PQN and DQN: algorithms for expression microarrays

An ideal expression algorithm should be able to tell truly different expression levels with small false positive errors and be robust to assay changes. We propose two algorithms. PQN is the non-central trimmed mean of perfect match intensities with quantile normalization. DQN is the non-central trimmed mean of differences between perfect match and mismatch intensities with quantile normalization. The quantiles for normalization can be either empirical or theoretical. When array types and/or assay change in a study, the normalization to common quantiles at the probe set level is essential. We compared DQN, PQN, RMA, GCRMA, DCHIP, PLIER and MAS5 for the Affymetrix Latin square data and our data of two sets of experiments using the same bone marrow but different types of microarrays and different assay. We found the computation for AUC of ROC at affycomp.biostat.jhsph.edu can be improved.     

cRNA target preparation for microarrays: comparison of gene expression profiles generated with different amplification procedures

Microarray technology has become a standard tool for generation of gene expression profiles to explore human disease processes. Being able to start from minute amounts of RNA extends the fields of application to core needle biopsies, laser capture microdissected cells, and flow-sorted cells. Several RNA amplification methods have been developed, but no extensive comparability and concordance studies of gene expression profiles are available. Different amplification methods may produce differences in gene expression patterns. Therefore, we compared profiles processed by a standard microarray protocol with three different types of RNA amplification: (i) two rounds of linear target amplification, (ii) random amplification, and (iii) amplification based on a template switching mechanism. The latter two methods accomplish target amplification in a nonlinear way using PCR technology. Starting from as little as 50 ng of total RNA, the yield of labeled cRNA was sufficient for hybridization to Affymetrix HG-U133A GeneChip array using the respective methods. Replicate experiments were highly reproducible for each method. In comparison with the standard protocol, all three approaches are less sensitive and introduced a minor but clearly detectable bias of the detection call. In conclusion, the three amplification protocols used are applicable for GeneChip analysis of small tissue samples.     

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     

Microarray gene expression analysis free of reverse transcription and dye labeling

A new microarray system has been developed for gene expression analysis using cationic gold nanoparticles with diameters of 250 nm as a target detection reagent. The approach utilizes nonlabeled target molecules hybridizing with complementary probes on the array, followed by incubation in a colloidal gold solution. The hybridization signal results from the precipitation of nanogold particles on the hybridized spots due to the electrostatic attraction of the cationic gold particles and the anionic phosphate groups in the target DNA backbone. In contrast to conventional fluorescent detection, this nanoparticle-based detection system eliminates the target labeling procedure. The visualization of hybridization signals can be accomplished with a flatbed scanner instead of a confocal laser scanner, which greatly simplifies the process and reduces the cost. The sensitivity is estimated to be less than 2 pg of DNA molecules captured on the array surface. The signal from hybridized spots quantitatively represents the amount of captured target DNA and therefore permits quantitative gene expression analysis. Cross-array reproducibility is adequate for detecting twofold or less signal changes across two microarray experiments.     

RNA expression analysis from formalin fixed paraffin embedded tissues

Formalin fixation and paraffin embedding (FFPE) is the most commonly used method worldwide for tissue storage. This method preserves the tissue integrity but causes extensive damage to nucleic acids stored within the tissue. As methods for measuring gene expression such as RT-PCR and microarray are adopted into clinical practice there is an increasing necessity to access the wealth of information locked in the Formalin fixation and paraffin embedding archives. This paper reviews the progress in this field and discusses the unique opportunities that exist for the application of these techniques in the development of personalized medicine.     

Outlier sums for differential gene expression analysis

We propose a method for detecting genes that, in a disease group, exhibit unusually high gene expression in some but not all samples. This can be particularly useful in cancer studies, where mutations that can amplify or turn off gene expression often occur in only a minority of samples. In real and simulated examples, the new method often exhibits lower false discovery rates than simple t-statistic thresholding. We also compare our approach to the recent cancer profile outlier analysis proposal of Tomlins and others (2005).     

Gene expression between a congenic strain that contains a quantitative trait locus of high bone density from CAST/EiJ and its wild-type strain C57BL/6J

Peak bone density is an important determining factor of future osteoporosis risk. We previously identified a quantitative trait locus (QTL) that contributes significantly to high bone density on mouse chromosome 1 from a cross between C57BL/6J (B6) and CAST/EiJ (CAST) mouse strains. We then generated a congenic strain, B6.CAST-1T, in which the chromosomal fragment containing this QTL had been transferred from CAST to the B6 background. The congenic mice have a significantly higher bone density than the B6 mice. In this study we performed cDNA microarray analysis to evaluate the gene expression profile that might yield insights into the mechanisms controlling the high bone density by this QTL. This study led to several interesting observations. First, approximately 60% of 8,734 gene accessions on GEM I chips were expressed in the femur of B6 mice. The expression and function of two-thirds of these expressed genes and ESTs have not been documented previously. Second, expression levels of genes related to bone formation were lower in congenic than in B6 mice. These data are consistent with a low bone formation in the congenic mice, a possibility that is confirmed by reduced skeletal alkaline phosphatase activity in serum compared with B6 mice. Third, expression levels of genes that might have negative regulatory action on bone resorption were higher in congenic than in B6 mice. Together these findings suggest that the congenic mice might have a lower bone turnover rate than B6 mice and raise the possibility that the high bone density in the congenic mice could be due to reduced bone resorption rather than increased bone formation. Electronic Publication     

Technological advances and genomics in metazoan parasites

Molecular biology has provided the means to identify parasite proteins, to define their function, patterns of expression and the means to produce them in quantity for subsequent functional analyses. Whole genome and expressed sequence tag programmes, and the parallel development of powerful bioinformatics tools, allow the execution of genome-wide between stage or species comparisons and meaningful gene-expression profiling. The latter can be undertaken with several new technologies such as DNA microarray and serial analysis of gene expression. Proteome analysis has come to the fore in recent years providing a crucial link between the gene and its protein product. RNA interference and ballistic gene transfer are exciting developments which can provide the means to precisely define the function of individual genes and, of importance in devising novel parasite control strategies, the effect that gene knockdown will have on parasite survival.     

RNA原位杂交技术及其在植物基因表达研究中的应用

原位杂交 ( In situ Hybridization)是一种在细胞水平上研究基因表达调控的最直接有效的分子生物学技术。这一技术最初应用于动物染色体上的基因物理定位 〔1〕和特定 m RNA在组织中的空间定位〔2〕,后来又作为诊断工具检测感染病毒的细胞 〔3〕。到 80年代后期 ,原位杂交技术开始应用于植物基因表达调控的研究 〔4～ 6〕。植物基因的时空表达研究是探讨植物生长发育机制的重要手段。由于 RNA原位杂交技术能够精确确定基因表达的时空分布 ,而得到了越来越广泛的应用 ;从营养器官生长发育〔7～ 9〕、生殖器官生长发育〔10～ 13〕、自交不…     

Time-synchronized clustering of gene expression trajectories

Current clustering methods are routinely applied to gene expressiontime course data to find genes with similar activation patternsand ultimately to understand the dynamics of biological processes.As the dynamic unfolding of a biological process often involvesthe activation of genes at different rates, successful clusteringin this context requires dealing with varying time and shapepatterns simultaneously. This motivates the combination of anovel pairwise warping with a suitable clustering method todiscover expression shape clusters. We develop a novel clusteringmethod that combines an initial pairwise curve alignment toadjust for time variation within likely clusters. The cluster-specifictime synchronization method shows excellent performance overstandard clustering methods in terms of cluster quality measuresin simulations and for yeast and human fibroblast data sets.In the yeast example, the discovered clusters have high concordancewith the known biological processes.     

DNA array profiling of gene expression changes during maize embryo development

We are using DNA microarray-based gene expression profiling to classify temporal patterns of gene expression during the development of maize embryos, to understand mRNA-level control of embryogenesis and to dissect metabolic pathways and their interactions in the maize embryo. Genes involved in carbohydrate, fatty acid, and amino acid metabolism, the tricarboxylic acid (TCA) cycle, glycolysis, the pentose phosphate pathway, embryogenesis, membrane transport, signal transduction, cofactor biosynthesis, photosynthesis, oxidative phosphorylation and electron transfer, as well as 600 random complementary DNA (cDNA) clones from maize embryos, were arrayed on glass slides. DNA arrays were hybridized with fluorescent dye-labeled cDNA probes synthesized from kernel and embryo poly(A)⁺RNA from different stages of maize seed development. Several characteristic developmental patterns of expression were identified and correlated with gene function. Patterns of coordinated gene expression in the TCA cycle and glycolysis were analyzed in detail. The steady state level of poly(A)⁺ RNA for many genes varies dramatically during maize embryo development. Expression patterns of genes coding for enzymes of fatty acid biosynthesis and glycolysis are coordinately regulated during development. Genes of unknown function may by assigned a hypothetical role based on their patterns of expression resembling well characterized genes. Electronic supplementary material to this paper can be obtained by using the Springer LINK server located at http://dx.doi.org/10.1007/s10142-002-0046-6. Electronic Publication     

基因芯片在衣原体研究中的应用

基因芯片又称为DNA微阵列,是指将大量核酸片段以预先设计的方式固定在载体上组成密集分子阵列,与荧光素或其他方式标记的样品进行杂交,通过检测杂交信号的强弱来判断样品中有无靶分子以及对靶分子进行定量,是一种研究生物大分子功能的新技术。在衣原体研究方面,基因芯片主要应用于衣原体的检测与分型、感染机制的研究、特定基因作用分析、毒力及耐药基因的筛选等。