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The Longhorn Array database (LAD): An open-source, MIAME compliant implementation of the stanford microarray database (SMD)

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BMC BIOINFORMATICS 2003年第1期4卷 32-32页

作者： Killion, PJ Sherlock, G Iyer, VR Univ Texas Inst Mol & Cellular Biol Sect Mol Genet & Microbiol Austin TX 78712 USA Stanford Univ Sch Med Dept Genet Stanford CA 94305 USA

Background: The power of microarray analysis can be realized only if data is systematically archived and linked to biological annotations as well as analysis algorithms. Description: The Longhorn Array database ( LAD) is a MIAME compliant microarray database that operates on PostgreSQL and Linux. It is a fully open source version of the stanford microarray database (SMD), one of the largest microarray databases. LAD is available at http:// *** Conclusions: Our development of LAD provides a simple, free, open, reliable and proven solution for storage and analysis of two-color microarray data.

关键词： Relational database microarray database Biological Annotation stanford microarray database Open Source Version

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IGF-I induced genes in stromal fibroblasts predict the clinical outcome of breast and lung cancer patients

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BMC MEDICINE 2010年第2010期8卷 1-18页

作者： Rajski, Michal Zanetti-Daellenbach, Rosanna Vogel, Brigitte Herrmann, Richard Rochlitz, Christoph Buess, Martin Univ Basel Dept Biomed CH-4031 Basel Switzerland St Clara Hosp Dept Internal Med Div Med Oncol CH-4016 Basel Switzerland Univ Basel Hosp Dept Internal Med Div Med Oncol CH-4031 Basel Switzerland Univ Basel Hosp Dept Gynecol & Obstet CH-4031 Basel Switzerland

Background: Insulin-like growth factor-1 (IGF-I) signalling is important for cancer initiation and progression. Given the emerging evidence for the role of the stroma in these processes, we aimed to characterize the effects of IGF-I on cancer cells and stromal cells separately. Methods: We used an ex vivo culture model and measured gene expression changes after IGF-I stimulation with cDNA microarrays. In vitro data were correlated with in vivo findings by comparing the results with published expression datasets on human cancer biopsies. Results: Upon stimulation with IGF-I, breast cancer cells and stromal fibroblasts show some common and other distinct response patterns. Among the up-regulated genes in the stromal fibroblasts we observed a significant enrichment in proliferation associated genes. The expression of the IGF-I induced genes was coherent and it provided a basis for the segregation of the patients into two groups. Patients with tumours with highly expressed IGF-I induced genes had a significantly lower survival rate than patients whose tumours showed lower levels of IGF-I induced gene expression (P = 0.029 - Norway/stanford and P = 7.96e-09 - NKI dataset). Furthermore, based on an IGF-I induced gene expression signature derived from primary lung fibroblasts, a separation of prognostically different lung cancers was possible (P = 0.007 - Bhattacharjee and P = 0.008 - Garber dataset). Conclusion: Expression patterns of genes induced by IGF-I in primary breast and lung fibroblasts accurately predict outcomes in breast and lung cancer patients. Furthermore, these IGF-I induced gene signatures derived from stromal fibroblasts might be promising predictors for the response to IGF-I targeted therapies. See the related commentary by Werner and Bruchim:http://***/1741-7015/8/2

关键词： Breast Cancer Lung Fibroblast Stromal Fibroblast Breast Cancer Dataset stanford microarray database

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Data analysis and bioinformatics tools for tandem mass spectrometry in proteomics

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PHYSIOLOGICAL GENOMICS 2008年第1期33卷 18-25页

作者： Deutsch, Eric W. Lam, Henry Aebersold, Ruedi Inst Syst Biol Seattle WA 98103 USA Swiss Fed Inst Technol Inst Mol Syst Biol Zurich Switzerland Univ Zurich Fac Sci Zurich Switzerland Ctr Syst Physiol & Metab Dis Zurich Switzerland

Data processing is a central and critical component of a successful proteomics experiment, and is often the most time-consuming step. There have been considerable advances in the field of proteomics informatics in the past 5 years, spurred mainly by free and open-source software tools. Along with the gains afforded by new software, the benefits of making raw data and processed results freely available to the community in data repositories are finally in evidence. In this review, we provide an overview of the general analysis approaches, software tools, and repositories that are enabling successful proteomics research via tandem mass spectrometry.

关键词： stanford microarray database PROTEIN IDENTIFICATION DATA EXPRESSION PROFILES SHOTGUN PROTEOMICS SPECTRUM LIBRARIES STATISTICAL-MODEL SOFTWARE SYSTEM PEPTIDEATLAS SEQUENCES

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Defining potentially conserved RNA regulons of homologous zinc-finger RNA-binding proteins

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GENOME BIOLOGY 2011年第1期12卷 R3-R3页

作者： Scherrer, Tanja Femmer, Christian Schiess, Ralph Aebersold, Ruedi Gerber, Andre P. ETH Inst Pharmaceut Sci Dept Chem & Appl Biosci CH-8093 Zurich Switzerland Univ Zurich PhD Program Mol Life Sci CH-8057 Zurich Switzerland ETH CH-8057 Zurich Switzerland ETH Bioproc Lab Dept Biosyst Sci & Engn CH-4058 Basel Switzerland ETH Inst Mol Syst Biol Dept Biol CH-8093 Zurich Switzerland

Background: Glucose inhibition of gluconeogenic growth suppressor 2 protein (Gis2p) and zinc-finger protein 9 (ZNF9) are conserved yeast and human zinc-finger proteins. The function of yeast Gis2p is unknown, but human ZNF9 has been reported to bind nucleic acids, and mutations in the ZNF9 gene cause the neuromuscular disease myotonic dystrophy type 2. To explore the impact of these proteins on RNA regulation, we undertook a systematic analysis of the RNA targets and of the global implications for gene expression. Results: Hundreds of mRNAs were associated with Gis2p, mainly coding for RNA processing factors, chromatin modifiers and GTPases. Target mRNAs contained stretches of G(A/U)(A/U) trinucleotide repeats located in coding sequences, which are sufficient for binding to both Gis2p and ZNF9, thus implying strong structural conservation. Predicted ZNF9 targets belong to the same functional categories as seen in yeast, indicating functional conservation, which is further supported by complementation of the large cell-size phenotype of gis2 mutants with ZNF9. We further applied a matched-sample proteome-transcriptome analysis suggesting that Gis2p differentially coordinates expression of RNA regulons, primarily by reducing mRNA and protein levels of genes required for ribosome assembly and by selectively up-regulating protein levels of myosins. Conclusions: This integrated systematic exploration of RNA targets for homologous RNA-binding proteins indicates an unexpectedly high conservation of the RNA-binding properties and of potential targets, thus predicting conserved RNA regulons. We also predict regulation of muscle-specific genes by ZNF9, adding a potential link to the myotonic dystrophy related phenotypes seen in ZNF9 mouse models.

关键词： Gene Ontology Synthetic Complete Saccharomyces Genome database stanford microarray database mRNA Fate

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Cancer gene map online

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Genome Biology 2004年第1期5卷 1-4页

作者： Choi, Charles Q.

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Free access costs money

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Genome Biology 2003年第1期4卷 1-4页

作者： Brickley, Peg

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GeneXplorer: an interactive web application for microarray data visualization and analysis

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BMC BIOINFORMATICS 2004年第1期5卷 141-141页

作者： Rees, CA Demeter, J Matese, JC Botstein, D Sherlock, G Stanford Univ Sch Med Dept Genet Stanford CA 94305 USA Stanford Univ Sch Med Dept Biochem Stanford CA 94305 USA Princeton Univ Lewis Sigler Inst Integrat Genom Carl Icahn Lab Princeton NJ 08544 USA

Background: When publishing large-scale microarray datasets, it is of great value to create supplemental websites where either the full data, or selected subsets corresponding to figures within the paper, can be browsed. We set out to create a CGI application containing many of the features of some of the existing standalone software for the visualization of clustered microarray data. Results: We present GeneXplorer, a web application for interactive microarray data visualization and analysis in a web environment. GeneXplorer allows users to browse a microarray dataset in an intuitive fashion. It provides simple access to microarray data over the Internet and uses only HTML and JavaScript to display graphic and annotation information. It provides radar and zoom views of the data, allows display of the nearest neighbors to a gene expression vector based on their Pearson correlations and provides the ability to search gene annotation fields. Conclusions: The software is released under the permissive MIT Open Source license, and the complete documentation and the entire source code are freely available for download from CPAN http://***/dist/microarray-GeneXplorer/.

关键词： Application Programming Interface microarray Dataset External database stanford microarray database Cluster Gene Expression Data

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Confirmation of human protein interaction data by human expression data -: art. no. 112

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BMC BIOINFORMATICS 2005年第1期6卷 112-112页

作者： Hahn, A Rahnenführer, JR Talwar, P Lengauer, T Max Planck Inst Informat D-66123 Saarbrucken Germany

Background: With microarray technology the expression of thousands of genes can be measured simultaneously. It is well known that the expression levels of genes of interacting proteins are correlated significantly more strongly in Saccharomyces cerevisiae than those of proteins that are not interacting. The objective of this work is to investigate whether this observation extends to the human genome. Results: We investigated the quantitative relationship between expression levels of genes encoding interacting proteins and genes encoding random protein pairs. Therefore we studied 1369 interacting human protein pairs and human gene expression levels of 155 arrays. We were able to establish a statistically significantly higher correlation between the expression levels of genes whose proteins interact compared to random protein pairs. Additionally we were able to provide evidence that genes encoding proteins belonging to the same GO-class show correlated expression levels. Conclusion: This finding is concurrent with the naive hypothesis that the scales of production of interacting proteins are linked because an efficient interaction demands that involved proteins are available to some degree. The goal of further research in this field will be to understand the biological mechanisms behind this observation.

关键词： Mutual Information Protein Pair Androgen Receptor Protein Interact Protein Pair stanford microarray database

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CLOE: Identification of putative functional relationships among genes by comparison of expression profiles between two species

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BMC BIOINFORMATICS 2004年第1期5卷 179-179页

作者： Pellegrino, M Provero, P Silengo, L Di Cunto, F Dept Genet Biol & Biochem I-10126 Turin Italy Fdn Biotecnol Turin Italy

Background: Public repositories of microarray data contain an incredible amount of information that is potentially relevant to explore functional relationships among genes by meta-analysis of expression profiles. However, the widespread use of this resource by the scientific community is at the moment limited by the limited availability of effective tools of analysis. We here describe CLOE, a simple cDNA microarray data mining strategy based on meta-analysis of datasets from pairs of species. The method consists in ranking EST probes in the datasets of the two species according to the similarity of their expression profiles with that of two EST probes from orthologous genes, and extracting orthologous EST pairs from a given top interval of the ranked lists. The Gene Ontology annotation of the obtained candidate partners is then analyzed for keywords overrepresentation. Results: We demonstrate the capabilities of the approach by testing its predictive power on three proteomically-defined mammalian protein complexes, in comparison with single and multiple species meta-analysis approaches. Our results show that CLOE can find candidate partners for a greater number of genes, if compared to multiple species co-expression analysis, but retains a comparable specificity even when applied to species as close as mouse and human. On the other hand, it is much more specific than single organisms co-expression analysis, strongly reducing the number of potential candidate partners for a given gene of interest. Conclusions: CLOE represents a simple and effective data mining approach that can be easily used for meta-analysis of cDNA microarray experiments characterized by very heterogeneous coverage. Importantly, it produces for genes of interest an average number of high confidence putative partners that is in the range of standard experimental validation techniques.

关键词： Gene Ontology Single Organism UniGene Cluster Orthologous Pair stanford microarray database

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A meta-data based method for DNA microarray imputation

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BMC BIOINFORMATICS 2007年第1期8卷 109-109页

作者： Joernsten, Rebecka Ouyang, Ming Wang, Hui-Yu Univ Med & Dent New Jersey Inst Informat Piscataway NJ 08854 USA Rutgers State Univ Dept Stat New Brunswick NJ 08903 USA

Background: DNA microarray experiments are conducted in logical sets, such as time course profiling after a treatment is applied to the samples, or comparisons of the samples under two or more conditions. Due to cost and design constraints of spotted cDNA microarray experiments, each logical set commonly includes only a small number of replicates per condition. Despite the vast improvement of the microarray technology in recent years, missing values are prevalent. Intuitively, imputation of missing values is best done using many replicates within the same logical set. In practice, there are few replicates and thus reliable imputation within logical sets is difficult. However, it is in the case of few replicates that the presence of missing values, and how they are imputed, can have the most profound impact on the outcome of downstream analyses ( e. g. significance analysis and clustering). This study explores the feasibility of imputation across logical sets, using the vast amount of publicly available microarray data to improve imputation reliability in the small sample size setting. Results: We download all cDNA microarray data of Saccharomyces cerevisiae, Arabidopsis thaliana, and Caenorhabditis elegans from the stanford microarray database. Through cross-validation and simulation, we find that, for all three species, our proposed imputation using data from public databases is far superior to imputation within a logical set, sometimes to an astonishing degree. Furthermore, the imputation root mean square error for significant genes is generally a lot less than that of non-significant ones. Conclusion: Since downstream analysis of significant genes, such as clustering and network analysis, can be very sensitive to small perturbations of estimated gene effects, it is highly recommended that researchers apply reliable data imputation prior to further analysis. Our method can also be applied to cDNA microarray experiments from other species, provided good reference d

关键词： Root Mean Square Error Imputation Accuracy Small Root Mean Square Error stanford microarray database cDNA microarray Data

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