基因芯片数据分析及在植物基因组研究中的应用

基因芯片作为一种新兴的技术手段已经在植物学、动物学、医学和农学等多个研究领域中发挥了重要作用。本文就基因芯片数据分析的各个环节,包括芯片数据的预处理、归一化、差异基因的判断、聚类分析以及基因芯片在植物功能基因组研究中的应用进行了综述。     

基因芯片及其在性传播疾病病原体研究中的应用

基因芯片是近年发展起来的一种高通量的核酸分析技术,已成为“后基因组时代”的重要分析工具之一。本简述了基因芯片的概念、分类及特点,并对基因芯片技术在性传播疾病病原体淋球菌、沙眼衣原体、解脲脲原体和人乳头瘤病毒研究中的应用作了综述。     

基因芯片技术及其在疾病基因诊断中的应用

基因芯片技术是伴随着人类基因组计划的实施而发展起来的生命科学领域里的前沿生物技术。它最显著的特点是高通量、高集成、微型化、平行化、多样化和自动化。经过短短十几年的发展,基因芯片技术现已在基因表达分析,基因突变及多态性分析,疾病基因诊断,药物及毒物基因组学等多个领域显示出重大的理论意义和实际应用价值,具有广阔的前景。本文专门介绍了基因芯片技术及其在疾病基因诊断上的应用。     

基因芯片技术及其在肿瘤基因组学中的应用

基因芯片又称DNA微阵列,分为cDNA微阵列和寡聚核苷酸微阵列。DNA微阵列技术是探索基因组功能的一种强有力工具。扼要介绍基因芯片、表达谱芯片技术和原理,以及基因芯片技术在肿瘤基因组学中的应用。     

基因芯片在衣原体研究中的应用

基因芯片又称为DNA微阵列,是指将大量核酸片段以预先设计的方式固定在载体上组成密集分子阵列,与荧光素或其他方式标记的样品进行杂交,通过检测杂交信号的强弱来判断样品中有无靶分子以及对靶分子进行定量,是一种研究生物大分子功能的新技术。在衣原体研究方面,基因芯片主要应用于衣原体的检测与分型、感染机制的研究、特定基因作用分析、毒力及耐药基因的筛选等。     

基因芯片技术及其在植物上的应用

基因芯片技术（gene chip technology)是采用光导原位合成或缩微印刷等方法,将大量特定的DNA探针片段有序地固定于固相载体的表面,形成DNA微阵列,然后与待测的标记样品靶DNA或RNA分子杂交,对杂交信号进行扫描及计算机检测分析,从而获取所需的生物信息。该技术在植物研究中广泛应用于寻找特异性相关基因和新基因,基因表达分析,基因突变和多态性检测,DNA测序等。     

基因芯片及其在环境微生物研究中的应用

基因芯片因其具有高密度、高灵敏度、快速 (实时 )检测、经济、自动化和低背景水平等特点 ,而广泛应用于不同的研究领域。目前 ,应用于环境微生物研究的基因芯片主要有功能基因芯片 (FGAs)、系统发育的寡核苷酸芯片 (POAs)和群落基因组芯片 (CGAs)。综述了基因芯片在环境微生物研究中的应用 ,包括自然环境中微生物的基因表达分析、比较基因组分析和混合微生物群落的分析等。讨论了基因芯片面临的挑战和前景展望     

基因芯片技术及其在补血中药研究中的应用

补血中药在我国中医药宝库中占有十分重要的地位,中药调控造血的分子机制与细胞因子网络直接相关。多维超高通量药物筛选体系的建立和从分子水平系统说明中药作用的药理学机制,使传统的中药理论与国际通用的医学理论模式接轨,是中药现代化、国际化的迫切要求。由于中药重整体、多靶点、多环节的作用方式,使得传统的研究技术难以完整地阐明其作用机制;而现今发展起来的高密度基因芯片技术,可以同时研究上千种基因的作用模式,进行平行基因分析,因此可以用来检测疾病状态下和中药作用后成千上万个基因的表达模式,并对其进行定性和定量分析,从而使从整体和分子水平上阐明中药作用机制成为可能。基因芯片这种高通量、快速、平行的基因信息处理和分析技术,是实现这一目标的绝佳实验手段。在药物领域对于药物靶标的发现、多靶位同步超高通量药物筛选、药物作用的分子机理、中医药基础理论现代化、药物活性及毒性评价等都有其他方法无可比拟的优越性。因此,建立以基因芯片技术为核心的多靶点、多层次、多水平的中药多维超高通量筛选体系具有极其重要的意义。     

基因芯片技术在植物基因克隆中的应用研究进展

基因芯片是以预先设计的方式将大量的生物讯息密码(寡核苷酸、cDNA、基因组DNA等)固定在玻片、硅片等固相载体上组成的密集分子阵列.基因芯片技术本质是生物信号的平行分析,它利用核酸分子杂交原理,通过荧光标记技术检测杂交亲和与否,再经过计算机分析处理可迅速获得所需信息.由于其具有高通量、微型化、连续化、自动化、快速和准确等特点,已引起国际国内广泛的关注和重视,在许多领域得到了广泛的应用.本文简述了基因芯片的概念,技术特点及主要分类,着重对其在基因表达水平检测,基因突变和多态性的分析,基因组DNA分析,后基因组学研究以及转基因农作物检测等方面进行阐述,并说明其存在的问题及展望.     

基因芯片技术在食品致病菌检测中的应用

基因芯片技术作为一种新的技术平台,已广泛地应用于食品研究领域。随着该技术的不断完善,其将在食品研究和食品安全中发挥越来越重要的作用。就基因芯片技术检测食品中常见致病菌的基本原理、检测过程、存在的问题、研究进展作一介绍。     

Differential coexpression analysis using microarray data and its application to human cancer

MOTIVATION: Microarrays have been used to identify differential expression of individual genes or cluster genes that are coexpressed over various conditions. However, alteration in coexpression relationships has not been studied. Here we introduce a model for finding differential coexpression from microarrays and test its biological validity with respect to cancer. RESULTS: We collected 10 published gene expression datasets from cancers of 13 different tissues and constructed 2 distinct coexpression networks: a tumor network and normal network. Comparison of the two networks showed that cancer affected many coexpression relationships. Functional changes such as alteration in energy metabolism, promotion of cell growth and enhanced immune activity were accompanied with coexpression changes. Coregulation of collagen genes that may control invasion and metastatic spread of tumor cells was also found. Cluster analysis in the tumor network identified groups of highly interconnected genes related to ribosomal protein synthesis, the cell cycle and antigen presentation. Metallothionein expression was also found to be clustered, which may play a role in apoptosis control in tumor cells. Our results show that this model would serve as a novel method for analyzing microarrays beyond the specific implications for cancer.     

DNA microarray data and contextual analysis of correlation graphs

Background  

DNA microarrays are used to produce large sets of expression measurements from which specific biological information is sought. Their analysis requires efficient and reliable algorithms for dimensional reduction, classification and annotation.     

Differential analysis of DNA microarray gene expression data

Here, we review briefly the sources of experimental and biological variance that affect the interpretation of high-dimensional DNA microarray experiments. We discuss methods using a regularized t-test based on a Bayesian statistical framework that allow the identification of differentially regulated genes with a higher level of confidence than a simple t-test when only a few experimental replicates are available. We also describe a computational method for calculating the global false-positive and false-negative levels inherent in a DNA microarray data set. This method provides a probability of differential expression for each gene based on experiment-wide false-positive and -negative levels driven by experimental error and biological variance.     

GenePublisher: Automated analysis of DNA microarray data

GenePublisher, a system for automatic analysis of data from DNA microarray experiments, has been implemented with a web interface at http://www.cbs.dtu.dk/services/GenePublisher. Raw data are uploaded to the server together with a specification of the data. The server performs normalization, statistical analysis and visualization of the data. The results are run against databases of signal transduction pathways, metabolic pathways and promoter sequences in order to extract more information. The results of the entire analysis are summarized in report form and returned to the user.     

Variable selection for model-based high-dimensional clustering and its application to microarray data

Summary .   Variable selection in high-dimensional clustering analysis is an important yet challenging problem. In this article, we propose two methods that simultaneously separate data points into similar clusters and select informative variables that contribute to the clustering. Our methods are in the framework of penalized model-based clustering. Unlike the classical L ₁-norm penalization, the penalty terms that we propose make use of the fact that parameters belonging to one variable should be treated as a natural "group." Numerical results indicate that the two new methods tend to remove noninformative variables more effectively and provide better clustering results than the L ₁-norm approach.     

DNA microarray data imputation and significance analysis of differential expression

MOTIVATION: Significance analysis of differential expression in DNA microarray data is an important task. Much of the current research is focused on developing improved tests and software tools. The task is difficult not only owing to the high dimensionality of the data (number of genes), but also because of the often non-negligible presence of missing values. There is thus a great need to reliably impute these missing values prior to the statistical analyses. Many imputation methods have been developed for DNA microarray data, but their impact on statistical analyses has not been well studied. In this work we examine how missing values and their imputation affect significance analysis of differential expression. RESULTS: We develop a new imputation method (LinCmb) that is superior to the widely used methods in terms of normalized root mean squared error. Its estimates are the convex combinations of the estimates of existing methods. We find that LinCmb adapts to the structure of the data: If the data are heterogeneous or if there are few missing values, LinCmb puts more weight on local imputation methods; if the data are homogeneous or if there are many missing values, LinCmb puts more weight on global imputation methods. Thus, LinCmb is a useful tool to understand the merits of different imputation methods. We also demonstrate that missing values affect significance analysis. Two datasets, different amounts of missing values, different imputation methods, the standard t-test and the regularized t-test and ANOVA are employed in the simulations. We conclude that good imputation alleviates the impact of missing values and should be an integral part of microarray data analysis. The most competitive methods are LinCmb, GMC and BPCA. Popular imputation schemes such as SVD, row mean, and KNN all exhibit high variance and poor performance. The regularized t-test is less affected by missing values than the standard t-test. AVAILABILITY: Matlab code is available on request from the authors.     

Three-parameter lognormal distribution ubiquitously found in cDNA microarray data and its application to parametric data treatment

Background  

To cancel experimental variations, microarray data must be normalized prior to analysis. Where an appropriate model for statistical data distribution is available, a parametric method can normalize a group of data sets that have common distributions. Although such models have been proposed for microarray data, they have not always fit the distribution of real data and thus have been inappropriate for normalization. Consequently, microarray data in most cases have been normalized with non-parametric methods that adjust data in a pair-wise manner. However, data analysis and the integration of resultant knowledge among experiments have been difficult, since such normalization concepts lack a universal standard.