Institute of Bioinformatics and Systems Biology, National Chiao-Tung University, Hsin-Chu, Taiwan.
Med Biol Eng Comput. 2011 Apr;49(4):463-72. doi: 10.1007/s11517-011-0732-4. Epub 2011 Feb 1.
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.
准确识别潜在的多聚腺苷酸化位点有助于研究可变多聚腺苷酸化。本研究的目的是开发一种机器学习方法，帮助区分基因组序列中真实的多聚腺苷酸化信号和随机出现的信号。由于之前的研究表明，某些基因中的 RNA 二级结构有显著影响，作者试图通过计算来确定多聚腺苷酸化位点周围的常见结构模式，并研究 RNA 二级结构如何影响多聚腺苷酸化。这涉及到对 RNA 结构影响的初步研究，通过 motif search 工具发现发夹结构可能很重要。因此，提出除了多聚腺苷酸化位点周围的序列模式外，还存在一种广泛存在的结构模式，该模式也用于人类 mRNA 多聚腺苷酸化。在这项研究中，作者提出了一种使用支持向量机预测人类多聚腺苷酸化位点的计算模型。结果表明，该预测模型的性能与当前的预测工具相当。此外，还使用几个 motif finding 程序确定了与多聚腺苷酸化相关的常见结构模式，这为 RNA 二级结构在多聚腺苷酸化中的作用提供了新的见解。
