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RefGenes: identification of reliable and condition specific reference genes for RT-qPCR data normalization

BMC GENOMICS 2011年第1期12卷 156-156页

作者： Hruz, Tomas Wyss, Markus Docquier, Mylene Pfaffl, Michael W. Masanetz, Sabine Borghi, Lorenzo Verbrugghe, Phebe Kalaydjieva, Luba Bleuler, Stefan Laule, Oliver Descombes, Patrick Gruissem, Wilhelm Zimmermann, Philip ETH Dept Biol CH-8092 Zurich Switzerland ETH Inst Theoret Comp Sci CH-8092 Zurich Switzerland Univ Geneva NCCR Frontiers Genet CH-1211 Geneva 4 Switzerland TUM D-85354 Freising Weihenstephan Germany NEBION AG CH-8048 Zurich Switzerland UWA Ctr Med Res Western Australian Inst Med Res Perth WA Australia

Background: RT-qPCR is a sensitive and increasingly used method for gene expression quantification. To normalize RT-qPCR measurements between samples, most laboratories use endogenous reference genes as internal controls. There is increasing evidence, however, that the expression of commonly used reference genes can vary significantly in certain contexts. Results: Using the Genevestigator database of normalized and well-annotated microarray experiments, we describe the expression stability characteristics of the transciptomes of several organisms. The results show that a) no genes are universally stable, b) most commonly used reference genes yield very high transcript abundances as compared to the entire transcriptome, and c) for each biological context a subset of stable genes exists that has smaller variance than commonly used reference genes or genes that were selected for their stability across all conditions. Conclusion: We therefore propose the normalization of RT-qPCR data using reference genes that are specifically chosen for the conditions under study. RefGenes is a community tool developed for that purpose. Validation RT-qPCR experiments across several organisms showed that the candidates proposed by RefGenes generally outperformed commonly used reference genes. RefGenes is available within Genevestigator at http://***.

关键词： Reference Gene microarray data Tissue Type Lymphoblastoid Cell Line Candidate Reference Gene

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Meta-analysis of muscle transcriptome data using the MADMuscle database reveals biologically relevant gene patterns

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BMC GENOMICS 2011年第1期12卷 113-113页

作者： Baron, Daniel Dubois, Emeric Bihouee, Audrey Teusan, Raluca Steenman, Marja Jourdon, Philippe Magot, Armelle Pereon, Yann Veitia, Reiner Savagner, Frederique Ramstein, Gerard Houlgatte, Remi INSERM U915 F-44000 Nantes France Univ Nantes Fac Med F-44000 Nantes France CHU Nantes CIC Inst Thorax F-44000 Nantes France CHU Nantes Lab Explorat Fonct Nantes F-44000 Nantes France CHU Nantes Ctr Reference Malad Neuromuscularies Rares Enfant F-44000 Nantes France CNRS UMR7592 Inst Jacques Monod F-75013 Paris France Univ Paris 07 F-75013 Paris France INSERM UMR 694 F-49033 Angers France Univ Angers F-49033 Angers France CHU Angers Lab Biochim & Biol Mol F-49033 Angers France Ecole Polytech Lab Informat Nantes Atlantique LINA F-44000 Nantes France

Background: DNA microarray technology has had a great impact on muscle research and microarray gene expression data has been widely used to identify gene signatures characteristic of the studied conditions. With the rapid accumulation of muscle microarray data, it is of great interest to understand how to compare and combine data across multiple studies. Meta-analysis of transcriptome data is a valuable method to achieve it. It enables to highlight conserved gene signatures between multiple independent studies. However, using it is made difficult by the diversity of the available data: different microarray platforms, different gene nomenclature, different species studied, etc. Description: We have developed a system tool dedicated to muscle transcriptome data. This system comprises a collection of microarray data as well as a query tool. This latter allows the user to extract similar clusters of co-expressed genes from the database, using an input gene list. Common and relevant gene signatures can thus be searched more easily. The dedicated database consists in a large compendium of public data (more than 500 data sets) related to muscle (skeletal and heart). These studies included seven different animal species from invertebrates (Drosophila melanogaster, Caenorhabditis elegans) and vertebrates (Homo sapiens, Mus musculus, Rattus norvegicus, Canis familiaris, Gallus gallus). After a renormalization step, clusters of co-expressed genes were identified in each dataset. The lists of co-expressed genes were annotated using a unified re-annotation procedure. These gene lists were compared to find significant overlaps between studies. Conclusions: Applied to this large compendium of data sets, meta-analyses demonstrated that conserved patterns between species could be identified. Focusing on a specific pathology (Duchenne Muscular Dystrophy) we validated results across independent studies and revealed robust biomarkers and new pathways of interest. The meta-analyses perf

关键词： Gene Ontology microarray data Duchenne Muscular Dystrophy Gene List microarray Platform

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Transcriptional regulatory network refinement and quantification through kinetic modeling, gene expression microarray data and information theory

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BMC BIOINFORMATICS 2007年第1期8卷 20-20页

作者： Sayyed-Ahmad, Abdallah Tuncay, Kagan Ortoleva, Peter J. Indiana Univ Dept Chem Ctr Cell & Virus Theory Bloomington IN 47405 USA Univ Minnesota Dept Chem Engn & Mat Sci Minneapolis MN 55455 USA

Background: Gene expression microarray and other multiplex data hold promise for addressing the challenges of cellular complexity, refined diagnoses and the discovery of well-targeted treatments. A new approach to the construction and quantification of transcriptional regulatory networks (TRNs) is presented that integrates gene expression microarray data and cell modeling through information theory. Given a partial TRN and time series data, a probability density is constructed that is a functional of the time course of transcription factor (TF) thermodynamic activities at the site of gene control, and is a function of mRNA degradation and transcription rate coefficients, and equilibrium constants for TF/gene binding. Results: Our approach yields more physicochemical information that compliments the results of network structure delineation methods, and thereby can serve as an element of a comprehensive TRN discovery/quantification system. The most probable TF time courses and values of the aforementioned parameters are obtained by maximizing the probability obtained through entropy maximization. Observed time delays between mRNA expression and activity are accounted for implicitly since the time course of the activity of a TF is coupled by probability functional maximization, and is not assumed to be proportional to expression level of the mRNA type that translates into the TF. This allows one to investigate post-translational and TF activation mechanisms of gene regulation. Accuracy and robustness of the method are evaluated. A kinetic formulation is used to facilitate the analysis of phenomena with a strongly dynamical character while a physically-motivated regularization of the TF time course is found to overcome difficulties due to omnipresent noise and data sparsity that plague other methods of gene expression data analysis. An application to Escherichia coli is presented. Conclusion: Multiplex time series data can be used for the construction of the network of

关键词： microarray data Independent Component Analysis Transcription Factor Activity Gene Expression microarray Gene Expression microarray data

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A Normalized Tree Index for identification of correlated clinical parameters in microarray experiments

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BIOdata MINING 2011年第1期4卷 2-2页

作者： Martin, Christian W. Tauchen, Anika Becker, Anke Nattkemper, Tim W. Univ Bielefeld Fac Technol Biodata Min & Appl Neuroinformat Grp D-33501 Bielefeld Germany Univ Bielefeld Grad Sch Bioinformat & Genome Res CeBiTec D-33501 Bielefeld Germany Univ Bielefeld Fac Publ Hlth D-33501 Bielefeld Germany Univ Freiburg Ctr Biol Syst Anal FRISYS AG Becker D-79104 Freiburg Germany

Background: Measurements on gene level are widely used to gain new insights in complex diseases e. g. cancer. A promising approach to understand basic biological mechanisms is to combine gene expression profiles and classical clinical parameters. However, the computation of a correlation coefficient between high-dimensional data and such parameters is not covered by traditional statistical methods. Methods: We propose a novel index, the Normalized Tree Index (NTI), to compute a correlation coefficient between the clustering result of high-dimensional microarray data and nominal clinical parameters. The NTI detects correlations between hierarchically clustered microarray data and nominal clinical parameters (labels) and gives a measurement of significance in terms of an empiric p-value of the identified correlations. Therefore, the microarray data is clustered by hierarchical agglomerative clustering using standard settings. In a second step, the computed cluster tree is evaluated. For each label, a NTI is computed measuring the correlation between that label and the clustered microarray data. Results: The NTI successfully identifies correlated clinical parameters at different levels of significance when applied on two real-world microarray breast cancer data sets. Some of the identified highly correlated labels confirm the actual state of knowledge whereas others help to identify new risk factors and provide a good basis to formulate new hypothesis. Conclusions: The NTI is a valuable tool in the domain of biomedical data analysis. It allows the identification of correlations between high-dimensional data and nominal labels, while at the same time a p-value measures the level of significance of the detected correlations.

关键词： microarray data Clinical Parameter Primary data Secondary data Cluster Tree

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Large-scale integration of cancer microarray data identifies a robust common cancer signature

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BMC BIOINFORMATICS 2007年第1期8卷 275-275页

作者： Xu, Lei Geman, Donald Winslow, Raimond L. Johns Hopkins Univ Inst Computat Med Baltimore MD 21218 USA Johns Hopkins Univ Ctr Cardiovasc Bioinformat & Modeling Baltimore MD 21218 USA Johns Hopkins Univ Dept Appl Math & Stat Baltimore MD 21218 USA Johns Hopkins Univ Ctr Imaging Sci Baltimore MD 21218 USA

Background: There is a continuing need to develop molecular diagnostic tools which complement histopathologic examination to increase the accuracy of cancer diagnosis. DNA microarrays provide a means for measuring gene expression signatures which can then be used as components of genomic-ased diagnostic tests to determine the presence of cancer. Results: In this study, we collect and integrate similar to 1500 microarray gene expression profiles from 26 published cancer data sets across 21 major human cancer types. We then apply a statistical method, referred to as the Top-Scoring Pair of Groups (TSPG) classifier, and a repeated random sampling strategy to the integrated training data sets and identify a common cancer signature consisting of 46 genes. These 46 genes are naturally divided into two distinct groups;those in one group are typically expressed less than those in the other group for cancer tissues. Given a new expression profile, the classifier discriminates cancer from normal tissues by ranking the expression values of the 46 genes in the cancer signature and comparing the average ranks of the two groups. This signature is then validated by applying this decision rule to independent test data. Conclusion: By combining the TSPG method and repeated random sampling, a robust common cancer signature has been identified from large-scale microarray data integration. Upon further validation, this signature may be useful as a robust and objective diagnostic test for cancer.

关键词： microarray data microarray Platform Independent Test data Prostate Cancer data Cancer microarray data

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Type-2 fuzzy Approach for Disease-Associated Gene Identification on microarrays

Type-2 fuzzy Approach for Disease-Associated Gene Identifica...

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International Conference on Bioscience, Biochemistry and Bioinformatics（ICBBB 2011）

作者： Vo Anh Discipline of Mathematical Sciences Faculty of Science and TechnologyQueensland University of TechnologyBrisbaneQ4001Australia

Fuzzy set theory has been widely used in the analysis of gene microarray ***,due to noise and uncertainty inherent in microarray data,traditional fuzzy methods sometimes do not perform *** this paper,we propose a type-2 fuzzy membership test(Type-2 FM test) for disease-associated gene identification on microarrays to improve traditional fuzzy *** apply this method on diabetes and lung cancer microarrays and make a comparison with traditional fuzzy *** diabetes data,we can identify 7 genes which have been confirmed to be related to diabetes treatment in the published literature and one more gene can be identified than original *** lung cancer data,we can also identify 7 genes which have been confirmed to be associated with lung cancer treatment in published literature and the type-2 d-values are significantly *** results show that our type-2 FM test performs better than traditional fuzzy methods when analyzing microarray data with similar expression values and noise.

关键词： type-2 fuzzy set microarray data uncertaintie and noises

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Integration of bioinformatics tools in candidate gene prioritization of co-regulated gene sets in Saccharomyces cerevisiae

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BMC BIOINFORMATICS 2011年第s7期12卷 1-2页

作者： Abedi, Vida Yeasin, Mohammed Sutter, Thomas R. Univ Memphis Dept Elect & Comp Engn Memphis TN 38152 USA Univ Memphis Dept Biol Sci Memphis TN 38152 USA Univ Memphis Coll Arts & Sci Bioinformat Program Memphis TN 38152 USA

An abstract for the article 'Integration of Bioinformatics Tools in Candidate Gene Prioritization of Co-Regulated Gene Sets in Saccharomyces Cerevisiae,' by Vida Abedi, Mohammed Yasin and Thomas R. Sutter is p... 详细信息

关键词： Gene Ontology Saccharomyces Cerevisiae microarray data microarray Experiment Biological data

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Cancer Classification Based on the "Fingerprint" of microarray data

Cancer Classification Based on the "Fingerprint" of Microarr...

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The 1st International Conference on Bioinformatics and Biomedical Engineering(iCBBE 2007)(首届IEEE生物信息与生物医学工程国际会议)

作者： Jinyong Cheng Yihui Liu Jinwen Shen School of Computer Science & IT Shandong Institute of Light Industry Jinan China 250353 College of Life Sciences Henan Agriculture University Zhengzhou China 450002

Recently there is an increasing interest in changing the criterion of tumor classification from morphologic to molecular. In this perspective, the problem can be regarded as a classifcation problem in machine learning. In this study wavelet analysis is used to extract the features from high dimensional microarray profiles. To make it easier to find the significant genes, we remove the small change contained in the high frequency part based on wavelet decomposition. A set of orthogonal wavelet approximation coefficients is used to compress gene profiles and reduce the dimensionality. Experimental results show that approximation coefficients at 1st and 2nd level achieve good performance.

关键词： microarray data wavelet analysis feature extraction dimensionality reduction support vector machine

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On the reproducibility of results of pathway analysis in genome-wide expression studies of colorectal cancers

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JOURNAL OF BIOMEDICAL INFORMATICS 2010年第3期43卷 397-406页

作者： Maglietta, Rosalia Distaso, Angela Piepoli, Ada Palumbo, Orazio Carella, Massimo D'Addabbo, Annarita Mukherjee, Sayan Ancona, Nicola CNR Ist Sistemi Intelligenti Automaz I-70126 Bari Italy Casa Sollievo della Sofferenza Osped Unita Operat Gastroenterol IRCCS I-71013 San Giovanni Rotondo FG Italy Casa Sollievo della Sofferenza Osped Serv Genet Med IRCCS I-71013 San Giovanni Rotondo FG Italy Duke Univ Inst Genome Sci & Policy Durham NC USA

One of the major problems in genomics and medicine is the identification of gene networks and pathways deregulated in complex and polygenic diseases, like cancer. In this paper, we address the problem of assessing the variability of results of pathways analysis identified in different and independent genome wide expression studies, in which the same phenotypic conditions are assayed. To this end, we assessed the deregulation of 1891 curated gene sets in four independent gene expression data sets of subjects affected by colorectal cancer (CRC). In this comparison we used two well-founded statistical models for evaluating deregulation of gene networks. We found that the results of pathway analysis in expression studies are highly reproducible. Our study revealed 53 pathways identified by the two methods in all the four data sets analyzed with high statistical significance and strong biological relevance with the pathology examined. This set of pathways associated to single markers as well as to whole biological processes altered constitutes a signature of the disease which sheds light on the genetics bases of CRC. (C) 2009 Elsevier Inc. All rights reserved.

关键词： Bioinformatics microarray data Pathway Colorectal cancer

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CORE-Net: exploiting prior knowledge and preferential attachment to infer biological interaction networks

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IET SYSTEMS BIOLOGY 2010年第5期4卷 296-310页

作者： Montefusco, F. Cosentino, C. Amato, F. Magna Graecia Univ Catanzaro Sch Comp & Biomed Engn Dept Expt & Clin Med I-88100 Catanzaro Italy Univ Exeter Coll Engn Math & Phys Sci Exeter EX4 4QF Devon England

The problem of reverse engineering in the topology of functional interaction networks from time-course experimental data has received considerable attention in literature, due to the potential applications in the most diverse fields, comprising engineering, biology, economics and social sciences. The present work introduces a novel technique, CORE-Net, which addresses this problem focusing on the case of biological interaction networks. The method is based on the representation of the network in the form of a dynamical system and on an iterative convex optimisation procedure. A first advantage of the proposed approach is that it allows to exploit qualitative prior knowledge about the network interactions, of the same kind as typically available from biological literature and databases. A second novel contribution consists of exploiting the growth and preferential attachment mechanisms to improve the inference performances when dealing with networks which exhibit a scale-free topology. The technique is first assessed through numerical tests on in silico random networks, subsequently it is applied to reverse engineering a cell cycle regulatory subnetwork in Saccharomyces cerevisiae from experimental microarray data. These tests show that the combined exploitation of prior knowledge and preferential attachment significantly improves the predictions with respect to other approaches.

关键词： Saccharomyces cerevisiae iterative convex optimisation procedure Biophysical instrumentation and techniques Biology and medical computing medical computing Optimisation techniques time-course experimental data Numerical approximation and analysis biological techniques optimisation in silico random networks functional interaction network topology dynamical system iterative methods arrays microarray data preferential attachment CORE-Net Interpolation and function approximation (numerical analysis) inference performances infer biological interaction networks reverse engineering

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