人类全基因组范围的CpG岛的预测与分析

CpG岛的甲基化是表观遗传中基因表达调控的重要机制。虽然目前已存在几个从DNA序列判别CpG岛的标准,但如何在标准中选择合适的参数仍是研究的焦点。文章通过分析比较两种经典CpG岛判定标准与三种预测方法,提出了改进的CpG岛预测方法——CpGISeeker。应用该预测方法,结合判定标准中的三个基本参数组合出的13组组合参数,在人类全基因组范围内进行了CpG岛预测,并统计分析了CpG岛的重复序列组成以及相对于基因转录起始位点的位置分布情况。分析结果表明CpGISeeker具有更精确判定CpG岛的特性;同时还提示,随着判定标准严格性的增加,CpG岛的重复序列含量降低,与基因转录起始位点的相关性提高。将CpG岛最小尺寸为500bp、GC含量为60%、CpG出现率达到0.65的组合参数作为标准,是目前预测CpG岛的最佳方式。     

预测DNA序列中短CpG岛新方法

在基因表达调控中,长度在200~500bp之间的短CpG岛具有非常重要的作用,然而目前并没有一种非常好的方法寻找短CpG岛。基于给定长度DNA片段上碱基随机分布的排列组合算法,我们定义了一种计算CpG观察预期比的新方法。结合DNA片段长度和GC含量这两个参数,该方法给出了人类21号和22号染色体上CpG岛分布的预测结果。根据CpG岛与基因功能区、Alu重复序列和UCSC的CpG岛对比分析,本研究给出了新的CpG岛判断准则:(1)CpG岛不小于200bp;(2)GC占比不小于50%;(3)CpG观察预期比不小于1.4。通过与Takai方法的对比分析显示,新方法能够显著地排除Alu重复序列对CpG岛预测的影响,并且能够准确预测具有更短长度的CpG岛在DNA片段上的分布。多基因转录起始位点基因分析结果表明,短CpG岛是UCSC的CpG岛的核心组成部分,短CpG岛是参与基因表达调控的核心元件。本研究为预测和分析短CpG岛在人类基因调控中的作用提供了必要的手段。     

人类基因组上CpG岛的定位和系统分析

为了研究CpG岛产生和消失机制以及位于基因启动子区域外的CpG岛保守性等问题,我们通过序列比对和进化保守性分析等方法,分析在人类和小鼠中保守的基因上的CpG岛。结果显示已有保守序列的突变以及序列插入删除是CpG岛产生和消失的主要原因,进一步分析发现52%的在小鼠基因组上保守序列完全缺失的CpG岛位于两个转座子之间,提示转座子所介导的序列插入是CpG岛形成和消失的重要原因。人类基因组上在启动子区域外的CpG岛中约有79%为新产生的CpG岛,显著高于启动子区域内新产生的CpG岛比例(41%)。GO分析表明与这些CpG岛相关的部分基因与神经系统发育显著相关,提示新产生的CpG岛参与神经发育过程。     

果蝇启动子区域CpG岛和TATA框预测与分析

CpG岛和TATA框在基因表达调控方面具有重要的作用,本研究从EBI核酸数据库中下载经试验证实的果蝇启动子序列共计1 917条,对其中的CpG岛、GC碱基分布和TATA框进行了分析。预测果蝇启动子序列中CpG岛的最佳参数组合为:GC最低含量为0.44,CpG最低出现率为0.6,CpG岛最小长度为200 bp,在此条件下,发现有84.82%的果蝇启动子中含有1~2个独立的CpG岛。果蝇启动子序列的平均GC含量为39.83%,GC碱基在启动子中的分布表现出规律性变化。果蝇启动子中TATA框的分布比较广泛,有32.86%的启动子中含有1个TATA框;有18.73%的启动子中含有2个TATA框;另有13.88%的启动子中含有2个以上的TATA框;此外,有34.53%的启动子没有TATA框。表明果蝇启动子中有比较丰富的CpG岛和TATA框。     

基于统计CPG岛的基因预测方法

随着以功能基因组学和蛋白质组学为主要研究内容的后基因组时代的来临,人们面对着生物信息的数据呈指数增长,如何通过有效的计算方法由核酸和蛋白质的序列推导出它们的结构和功能,特别是识别DNA序列中编码蛋白质的基因预测问题是迫切需要解决的研究课题之一.本文在CpG岛对研究基因编码的特殊生物意义下,通过三种方法确定CpG岛的位置,并在此基础上,结合一种新的DNA序列字母向量,利用信息熵离散量预测基因序列,提高了识别基因编码的效率,而且计算的时间有显著的减少.     

基于一致序列多样性分析的启动子预测方法

基于启动子的以下特点:(1)启动子区域有一些一致序列,但对于不同的启动子,一致序列在个别碱基上会有所改变,具有多样性;(2)一致序列的位置并不固定,总是在某个范围内波动;(3)大部分的真核生物启动子都和CpG岛有关。提出了一个新的启动子预测方法,即采用了一种新的统计建模策略,并首次提出了区间位置权重矩阵(IPWM)概率模型。大规模序列测试结果表明,新的启动子预测系统具有较好的敏感性和特异性。     

牛ASCL2基因生物信息学分析

在人与小鼠中,A SCL2基因是一个母源表达的印记基因,在早期胚胎和胎盘发育中起重要作用。牛A SCL2基因的印记状态和印记的分子机理还没有被研究。本研究采用生物信息学方法对牛A SCL2基因分子进化、启动子和CpG岛区域以及蛋白的高级结构进行分析和预测,为进一步揭示该基因生物学功能和其分子调控机理奠定基础。对21种哺乳动物A SCL2基因的mRNA序列进化分析表明：这21种哺乳动物间的遗传距离小于0.536,且牛与猪遗传距离最小,为0.106,与基因进化树分析结果一致。CpG岛在线软件预测显示,在牛中,该基因上游5 k序列中有三个CpG岛。启动子在线软件预测和转录因子分析相结合显示,启动子最可能位于该基因5'端上游4725~4775 bp处CpG岛区域内,此区域包括大量潜在转录因子结合位点,并在4734 bp处存在一个TATA框。蛋白质在线软件分析表明,A SCL2基因编码一种螺旋-环-螺旋形转录因子,有α-螺旋、β-转角和无规则卷曲3种二级结构。     

基因组差异甲基化片段筛选技术

分离和鉴定细胞之间的差异甲基化片段,不仅有助于了解基因的功能、分离疾病相关基因,而且可以发现与细胞分化或病变相关的甲基化标记。目前筛选差异甲基化DNA片段的方法主要有：甲基化敏感的限制性界标基因组扫描、甲基化敏感的代表性差异分析、甲基化敏感的限制性指纹技术、甲基化CpG岛扩增．代表性差异分析、微阵列技术等。其中微阵列法又先后建立有CpG岛微阵列、寡核苷酸微阵列和表达CpG岛序列标签微阵列。这些方法各有特点和适用范围,应根据具体研究目的和工作条件进行恰当的选择。     

人Toll样受体9基因启动子区的生物信息学分析

目的:探讨人Toll样受体9(TLR9)基因启动子区序列特征。方法:利用生物信息学技术预测人TLR9基因启动子区域、转录因子结合位点和CpG岛分布。结果:人TLR9基因启动子区有1434个转录因子结合位点,人和小鼠保守区域内存在23个共同的转录因子结合位点,人TLR9基因启动子区包含长572 bp的CpG岛。结论:人TLR9基因启动子区相关生物信息学的研究,提高了针对启动子的研究效率,并为预测基因启动子的功能提供了重要信息。     

小鼠骨髓细胞中p16和Ralb基因启动区CpG岛的甲基化位点分析

抑癌基因p16和白血病致癌因子Ralb与白血病的发生密切相关,其启动子区CpG岛的甲基化对基因表达具有重要作用.本文旨在分析p16、Ralb基因启动子区CpG岛甲基化位点信息,并比较这两个基因在小鼠骨髓细胞和原代培养的骨髓细胞中甲基化状态的差异.运用"MethPrimer"软件预测p16、Ralb基因启动子区的CpG岛,设计甲基化特异性引物.利用重亚硫酸盐测序法(BSP)检测甲基化位点信息.结果显示,p16有1个CpG岛,岛上21个CpG位点全部未发生甲基化;Ralb有2个CpG岛,CpG岛1上的5个CpG位点全部呈甲基化状态,而CpG岛2上的17个CpG位点全部呈非甲基化状态,且小鼠骨髓细胞和体外原代培养的骨髓细胞中两基因的甲基化状态一致.表明p16、Ralb基因甲基化状态未受外界培养条件的影响而改变,提示在与两基因甲基化相关的研究中体外试验可替代体内试验.     

CpG island methylation in human lymphocytes is highly correlated with DNA sequence, repeats, and predicted DNA structure

CpG island methylation plays an important role in epigenetic gene control during mammalian development and is frequently altered in disease situations such as cancer. The majority of CpG islands is normally unmethylated, but a sizeable fraction is prone to become methylated in various cell types and pathological situations. The goal of this study is to show that a computational epigenetics approach can discriminate between CpG islands that are prone to methylation from those that remain unmethylated. We develop a bioinformatics scoring and prediction method on the basis of a set of 1,184 DNA attributes, which refer to sequence, repeats, predicted structure, CpG islands, genes, predicted binding sites, conservation, and single nucleotide polymorphisms. These attributes are scored on 132 CpG islands across the entire human Chromosome 21, whose methylation status was previously established for normal human lymphocytes. Our results show that three groups of DNA attributes, namely certain sequence patterns, specific DNA repeats, and a particular DNA structure, are each highly correlated with CpG island methylation (correlation coefficients of 0.64, 0.66, and 0.49, respectively). We predicted, and subsequently experimentally examined 12 CpG islands from human Chromosome 21 with unknown methylation patterns and found more than 90% of our predictions to be correct. In addition, we applied our prediction method to analyzing Human Epigenome Project methylation data on human Chromosome 6 and again observed high prediction accuracy. In summary, our results suggest that DNA composition of CpG islands (sequence, repeats, and structure) plays a significant role in predisposing CpG islands for DNA methylation. This finding may have a strong impact on our understanding of changes in CpG island methylation in development and disease.     

Particle swarm optimization with reinforcement learning for the prediction of CpG islands in the human genome

Background

Regions with abundant GC nucleotides, a high CpG number, and a length greater than 200 bp in a genome are often referred to as CpG islands. These islands are usually located in the 5′ end of genes. Recently, several algorithms for the prediction of CpG islands have been proposed.

Methodology/Principal Findings

We propose here a new method called CPSORL to predict CpG islands, which consists of a complement particle swarm optimization algorithm combined with reinforcement learning to predict CpG islands more reliably. Several CpG island prediction tools equipped with the sliding window technique have been developed previously. However, the quality of the results seems to rely too much on the choices that are made for the window sizes, and thus these methods leave room for improvement.

Conclusions/Significance

Experimental results indicate that CPSORL provides results of a higher sensitivity and a higher correlation coefficient in all selected experimental contigs than the other methods it was compared to (CpGIS, CpGcluster, CpGProd and CpGPlot). A higher number of CpG islands were identified in chromosomes 21 and 22 of the human genome than with the other methods from the literature. CPSORL also achieved the highest coverage rate (3.4%). CPSORL is an application for identifying promoter and TSS regions associated with CpG islands in entire human genomic. When compared to CpGcluster, the islands predicted by CPSORL covered a larger region in the TSS (12.2%) and promoter (26.1%) region. If Alu sequences are considered, the islands predicted by CPSORL (Alu) covered a larger TSS (40.5%) and promoter (67.8%) region than CpGIS. Furthermore, CPSORL was used to verify that the average methylation density was 5.33% for CpG islands in the entire human genome.     

CpG islands of the X chromosome are gene associated.

Unmethylated CpG rich islands are a feature of vertebrate DNA: they are associated with housekeeping and many tissue specific genes. CpG islands on the active X chromosome of mammals are also unmethylated. However, islands on the inactive X chromosome are heavily methylated. We have identified a CpG island in the 5' region of the G6PD gene, and two islands forty Kb 3' from the G6PD gene, on the human X chromosome. Expression of the G6PD gene is associated with concordant demethylation of all three CpG islands. We have shown that one of the two islands is in the promoter region of a housekeeping gene, GdX. In this paper we show that the second CpG island is also associated with a gene, P3. The P3 gene has no homology to previously described genes. It is a single copy, 4 kb gene, conserved in evolution, and it has the features of a housekeeping two genes is within the CpG island and that sequences in the islands have promoter function.     

Histone methylation marks play important roles in predicting the methylation status of CpG islands

The methylation status of CpG islands is highly correlated with gene expression. Current methods for computational prediction of DNA methylation only utilize DNA sequence features. In this study, besides 35 DNA sequence features, we added four histone methylation marks to predict the methylation status of CpG islands, and improved the accuracy to 89.94%. Also we applied our model to predict the methylation pattern of all the CpG islands in the human genome, and the results are consistent with the previous reports. Our results imply the important roles of histone methylation marks in affecting the methylation status of CpG islands. H3K4me enriched in the methylation-resistant CpG islands could disrupt the contacts between nucleosomes, unravel chromatin and make DNA sequences accessible. And the established open environment may be a prerequisite for or a consequence of the function implementation of zinc finger proteins that could protect CpG islands from DNA methylation.     

Comparative Analysis of CpG Islands among HBV Genotypes

DNA methylation is being increasingly recognized to play a role in regulation of hepatitis B virus (HBV) gene expression. The aim of this study was to compare the CpG island distribution among different HBV genotypes. We analyzed 176 full-length HBV genomic sequences obtained from the GenBank database, belonging to genotypes A through J, to identify the CpG islands in the HBV genomes. Our results showed that while 79 out of 176 sequences contained three conventional CpG islands (I–III) as previously described, 83 HBV sequences harbored only two of the three known islands. Novel CpG islands were identified in the remaining 14 HBV isolates and named as CpG island IV, V, and VI. Among the eight known HBV genotypes and two putative genotypes, while HBV genomes containing three CpG islands were predominant in genotypes A, B, D, E, and I; genotypes C, F, G, and H tended to contain only two CpG islands (II and III). In conclusion, the CpG islands, which are potential targets for DNA methylation mediated by the host functions, differ among HBV genotypes, and these genotype-specific differences in CpG island distribution could provide new insights into the understanding of epigenetic regulation of HBV gene expression and hepatitis B disease outcome.