多效生长因子相关基因的生物信息学分析

目的:用生物信息学方法分析多效生长因子(PTN)潜在的分子功能。方法:利用由美国亚利桑那癌中心提供的生物信息学数据库,对前期用小鼠全基因组表达谱芯片检测到的Ptn相关基因进行生物信息学分析,通过GO Terms分析这些基因所属的功能群体,用Pathway Miner分析这些基因参与调控的信号通路。结果:370个由芯片检测得到的Ptn相关基因中,在GO Terms数据库中找到231个基因,其中参与细胞成分构成的基因占31.83%,具有分子功能的基因占35.34%,而参与生物学过程的基因占32.83%;在Pathway Miner数据库中找到105个基因。这些基因相关的信号通路有230条,分别属于细胞和调控过程通路以及代谢通路。结论:PTN是一个重要的细胞因子,可能参与机体的免疫与防御反应、炎症反应,以及细胞的增殖、凋亡调控等。     

生物信息学方法在基因芯片数据功能分析中的应用

基因芯片技术是当前功能基因组研究的重要工具.基因功能分析是将基因表达数据与基因功能或生物学通路相联系,寻找有意义的变化基因.本文介绍了GO分类法、信号通路和生物调控网络等常用的基因功能分析方法和工具.     

Genes Selection Comparative Study in Microarray Data Analysis

In response to the rapid development of DNA Microarray Technologies, many differentially expressed genes selection algorithms have been developed, and different comparison studies of these algorithms have been done. However, it is not clear how these methods compare with each other, especially when we used different developments tools. Here, we considered three commonly used differentially expressed genes selection approaches, namely: Fold Change, T-test and SAM, using Bioinformatics Matlab Toolbox and R/BioConductor. We used two datasets, issued from the affymetrix technology, to present results of used methods and software''s in gene selection process. The results, in terms of sensitivity and specificity, indicate that the behavior of SAM is better compared to Fold Change and T-test using R/BioConductor. While, no practical differences were observed between the three gene selection methods when using Bioinformatics Matlab Toolbox. In face of our result, the ROC curve shows that: on the one hand R/BioConductor using SAM is favored for microarray selection compared to the other methods. And, on the other hand, results of the three studied gene selection methods using Bioinformatics Matlab Toolbox are still comparable for the two datasets used.     

miR-29a靶基因预测及其相关生物信息学分析

目的:通过对miR-29a进行靶基因预测及相关生物信息学分析,为miR-29a靶基因的实验验证提供数据支持,以期为深入研究miR-29a的生物学功能和调控机制提供理论指导。方法:利用PubMed检索miR-29a相关文章,通过miRBase在线工具分析miR-29a序列。应用TargetScan及miRNAda两种计算方法预测miR-29a靶基因并取其交集作为分析的基因集合,分别进行基因本体(gene ontology,GO)中的分子功能和生物学过程以及KEGG(Kyoto Encyclopedia of Genes and Genomes)生物通路富集分析。结果:(1)miR-29a序列在多物种间具有高度保守性。(2)两种方法预测miR-29a靶基因交集共191个。(3)miR-29a靶基因GO分子功能集中于转录因子活性、DNA结合和钙离子结合等(P0.05);miR-29a靶基因GO生物学过程集中于调控转录、细胞粘附、细胞增殖与凋亡等(P0.05);KEGG生物通路主要富集于PI3K-AKT信号通路、JAK-STAT信号通路、T细胞受体信号通路和胰岛素信号通路等信号转导通路,以及肺小细胞癌和子宫内膜癌等疾病通路(P0.05)。结论:miR-29a可能通过参与多个靶基因信号通路的调控,在机体的多种生理病理过程中发挥重要作用,是一个颇有研究价值的生物学靶标。     

玉米耐铝毒基因的分离

以抑制消减杂交（SSH）为手段,以玉米对铝敏感的自交系Mo17和耐铝的自交系TL94B为材料,分别构建它们的正向和反向消减文库,分别筛选获得了124、47、103和64个阳性克隆。对文库的鉴定表明,插入片段分布在0.25-1．0kb之间,阳性克隆率在18％左右。对338个阳性克隆进行测序,得到232种表达序列标签（EST）,其中70．2％的EST可推测其功能。结果表明,玉米的铝离子胁迫反应涉及胁迫因子的信号传导、响应基因的转录表达与调控、物质的合成与运输、细胞结构和功能的改变等。     

生物信息学策略鉴定新基因

随着人类基因组计划的开展,在基因结构、定位、表达和功能研究等方面都积累了大量的数据,如何充分利用大量已有的网上数据库资源,加速人类基因克隆研究及功能初步研究,同时避免重复工作,节省开支,已成为我们面临的一个急迫而富有挑战性的课题.生物信息学是20世纪80年代末开始,随着基因组测序数据迅猛增加而逐渐兴起的一门新兴学科,它利用计算机对生命科学研究中的生物信息进行存储、检索和分析.它的产生和发展为人们提供了强大的工具,本文就生物信息学方法在识别和鉴定新基因中的应用予以综述.     

慢性酒精中毒大鼠在学习记忆方面差异基因的生物信息学分析

借助基因芯片获取慢性酒精中毒大鼠海马相关基因的表达数据集,通过生物信息学的分析方法对差异表达基因进行筛选与分析。从分子水平揭示慢性酒精中毒对大鼠大脑海马体的影响,为慢性酒精中毒的损伤机制以及相关疾病发病机制的基础研究与临床治疗提供新的方向。同时,还通过Y迷宫实验对实验大鼠的学习记忆功能进行了检测,借助电镜拍摄其线粒体。结果显示,我们一共筛选出208个差异表达基因,其中51个表达上调,157个表达下调。其中涉及的主要信号通路有氧化磷酸化通路、D-谷氨酰胺和谷氨酸代谢通路、阿尔茨海默病信号通路、帕金森病信号通路、膀胱癌信号通路、B细胞受体信号通路和亨廷顿病信号通路等。由此我们得出结论,慢性酒精中毒可能影响了海马多个基因的表达,其中包括Rpsa、Wdr31、Rps11、Rps9、Ndufa2、Mrto4、Rpl6、Dap3、Ndufb8、Ndufb6、Ephb2、Cox6c、Prkcd、Rela、Raf1、Ubd、Mrps28、Mrpl35等关键基因,进而损伤了电子传递链复合体Ⅰ,最终损伤线粒体,导致大鼠学习记忆能力的损伤。     

Microarray Data Analysis

The analysis of differential gene expression in microarray experiments requires the development of adequate statistical tools. This article describes a simple statistical method for detecting differential expression between two conditions with a low number of replicates. When comparing two group means using a traditional t-test, gene-specific variance estimates are unstable and can lead to wrong conclusions. We construct a likelihood ratio test while modelling these variances hierarchically across all genes, and express it as a t-test statistic. By borrowing information across genes we can take advantage of their large numbers, and still yield a gene-specific test statistic. We show that this hierarchical t-test is more powerful than its traditional version and generates less false positives in a simulation study, especially with small sample sizes. This approach can be extended to cases where there are more than two groups.     

胶质母细胞瘤预后相关基因的数据挖掘分析

胶质母细胞瘤（glioblastoma, GBM）是恶性程度最高的颅内恶性肿瘤，目前临床上缺乏有效治疗药物，复发率高且预后差，开发新的抗GBM药物是目前临床上亟待解决的问题。为了筛选与GBM预后密切相关的基因，为寻找新的药物靶点提供线索，采用GEO2R工具从GEO数据库中的269个肿瘤组织和61个正常组织中初步筛选出差异表达基因，然后利用Cluster Profiler数据库进行基因功能富集分析，STRING及Cytoscape进一步筛选出37个差异表达基因，采用GEPIA交互分析对这37个基因在GBM肿瘤组织中的表达进行验证。为了进一步探索这些差异表达基因与患者预后的关系，研究中利用GEPIA工具对TCGA数据库中与患者预后相关的数据进行深入挖掘，最终发现PTTG1、RRM2、E2F7与患者中位生存期呈显著性负相关。研究筛选出的与患者预后密切相关的基因不仅可以为评估患者预后提供参考，同时也为开发新的抗GBM药物提供了潜在的靶点。     

基于生物信息学技术筛选影响胶质母细胞瘤化疗敏感性相关基因的研究

目的：应用生物信息学技术筛选影响胶质母细胞瘤（GBM）化疗敏感性的相关基因。方法：对2批胶质瘤患者BIOSTAR基因芯片进行分析。通过随访完善临床资料,筛选芯片中胶质母细胞瘤患者生存期长、短两组间的差异基因,明确差异基因参与的功能和通路,并构建与烷化剂相关基因的信号传导网络,结合芯片数据、患者预后和信号传导网络,筛选GBM化疗敏感性的相关基因。结果：两组芯片中间差异基因有503条。2批芯片的差异基因主要参与62项基因功能,主要参与31条信号传导通路。通过对差异基因功能、通路,烷化剂信号转导网络的分析,得到影响胶质母细胞瘤化疗敏感性的核心的差异基因IFNGR2、IL8、ITGA5、TNFRSF1B。结论：通过严谨的实验设计和科学的统计学判别,结合患者完整的生存资料,本研究成功地应用生物信息学技术对基因芯片的大量数据进行挖掘和分析,并筛选出了可能影响GBM患者预后和化疗药物敏感性的基因,为进一步功能实验和患者个体化治疗奠定了基础。     

基于生物信息学技术筛选影响胶质母细胞瘤化疗敏感性 相关基因的研究

目的:应用生物信息学技术筛选影响胶质母细胞瘤(GBM)化疗敏感性的相关基因。方法:对2批胶质瘤患者BIOSTAR基因芯片进行分析。通过随访完善临床资料,筛选芯片中胶质母细胞瘤患者生存期长、短两组间的差异基因,明确差异基因参与的功能和通路,并构建与烷化剂相关基因的信号传导网络,结合芯片数据、患者预后和信号传导网络,筛选GBM化疗敏感性的相关基因。结果:两组芯片中间差异基因有503条。2批芯片的差异基因主要参与62项基因功能,主要参与31条信号传导通路。通过对差异基因功能、通路,烷化剂信号转导网络的分析,得到影响胶质母细胞瘤化疗敏感性的核心的差异基因IFNGR2、IL8、ITGA5、TNFRSF1B。结论:通过严谨的实验设计和科学的统计学判别,结合患者完整的生存资料,本研究成功地应用生物信息学技术对基因芯片的大量数据进行挖掘和分析,并筛选出了可能影响GBM患者预后和化疗药物敏感性的基因,为进一步功能实验和患者个体化治疗奠定了基础。     

玉米叶形相关性状的Meta-QTL及候选基因分析

叶长、叶宽、叶面积及叶夹角不仅影响玉米(Zea mays)光合效率, 也是株型的重要构成因素。通过对620个叶形QTL进行整合, 构建不同遗传背景下的叶形QTL整合图谱, 利用元分析发掘出22个叶长、22个叶宽、12个叶面积以及17个叶夹角mQTL; 进一步运用生物信息学手段, 确定44个与叶片发育密切相关的候选基因。分析发现, 仅有NAL7-like、YABBY6- like和GRF2等13个基因位于mQTL区间内, 而玉米中已克隆的KNOTTED1、AN3/GIF1、rgd1/lbl1、mwp1及SRL2-like、HYL1-like和CYCB2;4-like等水稻(Oryza sativa)和拟南芥(Arabidopsis thaliana)叶形同源基因位于未被整合的QTL内; 对44个候选基因在叶片长、宽、厚发育过程中基部-末端、中央-边缘、远轴-近轴的调控机理进行归纳分析, 发现玉米中仅有少数几个候选基因被报道, 揭示了叶形发育的部分分子机理。因此, 对玉米叶形相关mQTL/QTL及基因进行全面深入的分析, 不仅有助于增加对其遗传结构的了解, 发掘更多候选基因, 阐明叶形发育和形成的分子机制, 还可为耐密理想株型的分子标记辅助选择提供依据。     

生物信息学在新基因全长cDNA序列分析及功能预测中的应用

差异表达基因的检测与分析已成为研究具有差异的生物学表型的常规策略.对通过实验所获得的差异基因片段进行生物信息学分析,主要包括基于国际互联网的序列相似性分析、片段重叠群分析和全长cDNA序列分析,以及如何构建局域网并采用本地服务器进行规模化的数据分析,从而为研究人员提供可参考的生物信息学数据分析方案.     

Listen to Genes: Dealing with Microarray Data in the Frequency Domain

Background

We present a novel and systematic approach to analyze temporal microarray data. The approach includes normalization, clustering and network analysis of genes.

Methodology

Genes are normalized using an error model based uniform normalization method aimed at identifying and estimating the sources of variations. The model minimizes the correlation among error terms across replicates. The normalized gene expressions are then clustered in terms of their power spectrum density. The method of complex Granger causality is introduced to reveal interactions between sets of genes. Complex Granger causality along with partial Granger causality is applied in both time and frequency domains to selected as well as all the genes to reveal the interesting networks of interactions. The approach is successfully applied to Arabidopsis leaf microarray data generated from 31,000 genes observed over 22 time points over 22 days. Three circuits: a circadian gene circuit, an ethylene circuit and a new global circuit showing a hierarchical structure to determine the initiators of leaf senescence are analyzed in detail.

Conclusions

We use a totally data-driven approach to form biological hypothesis. Clustering using the power-spectrum analysis helps us identify genes of potential interest. Their dynamics can be captured accurately in the time and frequency domain using the methods of complex and partial Granger causality. With the rise in availability of temporal microarray data, such methods can be useful tools in uncovering the hidden biological interactions. We show our method in a step by step manner with help of toy models as well as a real biological dataset. We also analyse three distinct gene circuits of potential interest to Arabidopsis researchers.     

Identifying Differentially Expressed Genes in Time Course Microarray Data

Identifying differentially expressed (DE) genes across conditions or treatments is a typical problem in microarray experiments. In time course microarray experiments (under two or more conditions/treatments), it is sometimes of interest to identify two classes of DE genes: those with no time-condition interactions (called parallel DE genes, or PDE), and those with time-condition interactions (nonparallel DE genes, NPDE). Although many methods have been proposed for identifying DE genes in time course experiments, methods for discerning NPDE genes from the general DE genes are still lacking. We propose a functional ANOVA mixed-effect model to model time course gene expression observations. The fixed effect of (the mean curve) of the model decomposes bivariate functions of time and treatments (or experimental conditions) as in the classic ANOVA method and provides the associated notions of main effects and interactions. Random effects capture time-dependent correlation structures. In this model, identifying NPDE genes is equivalent to testing the significance of the time-condition interaction, for which an approximate F-test is suggested. We examined the performance of the proposed method on simulated datasets in comparison with some existing methods, and applied the method to a study of human reaction to the endotoxin stimulation, as well as to a cell cycle expression data set.     

Outcome-Driven Cluster Analysis with Application to Microarray Data

One goal of cluster analysis is to sort characteristics into groups (clusters) so that those in the same group are more highly correlated to each other than they are to those in other groups. An example is the search for groups of genes whose expression of RNA is correlated in a population of patients. These genes would be of greater interest if their common level of RNA expression were additionally predictive of the clinical outcome. This issue arose in the context of a study of trauma patients on whom RNA samples were available. The question of interest was whether there were groups of genes that were behaving similarly, and whether each gene in the cluster would have a similar effect on who would recover. For this, we develop an algorithm to simultaneously assign characteristics (genes) into groups of highly correlated genes that have the same effect on the outcome (recovery). We propose a random effects model where the genes within each group (cluster) equal the sum of a random effect, specific to the observation and cluster, and an independent error term. The outcome variable is a linear combination of the random effects of each cluster. To fit the model, we implement a Markov chain Monte Carlo algorithm based on the likelihood of the observed data. We evaluate the effect of including outcome in the model through simulation studies and describe a strategy for prediction. These methods are applied to trauma data from the Inflammation and Host Response to Injury research program, revealing a clustering of the genes that are informed by the recovery outcome.