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Identification of promoters bound by c-Jun/ATF2 during rapid large-scale gene activation following genotoxic stress

MOLECULAR CELL 2004年第4期16卷 521-535页

作者： Hayakawa, J Mittal, S Wang, Y Korkmaz, KS Adamson, E English, C Omichi, M McClelland, M Mercola, D Sidney Kimmel Canc Ctr San Diego CA 92121 USA Burnham Inst La Jolla CA 92037 USA Univ Calif San Diego Rebecca & John Moores Canc Ctr La Jolla CA 92093 USA Osaka Univ Sch Med Dept Obstet & Gynecol Suita Osaka 5650871 Japan

The NH2-terminal Jun kinases (JNKs) function in diverse roles through phosphorylation and activation of AP-1 components including ATF2 and c-Jun. However, the genes that mediate these processes are poorly understood. A model phenotype characterized by rapid activation of Jun kinase and enhanced DNA repair following cisplatin treatment was examined using chromatin immunoprecipitation with antibodies against ATF2 and c-Jun or their phosphorylated forms and hybridization to promoter arrays. Following genotoxic stress, we identified 269 genes whose promoters are bound upon phosphorylation of ATF2 and c-Jun. Binding did not occur following treatment with transplatin or the JNK inhibitor SP600125 or JNK-specific siRNA. Of 89 known DNA repair genes represented on the array, 23 are specifically activated by cisplatin treatment within 3-6 hr. Thus, the genotoxic stress response occurs at least partly via activation of ATF2 and c-Jun, leading to large-scale coordinate gene expression dominated by genes of DNA repair.

关键词： N-TERMINAL KINASE DNA-DAMAGING AGENTS C-JUN TRANSCRIPTION FACTORS microarray data TUMOR-CELLS EXPRESSION CISPLATIN REPAIR PROTEIN

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Visualization of microarray results to assist interpretation

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TUBERCULOSIS 2004年第3-4期84卷 275-281页

作者： Papatheodorou, I Sergot, M Randall, M Stewart, GR Robertson, BD Univ London Imperial Coll Sci & Technol Ctr Mol Microbiol & Infect London SW7 2AZ England Univ London Imperial Coll Sci & Technol Dept Comp London SW7 2AZ England

Whole genome microarrays allow assessment of the profile of genes expressed under particular experimental conditions, including external stimuti such as pH or temperature, and internal changes brought about by deleting or overexpressing a gene. Such experiments produce large data sets, for which sophisticated analysis software is available. What is tacking are toots for analysing data sets from different experiments, in order to test and generate hypotheses about the links between regulatory networks. We describe here a method for presenting results from different experiments as a directed graph constructed using an automated graph drawing program xneato, enhanced by a logic program designed to cluster data and aid in the generation of hypotheses about possible gene interactions. A web-based front-end to the system has been constructed to explore and manipulate the graphical displays produced. Results of microarray experiments on Mycobacterium tuberculosis were used to develop and evaluate the visualization too[ and initiate the development of an inference system for gene interactions based on such data. The GeneGraph project can be accessed at: *** (C) 2004 Elsevier Ltd. All rights reserved.

关键词： directed graphs visualization microarray data

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RAD and the RAD study-annotator: an approach to collection, organization and exchange of all relevant information for high-throughput gene expression studies

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BIOINFORMATICS 2004年第4期20卷 452-459页

作者： Manduchi, E Grant, GR He, H Liu, J Mailman, MD Pizarro, AD Whetzel, PL Stoeckert, CJ Univ Penn Ctr Bioinformat Philadelphia PA 19104 USA

Motivation: Gene expression array technology has become increasingly widespread among researchers who recognize its numerous promises. At the same time, bench biologists and bioinformaticians have come to appreciate increasingly the importance of establishing a collaborative dialog from the onset of a study and of collecting and exchanging detailed information on the many experimental and computational procedures using a structured mechanism. This is crucial for adequate analyses of this kind of data. Results: The RNA Abundance database (RAD;http://***/RAD) provides a comprehensive MIAME-supportive infrastructure for gene expression data management and makes extensive use of ontologies. Specific details on protocols, biomaterials, study designs, etc. are collected through a user-friendly suite of web annotation forms. Software has been developed to generate MAGE-ML documents to enable easy export of studies stored in RAD to any other database accepting data in this format (e.g. ArrayExpress). RAD is part of a more general Genomics Unified Schema (http://***), which includes a richly annotated gene index (http://***), thus providing a platform that integrates genomic and transcriptomic data from multiple organisms. This infrastructure enables a large variety of queries that incorporate visualization and analysis tools and have been tailored to serve the specific needs of projects focusing on particular organisms or biological systems.

关键词： microarray data Standards Ontology

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An example of slow convergence of the bootstrap in high dimensions

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AMERICAN STATISTICIAN 2004年第1期58卷 25-29页

作者： Troendle, JF Korn, EL McShane, LM NICHHD Div Epidemiol Stat & Prevent Res NIH Dept Hlth & Human Serv Bethesda MD 20892 USA NCI Biometr Res Branch DHHS Bethesda MD 20892 USA

This article examines the use of bootstrap hypothesis tests for testing the equality of two multivariate distributions. The test statistic used is the maximum of the univariate two-sample t-statistics. Depending upon the type of bootstrap resampling used, the simulation studies show that the test levels are conservative or anti-conservative when the sample sizes are small and the number of variables is large. For small sample sizes, using the bootstrap resampling that preserves the Type I error can lead to a testing procedure that has lower power, sometimes dramatically lower, than a permutation test.

关键词： hypothesis test microarray data multivariate data permutation test resampling small sample behavior

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Motif detection in Arabidopsis: Correlation with gene expression data

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In Silico Biology 2004年第2期4卷 149-161页

作者： Janaki, Chintalapati Joshi, Rajendra R. Bioinformatics Team Scientific/Eng. Computing Group Centre for Devmt. of Adv. Computing Ganeshkhind Pune 411007 Pune University Campus India

The genes having similar expression profiles are considered to have common regulatory mechanisms and are controlled by the binding of transcription factors to the regulatory elements present in their upstream regions. The detection of cis-regulatory elements can help in further understanding of co-expression of genes. This paper deals with the detection of motifs in the upstream regions of genes involved in diurnal rhythms of Arabidopsis and also deals with the correlation of expression data with sequence information. We detected motifs in the upstream regions of genes involved in diurnal cycles and checked for their presence in circadian regulated, dark induced and in light induced genes of Arabidopsis. Ten motifs were reported in this study, out of which five were already reported in available transcription factor databases as the elements involved in light responsiveness. Significance study of ten motifs was done by taking random sets of same data size. One of the ten motifs namely GGCCCA, which was found without any base variations in 62 genes, was further studied by analyzing the expression profiles of its respective genes within the set of diurnal regulated genes using SOM clustering method. It was found that the genes were clustered together into two major groups, out of which one group had glycine rich proteins and the second group had genes belonging to dehydrogenase and oxidoreductase family. © 2004 - IOS Press and Bioinformation Systems e.V. and the authors. All rights reserved.

关键词： Arabidopsis Diurnal genes Gene regulation Genesis MEME microarray data Motif finding PARAM Padma SOM

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An evaluation of microarray visualization tools for biological insight

An evaluation of microarray visualization tools for biologic...

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10th Annual IEEE Symposium on Information Visualization (InfoVis 2004)

作者： Saraiya, P North, C Duca, K Virginia Polytech Inst & State Univ Dept Comp Sci Blacksburg VA 24061 USA

ISBN: (纸本)0780387791

High-throughput experiments such as gene expression microarrays in the life sciences result in large datasets. In response, a wide variety of visualization tools have been created to facilitate data analysis. Biologists often face a dilemma in choosing the best tool for their situation. The tool that works best for one biologist may not work well for another due to differences in the type of insight they seek from their data. A primary purpose of a visualization tool is to provide domain-relevant insight into the data. Ideally, any user wants maximum information in the least possible time. In this paper we identify several distinct characteristics of insight that enable us to recognize and quantify it. Based on this, we empirically evaluate five popular microarray visualization tools. Our conclusions can guide biologists in selecting the best tool for their data, and computer scientists in developing and evaluating visualizations.

关键词： data visualization empirical evaluation insight high throughput experiments microarray data bioinformatics

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Combining microarrays and biological knowledge for estimating gene networks via Bayesian networks

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Journal of Bioinformatics and Computational Biology 2004年第1期2卷 77-98页

作者： Imoto, Seiya Higuchi, Tomoyuki Goto, Takao Tashiro, Kousuke Kuhara, Satoru Miyano, Satoru Human Genome Center Institute of Medical Science University of Tokyo Minato-ku Tokyo 108-8639 4-6-1 Shirokanedai Japan The Inst. of Statistical Mathematics Minato-ku Tokyo 106-8569 4-6-7 Minami-Azabu Japan Grad. Sch. of Genet. Rsrc. Technol. Kyushu University Higashi-ku Fukuoka 812-8581 6-10-1 Hakozaki Japan

We propose a statistical method for estimating a gene network based on Bayesian networks from microarray gene expression data together with biological knowledge including protein-protein interactions, protein-DNA interactions, binding site information, existing literature and so on. microarray data do not contain enough information for constructing gene networks accurately in many cases. Our method adds biological knowledge to the estimation method of gene networks under a Bayesian statistical framework, and also controls the trade-off between microarray information and biological knowledge automatically. We conduct Monte Carlo simulations to show the effectiveness of the proposed method. We analyze Saccharomyces cerevisiae gene expression data as an application. © Imperial College Press.

关键词： Bayesian network Biological knowledge Gene network microarray data

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Biclustering in gene expression data by tendency

Biclustering in gene expression data by tendency

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IEEE Computational Systems Bioinformatics Conference (CSB 2004)

作者： Liu, JZ Yang, J Wang, W Univ N Carolina Dept Comp Sci Chapel Hill NC 27599 USA

ISBN: (纸本)0769521940

The advent of DNA microarray technologies has revolutionized the experimental study of gene expression. Clustering is the most popular approach of analyzing gene expression data and has indeed proven to be successful in many applications. Our work focuses on discovering a subset of genes which exhibit similar expression patterns along a subset of conditions in the gene expression matrix. Specifically, we are looking for the Order Preserving clusters (OP-Cluster), in each of which a subset of genes induce a similar linear ordering along a subset of conditions. The pioneering work of the OPSM model[3], which enforces the strict order shared by the genes in a cluster, is included in our model as a special case. Our model is more robust than OPSM because similarly expressed conditions are allowed to form order equivalent groups and no restriction is placed on the order within a group. Guided by our model, we design and implement a deterministic algorithm, namely OPC-Tree, to discover OP-Clusters. Experimental study on two real datasets demonstrates the effectiveness of the algorithm in the application of tissue classification and cell cycle identification. In addition, a large percentage of OP-Clusters exhibit significant enrichment of one or more function categories, which implies that OP-Clusters indeed carry significant biological relevance.

关键词： gene expression data microarray data biclustering order preserving

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Modular sub-systems approach to predictive biomedicine

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Drug Discovery Today: BIOSILICO 2004年第1期2卷 2-3页

作者： Ghazal, Peter Beattie, John Scottish Ctr. Genomic Technol. Info. University of Edinburgh United Kingdom

…Can we predict a global genetic network on the basis of genomic/microarray data and is this the ‘right’ question to ask?

关键词： Bioinformatics Drug Discovery Techniques & Methods modular sub-systems approach microarray data global genetic network systems biology model organism systems GRID computing

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Bootstrapping of gene-expression data improves and controls the false discovery rate of differentially expressed genes

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Genetics, selection, evolution : GSE 2004年第2期36卷 191-205页

作者： Theo H E Meuwissen Mike E Goddard Institute for Animal Science Agricultural University of Norway 1432 As Norway. theo.meuwissen@iha.nlh.no

The ordinary-, penalized-, and bootstrap t-test, least squares and best linear unbiased prediction were compared for their false discovery rates (FDR), i.e. the fraction of falsely discovered genes, which was empirically estimated in a duplicate of the data set. The bootstrap-t-test yielded up to 80% lower FDRs than the alternative statistics, and its FDR was always as good as or better than any of the alternatives. Generally, the predicted FDR from the bootstrapped P-values agreed well with their empirical estimates, except when the number of mRNA samples is smaller than 16. In a cancer data set, the bootstrap-t-test discovered 200 differentially regulated genes at a FDR of 2.6%, and in a knock-out gene expression experiment 10 genes were discovered at a FDR of 3.2%. It is argued that, in the case of microarray data, control of the FDR takes sufficient account of the multiple testing, whilst being less stringent than Bonferoni-type multiple testing corrections. Extensions of the bootstrap simulations to more complicated test-statistics are discussed.

关键词： microarray data gene expression non-parametric bootstrapping -test false discovery rates

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