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At least 991 records · Page 55Linked to original sources
PubMed2007

DNA detection on plastic: surface activation protocol to convert polycarbonate substrates to biochip platforms.

A mild and efficient surface activation protocol to convert polycarbonate (PC) substrates, e.g., plastic bases of compact disks, to biochip platforms for DNA probe immobilization and target detection is described. The preparation procedure (activation, patterning, and coupling) is simple and effective; the on-chip hybridization is sensitive and selective. Particularly, UV/ozone treatment of PC sheets produces a hydrophilic surface with a high density of reactive carboxylic acid groups [(4.8 +/- 0.2) x 10-10 mol/cm2] in less than 10 min at ambient conditions, and no significant aging or physical damage to the substrate is observed. Covalent immobilization of DNA probes via both passive (reagent-less photopatterning and coupling in bulk solution phase) and flow-through (creation of microarrays with microfluidic channel plates) procedures has been demonstrated. Subsequent hybridization shows uniform and strong fluorescent signals for complementary target DNA and allows clear discrimination between fully complementary targets and strands with a single base-pair mismatch. The surface chemistry described herein will facilitate the development of disposable plastic biochips (not limited to DNA microarrays) and the fabrication of biomedical devices that are readable with conventional optical drives.

DNA↗
PubMed2005

Enzymatic control of metal deposition as key step for a low-background electrical detection for DNA chips.

Electrical detection of DNA using nanoparticle labels in combination with metal enhancement represents an interesting alternative to fluorescence readout schemes. This electrical method is hampered by unspecific metal deposition, resulting in a lower sensitivity of the assay. A novel enhancement technique based on an enzymatic process is introduced. This approach enables highly specific metal deposition only at the enzyme label, without the background that is typical in the case of the conventional metal enhancement process of growing nanoparticles. The enzymatic enhancement leads to a significant increase in sensitivity, and the detection of single base mismatches demonstrates the high specificity of the novel enhancement approach.

Biosensing Techniques↗
PubMed2006

Evaluation of external RNA controls for the assessment of microarray performance.

External RNA controls (ERCs), although important for microarray assay performance assessment, have yet to be fully implemented in the research community. As part of the MicroArray Quality Control (MAQC) study, two types of ERCs were implemented and evaluated; one was added to the total RNA in the samples before amplification and labeling; the other was added to the copyRNAs (cRNAs) before hybridization. ERC concentration-response curves were used across multiple commercial microarray platforms to identify problematic assays and potential sources of variation in the analytical process. In addition, the behavior of different ERC types was investigated, resulting in several important observations, such as the sample-dependent attributes of performance and the potential of using these control RNAs in a combinatorial fashion. This multiplatform investigation of the behavior and utility of ERCs provides a basis for articulating specific recommendations for their future use in evaluating assay performance across multiple platforms.

Algorithms↗
PubMed2006

The MicroArray Quality Control (MAQC) project shows inter- and intraplatform reproducibility of gene expression measurements.

Over the last decade, the introduction of microarray technology has had a profound impact on gene expression research. The publication of studies with dissimilar or altogether contradictory results, obtained using different microarray platforms to analyze identical RNA samples, has raised concerns about the reliability of this technology. The MicroArray Quality Control (MAQC) project was initiated to address these concerns, as well as other performance and data analysis issues. Expression data on four titration pools from two distinct reference RNA samples were generated at multiple test sites using a variety of microarray-based and alternative technology platforms. Here we describe the experimental design and probe mapping efforts behind the MAQC project. We show intraplatform consistency across test sites as well as a high level of interplatform concordance in terms of genes identified as differentially expressed. This study provides a resource that represents an important first step toward establishing a framework for the use of microarrays in clinical and regulatory settings.

Equipment Design↗
PubMed2006

Using RNA sample titrations to assess microarray platform performance and normalization techniques.

We have assessed the utility of RNA titration samples for evaluating microarray platform performance and the impact of different normalization methods on the results obtained. As part of the MicroArray Quality Control project, we investigated the performance of five commercial microarray platforms using two independent RNA samples and two titration mixtures of these samples. Focusing on 12,091 genes common across all platforms, we determined the ability of each platform to detect the correct titration response across the samples. Global deviations from the response predicted by the titration ratios were observed. These differences could be explained by variations in relative amounts of messenger RNA as a fraction of total RNA between the two independent samples. Overall, both the qualitative and quantitative correspondence across platforms was high. In summary, titration samples may be regarded as a valuable tool, not only for assessing microarray platform performance and different analysis methods, but also for determining some underlying biological features of the samples.

Algorithms↗
PubMed2006

Molecular analysis of flies selected for aggressive behavior.

Aggressive behavior is pervasive throughout the animal kingdom, and yet very little is known about its molecular underpinnings. To address this problem, we have developed a population-based selection procedure to increase aggression in Drosophila melanogaster. We measured changes in aggressive behavior in the selected subpopulations with a new two-male arena assay. In only ten generations of selection, the aggressive lines became markedly more aggressive than the neutral lines. After 21 generations, the fighting index increased more than 30-fold. Using microarray analysis, we identified genes with differing expression levels in the aggressive and neutral lines as candidates for this strong behavioral selection response. We tested a small set of these genes through mutant analysis and found that one significantly increased fighting frequency. These results suggest that selection for increases in aggression can be used to molecularly dissect this behavior.

Aggression↗
PubMed2006

Gene expression microarrays: glimpses of the immunological genome.

Successful microarray experimentation can generate enormous amounts of data, potentially very rich but also very unwieldy. Bold outlooks and new methods for data analysis and presentation should yield additional insight into the complexities of the immune system.

Animals↗
PubMed2006

3' UTR seed matches, but not overall identity, are associated with RNAi off-targets.

Off-target gene silencing can present a notable challenge in the interpretation of data from large-scale RNA interference (RNAi) screens. We performed a detailed analysis of off-targeted genes identified by expression profiling of human cells transfected with small interfering RNA (siRNA). Contrary to common assumption, analysis of the subsequent off-target gene database showed that overall identity makes little or no contribution to determining whether the expression of a particular gene will be affected by a given siRNA, except for near-perfect matches. Instead, off-targeting is associated with the presence of one or more perfect 3' untranslated region (UTR) matches with the hexamer or heptamer seed region (positions 2-7 or 2-8) of the antisense strand of the siRNA. These findings have strong implications for future siRNA design and the application of RNAi in high-throughput screening and therapeutic development.

3' Untranslated Regions↗
PubMed2004

Classification of cDNA array genes that have a highly significant discriminative power due to their unique distribution in four brain regions.

Novel statistical methods were used to distinguish functionally distinct brain regions using their cDNA array gene expression profiles, and it was found that one of four specific factors is often associated with the most regionally discriminative genes. The gene expression profiles for the substantia nigra (SN), striatum (STR), parietal cortex (PC), and posterolateral cortical amygdaloid nucleus (PLCo) brain regions were determined from each brain region. An F-test identified 339 genes of the 1185 array genes as having a P < or = 0.01 and applied a gene ranking and selection method based on Soft Independent Modeling of Class Analogy (SIMCA) to obtain 59 of the most discriminative genes. Their discriminative power was validated in three steps. The most convincing step showed their ability to correctly predict the brain regional classifications for 18 "test" gene expression sets obtained from the four regions. A two-way Hierarchical Cluster Analysis organized the 59 genes in six clusters according to their expression differences in the brain regions. Expression patterns in the SN and STR regions greatly differed from each other and the PC and PLCo. The closer similarity in the gene expression patterns of the PC and PLCo was probably due to their functional similarity. The important factors in determining differences in the regional gene expression profiles in six clusters were (1) regional myelin/oligodendrocyte levels, (2) resident neuron types, (3) neurotransmitter innervation profiles, and (4) Ca++-dependent signaling and second messenger systems.

Animals↗
PubMed2002

Large-scale analysis of gene expression profiles.

The accumulation of DNA microarray data has now made it possible to use gene expression profiles to analyse expression data. A gene expression profile contains the expression data for a given gene over various samples, and can be contrasted with an expression signature, which contains the expression data for a single sample. Gene expression profiles are most revealing when samples are grouped appropriately, either by standard clinical or pathological categories or by categories discovered through cluster analysis techniques. Expression profiles can exist at various levels of abstraction, yielding information across various tissues or across diseases within a particular tissue. Hypothesis tests may be applied to expression profiles on a large scale to identify candidate genes of interest.

Cluster Analysis↗
PubMed2005

There is no silver bullet--a guide to low-level data transforms and normalisation methods for microarray data.

To overcome random experimental variation, even for simple screens, data from multiple microarrays have to be combined. There are, however, systematic differences between arrays, and any bias remaining after experimental measures to ensure consistency needs to be controlled for. It is often difficult to make the right choice of data transformation and normalisation methods to achieve this end. In this tutorial paper we review the problem and a selection of solutions, explaining the basic principles behind normalisation procedures and providing guidance for their application.

Algorithms↗
PubMed2001

SVDMAN--singular value decomposition analysis of microarray data.

SUMMARY: We have developed two novel methods for Singular Value Decomposition analysis (SVD) of microarray data. The first is a threshold-based method for obtaining gene groups, and the second is a method for obtaining a measure of confidence in SVD analysis. Gene groups are obtained by identifying elements of the left singular vectors, or gene coefficient vectors, that are greater in magnitude than the threshold W N(-1/2), where N is the number of genes, and W is a weight factor whose default value is 3. The groups are non-exclusive and may contain genes of opposite (i.e. inversely correlated) regulatory response. The confidence measure is obtained by systematically deleting assays from the data set, interpolating the SVD of the reduced data set to reconstruct the missing assay, and calculating the Pearson correlation between the reconstructed assay and the original data. This confidence measure is applicable when each experimental assay corresponds to a value of parameter that can be interpolated, such as time, dose or concentration. Algorithms for the grouping method and the confidence measure are available in a software application called SVD Microarray ANalysis (SVDMAN). In addition to calculating the SVD for generic analysis, SVDMAN provides a new means for using microarray data to develop hypotheses for gene associations and provides a measure of confidence in the hypotheses, thus extending current SVD research in the area of global gene expression analysis.

Algorithms↗
PubMed2004

Enhancing scatterplots with smoothed densities.

MOTIVATION: Scatterplots of microarray data generally contain a very large number of dots, making it difficult to get a good impression of their distribution in dense areas. RESULTS: We present a fast and simple algorithm for two-dimensional histogram smoothing, to visually enhance scatterplots. AVAILABILITY: Functions for Matlab and R are available from the corresponding author.

Algorithms↗
PubMed2004

Optimization models for cancer classification: extracting gene interaction information from microarray expression data.

MOTIVATION: Microarray data appear particularly useful to investigate mechanisms in cancer biology and represent one of the most powerful tools to uncover the genetic mechanisms causing loss of cell cycle control. Recently, several different methods to employ microarray data as a diagnostic tool in cancer classification have been proposed. These procedures take changes in the expression of particular genes into account but do not consider disruptions in certain gene interactions caused by the tumor. It is probable that some genes participating in tumor development do not change their expression level dramatically. Thus, they cannot be detected by simple classification approaches used previously. For these reasons, a classification procedure exploiting information related to changes in gene interactions is needed. RESULTS: We propose a MAximal MArgin Linear Programming (MAMA) method for the classification of tumor samples based on microarray data. This procedure detects groups of genes and constructs models (features) that strongly correlate with particular tumor types. The detected features include genes whose functional relations are changed for particular cancer types. The proposed method was tested on two publicly available datasets and demonstrated a prediction ability superior to previously employed classification schemes. AVAILABILITY: The MAMA system was developed using the linear programming system LINDO http://www.lindo.com. A Perl script that specifies the optimization problem for this software is available upon request from the authors.

Algorithms↗
PubMed2004

A dynamically growing self-organizing tree (DGSOT) for hierarchical clustering gene expression profiles.

MOTIVATION: The increasing use of microarray technologies is generating large amounts of data that must be processed in order to extract useful and rational fundamental patterns of gene expression. Hierarchical clustering technology is one method used to analyze gene expression data, but traditional hierarchical clustering algorithms suffer from several drawbacks (e.g. fixed topology structure; mis-clustered data which cannot be reevaluated). In this paper, we introduce a new hierarchical clustering algorithm that overcomes some of these drawbacks. RESULT: We propose a new tree-structure self-organizing neural network, called dynamically growing self-organizing tree (DGSOT) algorithm for hierarchical clustering. The DGSOT constructs a hierarchy from top to bottom by division. At each hierarchical level, the DGSOT optimizes the number of clusters, from which the proper hierarchical structure of the underlying dataset can be found. In addition, we propose a new cluster validation criterion based on the geometric property of the Voronoi partition of the dataset in order to find the proper number of clusters at each hierarchical level. This criterion uses the Minimum Spanning Tree (MST) concept of graph theory and is computationally inexpensive for large datasets. A K-level up distribution (KLD) mechanism, which increases the scope of data distribution in the hierarchy construction, was used to improve the clustering accuracy. The KLD mechanism allows the data misclustered in the early stages to be reevaluated at a later stage and increases the accuracy of the final clustering result. The clustering result of the DGSOT is easily displayed as a dendrogram for visualization. Based on a yeast cell cycle microarray expression dataset, we found that our algorithm extracts gene expression patterns at different levels. Furthermore, the biological functionality enrichment in the clusters is considerably high and the hierarchical structure of the clusters is more reasonable. AVAILABILITY: DGSOT is available upon request from the authors.

Algorithms↗
PubMed2004

Faster cyclic loess: normalizing RNA arrays via linear models.

MOTIVATION: Our goal was to develop a normalization technique that yields results similar to cyclic loess normalization and with speed comparable to quantile normalization. RESULTS: Fastlo yields normalized values similar to cyclic loess and quantile normalization and is fast; it is at least an order of magnitude faster than cyclic loess and approaches the speed of quantile normalization. Furthermore, fastlo is more versatile than both cyclic loess and quantile normalization because it is model-based. AVAILABILITY: The Splus/R function for fastlo normalization is available from the authors.

Algorithms↗