Ji Guoli, Zheng Jianti, Shen Yingjia, Wu Xiaohui, Jiang Ronghan, Lin Yun, Loke Johnny C, Davis Kimberly M, Reese Greg J, Li Qingshun Quinn
Department of Automation, Xiamen University, Xiamen, Fujian, 361005, PR China.
BMC Bioinformatics. 2007 Feb 7;8:43. doi: 10.1186/1471-2105-8-43.
One of the essential processing events during pre-mRNA maturation is the post-transcriptional addition of a polyadenine [poly(A)] tail. The 3'-end poly(A) track protects mRNA from unregulated degradation, and indicates the integrity of mRNA through recognition by mRNA export and translation machinery. The position of a poly(A) site is predetermined by signals in the pre-mRNA sequence that are recognized by a complex of polyadenylation factors. These signals are generally tri-part sequence patterns around the cleavage site that serves as the future poly(A) site. In plants, there is little sequence conservation among these signal elements, which makes it difficult to develop an accurate algorithm to predict the poly(A) site of a given gene. We attempted to solve this problem.
Based on our current working model and the profile of nucleotide sequence distribution of the poly(A) signals and around poly(A) sites in Arabidopsis, we have devised a Generalized Hidden Markov Model based algorithm to predict potential poly(A) sites. The high specificity and sensitivity of the algorithm were demonstrated by testing several datasets, and at the best combinations, both reach 97%. The accuracy of the program, called poly(A) site sleuth or PASS, has been demonstrated by the prediction of many validated poly(A) sites. PASS also predicted the changes of poly(A) site efficiency in poly(A) signal mutants that were constructed and characterized by traditional genetic experiments. The efficacy of PASS was demonstrated by predicting poly(A) sites within long genomic sequences.
Based on the features of plant poly(A) signals, a computational model was built to effectively predict the poly(A) sites in Arabidopsis genes. The algorithm will be useful in gene annotation because a poly(A) site signifies the end of the transcript. This algorithm can also be used to predict alternative poly(A) sites in known genes, and will be useful in the design of transgenes for crop genetic engineering by predicting and eliminating undesirable poly(A) sites.
前体mRNA成熟过程中的一个基本加工事件是转录后添加多聚腺苷酸[poly(A)]尾巴。3'端的poly(A)序列可保护mRNA免受无节制的降解,并通过mRNA输出和翻译机制的识别来指示mRNA的完整性。poly(A)位点的位置由前体mRNA序列中的信号预先确定,这些信号被多聚腺苷酸化因子复合物识别。这些信号通常是围绕切割位点的三部分序列模式,该切割位点将成为未来的poly(A)位点。在植物中,这些信号元件之间几乎没有序列保守性,这使得开发一种准确的算法来预测给定基因的poly(A)位点变得困难。我们试图解决这个问题。
基于我们当前的工作模型以及拟南芥中poly(A)信号和poly(A)位点周围的核苷酸序列分布概况,我们设计了一种基于广义隐马尔可夫模型的算法来预测潜在的poly(A)位点。通过对几个数据集的测试证明了该算法具有高特异性和敏感性,在最佳组合下,两者均达到97%。通过对许多已验证的poly(A)位点进行预测,证明了名为poly(A)位点搜寻器或PASS的程序的准确性。PASS还预测了通过传统遗传实验构建和表征的poly(A)信号突变体中poly(A)位点效率的变化。通过预测长基因组序列中的poly(A)位点证明了PASS的有效性。
基于植物poly(A)信号的特征,构建了一个计算模型来有效预测拟南芥基因中的poly(A)位点。该算法将有助于基因注释,因为poly(A)位点表示转录本的末端。该算法还可用于预测已知基因中的可变poly(A)位点,并通过预测和消除不需要的poly(A)位点,在作物基因工程转基因设计中发挥作用。