Multicolor fluorescent differential display

Differential display and DNA microarray have emerged as the two most popular methods for gene expression profiling. Here, we developed a multicolor fluorescent differential display (FDD) method that combines the virtues of both differential display in signal amplification and DNA microarray in signal analysis. As in DNA microarray, RNA samples being compared can be labeled with either a red or green fluorescent dye and displayed in a single lane, allowing convenient scoring and quantification of the differentially expressed messages. In addition, the multicolor FDD has a built-in signal proofreading capability that is achieved by labeling each RNA sample from a comparative study with both red and green fluorescent dyes followed by their reciprocal mixings in color. Thus, the multicolor FDD provides a platform upon which a sensitive and accurate gene expression profiling by differential display can be automated and digitally analyzed. It is envisioned that cDNAs generated by the multicolor FDD may also be used directly as probes for DNA microarray, allowing an integration of the two most widely used technologies for comprehensive analysis of gene expression.     

Basic microarray analysis: grouping and feature reduction

DNA microarray technologies are useful for addressing a broad range of biological problems - including the measurement of mRNA expression levels in target cells. These studies typically produce large data sets that contain measurements on thousands of genes under hundreds of conditions. There is a critical need to summarize this data and to pick out the important details. The most common activities, therefore, are to group together microarray data and to reduce the number of features. Both of these activities can be done using only the raw microarray data (unsupervised methods) or using external information that provides labels for the microarray data (supervised methods). We briefly review supervised and unsupervised methods for grouping and reducing data in the context of a publicly available suite of tools called CLEAVER, and illustrate their application on a representative data set collected to study lymphoma.     

Genomic Portraits of the Nervous System in Health and Disease

As the human genome project moves toward its goal of sequencing the entire human genome, gene expression profiling by DNA microarray technology is being employed to rapidly screen genes for biological information. In this review, we will introduce DNA microarray technology, outline the basic experimental paradigms and data analysis methods, and then show with some examples how gene expression profiling can be applied to the study of the central nervous system in health and disease.     

DNA芯片技术中利用内标对数据归一化后检测基因的表达变化

在DNA芯片技术中 ,通过反转录反应 ,由mRNA合成带有荧光标记物的cDNA的过程中 ,往往要参入已知质量的poly(A) + RNA ,以对DNA芯片的检测灵敏度进行归一化处理 .通过体外转录的方法 ,以真核生物的cDNA克隆中的DNA片段为模板合成poly(A) +RNA ,对之定量后 ,以不同的质量比参入到样品的反转录体系中 ,代表不同的RNA拷贝丰度 ,从而对DNA芯片检测的灵敏度进行了定量 ,并得到DNA芯片上杂交点的荧光信号强度与基因表达的RNA拷贝数成正相关的关系 .利用含有内标的DNA芯片检测了热击反应后酵母细胞的基因表达变化 ,结果与Northern印迹方法检测结果是相符的     

Statistical approaches for the analysis of DNA methylation microarray data

Following the rapid development and adoption in DNA methylation microarray assays, we are now experiencing a growth in the number of statistical tools to analyze the resulting large-scale data sets. As is the case for other microarray applications, biases caused by technical issues are of concern. Some of these issues are old (e.g., two-color dye bias and probe- and array-specific effects), while others are new (e.g., fragment length bias and bisulfite conversion efficiency). Here, I highlight characteristics of DNA methylation that suggest standard statistical tools developed for other data types may not be directly suitable. I then describe the microarray technologies most commonly in use, along with the methods used for preprocessing and obtaining a summary measure. I finish with a section describing downstream analyses of the data, focusing on methods that model percentage DNA methylation as the outcome, and methods for integrating DNA methylation with gene expression or genotype data.     

Analysis of large-scale gene expression data.

DNA microarray technology has resulted in the generation of large complex data sets, such that the bottleneck in biological investigation has shifted from data generation, to data analysis. This review discusses some of the algorithms and tools for the analysis and organisation of microarray expression data, including clustering methods, partitioning methods, and methods for correlating expression data to other biological data.     

Statistical methods for microarray assays

The paper shortly reviews statistical methods used in the area of DNA microarray studies. All stages of the experiment are taken into account: planning, data collection, data preprocessing, analysis and validation. Among the methods of data analysis, the algorithms for estimating differential expression, multivariate approaches, clustering methods, as well as classification and discrimination are reviewed. The need is stressed for routine statistical data processing protocols and for the search of links of microarray data analysis with quantitative genetic models.     

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.     

Chasing the dream: plant EST microarrays

DNA microarray technology is poised to make an important contribution to the field of plant biology. Stimulated by recent funding programs, expressed sequence tag sequencing and microarray production either has begun or is being contemplated for most economically important plant species. Although the DNA microarray technology is still being refined, the basic methods are well established. The real challenges lie in data analysis and data management. To fully realize the value of this technology, centralized databases that are capable of storing microarray expression data and managing information from a variety of sources will be needed. These information resources are under development and will help usher in a new era in plant functional genomics.     

分析基因表达图式的新方法

随着基因组研究的深入进行,基因的分子生物学除了要寻找在生物学上重要的个别基因并研究其结构与功能外,更重要的应是了解整个基因组的功能活动,即细胞全部基因的表达图式.要解决如此复杂的问题就必须在研究方法上有所创新,基因表达系列分析法、cDNA微阵列分析法、DNA微芯片分析法等正是近几年发展起来的分析基因表达图式的新方法.     

A rapid and inexpensive labeling method for microarray gene expression analysis

Background  

Global gene expression profiling by DNA microarrays is an invaluable tool in biological research. However, existing labeling methods are time consuming and costly and therefore often limit the scale of microarray experiments and sample throughput. Here we introduce a new, fast, inexpensive method for direct random-primed fluorescent labeling of eukaryotic cDNA for gene expression analysis and compare the results obtained on the NimbleGen microarray platform with two other widely-used labeling methods, namely the NimbleGen-recommended double-stranded cDNA protocol and the indirect (aminoallyl) method.     

Quantitative assessment of DNA microarrays--comparison with Northern blot analyses

DNA microarray is a powerful technology that provides the expression profile of thousands of genes. However, less attention has been paid to its quantitative aspect. In this study, we constructed a small-scale DNA microarray that contains 84 genes and characterized its quantitative aspect. Analyses with this microarray showed that 17 genes were induced, whereas 8 genes were suppressed at least twofold during the differentiation of mouse embryonic stem cells. When repeated with the same combination of fluorescent dyes for probe labeling, the microarray produced consistent data (correlation coefficient = 0.991). In contrast, data were less consistent when repeated with the reverse combination of dyes (correlation coefficient = 0.945). The effect of dye combination was particularly evident in several genes. Total RNA (15 microg) and poly(A) RNA (0.5 microg) showed comparable sensitivity and produced essentially identical data (correlation coefficient = 0.983). The sensitivity of the DNA microarrays was slightly inferior to that of Northern blot analyses. In most genes, data obtained with the two methods were consistent. However, in 4 of 46 genes compared, DNA microarrays failed to detect the expression changes that were revealed by Northern blot. These data demonstrated that DNA microarrays provide quantitative data comparable to Northern blot in general, but a few issues must be considered when analyzing data.