Incorporating heterogeneous biological data sources in clustering gene expression data

In this paper, a similarity measure between genes with protein-protein interactions is pro-posed. The chip-chip data are converted into the same form of gene expression data with pear-son correlation as its similarity measure. On the basis of the similarity measures of protein- protein interaction data and chip-chip data, the combined dissimilarity measure is defined. The combined distance measure is introduced into K-means method, which can be considered as an improved K-means method. The improved K-means method and other three clustering methods are evaluated by a real dataset. Per-formance of these methods is assessed by a prediction accuracy analysis through known gene annotations. Our results show that the improved K-means method outperforms other clustering methods. The performance of the improved K-means method is also tested by varying the tuning coefficients of the combined dissimilarity measure. The results show that it is very helpful and meaningful to incorporate het-erogeneous data sources in clustering gene expression data, and those coefficients for the genome-wide or completed data sources should be given larger values when constructing the combined dissimilarity measure.

Experimental genomics:The application of DNA microarrays in cellular and molecular biology studies

The genome sequence information in combination with DNA microarrays promises to revolutionize the way of cellu-lar and molecular biological research by allowing complex mixtures of RNA and DNA to interrogated in a parallel and quantita-tive fashion. DNA microarrays can be used to measure levels of gene expression for tens of thousands of gene simultane-ously and take advantage of all available sequence information for experimental design and data interpretation in pursuit of biological understanding. Recent progress in experimental genomics allows DNA microarrays not simply to provide a cata-logue of all the genes and information about their function, but to understand how the components work together to comprise functioning cells and organisms. This brief review gives a survey of DNA microarrays technology and its applications in ge-nome and gene function analysis, gene expression studies, biological signal and defense system, cell cycle regulation, mechanism of transcriptional regulation, proteomics, and the functionality of food component.

Identify the signature genes for diagnose of uveal melanoma by weight gene co-expression network analysis

AIM: To identify and understand the relationship between co-expression pattern and clinic traits in uveal melanoma, weighted gene co-expression network analysis(WGCNA) is applied to investigate the gene expression levels and patient clinic features. Uveal melanoma is the most common primary eye tumor in adults. Although many studies have identified some important genes and pathways that were relevant to progress of uveal melanoma, the relationship between co-expression and clinic traits in systems level of uveal melanoma is unclear yet. We employ WGCNA to investigate the relationship underlying molecular and phenotype in this study.METHODS: Gene expression profile of uveal melanoma and patient clinic traits were collected from the Gene Expression Omnibus(GEO) database. The gene co-expression is calculated by WGCNA that is the R package software. The package is used to analyze the correlation between pairs of expression levels of genes.The function of the genes were annotated by gene ontology(GO).RESULTS: In this study, we identified four co-expression modules significantly correlated with clinictraits. Module blue positively correlated with radiotherapy treatment. Module purple positively correlates with tumor location(sclera) and negatively correlates with patient age. Module red positively correlates with sclera and negatively correlates with thickness of tumor. Module black positively correlates with the largest tumor diameter(LTD). Additionally, we identified the hug gene(top connectivity with other genes) in each module. The hub gene RPS15 A, PTGDS, CD53 and MSI2 might play a vital role in progress of uveal melanoma.CONCLUSION: From WGCNA analysis and hub gene calculation, we identified RPS15 A, PTGDS, CD53 and MSI2 might be target or diagnosis for uveal melanoma.

Applying a highly specific and reproducible cDNA RDA method to clone garlic up-regulated genes in human gastric cancer cells

AIM: To develop and optimize cDNA representational difference analysis (cDNA RDA) method and to identify and clone garlic up-regulated genes in human gastric cancer (HGC) cells. METHODS: We performed cDNA RDA method by using abundant double-stranded cDNA messages provided by two self-constructed cDNA libraries (Allitridi-treated and paternal HGC cell line BGC823 cells cDNA libraries respectively). Bam H I and Xho I restriction sites harbored in the library vector were used to select representations. Northern and Slot blots analyses were employed to identify the obtained difference products. RESULTS: Fragments released from the cDNA library vector after restriction endonuclease digestion acted as good marker indicating the appropriate digestion degree for library DNA. Two novel expressed sequence tags (ESTs) and a recombinant gene were obtained. Slot blots result showed a 8-fold increase of glia-derived nexin/protease nexin 1 (GDN/PN1) gene expression level and 4-fold increase of hepatitis B virus x-interacting protein (XIP) mRNA level in BGC823 cells after Allitridi treatment for 72h. CONCLUSION: Elevated levels of GDN/PN1 and XIP mRNAs induced by Allitridi provide valuable molecular evidence for elucidating the garlic's efficacies against neurodegenerative and inflammatory diseases. Isolation of a recombinant gene and two novel ESTs further show cDNA RDA based on cDNA libraries to be a powerful method with high specificity and reproducibility in cloning differentially expressed genes.

Studies of Differentially-Expressed Genes in Human Endometrial Cancer of Various Differentiated Grades

OBJECTIVE To study the gene expression profiles of human endometrial cancers at various differentia0ted grade levels and to identify the genes related to differentiation of the endometrial cancers. METHODS cDNA microarray technology was used to analyze the differentially-expressed genes among different differentiated grades of 32 cases of endometrial cancer. Hierarchical cluster analysis （HCA） for the gene expression profiles of the cases was employed. RESULTS The tissue samples were grouped based on the various differentiated tumor grades with 33 differentiation-related genes identified out （P〈0.001）. Based on the results from the HCA, the conformity rate was 91% among the 33 differentially-expressed genes, and the analysis of pathological classification.CONCLUSION Genes related to the differentiation of endometrial cancer can be identified by using gene chips to analyze the expression profiles of endometrial cancers at various differentiated grades; HCA of the gene expression profiles can be helpful for distinguishing high-risk endometrial cancers before surgery.

cDNA微阵列结合聚类分析探讨大鼠心肌缺血-再灌诱导的基因表达谱改变

目的 :探讨大鼠心肌缺血 再灌诱导的基因表达谱改变 ,为揭示心肌内源性保护的分子机制提供新的线索和思路。方法 :采用反复短时间结扎及松解左冠状动脉主支构建大鼠心肌缺血 再灌反应动物模型 ,运用含 4 0 97个大鼠基因点的cDNA芯片检测大鼠心肌缺血 再灌处理后不同时间点 (1,3,6 ,12 ,2 4h)的基因表达情况 ,并采用SOM算法将具有相似表达模式的基因进行聚类。结果 :短时间缺血后再灌 1,3,6 ,12 ,2 4h分别有 75 ,779,2 0 5 ,15 5 ,16 6个基因表达发生改变。具有相同表达模式的基因被聚为 12类。结论 :心肌缺血 再灌可以诱导心肌的基因表达谱发生改变 ,具有相似表达模式的基因可能具有相似的功能或相似的表达调控机制。

基于微分的cDNA基因芯片图像自动划格算法

针对cDNA基因芯片数据分析中划格对提取杂交荧光样点杂交强度信息的重要作用,提出了一种基于微分的cDNA基因芯片图像自动划格算法,采用NCBI上GEO数据库中cDNA基因芯片图像进行划格实验,验证了该算法的有效性。

基于图论的DNA微阵列数据聚类算法

传统的聚类算法用于DNA微阵列数据分析时,多数只能生成一种聚类结果,无法识别出与多组不同基因表达模式相类似的基因。针对该问题,提出一种基于图论的聚类算法,采用一个有向无权图来描述需要分析的DNA微阵列数据,分别计算该图具有最小割权值和第二小割权值的图割。测试结果表明,该算法可以有效地探测聚类结果空间并输出一组可能性较高的聚类结果,与Fuzzy-Max、Fuzzy-Alpha、Fuzzy-Clust等聚类算法相比具有更高的准确性。

DENGENE:一种高精度的基于密度的适用于基因表达数据的聚类算法

根据基因表达数据的特点,提出一种高精度的基于密度的聚类算法DENGENE。DENGENE通过定义一致性检测和引进峰点改进搜索方向,使得算法能够更好地处理基因表达数据。为了评价算法的性能,选取了两组广为使用的测试数据,即啤酒酵母基因表达数据集对算法来进行测试。实验结果表明,与基于模型的五种算法、CAST算法、K-均值聚类等相比,DENGENE在滤除噪声和聚类精度方面取得了显著的改善。

一种基于聚类和统计分析DNA基因芯片图像处理算法

DNA基因芯片可以同时监控成千上万个基因的表达信息。图像分析是基因芯片试验中一个重要的环节,直接影响到其后续的处理、分析和研究,比如鉴别预测具有不同表达信息的基因功能。基因芯片图像分析包括三个步骤:图像网格化,图像分割以及信息抽取。该文主要研究分割和信息抽取问题。首先基于K-Means聚类技术提出了一种新的分割方法;其次基于统计分析文章建议了一种新的背景和前景分割校正方法用于更准确的信息抽取。新方法的优点是对于基因芯片中spot图像没有任何形状限制。实际图像分析结果与目前最流行的基因芯片图像分析软件GenePix对比研究表明该文算法是精确有效的。

DNA芯片技术应用研究进展

DNA芯片技术是近年迅速发展的一门生物高新技术 ,其突出特点在于高度并行性、多样性 ,即能一次性对生物遗传信息进行大规模的快速、同步分析。DNA芯片技术目前已用于基因重复测序、基因表达分析、新基因的发现、基因单核苷酸多态性 ( SNPs)研究、基因诊断、药物筛选等领域 ,而且其应用范围还在不断扩展。本室运用这一新技术 ,检测水稻在受水稻黄单胞菌水稻致病变种及该菌株的 rpf C基因缺失突变体感染时基因表达的差异 。

Expression profiling analysis for genes related to meat quality and carcass traits during postnatal development of backfat in two pig breeds

The competitive equilibrium of fatty acid biosynthesis and oxidation in vivo determines porcine sub-cutaneous fat thickness(SFT) and intramuscular fat(IMF) content.Obese and lean-type pig breeds show obvious differences in adipose deposition;however, the molecular mechanism underlying this phenotypic variation remains unclear.We used pathway-focused oligo microarray studies to examine the expression changes of 140 genes associated with meat quality and carcass traits in backfat at five growth stages(1―5 months) of Landrace(a leaner, Western breed) and Taihu pigs(a fatty, indigenous, Chinese breed).Variance analysis(ANOVA) revealed that differences in the expression of 25 genes in Landrace pigs were significant(FDR adjusted permutation, P<0.05) among 5 growth stages.Gene class test(GCT) indicated that a gene-group was very significant between 2 pig breeds across 5 growth stages(PErmineJ<0.01), which consisted of 23 genes encoding enzymes and regulatory proteins associ-ated with lipid and steroid metabolism.These findings suggest that the distinct differences in fat deposition ability between Landrace and Taihu pigs may closely correlate with the expression changes of these genes.Clustering analysis revealed a very high level of significance(FDR adjusted, P<0.01) for 2 gene expression patterns in Landrace pigs and a high level of significance(FDR adjusted, P<0.05) for 2 gene expression patterns in Taihu pigs.Also, expression patterns of genes were more diversified in Taihu pigs than those in Landrace pigs, which suggests that the regulatory mechanism of micro-effect polygenes in adipocytes may be more complex in Taihu pigs than in Landrace pigs.Based on a dy-namic Bayesian network(DBN) model, gene regulatory networks(GRNs) were reconstructed from time-series data for each pig breed.These two GRNs initially revealed the distinct differences in physiological and biochemical aspects of adipose metabolism between the two pig breeds;from these results, some potential key genes could be identified.Quantitative, real-time RT-PCR(QRT-PCR) was used to verify the microarray data for five modulated genes, and a good correlation between the two measures of expression was observed for both 2 pig breeds at different growth stages(R=0.874±0.071).These results highlight some possible candidate genes for porcine fat characteristics and provide some data on which to base further study of the molecular basis of adipose metabolism.

Analysis of gene expression profile for identification of novel gene signatures during dengue infection

Background:Dengue is a major arthropod-borne viral disease spreading rapidly across the globe.The absence of vaccines and inadequate vector control measures leads to further expansion of dengue in many regions globally.Hence,the identification of genes involved in the pathogenesis of dengue will help to understand the molecular basis of the disease and the genes responsible for the disease progression.Methods:In the present study,a meta-analysis was carried out using dengue gene expression data obtained from Gene Expression Omnibus repository.The differentially expressed genes such as CCNB1 and CCNB2(G2/mitotic-specific cyclin-B2 and B1)were upregulated in dengue fever to control(DF-CO)and severe dengue(dengue hemorrhagic fever[DHF])to control(DHF-CO)were identified as key genes for controlling the major pathways(cell cycle,oocyte meiosis,p53 signaling pathway,cellular senescence and progesterone-mediated oocyte mat-uration).Similarly,interferon alpha-inducible(IFI27)genes,type-I and type-III interferon(IFN)signaling genes(STAT1 and STAT2),B cell activation and survival genes(TNFSF13B,TNFRSF17)and toll like receptor(TLR7)genes were differentially up activated during DF-CO and DHF-CO.Followed by,Cytoscape was used to identify the immune system process and topological analysis.Results:The results showed that the top differentially expressed genes under the statistical significance p<0.001,which is majorly involved in Kyoto Encyclopedia of Genes and Genomes orthology K05868 and K21770 with gene names CCNB1 and CCNB2.In addition to this,the immune system profile showed up-regulation of IL12A,CXCR3,TNFSF13B,IFI27,TNFRSF17,STAT,STAT2,and TLR7 genes in DF-CO and DHF-CO act as immunological signatures for inducing the immune response towards dengue infection.Conclusions:The current study could aid in understanding of molecular pathogenesis,genes and correspond-ing pathway upon dengue infection,and could facilitate for identification of novel drug targets and prognostic markers.

基因表达数据的聚类分析研究进展

基因表达数据的爆炸性增长迫切需求自动、有效的数据分析工具.目前聚类分析己成为分析基因表达数据获取生物学信息的有力工具.为了更好地挖掘基因表达数据,近年来提出了许多改进的传统聚类算法和新聚类算法.本文首先简单介绍了基因表达数据的获取和表示,之后系统地介绍了近年来应用在基因表达数据分析中的聚类算法.根据聚类目标的不同将算法分为基于基因的聚类、基于样本的聚类和两路聚类,并对每类算法介绍了其生物学的含义及其难点,详细讨论了各种算法的基本原理及优缺点.最后总结了当前的基因表达数据的聚类分析方法,并对发展趋势作了进一步的展望.

聚类分析中类数估计方法的实验比较

在基因表达数据的探索性聚类分析中,聚类个数的确定是决定聚类质量的关键因素。许多聚类有效性评价指标和方法可用于PAM聚类算法。该文讨论适合于PAM算法的7种常用评价指标和方法,采用4种不同聚类结构特征的基因表达数据对它们的性能进行实验比较。结果表明,系统演化方法和稳定性方法估计聚类个数的性能最好,正确率分别为100%与90%。

用于基因数据挖掘的基因表达数据库GEO

使用高通量方法学来检测基因表达情况在最近几年已非常普遍。微集芯片技术可同时定量成千上万的基因转录本。基因表达综合数据库(Gene Expression Omnibus简称GEO)是目前最大的而且完全公开的高通量分子丰度数据库,主要储存基因表达数据。该数据库以一个灵活开放的设计理念,允许用户或科研人员来递呈,保存和检索多种不同类型的数据。综述了近年来该数据库在基因表达数据挖掘中的应用,同时介绍一些通过使用用户友好网络界面能有效探索、查询和再现数百个实验和数百万个基因表达谱的工具,以方便数据进行挖掘和可视化。登录GEO公用数据库的网址为:http://www.ncbi.nlm.nih.gov/geo。

利用微阵列基因芯片研究东海海参(Acaudina leucoprocta)的生理功能

取喂食东海海参60d的ICR小鼠肝脏,制备微阵列基因表达芯片,并与模式小鼠芯片进行杂交,筛选功能基因和差异表达蛋白。从食用海参的鼠肝中共筛选出了4180个基因,其中差异表达基因184个,上调基因107个,下调基因77个。利用MAS.2.0系统对其功能进行分析,结果显示,分子功能上有催化活性的基因占39.86%,结合功能的基因占30.1%,信号转导、分子转导、电子传递、分子马达等功能基因占22.1%。从生化过程分析可见与新陈代谢有关的基因67.4%,调控基因16.2%,发育等相关基因9.1%。从细胞组成元件可见,各种细胞成分为51%,细胞器为21.6%,其差异表达蛋白均为参与细胞信号传导的因子、合成及分解代谢的酶和供能物质。

基于模糊c-均值聚类的微阵列基因表达数据分析

微阵列技术已成为染色体研究的主要工具,但是它所面临的挑战是如何对海量数据进行分析.利用模糊c 均值聚类对这些数据进行分析,从而发现有差异的基因表达.结果表明,模糊聚类是一种用来为微阵列基因表达数据寻找有差异的基因表达的一种有用工具.

基因表达聚类分析技术的现状与发展

随着多个生物基因组测序的完成、DNA芯片技术的广泛应用 ,基因表达数据分析已成为后基因组时代的研究热点 .聚类分析能将功能相关的基因按表达谱的相似程度归纳成类 ,有助于对未知功能的基因进行研究 ,是目前基因表达分析研究的主要计算技术之一 .已有多种聚类分析算法用于基因表达数据分析 ,各种算法因其着眼点、原理等方面的差异 ,而各有其优缺点 .如何对各种聚类算法的有效性进行分析、并开发新型的。

一种加权K-均值基因聚类算法

针对微阵列表达数据集中基因-基因之间存在复杂相关关系的问题,基于随机森林变量重要性分数,提出了一种新的加权K-均值基因聚类算法。首先,以微阵列表达数据中的样本为对象、基因为特征,训练随机森林分类器,计算每个基因的变量重要性分数;然后,以基因为对象、样本为特征、基因的变量重要性分数为权重进行K-均值聚类。在Leukemia、Breast、DLBCL等3个微阵列表数据集上进行了实验,结果表明:所提出的加权K-均值聚类算法与原始的K-均值聚类算法相比,类间距离与总距离的比值平均高出17.7个百分点,具有更好的同质性和差异性。