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Incorporating significant amino acid pairs and protein domains to predict RNA splicing-related proteins with functional roles

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Abstract

Machinery of pre-mRNA splicing is carried out through the interaction of RNA sequence elements and a variety of RNA splicing-related proteins (SRPs) (e.g. spliceosome and splicing factors). Alternative splicing, which is an important post-transcriptional regulation in eukaryotes, gives rise to multiple mature mRNA isoforms, which encodes proteins with functional diversities. However, the regulation of RNA splicing is not yet fully elucidated, partly because SRPs have not yet been exhaustively identified and the experimental identification is labor-intensive. Therefore, we are motivated to design a new method for identifying SRPs with their functional roles in the regulation of RNA splicing. The experimentally verified SRPs were manually curated from research articles. According to the functional annotation of Splicing Related Gene Database, the collected SRPs were further categorized into four functional groups including small nuclear Ribonucleoprotein, Splicing Factor, Splicing Regulation Factor and Novel Spliceosome Protein. The composition of amino acid pairs indicates that there are remarkable differences among four functional groups of SRPs. Then, support vector machines (SVMs) were utilized to learn the predictive models for identifying SRPs as well as their functional roles. The cross-validation evaluation presents that the SVM models trained with significant amino acid pairs and functional domains could provide a better predictive performance. In addition, the independent testing demonstrates that the proposed method could accurately identify SRPs in mammals/plants as well as effectively distinguish between SRPs and RNA-binding proteins. This investigation provides a practical means to identifying potential SRPs and a perspective for exploring the regulation of RNA splicing.

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Acknowledgments

The authors would like to sincerely thank the National Science Council of the Republic of China for financially supporting this research under Contract No. 101-2628-E-155-002-MY2 and 102-2221-E-155-069.

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Authors and Affiliations

  1. Institute of Bioinformatics and Systems Biology, National Chiao Tung University, Hsin-chu, 300, Taiwan

    Justin Bo-Kai Hsu

  2. Department of Computer Science and Engineering, Yuan Ze University, Taoyuan, 320, Taiwan

    Kai-Yao Huang, Tzu-Ya Weng & Tzong-Yi Lee

  3. Tao-Yuan Hospital, Ministry of Health and Welfare, Taoyuan, 320, Taiwan

    Chien-Hsun Huang

Authors
  1. Justin Bo-Kai Hsu
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  2. Kai-Yao Huang
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  3. Tzu-Ya Weng
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  4. Chien-Hsun Huang
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  5. Tzong-Yi Lee
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Corresponding author

Correspondence to Tzong-Yi Lee.

Additional information

Justin Bo-Kai Hsu and Kai-Yao Huang have contributed equally to this work.

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Hsu, J.BK., Huang, KY., Weng, TY. et al. Incorporating significant amino acid pairs and protein domains to predict RNA splicing-related proteins with functional roles. J Comput Aided Mol Des 28, 49–60 (2014). https://doi.org/10.1007/s10822-014-9706-6

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  • DOI: https://doi.org/10.1007/s10822-014-9706-6

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