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Popular Computational Tools Used for miRNA Prediction and Their Future Development Prospects

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Abstract

MicroRNAs (miRNAs) are 19–24 nucleotide (nt)-long noncoding, single-stranded RNA molecules that play significant roles in regulating the gene expression, growth, and development of plants and animals. From the year that miRNAs were first discovered until the beginning of the twenty-first century, researchers used experimental methods such as cloning and sequencing to identify new miRNAs and their roles in the posttranscriptional regulation of protein synthesis. Later, in the early 2000s, informatics approaches to the discovery of new miRNAs began to be implemented. With increasing knowledge about miRNA, more efficient algorithms have been developed for computational miRNA prediction. The miRNA research community, hoping for greater coverage and faster results, has shifted from cumbersome and expensive traditional experimental approaches to computational approaches. These computational methods started with homology-based comparisons of known miRNAs with orthologs in the genomes of other species; this method could identify a known miRNA in new species. Second-generation sequencing and next-generation sequencing of mRNA at different developmental stages and in specific tissues, in combination with a better search and alignment algorithm, have accelerated the process of predicting novel miRNAs in a particular species. Using the accumulated annotated miRNA sequence information, researchers have been able to design ab initio algorithms for miRNA prediction independent of genome sequence knowledge. Here, the methods recently used for miRNA computational prediction are summarized and classified into the following four categories: homology-based, target-based, scoring-based, and machine-learning-based approaches. Finally, the future developmental directions of miRNA prediction methods are discussed.

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Acknowledgement

We thank American Journal Experts for their help in revising the English grammar.

Funding

This work was supported by the National Natural Science Foundation of China (31770774); the Provincial Major Project of Basic or Applied Research in Natural Science, Guangdong Provincial Education Department (2016KZDXM038).

Author information

Author notes

  1. Tianyang Yu, Na Xu and Neshatul Haque have contributed equally to this work.

Authors and Affiliations

  1. Key Laboratory of Big Data Mining and Precision Drug Design of Guangdong Medical University, Guangdong Provincial Key Laboratory of Medical Molecular Diagnostics, Guangdong Medical University, Dongguan, Guangdong, 523808, China

    Tianyang Yu, Na Xu, Chang Gao, Wenhua Huang & Zunnan Huang

  2. Key Laboratory for Research and Development of Natural Drugs of Guangdong Province, School of Pharmacy, Guangdong Medical University, Dongguan, Guangdong, 523808, China

    Tianyang Yu, Na Xu & Zunnan Huang

  3. Structural Biology Laboratory, Division of Applied Biology, CSIR-Indian Institute of Chemical Technology, Hyderabad , 500007, India

    Neshatul Haque

  4. National Key Discipline of Human Anatomy, School of Basic Medical Sciences, Southern Medical University, No. 1838 Guangzhou Road North, Guangzhou, Guangdong, 510515, China

    Wenhua Huang

  5. Marine Biomedical Research Institute of Guangdong Zhanjiang, Zhanjiang, Guangdong, 524023, China

    Zunnan Huang

Authors
  1. Tianyang Yu
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  2. Na Xu
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  3. Neshatul Haque
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  4. Chang Gao
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  5. Wenhua Huang
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  6. Zunnan Huang
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Corresponding authors

Correspondence to Wenhua Huang or Zunnan Huang.

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Yu, T., Xu, N., Haque, N. et al. Popular Computational Tools Used for miRNA Prediction and Their Future Development Prospects. Interdiscip Sci Comput Life Sci 12, 395–413 (2020). https://doi.org/10.1007/s12539-020-00387-3

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  • DOI: https://doi.org/10.1007/s12539-020-00387-3

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