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Human microRNA target identification by RRSM

JOURNAL OF THEORETICAL BIOLOGY 2011年第1期286卷 79-84页

作者： Hsieh, Wan J. Wang, Hsiuying Natl Chiao Tung Univ Inst Stat Hsinchu Taiwan

MicroRNAs (miRNAs) are small endogenously expressed non-coding RNAs that regulate target messenger RNAs in various biological processes. In recent years, there have been many studies concentrated on the discovery of new miRNAs and identification of their mRNA targets. Although researchers have identified many miRNAs, few miRNA targets have been identified by actual experimental methods. To expedite the identification of miRNA targets for experimental verification, in the literature approaches based on the sequence or microarray expression analysis have been established to discover the potential miRNA targets. In this study, we focus on the human miRNA target prediction and propose a generalized relative R-2 method (RRSM) to find many high-confidence targets. Many targets have been confirmed from previous studies. The targets for several miRNAs discovered by the HITS-CLIP method in a recent study have also been selected by our study. (C) 2011 Elsevier Ltd. All rights reserved.

关键词： microarray expression miRNA Relative R-2 method Regression model Correlation

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Immune response in silico (IRIS): immune-specific genes identified from a compendium of microarray expression data

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GENES AND IMMUNITY 2005年第4期6卷 319-331页

作者： Abbas, AR Baldwin, D Ma, Y Ouyang, W Gurney, A Martin, F Fong, S Campagne, MV Godowski, P Williams, PM Chan, AC Clark, HF Genentech Inc Dept Bioinformat San Francisco CA 94080 USA Genentech Inc Dept Immunol San Francisco CA 94080 USA Genentech Inc Dept Mol Biol San Francisco CA 94080 USA

Immune cell-specific expression is one indication of the importance of a gene's role in the immune response. We have compiled a compendium of microarray expression data for virtually all human genes from six key immune cell types and their activated and differentiated states. Immune Response In Silico ( IRIS) is a collection of genes that have been selected for specific expression in immune cells. The expression pattern of IRIS genes recapitulates the phylogeny of immune cells in terms of the lineages of their differentiation. Gene Ontology assignments for IRIS genes reveal significant involvement in inflammation and immunity. Genes encoding CD antigens, cytokines, integrins and many other gene families playing key roles in the immune response are highly represented. IRIS also includes proteins of unknown function and expressed sequence tags that may not represent genes. The predicted cellular localization of IRIS proteins is evenly distributed between cell surface and intracellular compartments, indicating that immune specificity is important at many points in the signaling pathways of the immune response. IRIS provides a resource for further investigation into the function of the immune system and immune diseases.

关键词： microarray expression specific expression immune response

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Gene signatures reflect the marked heterogeneity of tissue-resident macrophages

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IMMUNOLOGY AND CELL BIOLOGY 2008年第3期86卷 246-254页

作者： Gorgani, Nick N. Ma, Yan Clark, Hilary F. Genentech Inc Dept Bioinformat San Francisco CA 94080 USA Genentech Inc Dept Immunol San Francisco CA 94080 USA

Tissue-resident macrophages play an important role in defense against pathogens and perform key functions in organ homeostasis, innate and adaptive immunity. Tissue macrophages originate from blood monocytes that infiltrate virtually every organ in the body. Macrophages in different tissues share many characteristics, including their ability to migrate, phagocytose particles, metabolize lipids and present antigens. Morphologically they are quite heterogeneous, and some distinct functions have been reported. The gene expression profile of macrophages is reflective of both their shared and distinct biological functions. Here, we show that macrophages from murine spleen, liver and peritoneum display dramatically different expression profiles. Clusters of genes were found to represent unique biological functions related to adhesion, antigen presentation, phagocytosis, lipid metabolism and signal transduction. Some gene families, such as integrins, are differentially expressed among the macrophages resident in different tissues, suggesting that the tissue of residence influences their biological function.

关键词： macrophage gene signature microarray expression

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A protein interaction network associated with asthma

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JOURNAL OF THEORETICAL BIOLOGY 2008年第4期252卷 722-731页

作者： Hwang, Sohyun Son, Seung-Woo Kim, Sang Cheol Kim, Young Joo Jeong, Hawoong Lee, Doheon Korea Adv Inst Sci & Technol Dept Bio & Brain Engn Taejon South Korea Korea Adv Inst Sci & Technol Inst BioCentury Dept Phys Taejon South Korea KRIBB Korean BioInformat Ctr Taejon South Korea Yonsei Univ Dept Appl Stat Seoul 120749 South Korea

Identifying candidate genes related to complex diseases or traits and mapping their relationships require a system-level analysis at a cellular scale. The objective of the present study is to systematically analyze the complex effects of interrelated genes and provide a framework for revealing their relationships in association with a specific disease (asthma in this case). We observed that protein-protein interaction (PPI) networks associated with asthma have a power-law connectivity distribution as many other biological networks have. The hub nodes and skeleton substructure of the result network are consistent with the prior knowledge about asthma pathways, and also suggest unknown candidate target genes associated with asthma, including GNB2L1, BRCA1, CBL, and VAV1. In particular, GNB2L1 appears to play a very important role in the asthma network through frequent interactions with key proteins in cellular signaling. This network-based approach represents an alternative method for analyzing the complex effects of candidate genes associated with complex diseases and suggesting a list of gene drug targets. The full list of genes and the analysis details are available in the following online supplementary materials: http://***:8080/resources/asthma-Ppi. (C) 2008 Elsevier Ltd. All rights reserved.

关键词： disease network protein-protein interaction microarray expression system biology

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Valproic acid as epigenetic cancer drug: Preclinical, clinical and transcriptional effects on solid tumors

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CANCER TREATMENT REVIEWS 2008年第3期34卷 206-222页

作者： Duenas-Gonzalez, Alfonso Candelaria, Myrna Perez-Plascencia, Carlos Perez-Cardenas, Enrique de la Cruz-Hernandez, Erick Herrera, Luis A. Univ Nacl Autonoma Mexico Inst Invest Biomed Unidad Invest Biomed Canc INCan Mexico City 04510 DF Mexico Inst Nacl Cancerol Div Invest Clin Mexico City DF Mexico

Among many anticancer drugs collectively named "targeted or molecular therapies" epigenetic drugs are clearly promising. Differently from other agents targeting a single gene product, epigenetic drugs have chromatin as their target through inhibition of histone deacetylases (HDACs) and DNA methyltransferases (DNMTs) therefore, yet unspecific, they may act upon most or all tumor types, as deregulation of the methylation and deacetylation machinery are a common hallmark of neoplasia. In the last years, valproic acid (VPA) as emerged as a promising drug for cancer treatment. VPA has shown potent antitumor effects in a variety of in vitro and in vivo systems, and encouraging results in early clinical. trials either atone or in combination with demethylating and/or cytotoxic agents. In addition, whole genome expression by microarray analysis from the primary tumors of patients treated with VPA show significant up-regulation of hundred of genes belonging to multiple pathways including ribosomal proteins, oxidative phosphorylation, MAPK signaling;focal adhesion, cell cycle, antigen processing and presentation, proteasome, apoptosis, PI3K, Wnt signaling, calcium signaling, TGF-beta signaling, and ubiquitin-mediated proteolysis among others. Despite in general, industry is not particularly interested in funding the clinical development of VPA, -at least in comparison to novel HDAC inhibitors-, existing preclinical and preliminary clinical data strongly suggest that VPA could be a drug that eventually will be used in combination therapies, either with classical cytotoxics, other molecular-targeted drugs or radiation in a number of solid tumors. (c) 2007 Elsevier Ltd. All rights reserved.

关键词： valproic acid epigenetic therapy solid tumors microarray expression

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Modeling nonlinearity in dilution design microarray data

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BIOINFORMATICS 2007年第11期23卷 1339-1347页

作者： Zheng, Xiuwen Huang, Hung-Chung Li, Wenyuan Liu, Peng Li, Quan-Zhen Liu, Ying Univ Texas Dept Math Sci Richardson TX 75083 USA Univ Texas Dept Comp Sci Richardson TX 75083 USA Univ Texas Dept Mol & Cell Biol Richardson TX 75083 USA Iowa State Univ Dept Stat Ames IA 50011 USA Univ Texas Med Ctr Microarray Core Facil Dallas TX 75390 USA

Motivation: Dilution design (Mixed tissue RNA) has been utilized by some researchers to evaluate and assess the performance of multiple microarray platforms. Current microarray data analysis approaches assume that the quantified signal intensities are linearly related to the expression of the corresponding genes in the sample. However, there are sources of nonlinearity in microarray expression measurements. Such nonlinearity study in the expressions of the RNA mixtures provides a new way to analyze gene expression data, and we argue that the nonlinearity can reveal novel information for microarray data analysis. Therefore, we proposed a statistical model, called proportion model, which is based on the linear regression analysis. To approximately quantify the nonlinearity in the dilution design, a new calibration, beta ratio (BR) was derived from the proportion model. Furthermore, a new adjusted fold change (adj-FC) was proposed to predict the true FC without nonlinearity, in particular for large FC. Results: We applied our method to one microarray dilution dataset. The experimental results indicated that, to some extent, there are global biases comparing with the linear assumption for the significant genes. Further analysis of those highly expressed genes with significant nonlinearity revealed some promising results, e.g. 'poison' effect was discovered for some genes in RNA mixtures. The adj-FCs of those genes with 'poison' effect, indicate that the nonlinearity can be also caused by the inherent feature of the genes besides signal noise and technical variation. Moreover, when percentage of overlapping genes (POG) was used as a cross-platform consistency measure, adj-FC outperformed simple fold change to show that Affymetrix and Illumina platforms are consistent.

关键词： .intensities microarray data utilized dilution calibration quantified data analysis microarray expression microarray platforms linearly Modeling nonlinearity sources of nonlinearity RNA mixtures beta ratio adj-FC

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Kernel hierarchical gene clustering from microarray expression data

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BIOINFORMATICS 2003年第16期19卷 2097-2104页

作者： Qin, J Lewis, DP Noble, WS Columbia Univ Columbia Genome Ctr New York NY 10032 USA Columbia Univ Dept Comp Sci New York NY 10027 USA Univ Washington Dept Genome Sci Seattle WA 98195 USA

Motivation: Unsupervised analysis of microarray gene expression data attempts to find biologically significant patterns within a given collection of expression measurements. For example, hierarchical clustering can be applied to expression profiles of genes across multiple experiments, identifying groups of genes that share similiar expression profiles. Previous work using the support vector machine supervised learning algorithm with microarray data suggests that higher-order features, such as pairwise and tertiary correlations across multiple experiments, may provide significant benefit in learning to recognize classes of co-expressed genes. Results: We describe a generalization of the hierarchical clustering algorithm that efficiently incorporates these higher-order features by using a kernel function to map the data into a high-dimensional feature space. We then evaluate the utility of the kernel hierarchical clustering algorithm using both internal and external validation. The experiments demonstrate that the kernel representation itself is insufficient to provide improved clustering performance. We conclude that mapping gene expression data into a high-dimensional feature space is only a good idea when combined with a learning algorithm, such as the support vector machine that does not suffer from the curse of dimensionality.

关键词： .pairwise microarray data feature space microarray expression kernel function hierarchical clustering algorithm

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Utilizing logical relationships in genomic data to decipher cellular processes

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FEBS JOURNAL 2005年第20期272卷 5110-5118页

作者： Bowers, PM O'Connor, BD Cokus, SJ Sprinzak, E Yeates, TO Eisenberg, D Univ Calif Los Angeles Howard Hughes Med Inst Los Angeles CA 90095 USA Univ Calif Los Angeles Inst Genom & Proteom Los Angeles CA 90095 USA Univ Calif Los Angeles Dept Chem & Biochem Los Angeles CA 90095 USA Univ Calif Los Angeles Dept Math Los Angeles CA 90095 USA

The wealth of available genomic data has spawned a corresponding interest in computational methods that can impart biological meaning and context to these experiments. Traditional computational methods have drawn relationships between pairs of proteins or genes based on notions of equality or similarity between their patterns of occurrence or behavior. For example, two genes displaying similar variation in expression, over a number of experiments, may be predicted to be functionally related. We have introduced a natural extension of these approaches, instead identifying logical relationships involving triplets of proteins. Triplets provide for various discrete kinds of logic relationships, leading to detailed inferences about biological associations. For instance, a protein C might be encoded within an organism if, and only if, two other proteins A and B are also both encoded within the organism, thus suggesting that gene C is functionally related to genes A and B. The method has been applied fruitfully to both phylogenetic and microarray expression data, and has been used to associate logical combinations of protein activity with disease state phenotypes, revealing previously unknown ternary relationships among proteins, and illustrating the inherent complexities that arise in biological data.

关键词： genomic data logic analysis microarray expression phylogenetic profile

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Analysis of microarray expression data

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Genome Biology 2000年第1期1卷 1-4页

作者： Paul Kellam

Detailed descriptions of the design and information content of EBI's database ArrayExpress, which is currently under development but aims to be a public repository of gene expression data, are the main feature of ... 详细信息

关键词： Gene expression Data microarray expression microarray Data Analysis microarray expression Data Public Repository

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Autocrine loops

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Genome Biology 2001年第1期2.0卷 1-2页

作者： Weitzman, Jonathan B.

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