An application for assessing quality of RNA hybridized to Affymetrix GeneChips

This paper describes a stand-alone application for estimating the 3' to 5' ratio by fitting a mixed effects model to the interior pixel intensities of perfect match probes for selected control probe sets from an Affymetrix *.DAT file. The effectiveness of this method was demonstrated previously by an application of the method to two microarray datasets for which external verification of RNA quality was known. This application provides a more objective assessment of sample quality in that both a point estimate and 95% confidence interval about the 3' to 5' ratio are provided. AVAILABILITY: The software and installation instructions are freely available for download at http://www.people.vcu.edu/~kjarcher/Research/Data.htm     

An overview of image-processing methods for Affymetrix GeneChips

We present an overview of image-processing methods for Affymetrix GeneChips. All GeneChips are affected to some extent by spatially coherent defects and image processing has a number of potential impacts on the downstream analysis of GeneChip data. Fortunately, there are now a number of robust and accurate algorithms, which identify the most disabling defects. One group of algorithms concentrate on the transformation from the original hybridisation DAT image to the representative CEL file. Another set uses dedicated pattern recognition routines to detect different types of hybridisation defect in replicates. A third type exploits the information provided by public repositories of GeneChips (such as GEO). The use of these algorithms improves the sensitivity of GeneChips, and should be a prerequisite for studies in which there are only few probes per relevant biological signal, such as exon arrays and SNP chips.     

Assessing the Quality of Hybridized RNA in Affymetrix GeneChips Using Linear Regression

The quality of data from microarray analysis is highly dependent on the quality of RNA. Because of the lability of RNA, steps involved in tissue sampling, RNA purification, and RNA storage are known to potentially lead to the degradation of RNAs; therefore, assessment of RNA quality and integrity is essential. Existing methods for estimating the quality of RNA hybridized to a GeneChip either suffer from subjectivity or are inefficient in performance. To overcome these drawbacks, we propose a linear regression method for assessing RNA quality for a hybridized Genechip. In particular, our approach used the probe intensities from the .cel files that the Affymetrix software associates with each microarray. The effectiveness and the improvements of the proposed method over the existing methods are illustrated by the application of the method to the previously published 19 human Affymetrix microarray data sets for which external verification of RNA quality is available.     

Using luminescent nanoparticles as staining probes for Affymetrix GeneChips

Microarray technology provides efficient access to genetic information using miniaturized, high-density arrays of DNA probes. We investigated the application of luminescent nanoparticles as probes for Affymetrix GeneChips detection without the need for signal amplification. Our goal is to investigate the feasibility of using luminescent nanoparticles as probes in a commercial microarray system without changing its configurations. With the present imaging modality and existing optical excitation and detection systems of the Affymetrix GeneChips, our early results indicate that nanoparticles not only can be used for GeneChip labeling but also are superior to the traditional fluorescent protein streptavidin-phycoerythrin (SAPE). The advantage of the particles lies in a simplified staining procedure, higher photobleaching threshold, and enhanced luminescence signal. The nanoparticles can be used for detection of low-abundance targets without any amplification step. A concentration detection limit of 50 fM has been achieved. This work demonstrates the feasibility of using luminescent nanoparticles as probes for commercial microarray systems, making them less costly, more reproducible, and potentially quantitative.     

Preferred analysis methods for Affymetrix GeneChips revealed by a wholly defined control dataset

Background  

As more methods are developed to analyze RNA-profiling data, assessing their performance using control datasets becomes increasingly important.     

Preferred analysis methods for Affymetrix GeneChips revealed by a wholly defined control dataset

Background

As more methods are developed to analyze RNA-profiling data, assessing their performance using control datasets becomes increasingly important.

Results

We present a 'spike-in' experiment for Affymetrix GeneChips that provides a defined dataset of 3,860 RNA species, which we use to evaluate analysis options for identifying differentially expressed genes. The experimental design incorporates two novel features. First, to obtain accurate estimates of false-positive and false-negative rates, 100-200 RNAs are spiked in at each fold-change level of interest, ranging from 1.2 to 4-fold. Second, instead of using an uncharacterized background RNA sample, a set of 2,551 RNA species is used as the constant (1x) set, allowing us to know whether any given probe set is truly present or absent. Application of a large number of analysis methods to this dataset reveals clear variation in their ability to identify differentially expressed genes. False-negative and false-positive rates are minimized when the following options are chosen: subtracting nonspecific signal from the PM probe intensities; performing an intensity-dependent normalization at the probe set level; and incorporating a signal intensity-dependent standard deviation in the test statistic.

Conclusions

A best-route combination of analysis methods is presented that allows detection of approximately 70% of true positives before reaching a 10% false-discovery rate. We highlight areas in need of improvement, including better estimate of false-discovery rates and decreased false-negative rates.     

Interpretation of multiple probe sets mapping to the same gene in Affymetrix GeneChips

Background  

Affymetrix GeneChip technology enables the parallel observations of tens of thousands of genes. It is important that the probe set annotations are reliable so that biological inferences can be made about genes which undergo differential expression. Probe sets representing the same gene might be expected to show similar fold changes/z-scores, however this is in fact not the case.     

Preferred analysis methods for Affymetrix GeneChips. II. An expanded,balanced, wholly-defined spike-in dataset

Background  

Concomitant with the rise in the popularity of DNA microarrays has been a surge of proposed methods for the analysis of microarray data. Fully controlled "spike-in" datasets are an invaluable but rare tool for assessing the performance of various methods.     

昆虫小器官RNA荧光原位杂交技术

【目的】在昆虫基因表达和功能研究中,RNA原位杂交技术越来越受到青睐。该技术不仅能定性定量反应基因表达的时空特异性,而且能在细胞水平上检测基因表达的调控模式。为了将该技术更好地在昆虫小器官研究中运用,我们以果蝇幼虫翅芽为例优化了改技术。【方法】解剖果蝇3龄幼虫翅芽进行原位杂交实验。【结果】我们发现影响原位杂交结果的因素十分复杂,包括取材时期,探针的合成,预杂交/杂交的时间和温度,清洗时间,适当的对照等。通过RNA荧光原位杂交实验,我们揭示了调控细胞记忆的trithorax基因在3龄翅芽广泛表达,并且受到转录因子Optomotor-blind的负调控。【结论】这一技术方法为研究昆虫小器官的基因表达和调控提供了便捷手段。