Large scale comparison of global gene expression patterns in human and mouse

Background  

It is widely accepted that orthologous genes between species are conserved at the sequence level and perform similar functions in different organisms. However, the level of conservation of gene expression patterns of the orthologous genes in different species has been unclear. To address the issue, we compared gene expression of orthologous genes based on 2,557 human and 1,267 mouse samples with high quality gene expression data, selected from experiments stored in the public microarray repository ArrayExpress.     

MAID : An effect size based model for microarray data integration across laboratories and platforms

Background  

Gene expression profiling has the potential to unravel molecular mechanisms behind gene regulation and identify gene targets for therapeutic interventions. As microarray technology matures, the number of microarray studies has increased, resulting in many different datasets available for any given disease. The increase in sensitivity and reliability of measurements of gene expression changes can be improved through a systematic integration of different microarray datasets that address the same or similar biological questions.     

Determination of reference genes for circadian studies in different tissues and mouse strains

Background  

Circadian rhythms have a profound effect on human health. Their disruption can lead to serious pathologies, such as cancer and obesity. Gene expression studies in these pathologies are often studied in different mouse strains by quantitative real time polymerase chain reaction (qPCR). Selection of reference genes is a crucial step of qPCR experiments. Recent studies show that reference gene stability can vary between species and tissues, but none has taken circadian experiments into consideration.     

Selection of reference genes for gene expression studies in human neutrophils by real-time PCR

Background  

Reference genes, which are often referred to housekeeping genes, are frequently used to normalize mRNA levels between different samples. However the expression level of these genes may vary among tissues or cells, and may change under certain circumstances. Thus the selection of reference gene(s) is critical for gene expression studies. For this purpose, 10 commonly used housekeeping genes were investigated in isolated human neutrophils.     

Detection of allelic variations of human gene expression by polymerase colonies

Background  

Quantification of variations of human gene expression is complicated by the small differences between different alleles. Recent work has shown that variations do exist in the relative allelic expression levels in certain genes of heterozygous individuals. Herein, we describe the application of an immobilized polymerase chain reaction technique as an alternative approach to measure relative allelic differential expression.     

Microarray data integration for genome-wide analysis of human tissue-selective gene expression

Background

Microarray gene expression data are accumulating in public databases. The expression profiles contain valuable information for understanding human gene expression patterns. However, the effective use of public microarray data requires integrating the expression profiles from heterogeneous sources.

Results

In this study, we have compiled a compendium of microarray expression profiles of various human tissue samples. The microarray raw data generated in different research laboratories have been obtained and combined into a single dataset after data normalization and transformation. To demonstrate the usefulness of the integrated microarray data for studying human gene expression patterns, we have analyzed the dataset to identify potential tissue-selective genes. A new method has been proposed for genome-wide identification of tissue-selective gene targets using both microarray intensity values and detection calls. The candidate genes for brain, liver and testis-selective expression have been examined, and the results suggest that our approach can select some interesting gene targets for further experimental studies.

Conclusion

A computational approach has been developed in this study for combining microarray expression profiles from heterogeneous sources. The integrated microarray data can be used to investigate tissue-selective expression patterns of human genes.

     

Feature extraction and signal processing for nylon DNA microarrays

Background  

High-density DNA microarrays require automatic feature extraction methodologies and softwares. These can be a potential source of non-reproducibility of gene expression measurements. Variation in feature location or in signal integration methodology may be a significant contribution to the observed variance in gene expression levels.     

Integrating gene expression and GO classification for PCA by preclustering

Background  

Gene expression data can be analyzed by summarizing groups of individual gene expression profiles based on GO annotation information. The mean expression profile per group can then be used to identify interesting GO categories in relation to the experimental settings. However, the expression profiles present in GO classes are often heterogeneous, i.e., there are several different expression profiles within one class. As a result, important experimental findings can be obscured because the summarizing profile does not seem to be of interest. We propose to tackle this problem by finding homogeneous subclasses within GO categories: preclustering.     

Effect of experimental treatment on GAPDH mRNA expression as a housekeeping gene in human diploid fibroblasts

Background  

Several genes have been used as housekeeping genes and choosing an appropriate reference gene is important for accurate quantitative RNA expression in real time RT-PCR technique. The expression levels of reference genes should remain constant between the cells of different tissues and under different experimental conditions. The purpose of this study was to determine the effect of different experimental treatments on the expression of glyceraldehyde 3-phosphate dehydrogenase (GAPDH) mRNA so that the reliability of GAPDH as reference gene for quantitative real time RT-PCR in human diploid fibroblasts (HDFs) can be validated. HDFs in 4 different treatment groups viz; young (passage 4), senescent (passage 30), H₂O₂-induced oxidative stress and γ-tocotrienol (GTT)-treated groups were harvested for total RNA extraction. Total RNA concentration and purity were determined prior to GAPDH mRNA quantification. Standard curve of GAPDH expression in serial diluted total RNA, melting curve analysis and agarose gel electrophoresis were used to determine the reliability of GAPDH as reference gene.     

Ultra-high throughput sequencing-based small RNA discovery and discrete statistical biomarker analysis in a collection of cervical tumours and matched controls

Background  

Ultra-high throughput sequencing technologies provide opportunities both for discovery of novel molecular species and for detailed comparisons of gene expression patterns. Small RNA populations are particularly well suited to this analysis, as many different small RNAs can be completely sequenced in a single instrument run.     

Network properties of human disease genes with pleiotropic effects

Background  

The ability of a gene to cause a disease is known to be associated with the topological position of its protein product in the molecular interaction network. Pleiotropy, in human genetic diseases, refers to the ability of different mutations within the same gene to cause different pathological effects. Here, we hypothesized that the ability of human disease genes to cause pleiotropic effects would be associated with their network properties.     

Comparative analysis of cis-regulation following stroke and seizures in subspaces of conserved eigensystems

Background  

It is often desirable to separate effects of different regulators on gene expression, or to identify effects of the same regulator across several systems. Here, we focus on the rat brain following stroke or seizures, and demonstrate how the two tasks can be approached simultaneously.     

Biologically relevant effects of mRNA amplification on gene expression profiles

Background  

Gene expression microarray technology permits the analysis of global gene expression profiles. The amount of sample needed limits the use of small excision biopsies and/or needle biopsies from human or animal tissues. Linear amplification techniques have been developed to increase the amount of sample derived cDNA. These amplified samples can be hybridised on microarrays. However, little information is available whether microarrays based on amplified and unamplified material yield comparable results.     

Response projected clustering for direct association with physiological and clinical response data

Background  

Microarray gene expression data are often analyzed together with corresponding physiological response and clinical metadata of biological subjects, e.g. patients' residual tumor sizes after chemotherapy or glucose levels at various stages of diabetic patients. Current clustering analysis cannot directly incorporate such quantitative metadata into the clustering heatmap of gene expression. It will be quite useful if these clinical response data can be effectively summarized in the high-dimensional clustering display so that important groups of genes can be intuitively discovered with different degrees of relevance to target disease phenotypes.     

Negative Subtraction Hybridization: An efficient method to isolate large numbers of condition-specific cDNAs

Background  

The construction of cDNA libraries is a useful tool to understand gene expression in organisms under different conditions, but random sequencing of unbiased cDNA collections is laborious and can give rise to redundant EST collections.     

Computational expression deconvolution in a complex mammalian organ

Background  

Microarray expression profiling has been widely used to identify differentially expressed genes in complex cellular systems. However, while such methods can be used to directly infer intracellular regulation within homogeneous cell populations, interpretation of in vivo gene expression data derived from complex organs composed of multiple cell types is more problematic. Specifically, observed changes in gene expression may be due either to changes in gene regulation within a given cell type or to changes in the relative abundance of expressing cell types. Consequently, bona fide changes in intrinsic gene regulation may be either mimicked or masked by changes in the relative proportion of different cell types. To date, few analytical approaches have addressed this problem.