Classification of Pre-cursor microRNAs from Different Species Using a New Set of Features
MicroRNAs (miRNAs) are short RNA sequences actively involved in post-transcriptional gene regulation. Such miRNAs have been discovered in most eukaryotic organisms. They also seem to exist in viruses and perhaps in microbial pathogens to target the host. Drosha is the enzyme which first cleaves the pre-miRNA from the nascent pri-miRNA. Previously, we showed that it is possible to distinguish between pre-miRNAs of different species depending on their evolutionary distance using just k-mers.
In this study, we introduce three new sets of features which are extracted from the precursor sequence and summarize the distance between k-mers. These new set of features, named inter k-mer distance, k-mer location distance and k-mer first-last distance, were compared to k-mer and all other published features describing a pre-miRNA. Classification at well above 80% (depending on the evolutionary distance) is possible with a combination of distance and regular k-mer features.
The novel features specifically aid classification at closer evolutionary distances when compared to k-mers only. K-mer and k-mer distance features together lead to accurate classification for larger evolutionary distances such as Homo sapiens versus Brassicaceae (93% ACC). Including the novel distance features further increases the average accuracy since they are more effective for lower evolutionary distances. Secondary structure-based features were not effective in this study. We hope that this will fuel further analysis of miRNA evolution. Additionally, our approach provides another line of evidence when predicting pre-miRNAs and can be used to ensure that miRNAs detected in NGS samples are indeed not contaminations. In the future, we aim for automatic categorization of unknown hairpins into all species/clades available in miRBase.
KeywordsmicroRNA sequence Machine learning Differentiate miRNAs among species k-mer miRNA categorization
- Berthold, M.R., Cebron, N., Dill, F., et al.: KNIME: the Konstanz information miner. In: Preisach, C., Burkhardt, H., Schmidt-Thime, L., Decker, R. (eds.) Data Analysis, Machine Learning and Applications, pp. 319–326. Springer, Heidelberg (2008). https://doi.org/10.1007/978-3-540-78246-9_38CrossRefGoogle Scholar
- Dang, H.T., Tho, H.P., Satou, K., Tu, B.H.: Prediction of microRNA hairpins using one-class support vector machines. In: 2nd International Conference on Bioinformatics and Biomedical Engineering, iCBBE 2008, pp. 33–36 (2008)Google Scholar
- Erson-Bensan, A.E.: Introduction to MicroRNAs in biological systems. In: Yousef, M., Allmer, J. (eds.) miRNomics: MicroRNA Biology and Computational Analysis, 1st edn. Humana Press, New York, pp. 1–14 (2014)Google Scholar
- Fromm, B., Billipp, T., Peck, L.E., et al.: A uniform system for the annotation of vertebrate microRNA genes and the evolution of the human microRNAome. Annu. Rev. Genet. 49, 213–242 (2015). https://doi.org/10.1146/annurev-genet-120213-092023CrossRefGoogle Scholar
- Griffiths-Jones, S.: miRBase: microRNA sequences and annotation. Curr. Protoc. Bioinf. 12.9.1–12.9.10 (2010). Chap. 12. Unit. https://doi.org/10.1002/0471250953.bi1209s29
- Saçar Demirci, M.D., Baumbach, J., Allmer, J.: On the performance of pre-microRNA detection algorithms. Nat. Commun. (2017). https://doi.org/10.1038/s41467-017-00403-z
- Saçar Demirci, M.D., Allmer, J.: Data mining for microRNA gene prediction: on the impact of class imbalance and feature number for microrna gene prediction. In: 2013 8th International Symposium on Health Informatics and Bioinformatics, pp. 1–6. IEEE (2013)Google Scholar
- Saçar Demirci, M.D., Allmer, J.: Machine learning methods for MicroRNA gene prediction. In: Yousef, M., Allmer, J. (eds.) miRNomics: MicroRNA Biology and Computational Analysis SE - 10, 1st edn., pp. 177–187. Humana Press, New York (2014)Google Scholar
- Yousef, M., Allmer, J., Khalifa, W.: Plant microRNA prediction employing sequence motifs achieves high accuracy (2016)Google Scholar
- Yousef, M., Khalifa, W., Acar, I.E., Allmer, J.: MicroRNA categorization using sequence motifs and k-mers. BMC Bioinf. 18(1), 170 (2017a). https://doi.org/10.1186/s12859-017-1584-1
- Yousef, M., Nigatu, D., Levy, D., et al.: Categorization of species based on their MicroRNAs employing sequence motifs, information-theoretic sequence feature extraction, and k-mers. EURASIP J. Adv. Sig. Process. 2017(70), 1–10 (2017b). https://doi.org/10.1186/s13634-017-0506-8