Large-scale analysis of gene expression profiles

The accumulation of DNA microarray data has now made it possible to use gene expression profiles to analyse expression data. A gene expression profile contains the expression data for a given gene over various samples, and can be contrasted with an expression signature, which contains the expression data for a single sample. Gene expression profiles are most revealing when samples are grouped appropriately, either by standard clinical or pathological categories or by categories discovered through cluster analysis techniques. Expression profiles can exist at various levels of abstraction, yielding information across various tissues or across diseases within a particular tissue. Hypothesis tests may be applied to expression profiles on a large scale to identify candidate genes of interest.     

Improved understanding of gene expression regulation using systems biology

This article reviews the current state of systems biology approaches, including the experimental tools used to generate ‘omic’ data and computational frameworks to interpret this data. Through illustrative examples, systems biology approaches to understand gene expression and gene expression regulation are discussed. Some of the challenges facing this field and the future opportunities in the systems biology era are highlighted.     

Global analysis of gene expression by differential display

Differential display (DD) is one of the most commonly used approaches for identifying differentially expressed genes. However, there has been lack of an accurate guidance on how many DD polymerase chain reaction (PCR) primer combinations are needed to display most of the genes expressed in a eukaryotic cell. This study critically evaluated the gene coverage by DD as a function of the number of arbitrary primers, the number of 3′ bases of an arbitrary primer required to completely match an mRNA target sequence, the additional 5′ base match(s) of arbitrary primers in first-strand cDNA recognition, and the length of mRNA tails being analyzed. The resulting new DD mathematical model predicts that 80 to 160 arbitrary 13mers, when used in combinations with 3 one-base anchored oligo-dT primers, would allow any given mRNA within a eukaryotic cell to be detected with a 74% to 93% probability, respectively. The prediction was supported by both computer simulation of the DD process and experimental data from a comprehensive fluorescent DD screening for target genes of tumor-suppressor p53. Thus, this work provides a theoretical foundation upon which global analysis of gene expression by DD can be pursued.     

合成基因线路规模化设计面临的挑战

随着合成基因线路规模的增加,传统的合成基因线路设计思路的瓶颈逐渐凸显,许多之前被忽略的因素对大规模基因线路的性能可能造成显著影响,这对合成基因线路的设计带来了新的挑战。本文重点梳理了基因表达噪声和竞争效应两方面对基因线路性能的影响,阐释了二者间的紧密联系,并基于理性设计的思路,从模拟-数字运算设计、网络拓扑设计、基因线路中的信息传递理论和动态信号等方面,归纳总结了解决这些问题的潜在方案,并展望了规模化合成基因线路理性设计的未来发展方向。     

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.     

基因表达数据聚类分析技术及其软件工具

随着DNA芯片技术的广泛应用,基因表达数据分析已成为生命科学的研究热点之一。概述基因表达聚类技术类型、算法分类与特点、结果可视化与注释;阐述一些流行的和新型的算法;介绍17个最新相关软件包和在线web服务工具;并说明软件工具的研究趋向。     

Feasibility of global gene expression analysis in testicular biopsies from infertile men

Numerous studies have documented the use of microarray analysis to identify patterns of global gene expression that distinguish normal development from that of the diseased state. Yet, there are no reports that compare global gene expression in the fertile and infertile human testis. Here, we report an initial study of global gene expression in testicular biopsies from several men with different infertility phenotypes. We found that microarray analysis of small biopsy samples was suitable for profiling expression of genes known to function in germ cell development and also identified expression of novel genes. Since it is now common for infertile men with spermatogenic failure to use intracytoplasmic sperm injection (ICSI) to achieve biological paternity, we hypothesize that molecular screening of testicular biopsies with microarrays may be suitable: (1) to categorize the molecular phenoytpes of infertile testes in a manner similar to standard morphologic analysis and (2) to initiate larger studies of gene expression in the infertile testes that may identify genetic signatures from biopsies that allow prediction of outcomes.     

基于信息论k—modes聚类法的基因表达数据分析

k-均值聚类算法是一种广泛应用于基因表达数据聚类分析中的迭代变换算法,它通常用距离法来表示基因间的关系,但不能有效的反应基因间的相互依赖的关系。为此,提出基于信息论的k-modes聚类算法,克服了以上缺点。另外,还引入了伪F统计量,一方面,可以对空间中有部分重叠的点进行有效的分类;另一方面,可以给出最佳聚类数目,从而弥补了k-modes聚类法的不足。使其成为一种非常有效的算法,从而达到较优的聚类效果。     

Exploiting big biology: integrating large-scale biological data for function inference.

The amount of data produced by molecular biologists is growing at an exponential rate. Some of the fastest growing sets of data are measurements of gene expression, comparable in quantity only to gene sequences and the vast biological literature. Both gene expression data and sequence data offer hints as to the functions of thousands of newly discovered genes, but neither give complete answers. Therefore, much effort is being focused on integrating these large data sets and combining them with all available functional data to draw inferences about the functions of uncharacterised genes. This review discusses the most pertinent functional data for genome-wide functional inference and describes several methods by which these disparate data types are being integrated.     

Genetic algorithms applied to multi-class clustering for gene expression data

A hybrid GA (genetic algorithm)-based clustering (HGACLUS) schema, combining merits of the Simulated Annealing, was described for finding an optimal or near-optimal set of medoids. This schema maximized the clustering success by achieving internal cluster cohesion and external cluster isolation. The performance     

Variation within and among species in gene expression: raw material for evolution

Heritable variation in regulatory or coding regions is the raw material for evolutionary processes. The advent of microarrays has recently promoted examination of the extent of variation in gene expression within and among taxa and examination of the evolutionary processes affecting variation. This review examines these issues. We find: (i) microarray-based measures of gene expression are precise given appropriate experimental design; (ii) there is large inter-individual variation, which is composed of a minor nongenetic component and a large heritable component; (iii) variation among populations and species appears to be affected primarily by neutral drift and stabilizing selection, and to a lesser degree by directional selection; and (iv) neutral evolutionary divergence in gene expression becomes nonlinear with greater divergence times due to functional constraint. Evolutionary analyses of gene expression reviewed here provide unique insights into partitioning of regulatory variation in nature. However, common limitations of these studies include the tendency to assume a linear relationship between expression divergence and species divergence, and failure to test explicit hypotheses that involve the ecological context of evolutionary divergence.     

Assessing reliability of gene clusters from gene expression data

The rapid development of microarray technologies has raised many challenging problems in experiment design and data analysis. Although many numerical algorithms have been successfully applied to analyze gene expression data, the effects of variations and uncertainties in measured gene expression levels across samples and experiments have been largely ignored in the literature. In this article, in the context of hierarchical clustering algorithms, we introduce a statistical resampling method to assess the reliability of gene clusters identified from any hierarchical clustering method. Using the clustering trees constructed from the resampled data, we can evaluate the confidence value for each node in the observed clustering tree. A majority-rule consensus tree can be obtained, showing clusters that only occur in a majority of the resampled trees. We illustrate our proposed methods with applications to two published data sets. Although the methods are discussed in the context of hierarchical clustering methods, they can be applied with other cluster-identification methods for gene expression data to assess the reliability of any gene cluster of interest. Electronic Publication     

Clustering gene expression data based on predicted differential effects of GV interaction

Microarray has become a popular biotechnology in biological and medical research. However, systematic and stochastic variabilities in microarray data are expected and unavoidable, resulting in the problem that the raw measurements have inherent “noise” within microarray experiments. Currently, logarithmic ratios are usually analyzed by various clustering methods directly, which may introduce bias interpretation in identifying groups of genes or samples. In this paper, a statistical method based on mixed model approaches was proposed for microarray data cluster analysis. The underlying rationale of this method is to partition the observed total gene expression level into various variations caused by different factors using an ANOVA model, and to predict the differential effects of GV （gene by variety） interaction using the adjusted unbiased prediction （AUP） method. The predicted GV interaction effects can then be used as the inputs of cluster analysis. We illustrated the application of our method with a gene expression dataset and elucidated the utility of our approach using an external validation.     

Evaluation of light regulatory potential of Calvin cycle steps based on large-scale gene expression profiling data

Although large-scale gene expression data have been studied from many perspectives, they have not been systematically integrated to infer the regulatory potentials of individual genes in specific pathways. Here we report the analysis of expression patterns of genes in the Calvin cycle from 95 Arabidopsis microarray experiments, which revealed a consistent gene regulation pattern in most experiments. This identified pattern, likely due to gene regulation by light rather than feedback regulations of the metabolite fluxes in the Calvin cycle, is remarkably consistent with the rate-limiting roles of the enzymes encoded by these genes reported from both experimental and modeling approaches. Therefore, the regulatory potential of the genes in a pathway may be inferred from their expression patterns. Furthermore, gene expression analysis in the context of a known pathway helps to categorize various biological perturbations that would not be recognized with the prevailing methods.     

基于基因表达谱的疾病亚型特征基因挖掘方法

在本研究中,提出了一种基于基因表达谱的疾病亚型特征基因挖掘方法,该方法基于过滤后基因表达谱,融合无监督聚类识别疾病亚型技术和提出的衡量特征基因对疾病亚型鉴别能力的模式质量测度,以嵌入的方式实现特征基因挖掘。最后将提出的方法应用于40例结肠癌组织与22例正常结肠组织中2000个基因的表达谱实验数据,结果显示:提出的方法是一种可行的疾病亚型特征基因挖掘方法,方法的优势在于可并行实现疾病亚型划分和特征基因识别。     

Comparisons of graph-structure clustering methods for gene expression data

Although many numerical clustering algorithms have been applied to gene expression dataanalysis,the essential step is still biological interpretation by manual inspection.The correlation betweengenetic co-regulation and affiliation to a common biological process is what biologists expect.Here,weintroduce some clustering algorithms that are based on graph structure constituted by biological knowledge.After applying a widely used dataset,we compared the result clusters of two of these algorithms in terms ofthe homogeneity of clusters and coherence of annotation and matching ratio.The results show that theclusters of knowledge-guided analysis are the kernel parts of the clusters of Gene Ontology (GO)-Clustersoftware,which contains the genes that are most expression correlative and most consistent with biologicalfunctions.Moreover,knowledge-guided analysis seems much more applicable than GO-Cluster in a largerdataset.