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Characterization and prediction of mRNA polyadenylation sites in human genes

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Abstract

The accurate identification of potential poly(A) sites has contributed to all many studies with regard to alternative polyadenylation. The aim of this study was the development of a machine-learning methodology that will help to discriminate real polyadenylation signals from randomly occurring signals in genomic sequence. Since previous studies have revealed that RNA secondary structure in certain genes has significant impact, the authors tried to computationally pinpoint common structural patterns around the poly(A) sites and to investigate how RNA secondary structure may influence polyadenylation. This involved an initial study on the impact of RNA structure and it was found using motif search tools that hairpin structures might be important. Thus, it was propose that, in addition to the sequence pattern around poly(A) sites, there exists a widespread structural pattern that is also employed during human mRNA polyadenylation. In this study, the authors present a computational model that uses support vector machines to predict human poly(A) sites. The results show that this predictive model has a comparable performance to the current prediction tool. In addition, it was identified common structural patterns associated with polyadenylation using several motif finding programs and this provides new insight into the role of RNA secondary structure plays in polyadenylation.

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Authors and Affiliations

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

    Tzu-Hao Chang & Hsien-Da Huang

  2. Institute of Systems Biology and Bioinformatics, National Central University, Jhongli, Taiwan

    Li-Ching Wu & Jorng-Tzong Horng

  3. Department of Computer Science and Information Engineering, National Central University, Jhongli, Taiwan

    Yu-Ting Chen & Jorng-Tzong Horng

  4. Department of Biological Science and Technology, National Chiao-Tung University, Hsin-Chu, Taiwan

    Hsien-Da Huang

  5. Department of Computer Science and Information Engineering, Yuan Ze University, Jhongli, Taiwan

    Baw-Jhiune Liu

  6. Biostatistics Center, China Medical University, Taichung, Taiwan

    Kuang-Fu Cheng

  7. Institute of Statistics, National Central University, Jhongli, Taiwan

    Kuang-Fu Cheng

  8. Department of Bioinformatics, Asia University, Taichung, Taiwan

    Jorng-Tzong Horng

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  1. Tzu-Hao Chang
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  2. Li-Ching Wu
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  3. Yu-Ting Chen
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  4. Hsien-Da Huang
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  5. Baw-Jhiune Liu
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Correspondence to Jorng-Tzong Horng.

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Chang, TH., Wu, LC., Chen, YT. et al. Characterization and prediction of mRNA polyadenylation sites in human genes. Med Biol Eng Comput 49, 463–472 (2011). https://doi.org/10.1007/s11517-011-0732-4

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  • DOI: https://doi.org/10.1007/s11517-011-0732-4

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