差别显示mRNA法的改良

本文介绍了一种筛选和克隆差异表达基因的新方法——差别显示mRNA法。并在模板、引物、反应体系、反应条件等方面,对该方法作了深入的探索和改进,与原方法相比,改良法具有高效、可靠性高、经济等优点。实验表明,该方法能有效地筛选到一对细胞或组织在不同状态下表达差异的基因。为新基因的发现和克隆提供了新的手段。     

宫颈癌差异表达基因筛选及功能分析

目的:筛选参与宫颈癌发生、发展的关键基因,为临床诊疗提供新的靶点。方法:在NCBI-GEO数据库中筛选多组宫颈癌基因表达检测数据集,利用GEO2R分析工具筛选各组数据集的差异表达基因;应用R分析筛选不同数据集之间共有的差异表达基因;利用DAVID在线分析对差异表达基因进行功能聚类和通路分析;利用STRING分析差异表达基因编码蛋白之间的相互作用关系。结果:共选择6组表达数据集,筛选得到59个差异表达基因(宫颈癌组织vs正常组织),表达差异至少达2倍,其中包含50个表达上调基因及9个表达下调基因。这些差异表达基因参与细胞周期、DNA复制、细胞分裂等生物进程。蛋白互作分析表明,这些差异表达基因多数存在相互作用。结论:利用生物信息学方法对不同来源的基因检测数据进行整合分析,有助于更准确的筛选对宫颈癌发生、发展过程具有重要作用的关键基因,本文筛选的宫颈癌差异基因为进一步研究宫颈癌发生、发展的分子机制及临床诊疗提供思路。     

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。     

乳腺癌基因芯片实验数据分析与挖掘

目的 对公共数据库上下载得到的乳腺癌基因芯片试验结果进行数据分析,找出在正常组织与癌组织中呈现差异表达的基因,并寻找差异表达基因的相关基因.方法 综合运用显著性分析（SAM）、顶级评分基因对（TSP）、关联规则挖掘等方法,对数据进行处理.结果 筛选出若干呈现差异表达的基因,并且寻找了其中一部分基因的可能高度相关的基因.结论 筛选出的基因及其相关基因可用于为进一步的研究提供候选基因.     

差异表达基因分离技术的研究进展

分离并克隆差异表达基因是生命科学的研究热点.近年来,以差示筛选、扣除杂交等基本方法为基础,先后出现了抑制差减杂交,微阵列技术等多种分析差异表达基因的技术, 使差异表达基因分离方法不断完善.对这些方法的优缺点、发展趋势及应用前景进行了简要综述.     

mRNA差别显示技术研究进展及其在水稻中的应用

随着基因组计划的顺利实施,大量的生物信息被解析,分离和鉴定差异表达基因已成为分子生物学研究的热点.mRNA差别显示技术(DDRT-PCR)是目前有效筛选、分离差异表达基因的方法之一.就DDRT-PCR的基本原理、存在的问题及相应的改进方法作了简要概述.阐明了该技术在水稻生长发育、杂种优势、抗逆性基因研究中的应用、取得的成绩,最后对该技术在水稻突变体及抗药性上的应用前景做了有益探讨.     

cDNA文库的快速构建法

ｃＤＮＡ文库的快速构建法周立,任东路（中国科学院发育生物学研究所,北京１０００８０）ｃＤＮＡ文库的构建是现代分子生物学中一门重要的技术。它主要用于基因的筛选。通过这种方法能够得到完整的编码蛋白质的目的片段。通过构建表达型ｃＤＮＡ文库还能分离到调控基因转录的蛋白质因子的基因。此外,差别筛选（ｄｉｆｆｅｒｅｎｔｉａｌｓｃｒｅｅｎｉｎｇ）技术有助于我们获得在发育的不同阶段特异表达的基因片段。     

抑制性扣除杂交技术(SSH)及其研究进展

一般认为,真核生物基因总数为100,000个左右。但在一定发育阶段,在某一类型的细胞当中,则只有15%左右的基因得以表达[1]。而生物体几乎所有的生命活动过程包括病理的变化,从本质上讲均是基因表达变化的结果。因此,关于真核生物发育过程中基因的表达与调控的研究已引起人们的高度重视。过去,人们对差异表达基因的分离主要依赖于示差筛选和差别杂交技术,但它们又存在着重复性差、敏感度低等缺点。近几年,随着ＰＣＲ技术的出现,涌现了许多基于ＰＣＲ的分离有差别表达基因的新方法。1992年ＬｉａｎｇＰ等[2]提出ｍＲＮＡ差异显示技术(ｍＲＮＡｄ…     

稳定转染结合流式分选技术筛选猪GBP1,GBP2基因高表达PK-15细胞

猪的GBP1,GBP2基因是重要的抗病候选基因,建立其高表达细胞模型可为深入研究基因的抗病能力及机理提供良好的素材。利用pEGFP载体上的Neor抗性筛选标记,采用G418药物筛选方法,结合利用GFP荧光标记,采用流式细胞分选技术,获得了超表达猪GBP1和GBP2基因的PK-15细胞,并通过定量PCR方法对筛选后细胞的超表达效果进行验证。结果显示猪GBP1和GBP2基因在转录水平的表达量相对于正常的PK-15细胞分别升高了近40倍和60倍,表明药物筛选结合流式分选是获得目的基因稳定高表达细胞株的快速便捷的方法。     

强直性脊柱炎差异表达基因的生物信息学分析

目的探讨强直性脊柱炎(AS)患者差异表达基因,并基于差异基因探讨强直性脊柱炎发病相关的可能生物学过程和信号通路。方法检索基因表达谱数据库(GEO)并筛选AS相关基因表达谱数据集。应用GEO在线分析功能GEO2R分析AS组和正常对照组的差异表达基因,用Cytoscape软件clueGO插件进行基因本体论和京都基因与基因组百科全书分析,采用String蛋白-蛋白相互作用(PPI)数据库分析差异表达基因编码蛋白间的相互作用;应用Cytoscape绘制蛋白相互作用网络图,并软件筛选信号通路关键基因分析。结果选取AS患者全血表达数据集GSE25101为研究对象,分析获得差异表达基因72个。72个差异表达基因分子功能主要为参与高迁移率族盒染色体蛋白1(HMGB1)转导机制;生物学过程主要富集于巨噬细胞迁移、骨髓细胞凋亡过程、线粒体呼吸链复合体装配、ATP合成偶联电子传输、线粒体ATP合成耦合电子输运等;细胞成分主要富集于呼吸链复合体、线粒体呼吸体等。信号通路富集于氧化磷酸化信号通路和帕金森综合征相关信号通路。PPI网络经过cytohubba插件筛选,ATP5J、NDUFS4、UQCRB、UQCRH、NDUFB3、COX7B、LSM3、ATP5EP2、ENY2、PSMA4被筛选为网络中的核心基因。结论通过生物信息学方法进行预测了AS的潜在机制,并筛选出10个潜在的与AS相关的重要分子,其中氧化磷酸化可能在AS发病机制中发挥了重要的作用。     

Detecting differentially expressed genes by relative entropy

DNA microarray experiments have generated large amount of gene expression measurements across different conditions. One crucial step in the analysis of these data is to detect differentially expressed genes. Some parametric methods, including the two-sample t-test (T-test) and variations of it, have been used. Alternatively, a class of non-parametric algorithms, such as the Wilcoxon rank sum test (WRST), significance analysis of microarrays (SAM) of Tusher et al. (2001), the empirical Bayesian (EB) method of Efron et al. (2001), etc., have been proposed. Most available popular methods are based on t-statistic. Due to the quality of the statistic that they used to describe the difference between groups of data, there are situations when these methods are inefficient, especially when the data follows multi-modal distributions. For example, some genes may display different expression patterns in the same cell type, say, tumor or normal, to form some subtypes. Most available methods are likely to miss these genes. We developed a new non-parametric method for selecting differentially expressed genes by relative entropy, called SDEGRE, to detect differentially expressed genes by combining relative entropy and kernel density estimation, which can detect all types of differences between two groups of samples. The significance of whether a gene is differentially expressed or not can be estimated by resampling-based permutations. We illustrate our method on two data sets from Golub et al. (1999) and Alon et al. (1999). Comparing the results with those of the T-test, the WRST and the SAM, we identified novel differentially expressed genes which are of biological significance through previous biological studies while they were not detected by the other three methods. The results also show that the genes selected by SDEGRE have a better capability to distinguish the two cell types.     

Comparison of methods for identifying differentially expressed genes across multiple conditions from microarray data

Identification of genes differentially expressed across multiple conditions has become an important statistical problem in analyzing large-scale microarray data. Many statistical methods have been developed to address the challenging problem. Therefore, an extensive comparison among these statistical methods is extremely important for experimental scientists to choose a valid method for their data analysis. In this study, we conducted simulation studies to compare six statistical methods: the Bonferroni (B-) procedure, the Benjamini and Hochberg (BH-) procedure, the Local false discovery rate (Localfdr) method, the Optimal Discovery Procedure (ODP), the Ranking Analysis of F-statistics (RAF), and the Significant Analysis of Microarray data (SAM) in identifying differentially expressed genes. We demonstrated that the strength of treatment effect, the sample size, proportion of differentially expressed genes and variance of gene expression will significantly affect the performance of different methods. The simulated results show that ODP exhibits an extremely high power in indentifying differentially expressed genes, but significantly underestimates the False Discovery Rate (FDR) in all different data scenarios. The SAM has poor performance when the sample size is small, but is among the best-performing methods when the sample size is large. The B-procedure is stringent and thus has a low power in all data scenarios. Localfdr and RAF show comparable statistical behaviors with the BH-procedure with favorable power and conservativeness of FDR estimation. RAF performs the best when proportion of differentially expressed genes is small and treatment effect is weak, but Localfdr is better than RAF when proportion of differentially expressed genes is large.     

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.     

Identifying differentially expressed genes in meta-analysis via Bayesian model-based clustering

A Bayesian model-based clustering approach is proposed for identifying differentially expressed genes in meta-analysis. A Bayesian hierarchical model is used as a scientific tool for combining information from different studies, and a mixture prior is used to separate differentially expressed genes from non-differentially expressed genes. Posterior estimation of the parameters and missing observations are done by using a simple Markov chain Monte Carlo method. From the estimated mixture model, useful measure of significance of a test such as the Bayesian false discovery rate (FDR), the local FDR (Efron et al., 2001), and the integration-driven discovery rate (IDR; Choi et al., 2003) can be easily computed. The model-based approach is also compared with commonly used permutation methods, and it is shown that the model-based approach is superior to the permutation methods when there are excessive under-expressed genes compared to over-expressed genes or vice versa. The proposed method is applied to four publicly available prostate cancer gene expression data sets and simulated data sets.     

Arrow Plot: a new graphical tool for selecting up and down regulated genes and genes differentially expressed on sample subgroups

ABSTRACT: BACKGROUND: A common task in analyzing microarray data is to determine which genes are differentially expressed across two (or more) kind of tissue samples or samples submitted under experimental conditions. Several statistical methods have been proposed to accomplish this goal, generally based on measures of distance between classes. It is well known that biological samples are heterogeneous because of factors such as molecular subtypes or genetic background that are often unknown to the experimenter. For instance, in experiments which involve molecular classification of tumors it is important to identify significant subtypes of cancer. Bimodal or multimodal distributions often reflect the presence of subsamples mixtures. Consequently, there can be genes differentially expressed on sample subgroups which are missed if usual statistical approaches are used. In this paper we propose a new graphical tool which not only identifies genes with up and down regulations, but also genes with differential expression in different subclasses, that are usually missed if current statistical methods are used. This tool is based on two measures of distance between samples, namely the overlapping coefficient (OVL) between two densities and the area under the receiver operating characteristic (ROC) curve. The methodology proposed here was implemented in the open-source R software. RESULTS: This method was applied to a publicly available dataset, as well as to a simulated dataset. We compared our results with the ones obtained using some of the standard methods for detecting differentially expressed genes, namely Welch t-statistic, fold change (FC), rank products (RP), average difference (AD), weighted average difference (WAD), moderated t-statistic (modT), intensity-based moderated t-statistic (ibmT), significance analysis of microarrays (samT) and area under the ROC curve (AUC). On both datasets all differentially expressed genes with bimodal or multimodal distributions were not selected by all standard selection procedures. We also compared our results with (i) area between ROC curve and rising area (ABCR) and (ii) the test for not proper ROC curves (TNRC). We found our methodology more comprehensive, because it detects both bimodal and multimodal distributions and different variances can be considered on both samples. Another advantage of our method is that we can analyze graphically the behavior of different kinds of differentially expressed genes. CONCLUSION: Our results indicate that the arrow plot represents a new flexible and useful tool for the analysis of gene expression profiles from microarrays.     

A global approach to identify differentially expressed genes in cDNA (two-color) microarray experiments

MOTIVATION: Currently most of the methods for identifying differentially expressed genes fall into the category of so called single-gene-analysis, performing hypothesis testing on a gene-by-gene basis. In a single-gene-analysis approach, estimating the variability of each gene is required to determine whether a gene is differentially expressed or not. Poor accuracy of variability estimation makes it difficult to identify genes with small fold-changes unless a very large number of replicate experiments are performed. RESULTS: We propose a method that can avoid the difficult task of estimating variability for each gene, while reliably identifying a group of differentially expressed genes with low false discovery rates, even when the fold-changes are very small. In this article, a new characterization of differentially expressed genes is established based on a theorem about the distribution of ranks of genes sorted by (log) ratios within each array. This characterization of differentially expressed genes based on rank is an example of all-gene-analysis instead of single gene analysis. We apply the method to a cDNA microarray dataset and many low fold-changed genes (as low as 1.3 fold-changes) are reliably identified without carrying out hypothesis testing on a gene-by-gene basis. The false discovery rate is estimated in two different ways reflecting the variability from all the genes without the complications related to multiple hypothesis testing. We also provide some comparisons between our approach and single-gene-analysis based methods. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.     

Finding differentially expressed genes in two-channel DNA microarray datasets: how to increase reliability of data preprocessing

Due to the great variety of preprocessing tools in two-channel expression microarray data analysis it is difficult to choose the most appropriate one for a given experimental setup. In our study, two independent two-channel inhouse microarray experiments as well as a publicly available dataset were used to investigate the influence of the selection of preprocessing methods (background correction, normalization, and duplicate spots correlation calculation) on the discovery of differentially expressed genes. Here we are showing that both the list of differentially expressed genes and the expression values of selected genes depend significantly on the preprocessing approach applied. The choice of normalization method to be used had the highest impact on the results. We propose a simple but efficient approach to increase the reliability of obtained results, where two normalization methods which are theoretically distinct from one another are used on the same dataset. Then the intersection of results, that is, the lists of differentially expressed genes, is used in order to get a more accurate estimation of the genes that were de facto differentially expressed.     

Identification of differentially expressed genes using multi-resolution wavelet transformation analysis combined with SAM

Although many statistical methods have been proposed for identifying differentially expressed genes, the optimal approach has still not been resolved. Therefore, it is necessary to develop more efficient methods of finding differentially expressed genes while accounting for noise and false discovery rate (FDR). We propose a method based on multi-resolution wavelet transformation analysis combined with SAM for identifying differentially expressed genes by adjusting the Δ and computing the FDR. This method was applied to a microarray expression dataset from adenoma patients and normal subjects. The number of differentially expressed genes gradually reduced with an increasing Δ value, and the FDR was reduced after wavelet transformation. At a given Δ value, the FDR was also reduced before and after wavelet transformation. In conclusion, a greater number and quality of differentially expressed genes were detected using the method when compared to non-transformed data, and the FDRs were notably more controlled and reduced.     

分离差异表达基因的方法

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,扣除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)等技术。这里着重对这些方法的优缺点及改进进行了论述和评介,并对技术的发展趋势进行了分析。     

Ranking analysis for identifying differentially expressed genes

Microarrays allow researchers to examine the expression of thousands of genes simultaneously. However, identification of genes differentially expressed in microarray experiments is challenging. With an optimal test statistic, we rank genes and estimate a threshold above which genes are considered to be differentially expressed genes (DE). This overcomes the embarrassing shortcoming of many statistical methods to determine the cut-off values in ranking analysis. Experiments demonstrate that our method is a good performance and avoids the problems with graphical examination and multiple hypotheses testing that affect alternative approaches. Comparing to those well known methods, our method is more sensitive to data sets with small differentially expressed values and not biased in favor of data sets based on certain distribution models.