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Identification of Real MicroRNA Precursors with a Pseudo Structure Status Composition Approach

Author

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  • Bin Liu
  • Longyun Fang
  • Fule Liu
  • Xiaolong Wang
  • Junjie Chen
  • Kuo-Chen Chou

Abstract

Containing about 22 nucleotides, a micro RNA (abbreviated miRNA) is a small non-coding RNA molecule, functioning in transcriptional and post-transcriptional regulation of gene expression. The human genome may encode over 1000 miRNAs. Albeit poorly characterized, miRNAs are widely deemed as important regulators of biological processes. Aberrant expression of miRNAs has been observed in many cancers and other disease states, indicating they are deeply implicated with these diseases, particularly in carcinogenesis. Therefore, it is important for both basic research and miRNA-based therapy to discriminate the real pre-miRNAs from the false ones (such as hairpin sequences with similar stem-loops). Particularly, with the avalanche of RNA sequences generated in the postgenomic age, it is highly desired to develop computational sequence-based methods in this regard. Here two new predictors, called “iMcRNA-PseSSC” and “iMcRNA-ExPseSSC”, were proposed for identifying the human pre-microRNAs by incorporating the global or long-range structure-order information using a way quite similar to the pseudo amino acid composition approach. Rigorous cross-validations on a much larger and more stringent newly constructed benchmark dataset showed that the two new predictors (accessible at http://bioinformatics.hitsz.edu.cn/iMcRNA/) outperformed or were highly comparable with the best existing predictors in this area.

Suggested Citation

  • Bin Liu & Longyun Fang & Fule Liu & Xiaolong Wang & Junjie Chen & Kuo-Chen Chou, 2015. "Identification of Real MicroRNA Precursors with a Pseudo Structure Status Composition Approach," PLOS ONE, Public Library of Science, vol. 10(3), pages 1-20, March.
    • Handle: RePEc:plo:pone00:0121501
    DOI: 10.1371/journal.pone.0121501
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    References listed on IDEAS

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    1. Wei Chen & Hao Lin & Peng-Mian Feng & Chen Ding & Yong-Chun Zuo & Kuo-Chen Chou, 2012. "iNuc-PhysChem: A Sequence-Based Predictor for Identifying Nucleosomes via Physicochemical Properties," PLOS ONE, Public Library of Science, vol. 7(10), pages 1-9, October.
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    4. Yan Xu & Jun Ding & Ling-Yun Wu & Kuo-Chen Chou, 2013. "iSNO-PseAAC: Predict Cysteine S-Nitrosylation Sites in Proteins by Incorporating Position Specific Amino Acid Propensity into Pseudo Amino Acid Composition," PLOS ONE, Public Library of Science, vol. 8(2), pages 1-7, February.
    5. Wei-Zhong Lin & Jian-An Fang & Xuan Xiao & Kuo-Chen Chou, 2011. "iDNA-Prot: Identification of DNA Binding Proteins Using Random Forest with Grey Model," PLOS ONE, Public Library of Science, vol. 6(9), pages 1-7, September.
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    2. Chih-Chou Chiu & Chung-Min Wu & Te-Nien Chien & Ling-Jing Kao & Chengcheng Li & Chuan-Mei Chu, 2023. "Integrating Structured and Unstructured EHR Data for Predicting Mortality by Machine Learning and Latent Dirichlet Allocation Method," IJERPH, MDPI, vol. 20(5), pages 1-22, February.

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