Bayesian Hierarchical Model for Estimating Gene Expression Intensity Using Multiple Scanned Microarrays

We propose a method for improving the quality of signal from DNA microarrays by using several scans at varying scanner sen-sitivities. A Bayesian latent intensity model is introduced for the analysis of such data. The method improves the accuracy at which expressions can be measured in all ranges and extends the dynamic range of measured gene expression at the high end. Our method is generic and can be applied to data from any organism, for imaging with any scanner that allows varying the laser power, and for extraction with any image analysis software. Results from a self-self hybridization data set illustrate an improved precision in the estimation of the expression of genes compared to what can be achieved by applying standard methods and using only a single scan.     

Response to comments on thermal polypeptides

Summary The above authors claim to have examined critically the thermal polycondensation of amino acids –as a possible prebiotic path of chemical evolution of life–. Some of the flaws in their premises and interpretations are discussed here.     

Response to Pearl's comments on principal stratification

Dr. Pearl invites researchers to justify their use of principal stratification. This comment explains how the use of principal stratification simplified a complex mediational problem encountered when evaluating a smoking cessation intervention's effect on reducing smoking withdrawal symptoms.     

Gene Expression Analysis Suggests Temporal Differential Response to Aluminum in Coffea arabica Cultivars

Aluminum (Al) is a limiting factor of crop yields on acidic soils. Ion aluminum (Al³⁺) acts primarily in plant root system retarding its growth and development, leading to the reduction of lateral roots number, and consequently the decrease of vegetal production. Most of coffee producing areas are located in acidic soils, which have Al³⁺ contents enough to damage plant development. Despite the advances in the understanding of physiological and genetic mechanisms of Al tolerance/susceptibility, few are known about Al ion action in coffee plants. This report describes the expression analysis of genes related to aluminum stress in germinating seeds of two cultivars of C. arabica (Catuaí Amarelo IAC 62 and Icatu Vermelho IAC 4045) when challenged with Al³⁺. In silico analyses of Brazilian Coffee Genome Project (BCGP) database were used to select genes previously found to be related with Al-stress. The expression profile of these genes in Catuaí and Icatu was evaluated through Quantitative PCR (qPCR). Based on our data, we suggest that both analyzed cultivars displays mechanisms of resistance or exclusion, which occurs outside the cell excluding Al³⁺ assimilation, and mechanisms of tolerance that occurs inside the cell after Al³⁺ absorption. The major difference is the timing of activation of each mechanism. While Catuaí tends to use resistance mechanisms in early stages of stress, Icatu uses tolerance strategies. In late stages, both cultivars seem to display tolerance mechanisms, but Icatu also displays Al-exclusion strategy.     

A Method for Multiplex Gene Synthesis Employing Error Correction Based on Expression

Our ability to engineer organisms with new biosynthetic pathways and genetic circuits is limited by the availability of protein characterization data and the cost of synthetic DNA. With new tools for reading and writing DNA, there are opportunities for scalable assays that more efficiently and cost effectively mine for biochemical protein characteristics. To that end, we have developed the Multiplex Library Synthesis and Expression Correction (MuLSEC) method for rapid assembly, error correction, and expression characterization of many genes as a pooled library. This methodology enables gene synthesis from microarray-synthesized oligonucleotide pools with a one-pot technique, eliminating the need for robotic liquid handling. Post assembly, the gene library is subjected to an ampicillin based quality control selection, which serves as both an error correction step and a selection for proteins that are properly expressed and folded in E. coli. Next generation sequencing of post selection DNA enables quantitative analysis of gene expression characteristics. We demonstrate the feasibility of this approach by building and testing over 90 genes for empirical evidence of soluble expression. This technique reduces the problem of part characterization to multiplex oligonucleotide synthesis and deep sequencing, two technologies under extensive development with projected cost reduction.     

青枯菌诱导的花生基因表达谱SSH分析

以抗青枯病花生种质‘J4’和‘中花6号’、感青枯病花生品种‘中花12号’为材料,用强产青枯菌毒菌株(Ralstonia solanacearum)对其根系分别接种,采用抑制差减杂交(SSH)技术检测花生根系应答侵染的基因表达谱变化,并对文库中差异基因进行Real-time PCR分析。结果表明:经菌液PCR检测对挑选出的1 036阳性克隆片段进行测序及片段整合分析,获得162条花生基因,有功能注释的基因58条,其中44条基因参与了细胞结构(6%)、信号转导(12%)、抗病防御(5%)、转录调控(12%)等生理过程。用Real-time PCR技术对7个基因在‘中花6号’和‘中花12号’中的表达模式分析结果表明,6个基因在青枯菌侵染早期在抗病材料‘中花6号’中呈上调表达,可能与青枯病抗性直接相关。     

Bayesian Hierarchical Clustering for Studying Cancer Gene Expression Data with Unknown Statistics

Clustering analysis is an important tool in studying gene expression data. The Bayesian hierarchical clustering (BHC) algorithm can automatically infer the number of clusters and uses Bayesian model selection to improve clustering quality. In this paper, we present an extension of the BHC algorithm. Our Gaussian BHC (GBHC) algorithm represents data as a mixture of Gaussian distributions. It uses normal-gamma distribution as a conjugate prior on the mean and precision of each of the Gaussian components. We tested GBHC over 11 cancer and 3 synthetic datasets. The results on cancer datasets show that in sample clustering, GBHC on average produces a clustering partition that is more concordant with the ground truth than those obtained from other commonly used algorithms. Furthermore, GBHC frequently infers the number of clusters that is often close to the ground truth. In gene clustering, GBHC also produces a clustering partition that is more biologically plausible than several other state-of-the-art methods. This suggests GBHC as an alternative tool for studying gene expression data.The implementation of GBHC is available at https://sites.google.com/site/gaussianbhc/     

Gene Expression Characteristics in Response to Abscisic Acid Under Shade

Plant Molecular Biology Reporter - Plant stress hormone ABA (abscisic acid) is induced by unfavorable environmental conditions such as drought, salt, oxidative, and cold stresses and leads to a...     

Gene Expression Analysis

The response of cells to extracellular signals usually requires altered expression of many genes, possibly including several distinct metabolic pathways. In some cases, only a subset of genes involved in such responses are known, which requires techniques to analyze changes in the expression of multiple genes, both known and unknown. Three techniques, two‐dimensional gel electrophoresis, differential display, and gene discovery arrays, provide opportunities for measuring changes in gene expression levels, as well as for identifying novel gene products.     

Response to the Richardson and Qian comments

Wetlands Ecology and Management -     

Time-Course Gene Set Analysis for Longitudinal Gene Expression Data

Gene set analysis methods, which consider predefined groups of genes in the analysis of genomic data, have been successfully applied for analyzing gene expression data in cross-sectional studies. The time-course gene set analysis (TcGSA) introduced here is an extension of gene set analysis to longitudinal data. The proposed method relies on random effects modeling with maximum likelihood estimates. It allows to use all available repeated measurements while dealing with unbalanced data due to missing at random (MAR) measurements. TcGSA is a hypothesis driven method that identifies a priori defined gene sets with significant expression variations over time, taking into account the potential heterogeneity of expression within gene sets. When biological conditions are compared, the method indicates if the time patterns of gene sets significantly differ according to these conditions. The interest of the method is illustrated by its application to two real life datasets: an HIV therapeutic vaccine trial (DALIA-1 trial), and data from a recent study on influenza and pneumococcal vaccines. In the DALIA-1 trial TcGSA revealed a significant change in gene expression over time within 69 gene sets during vaccination, while a standard univariate individual gene analysis corrected for multiple testing as well as a standard a Gene Set Enrichment Analysis (GSEA) for time series both failed to detect any significant pattern change over time. When applied to the second illustrative data set, TcGSA allowed the identification of 4 gene sets finally found to be linked with the influenza vaccine too although they were found to be associated to the pneumococcal vaccine only in previous analyses. In our simulation study TcGSA exhibits good statistical properties, and an increased power compared to other approaches for analyzing time-course expression patterns of gene sets. The method is made available for the community through an R package.