MicroRPM: A microRNA prediction model based only on plant small RNA sequencing data

Kuan Chieh Tseng, Yi Fan Chiang-Hsieh, Hsuan Pai, Chi Nga Chow, Shu Chuan Lee, Han Qin Zheng, Po Li Kuo, Guan Zhen Li, Yu Cheng Hung, Na Sheng Lin, Wen Chi Chang

Research output: Contribution to journalArticlepeer-review

20 Citations (Scopus)


Motivation MicroRNAs (miRNAs) are endogenous non-coding small RNAs (of about 22 nucleotides), which play an important role in the post-Transcriptional regulation of gene expression via either mRNA cleavage or translation inhibition. Several machine learning-based approaches have been developed to identify novel miRNAs from next generation sequencing (NGS) data. Typically, precursor/genomic sequences are required as references for most methods. However, the non-Availability of genomic sequences is often a limitation in miRNA discovery in non-model plants. A systematic approach to determine novel miRNAs without reference sequences is thus necessary. Results In this study, an effective method was developed to identify miRNAs from non-model plants based only on NGS datasets. The miRNA prediction model was trained with several duplex structure-related features of mature miRNAs and their passenger strands using a support vector machine algorithm. The accuracy of the independent test reached 96.61% and 93.04% for dicots (Arabidopsis) and monocots (rice), respectively. Furthermore, true small RNA sequencing data from orchids was tested in this study. Twenty-one predicted orchid miRNAs were selected and experimentally validated. Significantly, 18 of them were confirmed in the qRT-PCR experiment. This novel approach was also compiled as a user-friendly program called microRPM (miRNA Prediction Model). Availability and implementation This resource is freely available at http://microRPM.itps.ncku.edu.tw. Contact nslin@sinica.edu.tw or sarah321@mail.ncku.edu.tw Supplementary informationSupplementary dataare available at Bioinformatics online.

Original languageEnglish
Pages (from-to)1108-1115
Number of pages8
Issue number7
Publication statusPublished - 2018 Apr 1

All Science Journal Classification (ASJC) codes

  • Statistics and Probability
  • Biochemistry
  • Molecular Biology
  • Computer Science Applications
  • Computational Theory and Mathematics
  • Computational Mathematics


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