一种基于特征筛选的原核生物启动子判别分析方法

启动子识别是研究基因转录调控的重要环节,但目前方法的识别正确率偏低。在深入分析原核启动子特征的基础上,提出了一种基于特征筛选的原核启动子判别分析方法,首先在启动子序列的组成特征、信号特征和结构特征中选取备选特征,为每个特征建立适当的描述模型,并对主要的保守模式采用复合模式模型;再通过模型计算对备选特征进行逐步筛选,优化特征集,将序列表示为组合特征向量;最终利用二次判别分析实现识别。对大肠杆菌和枯草杆菌实际启动子数据进行的刀切法测试验证了方法的有效性和通用性。对于大肠杆菌非编码区(70启动子,识别的平均正确率达到了85.8%,优于其它几种典型识别方法;对于大肠杆菌编码区内部)70启动子和其它几种原核启动子,平均正确率也都超过了80%。方法框架还具有良好的可扩展性,能够方便地容纳新特征,使识别性能不断提高。     

利用位点特异性打分矩阵对大肠杆菌启动子的预测

启动子是基因转录起始的一个关键性元件。本研究利用数据库中提供的大肠杆菌启动子数据,基于位点特异性打分矩阵(Position-specific scoring matrix,PSSM)算法建立了大肠杆菌启动子预测方法,并采用ROC曲线对预测结果进行评估。结果显示,本方法对大肠杆菌sigma24、sigma28、sigma32、sigma38、sigma54和sigma70启动子预测的准确度分别达到86%,96%,93%,96%,97%和74%。由于原核生物启动子序列的保守性,可将该方法推广至其他原核生物的启动子预测。     

A compression-based approach for coding sequences identification. I. Application to prokaryotic genomes.

Most of the gene prediction algorithms for prokaryotes are based on Hidden Markov Models or similar machine-learning approaches, which imply the optimization of a high number of parameters. The present paper presents a novel method for the classification of coding and non-coding regions in prokaryotic genomes, based on a suitably defined compression index of a DNA sequence. The main features of this new method are the non-parametric logic and the costruction of a dictionary of words extracted from the sequences. These dictionaries can be very useful to perform further analyses on the genomic sequences themselves. The proposed approach has been applied on some prokaryotic complete genomes, obtaining optimal scores of correctly recognized coding and non-coding regions. Several false-positive and false-negative cases have been investigated in detail, which have revealed that this approach can fail in the presence of highly structured coding regions (e.g., genes coding for modular proteins) or quasi-random non-coding regions (e.g., regions hosting non-functional fragments of copies of functional genes; regions hosting promoters or other protein-binding sequences). We perform an overall comparison with other gene-finder software, since at this step we are not interested in building another gene-finder system, but only in exploring the possibility of the suggested approach.     

一种基于多特征的大肠杆菌启动子判别算法

主要对从一段DNA序列中提取出信息以判别其中是否含有启动子的问题进行了研究。首先从固定长度的序歹4中提取成分特征和结构特征,然后将这些特征输入到一个非线性分类器中进行判别。测试结果显示,在正集＆非编码区负集中,平均错误率降低为13．4％;在正集＆编码区负集中,平均错误率降低到17．0％。表明该方法是非常有效的。     

A fractal method to distinguish coding and non-coding sequences in a complete genome based on a number sequence representation

A fractal method to distinguish coding and non-coding sequences in a complete genome is proposed, based on different statistical behaviors between these two kinds of sequences. We first propose a number sequence representation of DNA sequences. Multifractal analysis is then performed on the measure representation of the obtained number sequence. The three exponents C(-1), C1 and C2 are selected from the result of multifractal analysis. Each DNA may be represented by a point in the three-dimensional space generated by these three-component vectors. It is shown that points corresponding to coding and non-coding sequences in the complete genome of many prokaryotes are roughly distributed in different regions. Fisher's discriminant algorithm can be used to separate these two regions in the spanned space. If the point (C(-1),C1,C2) for a DNA sequence is situated in the region corresponding to coding sequences, the sequence is discriminated as a coding sequence; otherwise, the sequence is classified as a non-coding one. For all 51 prokaryotes we considered , the average discriminant accuracies pc,pnc,qc and qnc reach 72.28%, 84.65%, 72.53% and 84.18%, respectively.     

Two separable functional domains in the sigma-subunit of RNA polymerase in Bacillus subtilis?

The sigma-subunit of RNA polymerase is responsible for promoter recognition in prokaryotes [(1969) Nature 221, 43-46]. Alterations in the sigma-subunit are thought to be involved in controlling 'global' changes in gene expression, such as those involved in differentiation in the spore-forming bacterium Bacillus subtilis [(1981) Cell 25, 582-584]. Stragier et al. [(1985) FEBS Lett. 195, 3-11] have proposed that sigma-factors are composed of two domains: a C-terminal domain involved in promoter recognition and an N-terminal domain involved in interactions with RNA polymerase. We have sequenced another developmental gene from B. subtilis, spoIIIC, and the strong homology of its predicted product suggests that it too may be a sigma-factor. However, the spoIIIC product is small and lacks completely the conserved N-terminal domain of the sigma-subunits. I propose that the product of the spoIIIC gene may carry out the DNA-recognition functions of a sigma-factor but that it probably requires an auxiliary factor to interact with core RNA polymerase.     

基于卷积神经网络的大肠杆菌启动子预测

目的 基于位点特异性打分矩阵（position-specific scoring matrices,PSSM）的预测模型已经取得了良好的效果,基于PSSM的各种优化方法也在不断发展,但准确率相对较低,为了进一步提高预测准确率,本文基于卷积神经网络（convolutional neural networks,CNN）算法做了进一步研究。方法 采用PSSM将启动子序列处理成数值矩阵,通过CNN算法进行分类。大肠杆菌K-12（Escherichia coli K-12,E.coli K-12,下文简称大肠杆菌）的Sigma38、Sigma54和Sigma70 3种启动子序列被作为正集,编码（Coding）区和非编码（Non-coding）区的序列为负集。结果 在预测大肠杆菌启动子的二分类中,准确率达到99%,启动子预测的成功率接近100%;在对Sigma38、Sigma54、Sigma70 3种启动子的三分类中,预测准确率为98%,并且针对每一种序列的预测准确率均可以达到98%以上。最后,本文以Sigma38、Sigma54、Sigma70 3种启动子分别和Coding区或者Non-coding区序列做四分类,预测得到的准确性为0.98,对3种Sigma启动子均衡样本的十交叉检验预测精度均可以达到0.95以上,海明距离为0.016,Kappa系数为0.97。结论 相较于支持向量机（support vector machine,SVM）等其他分类算法,CNN分类算法更具优势,并且基于CNN的分类优势,编码方式亦可以得到简化。     

Assay and characterization of a strong promoter element from B. subtilis

A new strong promoter fragment isolated from Bacillus subtilis was identified and characterized. Using the heat stable beta-galactosidase as reporter, the promoter fragment exhibited high expression strength both in Escherichia coli and B. subtilis. The typical prokaryotic promoter conservation regions were found in the promoter fragment and the putative promoter was identified as the control element of yxiE gene via sequencing assay and predication of promoter. To further verify and characterize the cloned strong promoter, the putative promoter was sub-cloned and the beta-Gal directed by the promoters was high-level expressed both in E. coli and B. subtilis. By means of the isolated promoter, an efficient expression system was developed in B. subtilis and the benefit and usefulness was demonstrated through expression of three heterologous and homogenous proteins. Thus, we identified a newly strong promoter of B. subtilis and provided a robust expression system for genetic engineering of B. subtilis.