聚类分析在黄霉素发酵过程中的应用

【目的】将聚类分析的方法应用于黄霉素摇瓶发酵条件的优化过程中。【方法】通过系统聚类算法、K均值聚类算法和模糊C均值聚类算法对不同批次黄霉素发酵的摇瓶数据的聚类分析进行比较,发现模糊C均值聚类算法优于其他聚类算法,确定了以模糊C均值聚类算法对黄霉素摇瓶发酵数据进行聚类分析。【结果】然后利用模糊C均值聚类算法选取优质组样本,并利用优质样本优化了黄霉素摇瓶发酵的控制参数分布范围。【结论】这充分证明了聚类分析在发酵过程的优化过程中有良好的实用性。     

基于耦合双向聚类技术的DLBCL异质性分析

基因芯片技术为疾病异质性研究提供了有力的工具。当前基于传统聚类分析的方法一般利用芯片上大量基因作为特征来发现疾病的亚型, 因此它们没有考虑到特征中包含的大量无关基因会掩盖有意义的疾病样本的分割。为了避免这个缺点, 提出了基于耦合双向聚类的异质性分析方法(Heterogeneous Analysis Based on Coupled Two-Way Clustering, HCTWC)来搜索有意义的基因簇以便发现样本的内在分割。该方法被应用于弥漫性大B细胞淋巴瘤(diffuse large B-cell lymphoma DLBCL)芯片数据集, 通过识别的基因簇作为特征对DLBCL样本聚类发现生存期分别为55%和25%的两类DLBCL亚型(P<0.05), 因此, HCTWC方法在解决疾病异质性是有效的。     

人类基因组DNA甲基化数据分析的研究现状

作为人类基因组最为典型的表观遗传现象,DNA甲基化在多种关键生理活动中扮演重要角色.系统分析基因组尺度的DNA甲基化概况意义重大.从Cp G岛等基本定义出发,阐述了高通量DNA甲基化的检测技术以及针对芯片技术与下一代测序技术的低水平数据处理方法;重点对比了基于机器学习理论对Cp G位点及Cp G岛甲基化水平的预测算法,以及所利用的特征对预测效果的影响与发展趋势;并对DNA差异甲基化在组织特异性、癌症等多种疾病中的计算分析进行了全面的综述.     

NSCLC分类及生存分析预测的全基因组特征基因识别

基于NSCLC(非小细胞肺癌)子类分类在临床和生物医学研究方面的意义,利用全基因组基因表达水平(GE)和甲基化(ME)水平的微阵列数据对NSCLC子类分类进行全基因组特征基因识别分析。针对全基因组微阵列数据的高噪声、超高维小样本特性,利用弹性正交贝叶斯算法对全基因组基因进行递归筛选,识别分类精度最优的特征基因集。以TCGA的490的基因表达数据和378个甲基化数据为例,分别识别出52个GE特征基因和25个ME特征基因,相应的分类准确率分别为99%和98%。结合特征基因和临床数据建立的多变量Cox模型明确说明了特征基因在病人生存分析方面的重要作用:仅利用相应的基因表达数据和甲基化数据即可对病人样本的"高/低风险"进行正确分类,显著性水平均低于0.05。特征基因参与的代谢通路与p53、TGF-beta、Wnt等重要的癌症分类和发展的代谢通路的密切关系进一步证实了特征基因对NSCLC分类的重要性。     

基于改进的模糊C均值聚类的肺结节计算机辅助诊断算法研究

目的:研究基于改进的模糊C均值聚类计算机辅助诊断算法对肺结节的诊断价值,降低对肺结节的漏诊率,提高病人的生存率.方法:基于模糊C均值聚类的算法,利用直方图统计特性对数据进行优化,在此基础上利用像素的邻域特性,将数据样本对各聚类中心约束条件为1,改变为隶属度之和为样本总数.用改进的FCM对肺实质图像进行分割,将分割后的图像应用区域标记算法去除小面积区域.利用肺结节的关键特征,提取可疑区域.结果:运用改进算法后,区域分割效果更好.仿真结果证明算法很好的将"线"形或分枝状结构的血管去除.结论:改进的FCM有很好的实时性和对噪声的鲁棒性,分离血管后,将可疑区域在原图标记出来,使医生的工作更加明确.     

昆虫DNA甲基化的研究进展

DNA甲基化是表观遗传学的一个分支学科,因其在调控基因表达和生物体许多生理生化过程具有重要的作用,近年来逐渐受到广泛关注。昆虫由于种类繁多,变态发育和表型复杂,研究DNA甲基化的功能具有重要的意义。昆虫DNA甲基化需要由DNA甲基化转移酶(Dnmts)参与完成,其数量和结构在不同物种中差异较大。大多数昆虫均具有维持DNA甲基化水平的Dnmt1,缺乏行使从头DNA甲基化功能的Dnmt3,Dnmt2(也称为TRDMT1)虽保守存在但其DNA结合的能力极其微弱。昆虫DNA甲基化的总体水平较低,并且呈现不同龄期和组织的时空分布特异性。在昆虫基因组中,以外显子区的DNA甲基化水平最为显著,具有增强基因表达的功能,表现出在序列保守和广泛表达的基因中水平高、在特异表达的基因中水平低的进化特征。目前用于研究昆虫DNA甲基化的方法主要有生物信息学预测和甲基化特异限制性内切酶、甲基化敏感扩增多态性、基因组DNA甲基化测序等实验研究方法。本文就昆虫DNA甲基化转移酶的特性、DNA甲基化的分布与进化及其研究方法进行综述,旨在深入了解昆虫DNA甲基化的研究现状及其重要作用,为促进该研究领域的发展提供新的思路和方法。     

DNA甲基化——肿瘤产生的一种表观遗传学机制

在人类基因组中,DNA甲基化是一种表观遗传修饰,它与肿瘤的发生关系密切。抑癌基因和DNA修复基因的高甲基化、重复序列DNA的低甲基化、某些印记基因的印记丢失与多种肿瘤的发生有关。目前研究发现,基因组中甲基化的水平不仅受DNA 甲基化转移酶（DNMT）的影响,还与组蛋白甲基化、叶酸摄入、RNA干扰等多种因素有关。DNA甲基化在基因转录过程中扮有重要角色,并与组蛋白修饰、染色质构型重塑共同参与转录调控。     

TET酶的生物学功能及相关机制的研究进展

DNA甲基化（DNA methylation）及去甲基化属于常见的表观遗传修饰,可介导多种生理和病理过程。DNA甲基化及去甲基化修饰参与基因的表达调控,且二者的动态平衡可以维持遗传表达稳定性。DNA甲基转移酶（DNA methyltransferase,DNMT）主要包括DNMT1、DNMT3A、DNMT3B、DNMT3L,DNA去甲基化酶（DNA demethylase）主要指10-11易位蛋白（ten-eleven-translocation protein,TET）家族,包括TET1、TET2、TET3,是调节DNA甲基化和去甲基化的重要酶类。TET酶是目前发现的调节DNA去甲基化（DNA demethylation）过程中最重要的酶。综述了TET酶在DNA去甲基化修饰中的作用机制,探讨了DNA去甲基化酶在生长发育和疾病中的关键作用,以期为今后表观遗传学的相关研究提供新思路。     

不同倍性西瓜基因组DNA甲基化水平与模式的MSAP分析

DNA甲基化是表观遗传修饰的主要方式之一,在基因表达调控中发挥重要作用。本研究以不同倍性（2x、3x、4x）西瓜为试材,采用基于DNA甲基化敏感酶的扩增多态性分析（Methylation-Sensitive Ampliftcation Polymorphism,MSAP）方法,在全基因组水平上探究西瓜同源多倍化过程中DNA序列中CCGG位点的甲基化水平及模式变化特征。研究中选用23对选扩引物,共检测到1883个基因位点。二倍体、三倍体、四倍体中检测到的位点数分别为647、655和581;其中发生甲基化的位点数分别为181、150和159。相应的扩增总甲基化率分别为28．0％、22．9％和27．4％：全甲基化位点数分别为121、80和82,相应的全甲基化率分别为18．7％、12．2％和14．1％。进一步对不同倍性西瓜DNA甲基化模式的变化特征进行分析,结果显示：四倍体西瓜与二倍体西瓜相比有超过半数的位点（54．4％）DNA甲基化模式发生了变化,其与三倍体西瓜相比也有近一半的位点（45．4％）DNA甲基化模式发生了变化,并且变化趋势都以四倍体西瓜甲基化程度升高为主：而三倍体西瓜与二倍体西瓜相比．虽然也有41．6％的位点DNA甲基化模式发生了改变,但变化趋势以三倍体西瓜甲基化程度降低略占优势：与之相似,三倍体西瓜与四倍体相比较。甲基化的变化趋势也是以三倍体西瓜甲基化程度降低为主。以上结果表明：不同倍性西瓜中DNA甲基化事件虽均有发生．但不论是从总甲基化率还是全甲基化率来看,DNA甲基化水平与倍性高低关系不大．三倍体西瓜表现出较为显著的低甲基化水平特征。DNA甲基化模式的分析也表明。与二倍体及四倍体西瓜相比．三倍体西瓜DNA甲基化模式的调整主要以去甲基化为主。显示出三倍体西瓜基因组独特的DNA甲基化特征。本研究为进一步从表观遗传学的角度探讨西瓜的三倍体优?     

Bayes框架下DNA证据的量化研究

针对DNA（脱氧核糖核酸）证据的量化过程中常用的插入算法存在的缺陷，即量化结果与样本大小无关，小样本时过分量化了DNA证据，本文考虑了样本大小的影响，引入了Bayes模型。给出了基于Bayes模型下的似然比的计算公式，结合实际案例，对比了两种方法下的计算结果，数据结果表明基于Bayes模型下的算法比插入算法更加精确和合理。     

Fuzzy C-means method for clustering microarray data

MOTIVATION: Clustering analysis of data from DNA microarray hybridization studies is essential for identifying biologically relevant groups of genes. Partitional clustering methods such as K-means or self-organizing maps assign each gene to a single cluster. However, these methods do not provide information about the influence of a given gene for the overall shape of clusters. Here we apply a fuzzy partitioning method, Fuzzy C-means (FCM), to attribute cluster membership values to genes. RESULTS: A major problem in applying the FCM method for clustering microarray data is the choice of the fuzziness parameter m. We show that the commonly used value m = 2 is not appropriate for some data sets, and that optimal values for m vary widely from one data set to another. We propose an empirical method, based on the distribution of distances between genes in a given data set, to determine an adequate value for m. By setting threshold levels for the membership values, genes which are tigthly associated to a given cluster can be selected. Using a yeast cell cycle data set as an example, we show that this selection increases the overall biological significance of the genes within the cluster. AVAILABILITY: Supplementary text and Matlab functions are available at http://www-igbmc.u-strasbg.fr/fcm/     

A comparison of cluster analysis methods using DNA methylation data

MOTIVATION: Aberrant DNA methylation is common in cancer. DNA methylation profiles differ between tumor types and subtypes and provide a powerful diagnostic tool for identifying clusters of samples and/or genes. DNA methylation data obtained with the quantitative, highly sensitive MethyLight technology is not normally distributed; it frequently contains an excess of zeros. Established tools to analyze this type of data do not exist. Here, we evaluate a variety of methods for cluster analysis to determine which is most reliable. RESULTS: We introduce a Bernoulli-lognormal mixture model for clustering DNA methylation data obtained using MethyLight. We model the outcomes using a two-part distribution having discrete and continuous components. It is compared with standard cluster analysis approaches for continuous data and for discrete data. In a simulation study, we find that the two-part model has the lowest classification error rate for mixture outcome data compared with other approaches. The methods are illustrated using DNA methylation data from a study of lung cancer cell lines. Compared with competing hierarchical clustering methods, the mixture model approaches have the lowest cross-validation error for detecting lung cancer subtype (non-small versus small cell). The Bernoulli-lognormal mixture assigns observations to subgroups with the lowest uncertainty. AVAILABILITY: Software is available upon request from the authors. SUPPLEMENTARY INFORMATION: http://www-rcf.usc.edu/~kims/SupplementaryInfo.html     

A Bayesian hierarchical model to detect differentially methylated loci from single nucleotide resolution sequencing data

DNA methylation is an important epigenetic modification that has essential roles in cellular processes including gene regulation, development and disease and is widely dysregulated in most types of cancer. Recent advances in sequencing technology have enabled the measurement of DNA methylation at single nucleotide resolution through methods such as whole-genome bisulfite sequencing and reduced representation bisulfite sequencing. In DNA methylation studies, a key task is to identify differences under distinct biological contexts, for example, between tumor and normal tissue. A challenge in sequencing studies is that the number of biological replicates is often limited by the costs of sequencing. The small number of replicates leads to unstable variance estimation, which can reduce accuracy to detect differentially methylated loci (DML). Here we propose a novel statistical method to detect DML when comparing two treatment groups. The sequencing counts are described by a lognormal-beta-binomial hierarchical model, which provides a basis for information sharing across different CpG sites. A Wald test is developed for hypothesis testing at each CpG site. Simulation results show that the proposed method yields improved DML detection compared to existing methods, particularly when the number of replicates is low. The proposed method is implemented in the Bioconductor package DSS.     

ENmix: a novel background correction method for Illumina HumanMethylation450 BeadChip

The Illumina HumanMethylation450 BeadChip is increasingly utilized in epigenome-wide association studies, however, this array-based measurement of DNA methylation is subject to measurement variation. Appropriate data preprocessing to remove background noise is important for detecting the small changes that may be associated with disease. We developed a novel background correction method, ENmix, that uses a mixture of exponential and truncated normal distributions to flexibly model signal intensity and uses a truncated normal distribution to model background noise. Depending on data availability, we employ three approaches to estimate background normal distribution parameters using (i) internal chip negative controls, (ii) out-of-band Infinium I probe intensities or (iii) combined methylated and unmethylated intensities. We evaluate ENmix against other available methods for both reproducibility among duplicate samples and accuracy of methylation measurement among laboratory control samples. ENmix out-performed other background correction methods for both these measures and substantially reduced the probe-design type bias between Infinium I and II probes. In reanalysis of existing EWAS data we show that ENmix can identify additional CpGs, and results in smaller P-value estimates for previously-validated CpGs. We incorporated the method into R package ENmix, which is freely available from Bioconductor website.     

MBD-isolated Genome Sequencing provides a high-throughput and comprehensive survey of DNA methylation in the human genome

DNA methylation is an epigenetic modification involved in both normal developmental processes and disease states through the modulation of gene expression and the maintenance of genomic organization. Conventional methods of DNA methylation analysis, such as bisulfite sequencing, methylation sensitive restriction enzyme digestion and array-based detection techniques, have major limitations that impede high-throughput genome-wide analysis. We describe a novel technique, MBD-isolated Genome Sequencing (MiGS), which combines precipitation of methylated DNA by recombinant methyl-CpG binding domain of MBD2 protein and sequencing of the isolated DNA by a massively parallel sequencer. We utilized MiGS to study three isogenic cancer cell lines with varying degrees of DNA methylation. We successfully detected previously known methylated regions in these cells and identified hundreds of novel methylated regions. This technique is highly specific and sensitive and can be applied to any biological settings to identify differentially methylated regions at the genomic scale.     

Accurate quantification of DNA methylation using combined bisulfite restriction analysis coupled with the Agilent 2100 Bioanalyzer platform

DNA methylation is the best-studied epigenetic modification and describes the conversion of cytosine to 5-methylcytosine. The importance of this phenomenon is that aberrant promoter hypermethylation is a common occurrence in cancer and is frequently associated with gene silencing. Various techniques are currently available for the analysis of DNA methylation. However, accurate and reproducible quantification of DNA methylation remains challenging. In this report, we describe Bio-COBRA (combined bisulfite restriction analysis coupled with the Agilent 2100 Bioanalyzer platform), as a novel approach to quantitative DNA methylation analysis. The combination of a well-established method, COBRA, which interrogates DNA methylation via the restriction enzyme analysis of PCR-amplified bisulfite treated DNAs, with the Bioanalyzer platform allows for the rapid and quantitative assessment of DNA methylation patterns in large sample sets. The sensitivity and reproducibility of Bio-COBRA make it a valuable tool for the analysis of DNA methylation in clinical samples, which could aid in the development of diagnostic and prognostic parameters with respect to disease detection and management.     

Rapid and quantitative method of allele-specific DNA methylation analysis

Several biological phenomena depend on differential methylation of chromosomal strands. While understanding the role of these processes requires information on allele-specific methylation, the available methodologies are not quantitative or labor-intensive. We describe a novel, rapid method to quantitate allele-specific DNA methylation based on the combination of bisulfite PCR and Pyrosequencing. In this method, DNA is first treated with sodium bisulfite, which converts cytosine but not 5-methylcytosine to uracil. Genes of interest are subsequently amplified using PCR. Allele-specific methylation can then be determined by pyrosequencing each allele individually using sequencing primers that incorporate single nucleotide polymorphisms (SNPs) that allow differentiation between the two parental alleles. This allele-specific methylation methodology can potentially afford quantitative analyses relevant to the regulation of X chromosome inactivation, allele-specific expression of genes in the immune system, repetitive elements, and genomic imprinting. As an illustration of our new method, we quantitated allele-specific methylation of the differentially methylated region of the H19 gene, which is imprinted. Although we could reliably determine allele-specific methylation with our technique, additional studies will be required to confirm the ability of our assay to measure loss of imprinting.     

GeMes,Clusters of DNA Methylation under Genetic Control,Can Inform Genetic and Epigenetic Analysis of Disease

Epigenetic marks such as DNA methylation have generated great interest in the study of human disease. However, studies of DNA methylation have not established population-epigenetics principles to guide design, efficient statistics, or interpretation. Here, we show that the clustering of correlated DNA methylation at CpGs was similar to that of linkage-disequilibrium (LD) correlation in genetic SNP variation but for much shorter distances. Some clustering of methylated CpGs appeared to be genetically driven. Further, a set of correlated methylated CpGs related to a single SNP-based LD block was not always physically contiguous—segments of uncorrelated methylation as long as 300 kb could be interspersed in the cluster. Thus, we denoted these sets of correlated CpGs as GeMes, defined as potentially noncontiguous methylation clusters under the control of one or more methylation quantitative trait loci. This type of correlated methylation structure has implications for both biological functions of DNA methylation and for the design, analysis, and interpretation of epigenome-wide association studies.     

Development of bioluminescent pyrophosphate assay using pyruvate phosphate dikinase and its application to single-nucleotide polymorphism analysis

DNA analysis is an important technology with respect to diagnosis of infectious disease and tailored medication. In this study, we developed a novel bioluminescent assay for pyrophosphate, and it was applied to single-nucleotide polymorphism (SNP) analysis using one-base extension reaction. The principle of this method is as follows. A specific primer within each aliquot possessing a short 3′ end of the base of interest was hybridized to the single-stranded DNA template. Subsequently, (exo-)Klenow DNA polymerase and one of either α-thio-dATP, dTTP, dGTP, or dCTP were added and incubated for 1 min. Pyrophosphate released by DNA polymerase is converted to ATP by pyruvate phosphate dikinase (PPDK), and the concentration of ATP is determined using the firefly luciferase reaction. This method, which does not require expensive equipment, can be used to rapidly monitor one point mutation in the gene.