Creation of the whole human genome microarray

Several companies have recently announced the availability of products that enable a scientist to probe gene expression from the entire human genome on a single DNA microarray. This review will focus on the underlying technological trends that have made this achievement possible, the particular methodologies which are employed to create such microarrays and the implications of the whole human genome microarray for future biological studies. The single genome array represents an important milestone on the path to unraveling the complexity of the cellular networks that control living processes. The microarrays being designed today may, however, become distant ancestors to the whole human genome arrays of the future as our understanding of the functioning of the human genome increases.     

DNA microarrays for functional plant genomics

DNA microarray technology is a key element in today's functional genomics toolbox. The power of the method lies in miniaturization, automation and parallelism permitting large-scale and genome-wide acquisition of quantitative biological information from multiple samples. DNA microarrays are currently fabricated and assayed by two main approaches involving either in situ synthesis of oligonucleotides (`oligonucleotide microarrays') or deposition of pre-synthesized DNA fragments (`cDNA microarrays') on solid surfaces. To date, the main applications of microarrays are in comprehensive, simultaneous gene expression monitoring and in DNA variation analyses for the identification and genotyping of mutations and polymorphisms. Already at a relatively early stage of its application in plant science, microarrays are being utilized to examine a range of biological issues including the circadian clock, plant defence, environmental stress responses, fruit ripening, phytochrome A signalling, seed development and nitrate assimilation. Novel insights are obtained into the molecular mechanisms co-ordinating metabolic pathways, regulatory and signalling networks. Exciting new information will be gained in the years to come not only from genome-wide expression analyses on a few model plant species, but also from extensive studies of less thoroughly studied species on a more limited scale. The value of microarray technology to our understanding of living processes will depend both on the amount of data to be generated and on its clever exploration and integration with other biological knowledge arising from complementary functional genomics tools for `profiling' the genome, proteome, metabolome and phenome.     

Rapid divergence in expression between duplicate genes inferred from microarray data

For more than 30 years, expression divergence has been considered as a major reason for retaining duplicated genes in a genome, but how often and how fast duplicate genes diverge in expression has not been studied at the genomic level. Using yeast microarray data, we show that expression divergence between duplicate genes is significantly correlated with their synonymous divergence (K_S) and also with their nonsynonymous divergence (K_A) if K_A ≤ 0.3. Thus, expression divergence increases with evolutionary time, and K_A is initially coupled with expression divergence. More interestingly, a large proportion of duplicate genes have diverged quickly in expression and the vast majority of gene pairs eventually become divergent in expression. Indeed, more than 40% of gene pairs show expression divergence even when K_S is ≤ 0.10, and this proportion becomes >80% for K_S > 1.5. Only a small fraction of ancient gene pairs do not show expression divergence.     

Promise and progress in environmental genomics: a status report on the applications of gene expression-based microarray studies in ecologically relevant fish species

The advent of any new technology is typically met with great excitement. So it was a few years ago, when the combination of advances in sequencing technology and the development of microarray technology made measurements of global gene expression in ecologically relevant species possible. Many of the review papers published around that time promised that these new technologies would revolutionize environmental biology as they had revolutionized medicine and related fields. A few years have passed since these technological advancements have been made, and the use of microarray studies in non‐model fish species has been adopted in many laboratories internationally. Has the relatively widespread adoption of this technology really revolutionized the fields of environmental biology, including ecotoxicology, aquaculture and ecology, as promised? Or have these studies merely become a novelty and a potential distraction for scientists addressing environmentally relevant questions? In this review, the promises made in early review papers, in particular about the advances that the use of microarrays would enable, are summarized; these claims are compared to the results of recent studies to determine whether the forecasted changes have materialized. Some applications, as discussed in the paper, have been realized and have led to advances in their field, others are still under development.     

Effective feature selection framework for cluster analysis of microarray data

The microarray technique has become a standard means in simultaneously examining expression of all genes measured in different circumstances. As microarray data are typically characterized by high dimensional features with a small number of samples, feature selection needs to be incorporated to identify a subset of genes that are meaningful for biological interpretation and accountable for the sample variation. In this article, we present a simple, yet effective feature selection framework suitable for two-dimensional microarray data. Our correlation-based, nonparametric approach allows compact representation of class-specific properties with a small number of genes. We evaluated our method using publicly available experimental data and obtained favorable results.     

Rice functional genomics research in China

Rice functional genomics is a scientific approach that seeks to identify and define the function of rice genes, and uncover when and how genes work together to produce phenotypic traits. Rapid progress in rice genome sequencing has facilitated research in rice functional genomics in China. The Ministry of Science and Technology of China has funded two major rice functional genomics research programmes for building up the infrastructures of the functional genomics study such as developing rice functional genomics tools and resources. The programmes were also aimed at cloning and functional analyses of a number of genes controlling important agronomic traits from rice. National and international collaborations on rice functional genomics study are accelerating rice gene discovery and application.     

Drosophila microarray platforms.

The advent of high throughput microarrays and the complete sequencing of the Drosophila melanogaster genome have enabled global gene expression analysis in this powerful genetic model organism. Currently, researchers are using three main Drosophila array platform types, with elements composed of cDNA amplicons, oligonucleotides (short and long) or genomic amplicons. This paper provides a broad overview of these platforms.     

机械点样DNA微点阵技术及其在基因表达分析上的应用(I)

从芯片制作、芯片杂交、芯片扫读与图像分析、基因表达数据分析等方面,详细介绍了机械点样DNA微点阵技术及其应用于多基因表达分析的基本步骤与原理。     

机械点样DNA微点阵技术及其在基因表达分析上的应用(II)

从芯片制作、芯片杂交、芯片扫读与图像分析、基因表达数据分析等方面,详细介绍了机械点样DNA微点阵技术及其应用于多基因表达分析的基本步骤与原理。     

Tissue-specific gene expression in soybean (Glycine max) detected by cDNA microarray analysis

We have constructed cDNA microarrays for soybean (Glycine maxL. Merrill), containing approximately 4,100 Unigene ESTs derived from axenic roots, to evaluate their application and utility for functional genomics of organ differentiation in legumes. We assessed microarray technology by conducting studies to evaluate the accuracy of microarray data and have found them to be both reliable and reproducible in repeat hybridisations. Several ESTs showed high levels (50 fold) of differential expression in either root or shoot tissue of soybean. A small number of physiologically interesting, and differentially expressed sequences found by microarray analysis were verified by both quantitative real-time RT-PCR and Northern blot analysis. There was a linear correlation (r² = 0.99, over 5 orders of magnitude) between microarray and quantitative real-time RT-PCR data. Microarray analysis of soybean has enormous potential not only for the discovery of new genes involved in tissue differentiation and function, but also to study the expression of previously characterised genes, gene networks and gene interactions in wild-type, mutant or transgenic plants.     

蛋白微阵列研究进展

蛋白微阵列是随着基因微阵列技术发展起来的,用于基因微阵列的制备方法、信号的检测及分析系统,也可用于蛋白微阵列。各种蛋白微阵列基质的发展,提高了蛋白的固定效率。放射性同位数、化学发光、激光共聚焦荧光扫描等技术都已用于微阵列的检测。重组蛋白技术的发展,提高了蛋白微阵列检测的通量和灵敏度。蛋白微阵列具有通量高、使用样品少、重复性好、可定量的特点,使其在生物医药科学研究中得到了广泛应用。本综述了蛋白微阵列的制备及其在免疫检测、医学诊断及蛋白组研究中的应用。     

cDNA芯片阳性对照的制备及在芯片敏感性分析中的应用

cDNA芯片是一种高通量基因表达谱分析技术,在生理病理条件下细胞基因表达谱分析,新基因发现和功能研究等方面具有广阔应用前景。CDNA芯片阳性对照的选取以及CDNA芯片检测敏感性是芯片成功应用的关键问题之一。以在系统发育上与人类基因同源性小的荧火虫荧光素酶基因材料,制备了用于人类和其他动物基因表达谱CDNA芯片的通用型阳性对照探针和相应的mRNA参照物,经反转录对mRNA参照物进行Cy3荧光标记并与DNA芯片杂交后发现,mRNA参照物能特异性地与荧光酶基因cDNA片断杂交,而与人β-肌动蛋白基因,人G3PDH基因以及λDNA/HINDⅢ无杂交反应。把mRNA参照物以不同比例加入HepG2总RNA中,以反转录荧光标记后与CDNA芯片杂交,结果发现当总RNA中的MRNA含量为1/10^4稀释（即mRNA分子个数约为10^8个）时,CDNA芯片基本检测不出mRNA标记产物的杂交信号。而且,cDNA芯片检测的信号强度与芯片上固定的探针浓度密切相关,当探针浓度为2g/L时,杂交信号最强,随着探针浓度下降芯片的杂交信号趋于减弱。CDNA芯片通用型阳性参照物的制备以及应用于CDNA芯片检测敏感性研究为CDNA芯片应用于人和其他动物基因表达谱高通量分析和新基因功能研究提供了技术基础和理论依据。     

肺癌相关基因芯片的制作

探讨肺癌相关基因芯片的制作过程及其在基因表达谱方面的应用,同时对一些传统的技术环节进行了改进。首先有目的搜集了60个肺癌相关基因,用修饰后的引物将其分别克隆,经纯化、变性后,用Cartesian PinSys5500 cDNA Microarray点样仪以微阵列的形式将其点布于醛基化的玻璃片上;然后将人支气管上皮恶性转化细胞模型BEP2D细胞的原代、20代、35代的总RNA逆转录后再经LD PCR标记成荧光探讨与基因芯片进行杂交。ScanArray 3000荧光扫描仪扫描后显示,图象背景均匀、信号清晰,证实该芯片制作成功,并能取得良好的杂交效果。     

Random forest for gene selection and microarray data classification

A random forest method has been selected to perform both gene selection and classification of the microarray data. In this embedded method, the selection of smallest possible sets of genes with lowest error rates is the key factor in achieving highest classification accuracy. Hence, improved gene selection method using random forest has been proposed to obtain the smallest subset of genes as well as biggest subset of genes prior to classification. The option for biggest subset selection is done to assist researchers who intend to use the informative genes for further research. Enhanced random forest gene selection has performed better in terms of selecting the smallest subset as well as biggest subset of informative genes with lowest out of bag error rates through gene selection. Furthermore, the classification performed on the selected subset of genes using random forest has lead to lower prediction error rates compared to existing method and other similar available methods.     

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。