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Identifying synergistic regulation involving c-Myc and sp1 in human tissues

NUCLEIC ACIDS RESEARCH 2007年第4期35卷 1098-1107页

作者： Parisi, Fabio Wirapati, Pratyaksha Naef, Felix ISREC Lausanne Switzerland NCCR Mol Oncol Lausanne Switzerland Swiss Inst Bioinformat CH-1015 Lausanne Switzerland Ecole Polytech Fed Lausanne Sch Life Sci CH-1015 Lausanne Switzerland

Combinatorial gene regulation largely contributes to phenotypic versatility in higher eukaryotes. Genome-wide chromatin immuno-precipitation (ChIP) combined with expression profiling can dissect regulatory circuits around transcriptional regulators. Here, we integrate tiling array measurements of DNA-binding sites for c-Myc, sp1, TFIID and modified histones with a tissue expression atlas to establish the functional correspondence between physical binding, promoter activity and transcriptional regulation. For this we develop SLM, a methodology to map c-Myc and sp1-binding sites and then classify sites as sp1-only, c-Myc-only or dual. Dual sites show several distinct features compared to the single regulator sites: specifically, they exhibit overall higher degree of conservation between human and rodents, stronger correlation with TFIID-bound promoters, and preference for permissive chromatin state. By applying regression models to an expression atlas we identified a functionally distinct signature for strong dual c-Myc/sp1 sites. Namely, the correlation with c-Myc expression in promoters harboring dual-sites is increased for stronger sp1 sites by strong sp1 binding and the effect is largest in proliferating tissues. Our approach shows how integrated functional analyses can uncover tissue-specific and combinatorial regulatory dependencies in mammals.

关键词： TRANSCRIPTION FACTOR SP1 MODEL-BASED ANALYSIS HIGH-RESOLUTION MAP OLIGONUCLEOTIDE ARRAYS GENE-EXPRESSION microarray data BINDING SITES TILING-ARRAYS HUMAN GENOME CHIP-CHIP human tissue Binding Sites Sp1 Transcription Factor oncogene myc Human Genome

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From access and integration to mining of secure genomic data sets across the Grid

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FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE 2007年第3期23卷 447-456页

作者： Sinnott, Richard O. Univ Glasgow Natl e Sci Ctr Glasgow G12 8QQ Lanark Scotland

The UK Department of Trade and Industry (DTI) funded BRIDGES project (Biomedical Research Informatics Delivered by Grid Enabled Services) has developed a Grid infrastructure to support cardiovascular research. This includes the provision of a compute Grid and a data Grid infrastructure with Security at its heart. In this paper we focus on the BRIDGES data Grid. A primary aim of the BRIDGES data Grid is to help control the complexity in access to and integration of a myriad of genomic data sets through simple Grid-based tools. We outline these tools, how they are delivered to the end user scientists. We also describe how these tools are to be extended in the BBSRC funded Grid Enabled microarray Expression Profile Search (GEMEPS) to support a richer vocabulary of search capabilities to Support mining of microarray data sets. As with BRIDGES, fine grain Grid security underpins GEMEPS. (c) 2006 Elsevier B.V. All rights reserved.

关键词： grid security genomics data grid microarray data

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Robust semiparametric mixing for detecting differentially expressed genes in microarray experiments

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COMPUTATIONAL STATISTICS & data ANALYSIS 2007年第11期51卷 5253-5265页

作者： Alfo, Marco Farcomeni, Alessio Tardella, Luca Univ Roma La Sapienza Dipartimento Stat Probabil & Stat Applicate I-00185 Rome Italy

An important goal of microarray studies is the detection of genes that show significant changes in observed expressions when two or more classes of biological samples such as treatment and control are compared. Using the c-fold rule, a gene is declared to be differentially expressed if its average expression level varies by more than a constant factor c between treatment and control (typically c = 2). While often used, however, this simple rule is not completely convincing. By modeling this filter, a binary variable is defined at the gene x experiment level, allowing for a more powerful treatment of the corresponding information. A gene-specific random term is introduced to control for both dependence among genes and variability with respect to the c-fold threshold. Inference is carried out via a two-level finite mixture model under a likelihood approach. Then, parameter estimates are also derived using the counting distribution under a Bayesian nonparametric approach which allows to keep under control some error rate of erroneous discoveries. The effectiveness of both proposed approaches is illustrated through a large-scale simulation study and a well known benchmark data set. (C) 2006 Elsevier B.V. All rights reserved.

关键词： microarray data up-regulated genes mixture models counting distribution false discovery rate

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A robust measure of correlation between two genes on a microarray

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BMC BIOINFORMATICS 2007年第1期8卷 1-13页

作者： Hardin, Johanna Mitani, Aya Hicks, Leanne VanKoten, Brian Pomona Coll Dept Math Claremont CA 91711 USA Pitzer Coll Dept Math Claremont CA 91711 USA Univ Nebraska Dept Stat Lincoln NE 68588 USA Lewis & Clark Coll Dept Math Portland OR 97219 USA

Background: The underlying goal of microarray experiments is to identify gene expression patterns across different experimental conditions. Genes that are contained in a particular pathway or that respond similarly to experimental conditions could be co-expressed and show similar patterns of expression on a microarray. Using any of a variety of clustering methods or gene network analyses we can partition genes of interest into groups, clusters, or modules based on measures of similarity. Typically, Pearson correlation is used to measure distance (or similarity) before implementing a clustering algorithm. Pearson correlation is quite susceptible to outliers, however, an unfortunate characteristic when dealing with microarray data (well known to be typically quite noisy.) Results: We propose a resistant similarity metric based on Tukey's biweight estimate of multivariate scale and location. The resistant metric is simply the correlation obtained from a resistant covariance matrix of scale. We give results which demonstrate that our correlation metric is much more resistant than the Pearson correlation while being more efficient than other nonparametric measures of correlation (e.g., Spearman correlation.) Additionally, our method gives a systematic gene flagging procedure which is useful when dealing with large amounts of noisy data. Conclusion: When dealing with microarray data, which are known to be quite noisy, robust methods should be used. Specifically, robust distances, including the biweight correlation, should be used in clustering and gene network analysis.

关键词： microarray data Gene Pair Multivariate Location Minimum Covariance Determinant Outlying data Point

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A new framework for identifying differentially expressed genes

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PATTERN RECOGNITION 2007年第11期40卷 3249-3262页

作者： Li, Jie Tang, Xianglong Zhao, Wei Huang, Jianhua Harbin Inst Technol Dept Comp Sci & Technol Harbin 150001 Peoples R China

microarrays have been widely used to classify cancer samples and discover the biological types, for example tumor versus normal phenotypes in cancer research. One of the challenging scientific tasks in the post-genomic epoch is how to identify a subset of differentially expressed genes from thousands of genes in microarray data which will enable us to understand the underlying molecular mechanisms of diseases, accurately diagnosing diseases and identifying novel therapeutic targets. In this paper, we propose a new framework for identifying differentially expressed genes. In the proposed framework, genes are ranked according to their residuals. The performance of the framework is assessed through applying it to several public microarray data. Experimental results show that the proposed method gives more robust and accurate rank than other statistical test methods, such as t-test, Wilcoxon rank sum test and KS-test. Another novelty of the method is that we design an algorithm for selecting a small subset of genes that show significant variation in expression ("outlier" genes). The number of genes in the small subset can be controlled via an alterable window of confidence level. In addition, the results of the proposed method can be visualized. By observing the residual plot, we can easily find genes that show significant variation in two groups of samples and learn the degrees of differential expression of genes. Through a comparison study, we found several "outlier" genes which had been verified in previous biological experiments while they were either not identified by other methods or had lower ranks in standard statistical tests. (c) 2007 Pattern Recognition Society. Published by Elsevier Ltd. All rights reserved.

关键词： microarray data t-test Wilcoxon rank sum test KS-test differentially expressed genes "outlier" gene regression model window of confidence level

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Robust filtering for gene expression time series data with variance constraints

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INTERNATIONAL JOURNAL OF COMPUTER MATHEMATICS 2007年第5期84卷 619-633页

作者： Wei, Guoliang Wang, Zidong Shu, Huisheng Fraser, Karl Liu, Xiaohui Brunel Univ Dept Informat Syst & Comp Uxbridge UB8 3PH Middx England Donghua Univ Sch Informat Sci & Technol Shanghai 200051 Peoples R China Donghua Univ Dept Appl Math Shanghai 200051 Peoples R China

In this paper, an uncertain discrete-time stochastic system is employed to represent a model for gene regulatory networks from time series data. A robust variance-constrained filtering problem is investigated for a gene expression model with stochastic disturbances and norm-bounded parameter uncertainties, where the stochastic perturbation is in the form of a scalar Gaussian white noise with constant variance and the parameter uncertainties enter both the system matrix and the output matrix. The purpose of the addressed robust filtering problem is to design a linear filter such that, for the admissible bounded uncertainties, the filtering error system is Schur stable and the individual error variance is less than a prespecified upper bound. By using the linear matrix inequality (LMI) technique, sufficient conditions are first derived for ensuring the desired filtering performance for the gene expression model. Then the filter gain is characterized in terms of the solution to a set of LMIs, which can easily be solved by using available software packages. A simulation example is exploited for a gene expression model in order to demonstrate the effectiveness of the proposed design procedures.

关键词： microarray data gene expression model robust filtering stochastic disturbances error variance constraints

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Revisiting adverse effects of cross-hybridization in Affymetrix gene expression data: do they matter for correlation analysis?

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BIOLOGY DIRECT 2007年第1期2卷 1-13页

作者： Klebanov, Lev Chen, Linlin Yakovlev, Andrei Univ Rochester Dept Biostat & Computat Biol Rochester NY 14642 USA Charles Univ Prague Dept Probabil & Stat CZ-18675 Prague Czech Republic

Background.: This work was undertaken in response to a recently published paper by Okoniewski and Miller (BMC Bioinformatics 2006, 7: Article 276). The authors of that paper came to the conclusion that the process of multiple targeting in short oligonucleotide microarrays induces spurious correlations and this effect may deteriorate the inference on correlation coefficients. The design of their study and supporting simulations cast serious doubt upon the validity of this conclusion. The work by Okoniewski and Miller drove us to revisit the issue by means of experimentation with biological data and probabilistic modeling of cross-hybridization effects. Results.: We have identified two serious flaws in the study by Okoniewski and Miller: (1) The data used in their paper are not amenable to correlation analysis;(2) The proposed simulation model is inadequate for studying the effects of cross-hybridization. Using two other data sets, we have shown that removing multiply targeted probe sets does not lead to a shift in the histogram of sample correlation coefficients towards smaller values. A more realistic approach to mathematical modeling of cross-hybridization demonstrates that this process is by far more complex than the simplistic model considered by the authors. A diversity of correlation effects (such as the induction of positive or negative correlations) caused by cross-hybridization can be expected in theory but there are natural limitations on the ability to provide quantitative insights into such effects due to the fact that they are not directly observable. Conclusion.: The proposed stochastic model is instrumental in studying general regularities in hybridization interaction between probe sets in microarray data. As the problem stands now, there is no compelling reason to believe that multiple targeting causes a large-scale effect on the correlation structure of Affymetrix gene expression data. Our analysis suggests that the observed long-range correlations i

关键词： microarray data Gene Pair Correlation Structure Multiple Target Gene Expression Signal

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Inferring pairwise regulatory relationships from multiple time series datasets

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BIOINFORMATICS 2007年第6期23卷 755-763页

作者： Shi, Yanxin Mitchell, Tom Bar-Joseph, Ziv Carnegie Mellon Univ Machine Learning Dept Pittsburgh PA 15213 USA Carnegie Mellon Univ Language Technol Inst Pittsburgh PA 15213 USA Carnegie Mellon Univ Dept Comp Sci Pittsburgh PA 15213 USA Carnegie Mellon Univ Dept Sci Biol Pittsburgh PA 15213 USA

Motivation: Time series expression experiments have emerged as a popular method for studying a wide range of biological systems under a variety of conditions. One advantage of such data is the ability to infer regulatory relationships using time lag analysis. However, such analysis in a single experiment may result in many false positives due to the small number of time points and the large number of genes. Extending these methods to simultaneously analyze several time series datasets is challenging since under different experimental conditions biological systems may behave faster or slower making it hard to rely on the actual duration of the experiment. Results: We present a new computational model and an associated algorithm to address the problem of inferring time-lagged regulatory relationships from multiple time series expression experiments with varying (unknown) time-scales. Our proposed algorithm uses a set of known interacting pairs to compute a temporal transformation between every two datasets. Using this temporal transformation we search for new interacting pairs. As we show, our method achieves a much lower false-positive rate compared to previous methods that use time series expression data for pairwise regulatory relationship discovery. Some of the new predictions made by our method can be verified using other high throughput data sources and functional annotation databases.

关键词： GENE-EXPRESSION data microarray data PROFILES NETWORKS

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The discovery of transcriptional modules by a two-stage matrix decomposition approach

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BIOINFORMATICS 2007年第4期23卷 473-479页

作者： Li, Huai Sun, Yu Zhan, Ming NIA Bioinformat Unit Branch Res Resources Baltimore MD 21224 USA

Motivation: We address the problem of identifying gene transcriptional modules from gene expression data by proposing a new approach. Genes mostly interact with each other to form transcriptional modules for context-specific cellular activities or functions. Unraveling such transcriptional modules is important for understanding biological network, deciphering regulatory mechanisms and identifying biomarkers. Method: The proposed algorithm is based on two-stage matrix decomposition. We first model microarray data as non-linear mixtures and adopt the non-linear independent component analysis to reduce the non-linear distortion and separate the data into independent latent components. We then apply the probabilistic sparse matrix decomposition approach to model the 'hidden' expression profiles of genes across the independent latent components as linear weighted combinations of a small number of transcriptional regulator profiles. Finally, we propose a general scheme for identifying gene modules from the outcomes of the matrix decomposition. Results: The proposed algorithm partitions genes into non-mutually exclusive transcriptional modules, independent from expression profile similarity measurement. The modules contain genes with not only similar but different expression patterns, and show the highest enrichment of biological functions in comparison with those by other methods. The usefulness of the algorithm was validated by a yeast microarray data analysis. Availability: The software is available upon request to the authors. Contact: zhanmi@***

关键词： INDEPENDENT COMPONENT ANALYSIS GENE-EXPRESSION data BLIND SOURCESEPARATION microarray data FACTORIZATION NETWORK RECONSTRUCTION IDENTIFICATION ANNOTATION ALGORITHMS

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Unsupervised feature selection under perturbations: meeting the challenges of biological data

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BIOINFORMATICS 2007年第24期23卷 3343-3349页

作者： Varshavsky, Roy Gottlieb, Assaf Horn, David Linial, Michal Hebrew Univ Jerusalem Sch Comp Sci & Engn IL-91904 Jerusalem Israel Tel Aviv Univ Sch Phys & Astron IL-69978 Tel Aviv Israel Hebrew Univ Jerusalem Inst Life Sci Dept Biol Chem IL-91904 Jerusalem Israel

Motivation: Feature selection methods aim to reduce the complexity of data and to uncover the most relevant biological variables. In reality, information in biological datasets is often incomplete as a result of untrustworthy samples and missing values. The reliability of selection methods may therefore be questioned. Method: Information loss is incorporated into a perturbation scheme, testing which features are stable under it. This method is applied to data analysis by unsupervised feature filtering (UFF). The latter has been shown to be a very successful method in analysis of gene-expression data. Results: We find that the UFF quality degrades smoothly with information loss. It remains successful even under substantial damage. Our method allows for selection of a best imputation method on a dataset treated by UFF. More importantly, scoring features according to their stability under information loss is shown to be correlated with biological importance in cancer studies. This scoring may lead to novel biological insights.

关键词： GENE-EXPRESSION PROFILES MISSING VALUE IMPUTATION microarray data CLASSIFICATION

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