Two-stage normalization using background intensities in cDNA microarray data

Background  

In the microarray experiment, many undesirable systematic variations are commonly observed. Normalization is the process of removing such variation that affects the measured gene expression levels. Normalization plays an important role in the earlier stage of microarray data analysis. The subsequent analysis results are highly dependent on normalization. One major source of variation is the background intensities. Recently, some methods have been employed for correcting the background intensities. However, all these methods focus on defining signal intensities appropriately from foreground and background intensities in the image analysis. Although a number of normalization methods have been proposed, no systematic methods have been proposed using the background intensities in the normalization process.     

Profound influence of microarray scanner characteristics on gene expression ratios: analysis and procedure for correction

Background  

High throughput gene expression data from spotted cDNA microarrays are collected by scanning the signal intensities of the corresponding spots by dedicated fluorescence scanners. The major scanner settings for increasing the spot intensities are the laser power and the voltage of the photomultiplier tube (PMT). It is required that the expression ratios are independent of these settings. We have investigated the relationships between PMT voltage, spot intensities, and expression ratios for different scanners, in order to define an optimal scanning procedure.     

Application of a correlation correction factor in a microarray cross-platform reproducibility study

Background  

Recent research examining cross-platform correlation of gene expression intensities has yielded mixed results. In this study, we demonstrate use of a correction factor for estimating cross-platform correlations.     

Web-based interrogation of gene expression signatures using EXALT

Background  

Widespread use of high-throughput techniques such as microarrays to monitor gene expression levels has resulted in an explosive growth of data sets in public domains. Integration and exploration of these complex and heterogeneous data have become a major challenge.     

The utility of MAS5 expression summary and detection call algorithms

Background  

Used alone, the MAS5.0 algorithm for generating expression summaries has been criticized for high False Positive rates resulting from exaggerated variance at low intensities.     

An interactive tool for visualization of relationships between gene expression profiles

Background  

Application of phenetic methods to gene expression analysis proved to be a successful approach. Visualizing the results in a 3-dimentional space may further enhance these techniques.     

Normalization and analysis of DNA microarray data by self-consistency and local regression

Background  

With the advent of DNA hybridization microarrays comes the remarkable ability, in principle, to simultaneously monitor the expression levels of thousands of genes. The quantiative comparison of two or more microarrays can reveal, for example, the distinct patterns of gene expression that define different cellular phenotypes or the genes induced in the cellular response to insult or changing environmental conditions. Normalization of the measured intensities is a prerequisite of such comparisons, and indeed, of any statistical analysis, yet insufficient attention has been paid to its systematic study. The most straightforward normalization techniques in use rest on the implicit assumption of linear response between true expression level and output intensity. We find that these assumptions are not generally met, and that these simple methods can be improved.     

A BAC-bacterial recombination method to generate physically linked multiple gene reporter DNA constructs

Background  

Reporter gene mice are valuable animal models for biological research providing a gene expression readout that can contribute to cellular characterization within the context of a developmental process. With the advancement of bacterial recombination techniques to engineer reporter gene constructs from BAC genomic clones and the generation of optically distinguishable fluorescent protein reporter genes, there is an unprecedented capability to engineer more informative transgenic reporter mouse models relative to what has been traditionally available.     

Reverse engineering a gene network using an asynchronous parallel evolution strategy

Background  

The use of reverse engineering methods to infer gene regulatory networks by fitting mathematical models to gene expression data is becoming increasingly popular and successful. However, increasing model complexity means that more powerful global optimisation techniques are required for model fitting. The parallel Lam Simulated Annealing (pLSA) algorithm has been used in such approaches, but recent research has shown that island Evolutionary Strategies can produce faster, more reliable results. However, no parallel island Evolutionary Strategy (piES) has yet been demonstrated to be effective for this task.     

Partial mixture model for tight clustering of gene expression time-course

Background  

Tight clustering arose recently from a desire to obtain tighter and potentially more informative clusters in gene expression studies. Scattered genes with relatively loose correlations should be excluded from the clusters. However, in the literature there is little work dedicated to this area of research. On the other hand, there has been extensive use of maximum likelihood techniques for model parameter estimation. By contrast, the minimum distance estimator has been largely ignored.     

Detecting microRNA activity from gene expression data

Background  

MicroRNAs (miRNAs) are non-coding RNAs that regulate gene expression by binding to the messenger RNA (mRNA) of protein coding genes. They control gene expression by either inhibiting translation or inducing mRNA degradation. A number of computational techniques have been developed to identify the targets of miRNAs. In this study we used predicted miRNA-gene interactions to analyse mRNA gene expression microarray data to predict miRNAs associated with particular diseases or conditions.     

A Web-based and Grid-enabled dChip version for the analysis of large sets of gene expression data

Background  

Microarray techniques are one of the main methods used to investigate thousands of gene expression profiles for enlightening complex biological processes responsible for serious diseases, with a great scientific impact and a wide application area. Several standalone applications had been developed in order to analyze microarray data. Two of the most known free analysis software packages are the R-based Bioconductor and dChip. The part of dChip software concerning the calculation and the analysis of gene expression has been modified to permit its execution on both cluster environments (supercomputers) and Grid infrastructures (distributed computing).     

Conditional gene expression in the mouse using a Sleeping Beauty gene-trap transposon

Background  

Insertional mutagenesis techniques with transposable elements have been popular among geneticists studying model organisms from E. coli to Drosophila and, more recently, the mouse. One such element is the Sleeping Beauty (SB) transposon that has been shown in several studies to be an effective insertional mutagen in the mouse germline. SB transposon vector studies have employed different functional elements and reporter molecules to disrupt and report the expression of endogenous mouse genes. We sought to generate a transposon system that would be capable of reporting the expression pattern of a mouse gene while allowing for conditional expression of a gene of interest in a tissue- or temporal-specific pattern.     

A simplified,robust, and streamlined procedure for the production of <Emphasis Type="Italic">C. elegans</Emphasis>transgenes via recombineering

Background  

The nematode Caenorhabditis elegans has emerged as a powerful system to study biologic questions ranging from development to aging. The generation of transgenic animals is an important experimental tool and allows use of GFP fusion proteins to study the expression of genes of interest or generation of epitope tagged versions of specific genes. Transgenes are often generated by placing a promoter upstream of a reporter gene or cDNA. This often produces a representative expression pattern, but important exceptions have been observed. To better capture the genuine expression pattern and timing, several investigators have modified large pieces of DNA carried by BACs or fosmids for use in the construction of transgenic animals via recombineering. However, these techniques are not in widespread use despite the advantages when compared to traditional approaches. Additionally, some groups have encountered problems with employing these techniques. Hence, we sought identify ways to improve the simplicity and reliability of the procedure.     

Quality assessment of microarrays: Visualization of spatial artifacts and quantitation of regional biases

Background  

Quality-control is an important issue in the analysis of gene expression microarrays. One type of problem is regional bias, in which one region of a chip shows artifactually high or low intensities (or ratios in a two-channel array) relative to the majority of the chip. Current practice in quality assessment for microarrays does not address regional biases.     

Additive risk survival model with microarray data

Background  

Microarray techniques survey gene expressions on a global scale. Extensive biomedical studies have been designed to discover subsets of genes that are associated with survival risks for diseases such as lymphoma and construct predictive models using those selected genes. In this article, we investigate simultaneous estimation and gene selection with right censored survival data and high dimensional gene expression measurements.     

Increased retention of functional fusions to toxic genes in new two-hybrid libraries of the E. coli strain MG1655 and B. subtilis strain 168 genomes,prepared without passaging through E. coli

Background  

Cloning of genes in expression libraries, such as the yeast two-hybrid system (Y2H), is based on the assumption that the loss of target genes is minimal, or at worst, managable. However, the expression of genes or gene fragments that are capable of interacting with E. coli or yeast gene products in these systems has been shown to be growth inhibitory, and therefore these clones are underrepresented (or completely lost) in the amplified library.     

A feature selection approach for identification of signature genes from SAGE data

Background  

One goal of gene expression profiling is to identify signature genes that robustly distinguish different types or grades of tumors. Several tumor classifiers based on expression profiling have been proposed using microarray technique. Due to important differences in the probabilistic models of microarray and SAGE technologies, it is important to develop suitable techniques to select specific genes from SAGE measurements.