基因芯片筛选新疆维吾尔妇女宫颈癌差异表达基因

新疆南部维吾尔族聚居区是宫颈癌高发区. 本文旨在利用基因芯片技术筛选与维吾尔族妇女宫颈癌发生相关的基因. 首先,分别提取5例新疆维吾尔妇女宫颈癌和5例子宫肌瘤组织(对照)的mRNA,逆转录成cDNA,并用Cy3-dUTP标记子宫肌瘤组织的cDNA,用Cy5 dUTP标记宫颈癌组织的cDNA,制成芯片杂交探针.为筛选出宫颈癌组织中差异表达的基因,上述标记探针分别与含有20 000条人类基因的Affymetrix基因芯片进行杂交,杂交信号用GeneChip Scanner 3000扫描仪扫描,并用芯片图像分析软件(SAM software)分析扫描结果.筛选出的差异表达基因经GO(Gene Ontology)分析和KEGG（Kyoto Encyclopedia of Genes and Genomes）信号通路分析,确定其在宫颈癌中的作用.基因芯片筛选结果显示,在宫颈癌组织中发现2 758个差异表达基因,其中1 326个上调基因,1 432个下调基因.GO分析和KEGG信号通路分析表明,表达差异在两倍以上的基因涉及168个信号通路,包括细胞粘附分子、细胞周期以及MAPK和mTOR信号通路等.上述结果表明,基因芯片技术筛选出大量与宫颈癌发生相关的基因,其中表达差异显著的基因涉及细胞粘附分子、细胞周期和mTOR等信号通路.     

鸡胚公母性腺差异表达基因的基因芯片杂交筛选

采用Affymetrix公司鸡基因组芯片对9日龄鸡胚公母性腺总RNA进行了芯片杂交, 并对基因表达谱进行了分析。统计结果显示, 9日龄母鸡性腺表达基因数19 368个, 公鸡性腺表达基因数19 493个; 公母性腺绝对差异表达基因,即公鸡性腺表达而母鸡性腺不表达基因145个, 母鸡性腺表达而公鸡性腺不表达基因189个。绝对差异表达基因功能分类结果显示, 参与细胞组成、细胞加工和分子结合基因占多数, 部分基因参与细胞器组成、代谢加工、生物学调控以及催化反应和细胞信号转导等。值得注意的是, 本研究发现了一些已经报道同性别决定和分化有一定关联的基因, 如ASW、CHD1和SOX9等, 同时也发现了一些未知其同性腺分化和发育有关联的基因和编码假想蛋白的表达序列。进一步分析这些基因和表达序列的生物学功能和表达模式, 将对鸟类性别决定和分化机制的了解提供有益参考。     

筛选差异表达基因方法的新进展

了解不同细胞或同类细胞在不同发育阶段、不同生理状态下的基因表达状况,可以为研究生命活动过程提供重要信息。以差别筛选、削减杂交等基本方法为出发点,研究基因表达差异的方法不断完善,先后出现了DDRT—PCR、RDA、SSH、cDNA微阵列(基因芯片)、基因表达的系统分析(SAGE)等技术。本着重对这些方法的优缺点及改进进行论述和评介,并对技术的发展趋势进行了分析。     

利用表达谱基因芯片筛选溃疡性结肠炎差异表达基因

目的:利用人类全基因组表达谱芯片技术,分析溃疡性结肠炎患者和健康者基因表达谱差异,筛选出溃疡性结肠炎相关基因。方法:采用Trizol法提取8例溃疡性结肠炎患者和8例健康对照者结肠粘膜组织总RNA并纯化,逆转录合成c DNA,利用荧光染料Cy3标记aa UTP,转录合成标记的c RNA,并与Agilent人类全基因组表达谱芯片杂交,扫描荧光信号图像,对芯片原始数据进行归一化处理,利用倍数差异和t检验计算筛选出相关差异表达基因,采用DAVID在线分析系统进行基因的功能注释和关联分析,明确差异基因的生物学功能,并对部分差异表达基因进行实时荧光定量PCR验证。结果:筛查出溃疡性结肠炎结肠粘膜组织差异表达基因4132个,其中上调基因2004个,下调基因2128个。选取6条差异表达基因进行PCR验证,结果有3条基因表达上调,3条基因表达下调,表达趋势与芯片结果一致。结论:溃疡性结肠炎患者与健康对照者基因表达存在明显差异,分析这些差异表达基因有助于我们探索溃疡性结肠炎的发病机制,为疾病的治疗提供理论依据。     

筛选差异表达基因和蛋白质的方法进展

分离和鉴定差异表达基因和蛋白质不仅有助于发现基因和蛋白质的功能,更有助于揭示某些疾病的发生机理.目前筛选差异表达基因的方法主要有差异显示PCR方法(differential display RT-PCR,DDRT-PCR)、消减杂交法(subtractive hybridization,SH)、基因芯片技术(DNA chip technique)和基因表达的系统分析(serial analysis of gene expression,SAGE)等,其中消减杂交法中又先后建立了代表性差异分析技术(representational difference analysis,RDA)、抑制消减杂交法(suppression subtractive hybridization,SSH)和获得全长基因的消减杂交法(full-length-gene-obtainable subtractive hybridization).筛选差异表达蛋白质的方法主要有双向电泳技术(two-dimentional gel electrophoresis)和噬菌体全套抗体库技术(phage display antibody repertoire library technique).这些方法各有特点,各有利弊,研究者可根据自己的需要选择适合于自己的方法.     

基因芯片技术筛选转SlNAC1基因拟南芥差异表达基因

研究已表明植物特有的一些NAC(NAM,ATAF1/2,CUC2)转录因子可提高植物抗逆性,利用基因芯片技术筛选转SlNAC1基因拟南芥与野生型拟南芥间差异表达基因,能够为研究转基因拟南芥非生物胁迫抗性相关基因提供依据。结果显示,在转SlNAC1基因拟南芥43 604个基因中有3 046个差异表达2倍以上的基因。对差异表达5倍以上基因经过GO富集度统计学分析表明,细胞组分相关基因占33.05%;分子功能相关基因占33.95%;生物学过程相关基因占33.00%。对差异表达2倍以上基因进行KEGG信号通路分析,结果表明有2 431个基因涉及到88个不同的信号通路。通过筛选获得转基因拟南芥非生物胁迫抗性相关候选基因,为后续研究NAC转录因子的下游基因及其调控网络的构建提供方向和理论支撑。     

基因芯片与植物基因差异表达分析

基因芯片为研究植物不同个体或物种之间以及同一个体在不同生长发育阶段、正常和疾病状态下基因表达的差异、某一性状多基因的协同作用,寻找和定位新的目的基因等方面带来了革命性的变革。与传统研究基因差异表达的方法相比,它具有微型化、用材少、快速、准确、灵敏度能高基、在因同等一研究方面已取得了显著的成绩,如拟南芥、酵母、水稻等。     

糖尿病小鼠胚胎早期发育的差异表达基因

目的利用小鼠糖尿病模型,探讨母体糖尿病环境对早期胚胎基因表达的影响。方法ICR雌性小鼠腹腔注射150mg／kg剂量STZ诱发糖尿病,与正常雄鼠交配受孕,取14d胎龄的胚胎,提取胚胎的总RNA。将Cy3和Cy52种荧光分别标记到实验组和对照组的RNA上,制成RNA探针,并与包含24859个基因的表达谱芯片进行杂交及扫描,重复3次实验,采用Agilent扫描仪进行扫描软件读取数据。结果筛选出差异表达基因397个,其中有328个基因在实验组表达量比对照组大2倍,69个基因在实验组表达量比对照组小2倍。结论母体糖尿病环境能影响早期胎儿的基因表达,通过上调代谢相关基因和下调发育相关基因影响小鼠胚胎的早期发育。为深入探讨糖尿病胚胎病理和代谢疾病的分子机理提供了基本数据和研究的方向。     

用基因芯片筛选前列腺癌LNCaP和C4-2细胞系中差异表达的基因

目的：筛选与前列腺癌进展相关的功能基因。方法：利用Affymetrix公司的人类金基因组U133A芯片,筛选在前列腺癌细胞系LNCaP和C4-2中差异表达的基因,并用RT-PCR方法验证部分差异基因的表达。结果：芯片筛选结果表明,与LNCaP细胞系相比,C4-2细胞系中217个基因转录本表达上调,101个基因转录本表达下调;对其中45个基因进行了RT-PCR验证,证明35个基因转录本的表达结果与芯片筛选一致。结论：筛选出了在LNCaP和C4-2细胞系中显著差异表达的基因35个,为进一步研究前列腺癌进展的机制奠定了基础。     

A statistical framework for differential network analysis from microarray data

Background  

It has been long well known that genes do not act alone; rather groups of genes act in consort during a biological process. Consequently, the expression levels of genes are dependent on each other. Experimental techniques to detect such interacting pairs of genes have been in place for quite some time. With the advent of microarray technology, newer computational techniques to detect such interaction or association between gene expressions are being proposed which lead to an association network. While most microarray analyses look for genes that are differentially expressed, it is of potentially greater significance to identify how entire association network structures change between two or more biological settings, say normal versus diseased cell types.     

Weighted change-point method for detecting differential gene expression in breast cancer microarray data

In previous work, we proposed a method for detecting differential gene expression based on change-point of expression profile. This non-parametric change-point method gave promising result in both simulation study and public dataset experiment. However, the performance is still limited by the less sensitiveness to the right bound and the statistical significance of the statistics has not been fully explored. To overcome the insensitiveness to the right bound we modified the original method by adding a weight function to the D(n) statistic. Simulation study showed that the weighted change-point statistics method is significantly better than the original NPCPS in terms of ROC, false positive rate, as well as change-point estimate. The mean absolute error of the estimated change-point by weighted change-point method was 0.03, reduced by more than 50% comparing with the original 0.06, and the mean FPR was reduced by more than 55%. Experiment on microarray Dataset I resulted in 3974 differentially expressed genes out of total 5293 genes; experiment on microarray Dataset II resulted in 9983 differentially expressed genes among total 12576 genes. In summary, the method proposed here is an effective modification to the previous method especially when only a small subset of cancer samples has DGE.     

Visualization methods for statistical analysis of microarray clusters

Background  

The most common method of identifying groups of functionally related genes in microarray data is to apply a clustering algorithm. However, it is impossible to determine which clustering algorithm is most appropriate to apply, and it is difficult to verify the results of any algorithm due to the lack of a gold-standard. Appropriate data visualization tools can aid this analysis process, but existing visualization methods do not specifically address this issue.     

A comparison of statistical methods for detecting differentially expressed genes from RNA-seq data

RNA-Seq technologies are quickly revolutionizing genomic studies, and statistical methods for RNA-seq data are under continuous development. Timely review and comparison of the most recently proposed statistical methods will provide a useful guide for choosing among them for data analysis. Particular interest surrounds the ability to detect differential expression (DE) in genes. Here we compare four recently proposed statistical methods, edgeR, DESeq, baySeq, and a method with a two-stage Poisson model (TSPM), through a variety of simulations that were based on different distribution models or real data. We compared the ability of these methods to detect DE genes in terms of the significance ranking of genes and false discovery rate control. All methods compared are implemented in freely available software. We also discuss the availability and functions of the currently available versions of these software.     

Evaluation of normalization methods for microarray data

Background

Microarray technology allows the monitoring of expression levels for thousands of genes simultaneously. This novel technique helps us to understand gene regulation as well as gene by gene interactions more systematically. In the microarray experiment, however, many undesirable systematic variations are observed. Even in replicated experiment, some variations are commonly observed. Normalization is the process of removing some sources of variation which affect the measured gene expression levels. Although a number of normalization methods have been proposed, it has been difficult to decide which methods perform best. Normalization plays an important role in the earlier stage of microarray data analysis. The subsequent analysis results are highly dependent on normalization.

Results

In this paper, we use the variability among the replicated slides to compare performance of normalization methods. We also compare normalization methods with regard to bias and mean square error using simulated data.

Conclusions

Our results show that intensity-dependent normalization often performs better than global normalization methods, and that linear and nonlinear normalization methods perform similarly. These conclusions are based on analysis of 36 cDNA microarrays of 3,840 genes obtained in an experiment to search for changes in gene expression profiles during neuronal differentiation of cortical stem cells. Simulation studies confirm our findings.

     

Whole-tree methods for detecting differential diversification rates

Prolific cladogenesis, adaptive radiation, species selection, key innovations, and mass extinctions are a few examples of biological phenomena that lead to differential diversification among lineages. Central to the study of differential diversification rates is the ability to distinguish chance variation from that which requires deterministic explanation. To detect diversification rate variation among lineages, we propose a number of methods that incorporate information on the topological distribution of species diversity from all internal nodes of a phylogenetic tree. These whole-tree methods (M(Pi), M(Sigma), and M(R)) are explicitly connected to a null model of random diversification--the equal-rates Markov (ERM) random branching model--and an alternative model of differential diversification: M(Pi) is based on the product of individual nodal ERM probabilities; M(Sigma) is based on the sum of individual nodal ERM probabilities, and M(R) is based on a transformation of ERM probabilities that corresponds to a formalized system that orders trees by their relative symmetry. These methods have been implemented in a freely available computer program, SYMMETREE, to detect clades with variable diversification rates, thereby allowing the study of biological processes correlated with and possibly causal to shifts in diversification rate. Application of these methods to several published phylogenies demonstrates their ability to contend with relatively large, incompletely resolved trees. These topology-based methods do not require estimates of relative branch lengths, which should facilitate the analysis of phylogenies, such as supertrees, for which such data are unreliable or unavailable.     

Comparing transformation methods for DNA microarray data

Background  

When DNA microarray data are used for gene clustering, genotype/phenotype correlation studies, or tissue classification the signal intensities are usually transformed and normalized in several steps in order to improve comparability and signal/noise ratio. These steps may include subtraction of an estimated background signal, subtracting the reference signal, smoothing (to account for nonlinear measurement effects), and more. Different authors use different approaches, and it is generally not clear to users which method they should prefer.     

Clustering methods for microarray gene expression data

Within the field of genomics, microarray technologies have become a powerful technique for simultaneously monitoring the expression patterns of thousands of genes under different sets of conditions. A main task now is to propose analytical methods to identify groups of genes that manifest similar expression patterns and are activated by similar conditions. The corresponding analysis problem is to cluster multi-condition gene expression data. The purpose of this paper is to present a general view of clustering techniques used in microarray gene expression data analysis.     

Some statistical issues in microarray gene expression data

In this paper we discuss some of the statistical issues that should be considered when conducting experiments involving microarray gene expression data. We discuss statistical issues related to preprocessing the data as well as the analysis of the data. Analysis of the data is discussed in three contexts: class comparison, class prediction and class discovery. We also review the methods used in two studies that are using microarray gene expression to assess the effect of exposure to radiofrequency (RF) fields on gene expression. Our intent is to provide a guide for radiation researchers when conducting studies involving microarray gene expression data.     

New normalization methods for cDNA microarray data

MOTIVATION: The focus of this paper is on two new normalization methods for cDNA microarrays. After the image analysis has been performed on a microarray and before differentially expressed genes can be detected, some form of normalization must be applied to the microarrays. Normalization removes biases towards one or other of the fluorescent dyes used to label each mRNA sample allowing for proper evaluation of differential gene expression. RESULTS: The two normalization methods that we present here build on previously described non-linear normalization techniques. We extend these techniques by firstly introducing a normalization method that deals with smooth spatial trends in intensity across microarrays, an important issue that must be dealt with. Secondly we deal with normalization of a new type of cDNA microarray experiment that is coming into prevalence, the small scale specialty or 'boutique' array, where large proportions of the genes on the microarrays are expected to be highly differentially expressed. AVAILABILITY: The normalization methods described in this paper are available via http://www.pi.csiro.au/gena/ in a software suite called tRMA: tools for R Microarray Analysis upon request of the authors. Images and data used in this paper are also available via the same link.