The impact of quantitative optimization of hybridization conditions on gene expression analysis

Background  

With the growing availability of entire genome sequences, an increasing number of scientists can exploit oligonucleotide microarrays for genome-scale expression studies. While probe-design is a major research area, relatively little work has been reported on the optimization of microarray protocols.     

Sample size for gene expression microarray experiments

MOTIVATION: Microarray experiments often involve hundreds or thousands of genes. In a typical experiment, only a fraction of genes are expected to be differentially expressed; in addition, the measured intensities among different genes may be correlated. Depending on the experimental objectives, sample size calculations can be based on one of the three specified measures: sensitivity, true discovery and accuracy rates. The sample size problem is formulated as: the number of arrays needed in order to achieve the desired fraction of the specified measure at the desired family-wise power at the given type I error and (standardized) effect size. RESULTS: We present a general approach for estimating sample size under independent and equally correlated models using binomial and beta-binomial models, respectively. The sample sizes needed for a two-sample z-test are computed; the computed theoretical numbers agree well with the Monte Carlo simulation results. But, under more general correlation structures, the beta-binomial model can underestimate the needed samples by about 1-5 arrays. CONTACT: jchen@nctr.fda.gov.     

Fundamentals of DNA hybridization arrays for gene expression analysis

DNA hybridization arrays [also known as macroarrays, microarrays and/or high-density oligonucleotide arrays (Gene Chips)] bring gene expression analysis to a genomic scale by permitting investigators to simultaneously examine changes in the expression of literally thousands of genes. For hybridization arrays, the general approach is to immobilize gene-specific sequences (probes) on a solid state matrix (nylon membranes, glass microscope slides, silicon/ceramic chips). These sequences are then queried with labeled copies of nucleic acids from biological samples (targets). The underlying theory is that the greater the expression of a gene, the greater the amount of labeled target, and hence, the greater output signal. In spite of the simplicity of the experimental design, there are at least four different platforms and several different approaches to processing and labeling the biological samples. Moreover, investigators must also determine whether they will utilize commercially available arrays or generate their own. This review will cover the status of the hybridization array field with an eye toward underlying principles and available technologies. Future developments and technological trends will also be evaluated.     

Mixture models for gene expression experiments with two species

Cross-species research in drug development is novel and challenging. A bivariate mixture model utilizing information across two species was proposed to solve the fundamental problem of identifying differentially expressed genes in microarray experiments in order to potentially improve the understanding of translation between preclinical and clinical studies for drug development. The proposed approach models the joint distribution of treatment effects estimated from independent linear models. The mixture model posits up to nine components, four of which include groups in which genes are differentially expressed in both species. A comprehensive simulation to evaluate the model performance and one application on a real world data set, a mouse and human type II diabetes experiment, suggest that the proposed model, though highly structured, can handle various configurations of differential gene expression and is practically useful on identifying differentially expressed genes, especially when the magnitude of differential expression due to different treatment intervention is weak. In the mouse and human application, the proposed mixture model was able to eliminate unimportant genes and identify a list of genes that were differentially expressed in both species and could be potential gene targets for drug development.     

Modeling hybridization kinetics

Formation of complementary base pairs between nucleic acids over a short region (相似文献   

Computational strategies for analyzing data in gene expression microarray experiments

Microarray analysis has become a widely used method for generating gene expression data on a genomic scale. Microarrays have been enthusiastically applied in many fields of biological research, even though several open questions remain about the analysis of such data. A wide range of approaches are available for computational analysis, but no general consensus exists as to standard for microarray data analysis protocol. Consequently, the choice of data analysis technique is a crucial element depending both on the data and on the goals of the experiment. Therefore, basic understanding of bioinformatics is required for optimal experimental design and meaningful interpretation of the results. This review summarizes some of the common themes in DNA microarray data analysis, including data normalization and detection of differential expression. Algorithms are demonstrated by analyzing cDNA microarray data from an experiment monitoring gene expression in T helper cells. Several computational biology strategies, along with their relative merits, are overviewed and potential areas for additional research discussed. The goal of the review is to provide a computational framework for applying and evaluating such bioinformatics strategies. Solid knowledge of microarray informatics contributes to the implementation of more efficient computational protocols for the given data obtained through microarray experiments.     

A computer program for modeling the kinetics of gene expression

Enzyme induction may be modeled on the basis of four, quantifiable processes that control the rates at which specific gene products accumulate and decay. These processes include synthesis of functional mRNA, translation and degradation of mRNA, and degradation of the protein product. We present a simple computer program that permits mathematical simulation of gene expression on the basis of experimentally determined rates of synthesis and degradation. The program was implemented as a spreadsheet using Microsoft Excel for Macintosh and MS-DOS operating systems and also was adapted for HyperCard on the Macintosh. It contains a formula to account for growth of tissue or cell populations. The program predicts amounts of individual mRNAs and proteins (or enzyme activities) in cells as a function of time after a stimulus alters their rates of synthesis or degradation.     

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.     

Subtractive hybridization for differential gene expression in mechanically unloaded rat heart

The objective of this study was to identify differentially expressed genes in the mechanically unloaded rat heart by suppression subtractive hybridization. In male Wistar-Kyoto rats, mechanical unloading was achieved by infrarenal heterotopic heart transplantation. Differentially expressed genes were investigated systematically by suppression subtractive hybridization. Selected targets were validated by Northern blot analysis, real-time RT-PCR, and immunoblot analysis. Maximal ADP-stimulated oxygen consumption (state 3) was measured in isolated mitochondria. Transplantation caused atrophy (heart-to-body weight ratio: 1.6 +/- 0.1 vs. 2.4 +/- 0.1, P < 0.001). We selected 1,880 clones from the subtractive hybridization procedure (940 forward and 940 reverse runs assessing up- or downregulation). The first screen verified 465 forward and 140 reverse clones, and the second screen verified 67 forward and 30 reverse clones. On sequencing of 24 forward and 23 reverse clones, 9 forward and 14 reverse homologies to known genes were found. Specifically, we identified reduced mRNA expression of complex I (-49%, P < 0.05) and complex II (-61%, P < 0.001) of the respiratory chain. Significant reductions were also observed on the respiratory chain protein level: -42% for complex I (P < 0.01), -57% for complex II (P < 0.05), and -65% for complex IV (P < 0.05). Consistent with changes in gene and protein expression, state 3 respiration was significantly decreased in isolated mitochondria of atrophied hearts, with glutamate and succinate as substrates: 85 +/- 27 vs. 224 +/- 32 natoms O.min(-1).mg(-1) with glutamate (P < 0.01) and 59 +/- 18 vs. 154 +/- 30 natoms O.min(-1).mg(-1) with succinate (P < 0.05). Subtractive hybridization indicates major changes in overall gene expression by mechanical unloading and specifically identified downregulation of respiratory chain genes. This observation is functionally relevant and provides a mechanism for the regulation of respiratory capacity in response to chronic mechanical unloading.     

Automated image analysis for array hybridization experiments.

MOTIVATION: Image analysis is a major part of data evaluation for array hybridization experiments in molecular biology. The program presented here is designed to analyze automatically images from hybridization experiments with various arrangements: different kinds of probes (oligonucleotides or complex probes), different supports (nylon filters or glass slides), different labeling of probes (radioactively or fluorescently). The program is currently applied to oligonucleotide fingerprinting projects and complex hybridizations. The only precondition for the use of the program is that the targets are arrayed in a grid, which can be approximately transformed to an orthogonal equidistant grid by a projective mapping. RESULTS: We demonstrate that our program can cope with the following problems: global distortion of the grid, missing of grid nodes, local deviation of the spot from its specified grid position. This is checked by different quality measures. The image analysis of oligonucleotide fingerprint experiments on an entire genetic library is used, in clustering procedures, to group related clones together. The results show that the program yields automatically generated high quality input data for follow up analysis such as clustering procedures. AVAILABILITY: The executable files will be available upon request for academics.     

Wide hybridization experiments in cereals

Summary Wide hybridization is a useful tool in plant breeding, but little is known about its possible range. For the cereals, wheat, barley and rye, this was tested with 15 different species of the Poaceae and Panicoideae. Embryo formation could be obtained with Agropyron repens, Alopecurus agrestis, Dactylis glomerata, Festuca glauca, Hordeum bulbosum, Lolium perenne, Pennisetum americanum, and Zea mays. As well, haploid as diploid embryos occurred. New embryo culture techniques should enable these embryos to grow to plants.     

Probe signal correction for differential methylation hybridization experiments

Background  

Non-biological signal (or noise) has been the bane of microarray analysis. Hybridization effects related to probe-sequence composition and DNA dye-probe interactions have been observed in differential methylation hybridization (DMH) microarray experiments as well as other effects inherent to the DMH protocol.     

SeqExpress: desktop analysis and visualization tool for gene expression experiments

SUMMARY: SeqExpress is a stand-alone desktop application for the identification of relevant genes within collections of microarray or SAGE experiments. A number of analysis, filtering and visualization tools are provided to aid in the selection of groups of genes. If R is installed then the application can use this to provide further analysis. AVAILABILITY: SeqExpress is available at: http://www.seqexpress.com     

Design and analysis of two-phase experiments for gene expression microarrays-part I.

Gene expression microarray experiments are intrinsically two-phase experiments. Messenger RNA (mRNA), required for the microarray experiment, must first be derived from plants or animals that are exposed to a set of treatments in a previous experiment (Phase 1). The mRNA is then used in the subsequent laboratory-based microarray experiment (Phase 2) from which gene expression is measured and ultimately analyzed. We show that obtaining a valid test for the effects of treatments on gene expression depends on the design of both the Phase 1 and Phase 2 experiments. Examples show that the multiple dye-swap design at Phase 2 is more robust than the alternating loop design in the absence of prior knowledge of the relative size of variation in the Phase 1 and Phase 2 experiments.