Dynamic models of gene expression and classification

Powerful new methods, like expression profiles using cDNA arrays, have been used to monitor changes in gene expression levels as a result of a variety of metabolic, xenobiotic or pathogenic challenges. This potentially vast quantity of data enables, in principle, the dissection of the complex genetic networks that control the patterns and rhythms of gene expression in the cell. Here we present a general approach to developing dynamic models for analyzing time series of whole genome expression. In this approach, a self-consistent calculation is performed that involves both linear and non-linear response terms for interrelating gene expression levels. This calculation uses singular value decomposition (SVD) not as a statistical tool but as a means of inverting noisy and near-singular matrices. The linear transition matrix that is determined from this calculation can be used to calculate the underlying network reflected in the data. This suggests a direct method of classifying genes according to their place in the resulting network. In addition to providing a means to model such a large multivariate system this approach can be used to reduce the dimensionality of the problem in a rational and consistent way, and suppress the strong noise amplification effects often encountered with expression profile data. Non-linear and higher-order Markov behavior of the network are also determined in this self-consistent method. In data sets from yeast, we calculate the Markov matrix and the gene classes based on the linear-Markov network. These results compare favorably with previously used methods like cluster analysis. Our dynamic method appears to give a broad and general framework for data analysis and modeling of gene expression arrays. Electronic Publication     

Random forests-based differential analysis of gene sets for gene expression data

In DNA microarray studies, gene-set analysis (GSA) has become the focus of gene expression data analysis. GSA utilizes the gene expression profiles of functionally related gene sets in Gene Ontology (GO) categories or priori-defined biological classes to assess the significance of gene sets associated with clinical outcomes or phenotypes. Many statistical approaches have been proposed to determine whether such functionally related gene sets express differentially (enrichment and/or deletion) in variations of phenotypes. However, little attention has been given to the discriminatory power of gene sets and classification of patients.     

数字化表达谱差异分析方法-DGE-P

目的：目前,关于数字化表达谱差异分析的方法及软件极少,且需懂得R语言等,操作繁琐,这给数字表达谱分析带来了不少困难,DGE-P软件针对数字化表达谱开发的差异分析软件。方法：DGE-P软件,利用倍数分析及数字化基因表达谱差异基因检测方法,对通过本软件标准化后的数据进行差异显著性分析。结果：DGE-P软件包含了丰度统计、数据标准化、求倍数分析和p-value值三个模块。可得出倍数分析与数字化基因表达谱差异基因检测方法（p-value）两个值。结论：DGE-P较以前的差异分析软件相比是一款针对数字化表达谱分析的软件,克服了其他软件在无重复实验数据时无法避免误差的缺陷。并且DGE-P较其他的软件相比使用方便,可在windows系统下运行,操作简单。     

Approach for defining endogenous reference genes in gene expression experiments

The quantification of gene expression by real-time polymerase chain reaction (PCR) has revolutionized the field of gene expression analysis. Due to its sensitivity and flexibility it is becoming the method of choice for many investigators. However, good normalization protocols still have to be implemented to facilitate data exchange and comparison. We have designed primers for 10 unrelated genes and developed a simple protocol to detect genes with stable expression that are suitable for use as endogenous reference genes for further use in the normalization of gene expression data obtained by real-time PCR. Using this protocol, we were able to identify human proteosome subunit Y as a reliable endogenous reference gene for human umbilical vein endothelial cells treated for up to 18 h with TNFalpha, IL-4, or IFNgamma and for B cells isolated from healthy controls and patients suffering from IgA nephropathy. Other optional endogenous reference genes that can be considered are phosphomannomutase (PPMM) and actin for endothelial cells and glyceraldehyde-3-phosphate dehydrogenase and PPMM for B cells.     

Large-scale analysis of gene expression profiles

The accumulation of DNA microarray data has now made it possible to use gene expression profiles to analyse expression data. A gene expression profile contains the expression data for a given gene over various samples, and can be contrasted with an expression signature, which contains the expression data for a single sample. Gene expression profiles are most revealing when samples are grouped appropriately, either by standard clinical or pathological categories or by categories discovered through cluster analysis techniques. Expression profiles can exist at various levels of abstraction, yielding information across various tissues or across diseases within a particular tissue. Hypothesis tests may be applied to expression profiles on a large scale to identify candidate genes of interest.     

The clustering of regression models method with applications in gene expression data

Identification of differentially expressed genes and clustering of genes are two important and complementary objectives addressed with gene expression data. For the differential expression question, many "per-gene" analytic methods have been proposed. These methods can generally be characterized as using a regression function to independently model the observations for each gene; various adjustments for multiplicity are then used to interpret the statistical significance of these per-gene regression models over the collection of genes analyzed. Motivated by this common structure of per-gene models, we proposed a new model-based clustering method--the clustering of regression models method, which groups genes that share a similar relationship to the covariate(s). This method provides a unified approach for a family of clustering procedures and can be applied for data collected with various experimental designs. In addition, when combined with per-gene methods for assessing differential expression that employ the same regression modeling structure, an integrated framework for the analysis of microarray data is obtained. The proposed methodology was applied to two microarray data sets, one from a breast cancer study and the other from a yeast cell cycle study.     

Application of the S-score algorithm for analysis of oligonucleotide microarrays

In the past several years, oligonucleotide microarrays have emerged as a widely used tool for the simultaneous, non-biased measurement of expression levels for thousands of genes. Several challenges exist in successfully utilizing this biotechnology; principal among these is analysis of microarray data. An experiment to measure differential gene expression can consist of a dozen microarrays, each consisting of over a hundred thousand data points. Previously, we have described the use of a novel algorithm for analyzing oligonucleotide microarrays and assessing changes in gene expression [J. Mol. Biol. 317 (2002) 225]. This algorithm describes changes in expression in terms of the statistical significance (S-score) of change, which combines signals detected by multiple probe pairs according to an error model characteristic of oligonucleotide arrays. Software is available that simplifies the use of the application of this algorithm so that it may be applied to improving the analysis of oligonucleotide microarray data. The application of this method to problems of the central nervous system is discussed.     

GenOrigin: A comprehensive protein-coding gene origination database on the evolutionary timescale of life

The origination of new genes contributes to the biological diversity of life. New genes may quickly build their network, exert important functions, and generate novel phenotypes. Dating gene age and inferring the origination mechanisms of new genes, like primate-specific genes, is the basis for the functional study of the genes. However, no comprehensive resource of gene age estimates across species is available. Here, we systematically date the age of 9,102,113 protein-coding genes from 565 species in the Ensembl and Ensembl Genomes databases, including 82 bacteria, 57 protists, 134 fungi, 58 plants, 56 metazoa, and 178 vertebrates, using a protein-family-based pipeline with Wagner parsimony algorithm. We also collect gene age estimate data from other studies and uniformly distribute the gene age estimates to time ranges in a million years for comparison across studies. All the data are cataloged into GenOrigin (http://genorigin.chenzxlab.cn/), a user-friendly new database of gene age estimates, where users can browse gene age estimates by species, age, and gene ontology. In GenOrigin, the information such as gene age estimates, annotation, gene ontology, ortholog, and paralog, as well as detailed gene presence/absence views for gene age inference based on the species tree with evolutionary timescale, is provided to researchers for exploring gene functions.     

Characterizing the role of mechanical signals in gene regulatory networks using Long SAGE

A systems understanding of mechanical regulation is critical for determining how cells proliferate and differentiate. To better understand the biological process in which mechanical signals regulate cells, we globally investigated the gene expression profiling via long serial analysis of gene expression (Long SAGE) in osteoblasts after exposure to mechanical stretching. The analysis showed that the differentially expressed genes were related with many physiological processes, including signal transduction, cell proliferation and apoptosis. Several genes that were seldom or never studied in osteoblasts have been found in this study. We further analyzed the signal pathways and provided gene regulatory networks activated by mechanical signals. Many changed genes in our data were contributed to ECM-integrin-FAK mediated pathway and mainly influenced actin-cytoskeleton dynamic remodeling, cell proliferation and differentiation. We also provided evidence supporting the hypothesis that endoplasmic reticulum and mitochondrion were combined to dedicate to calcium regulation. Taken together, our experiments provided a systemic view on biological processes and mechanotransduction network in osteoblasts, suggesting that mechanical signals regulate osteoblast through a greater diversity of interactions and pathways than previously appreciated.     

Cowpea mosaic virus nanoscaffold as signal enhancement for DNA microarrays

Previous studies have shown that a functionalized viral nanoparticle can be used as a fluorescent signal-generating element and enhance detection sensitivity for immunoassays and low density microarrays. In this study, we further tested this ability in commercial DNA microarrays, including Affymetrix high density resequencing microarray. Optimum conditions for NeutrAvidin and dye coupling to a double-cysteine mutant of cowpea mosaic virus (CPMV) were found to be comparable to the commonly used streptavidin-phycoerythrin (SAPE) for high density resequencing microarray. A 3-fold signal enhancement in comparison to Cy5-dCTP controls was obtained when using nanoparticles on control scorecard expression microarrays. Hybridization results from commercially available 8000 rat expression arrays indicate an increment of 14% on the detected features when the virus complex was used as the staining reagent in comparison to Cy5-dCTP controls. The current work shows the utility of the CPMV-dye nanoparticles as a detection reagent in well-established detection platforms.     

Prediction of non-genotoxic carcinogenesis in rats using changes in gene expression following acute dosing

Non-genotoxic carcinogenicity of chemicals is currently routinely evaluated in 2-year rodent bioassays. Therefore, the development of early biomarkers for non-genotoxic carcinogenesis would result in substantial savings in time and expense. The current study investigates whether early changes in gene expression may be developed as markers for cancer. Animals were treated for 1 or 5 days with either non-genotoxic carcinogens (NGTCs) or non-carcinogens and gene expression was analyzed by quantitative PCR (qPCR). We tested two gene signatures previously reported to detect non-genotoxic carcinogens. Using one gene signature it was confirmed that 3/3 non-genotoxic carcinogens and 2/2 non-carcinogens are correctly identified with data from 1 or 5 days of dosing. In contrast an alternative signature correctly identified 0/3 and 2/3 non-genotoxic carcinogens at 1 and 5 days of treatment, respectively and 2/2 non-carcinogens at both time-points. Additionally, we evaluated a novel panel of putative biomarker genes, from the literature, many of which have roles in cell growth and division, including myc, cdc2 and mcm6. These genes were significantly induced by non-genotoxic carcinogens and not by non-carcinogens. Using the average fold-induction across this panel, 2/3 non-genotoxic carcinogens were detected at both 1 and 5 days. These data support the idea that acute changes in gene expression may provide biomarkers for non-genotoxic carcinogenesis but also highlight interesting differences in the sensitivities of distinct gene signatures.