with discriminative features and modified-SMOTEbagging for pre-miRNA classification. Nucleic Acids Res. 2013;41(1):e21. https://doi.org/10.1093/nar/gks878. 22. Lertampaiporn S, Thammarongtham C, Nukoolkit C, Kaewkamnerdpong B, Ruengjitchatchawalya M. Identification of noncoding RNAs with a new composite feature in the hybrid random Forest Ensemble algorithm. Nucleic Acids Res. 2014;42(11):e93. https://doi.org/10.1093/nar/gku325. 23. Kent JT. Information Gain and a General Measure of Correlation. Biometrika. 1983;70(1):163-73. http://www.jstor.org/sta ble/2335954 Accessed 06 Oct 2016. 24. Holte RC. Very simple classification rules perform well on Most commonly used datasets. Mach Learn. 1993;11(1):63–91. https://doi.org/10.1023/A:1022631118932. 25. Robnik-Šikonja M, Kononenko I. An adaptation of Relief for attribute estimation in regression. Mach Learning Proc Fourteenth Int Conf. 1997;5:296–304. 26. Ahmad MW, Mourshed M, Rezgui Y. Trees vs neurons: comparison between random forest and ANN for high-resolution prediction of building energy consumption. Energy Build. 2017;147:77–89. https://doi.org/10.1016/j.enbuild.2017.04.038. 27. Wehenkel M, Sutera A, Bastin C, Geurts P, Phillips C. Random forests based group importance scores and their statistical interpretation: application for Alzheimer’s disease. Front Neurosci. 2018;12:1–19. https://doi.org/10.3389/fnins.2018.00411. 28. Urbanowicz RJ, Olson RS, Schmitt P, Meeker M, Moore JH. Benchmarking relief-based feature selection methods for bioinformatics data mining. J Biomed Inform. 2018;85:168–88. https://doi.org/10.1016/j.jbi.2018.07.015. 29. Shaw TI, Manzour A, Wang Y, Malmberg RL, Cai L. Analyzing modular RNA structure reveals low global structural entropy in microRNA sequence. J Bioinform Comput Biol. 2011;9(2):283–98. https://doi.org/10.1142/S0219720011005495. 30. Wan Y, Qu K, Ouyang Z, Kertesz M, Li J, Tibshirani R, et al. Genome-wide measurement of RNA folding energies. Mol Cell. 2012;48(2):169–81. https://doi.org/10.1016/j.molcel.2012.08.008. 31. Leclercq M, Diallo AB, Blanchette M. Computational prediction of the localization of microRNAs within their pre-miRNA. Nucleic Acids Res. 2013;41(15):7200–11. https://doi.org/10.1093/nar/gkt466. 32. Winkler WC, Grundy FJ, Murphy BA, Henkin TM. The GA motif: an RNA element common to bacterial antitermination systems, rRNA, and eukaryotic RNAs. RNA. 2001;7(8):1165–72. https://doi.org/10.1017/S1355838201002370. 33. Wilde A, Hihara Y. Transcriptional and posttranscriptional regulation of cyanobacterial photosynthesis. Biochim Biophys Acta. 2016;1857(3):296–308. https://doi.org/10.1016/j.bbabio.2015.11.002. Publisher’sNote Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Journal

BioData Mining – Springer Journals

Published: Mar 21, 2022

Keywords: Microalgae; Machine learning; Non-coding RNAs; Random Forest; Signature feature; SMOTE

There are no references for this article.