Integrating probe-level expression changes across generations of Affymetrix arrays

There is an urgent need for bioinformatic methods that allow integrative analysis of multiple microarray data sets. While previous studies have mainly concentrated on reproducibility of gene expression levels within or between different platforms, we propose a novel meta-analytic method that takes into account the vast amount of available probe-level information to combine the expression changes across different studies. We first show that the comparability of relative expression changes and the consistency of differentially expressed genes between different Affymetrix array generations can be considerably improved by determining the expression changes at the probe-level and by considering the latest information on probe-level sequence matching instead of the probe annotations provided by the manufacturer. With the improved probe-level expression change estimates, data from different generations of Affymetrix arrays can be combined more effectively. This will allow for the full exploitation of existing results when designing and analyzing new experiments.     

AffyTrees: facilitating comparative analysis of Affymetrix plant microarray chips

Microarrays measure the expression of large numbers of genes simultaneously and can be used to delve into interaction networks involving many genes at a time. However, it is often difficult to decide to what extent knowledge about the expression of genes gleaned in one model organism can be transferred to other species. This can be examined either by measuring the expression of genes of interest under comparable experimental conditions in other species, or by gathering the necessary data from comparable microarray experiments. However, it is essential to know which genes to compare between the organisms. To facilitate comparison of expression data across different species, we have implemented a Web-based software tool that provides information about sequence orthologs across a range of Affymetrix microarray chips. AffyTrees provides a quick and easy way of assigning which probe sets on different Affymetrix chips measure the expression of orthologous genes. Even in cases where gene or genome duplications have complicated the assignment, groups of comparable probe sets can be identified. The phylogenetic trees provide a resource that can be used to improve sequence annotation and detect biases in the sequence complement of Affymetrix chips. Being able to identify sequence orthologs and recognize biases in the sequence complement of chips is necessary for reliable cross-species microarray comparison. As the amount of work required to generate a single phylogeny in a nonautomated manner is considerable, AffyTrees can greatly reduce the workload for scientists interested in large-scale cross-species comparisons.     

FGX: a frequentist gene expression index for Affymetrix arrays

We consider a new frequentist gene expression index for Affymetrix oligonucleotide DNA arrays, using a similar probe intensity model as suggested by Hein and others (2005), called the Bayesian gene expression index (BGX). According to this model, the perfect match and mismatch values are assumed to be correlated as a result of sharing a common gene expression signal. Rather than a Bayesian approach, we develop a maximum likelihood algorithm for estimating the underlying common signal. In this way, estimation is explicit and much faster than the BGX implementation. The observed Fisher information matrix, rather than a posterior credibility interval, gives an idea of the accuracy of the estimators. We evaluate our method using benchmark spike-in data sets from Affymetrix and GeneLogic by analyzing the relationship between estimated signal and concentration, i.e. true signal, and compare our results with other commonly used methods.     

A comparison of Affymetrix gene expression arrays

Background  

Affymetrix GeneChips™ are an important tool in many facets of biological research. Recently, notable design changes to the chips have been made. In this study, we use publicly available data from Affymetrix to gauge the performance of three human gene expression arrays: Human Genome U133 Plus 2.0 (U133), Human Exon 1.0 ST (HuEx) and Human Gene 1.0 ST (HuGene).     

OpenADAM: an open source genome-wide association data management system for Affymetrix SNP arrays

Background

The goal of DNA barcoding is to develop a species-specific sequence library for all eukaryotes. A 650 bp fragment of the cytochrome c oxidase 1 (CO1) gene has been used successfully for species-level identification in several animal groups. It may be difficult in practice, however, to retrieve a 650 bp fragment from archival specimens, (because of DNA degradation) or from environmental samples (where universal primers are needed).

Results

We used a bioinformatics analysis using all CO1 barcode sequences from GenBank and calculated the probability of having species-specific barcodes for varied size fragments. This analysis established the potential of much smaller fragments, mini-barcodes, for identifying unknown specimens. We then developed a universal primer set for the amplification of mini-barcodes. We further successfully tested the utility of this primer set on a comprehensive set of taxa from all major eukaryotic groups as well as archival specimens.

Conclusion

In this study we address the important issue of minimum amount of sequence information required for identifying species in DNA barcoding. We establish a novel approach based on a much shorter barcode sequence and demonstrate its effectiveness in archival specimens. This approach will significantly broaden the application of DNA barcoding in biodiversity studies.     

Genotyping and annotation of Affymetrix SNP arrays

In this paper we develop a new method for genotyping Affymetrix single nucleotide polymorphism (SNP) array. The method is based on (i) using multiple arrays at the same time to determine the genotypes and (ii) a model that relates intensities of individual SNPs to each other. The latter point allows us to annotate SNPs that have poor performance, either because of poor experimental conditions or because for one of the alleles the probes do not behave in a dose–response manner. Generally, our method agrees well with a method developed by Affymetrix. When both methods make a call they agree in 99.25% (using standard settings) of the cases, using a sample of 113 Affymetrix 10k SNP arrays. In the majority of cases where the two methods disagree, our method makes a genotype call, whereas the method by Affymetrix makes a no call, i.e. the genotype of the SNP is not determined. By visualization it is indicated that our method is likely to be correct in majority of these cases. In addition, we demonstrate that our method produces more SNPs that are in concordance with Hardy–Weinberg equilibrium than the method by Affymetrix. Finally, we have validated our method on HapMap data and shown that the performance of our method is comparable to other methods.     

Combining gene expression data from different generations of oligonucleotide arrays

Background  

One of the important challenges in microarray analysis is to take full advantage of previously accumulated data, both from one's own laboratory and from public repositories. Through a comparative analysis on a variety of datasets, a more comprehensive view of the underlying mechanism or structure can be obtained. However, as we discover in this work, continual changes in genomic sequence annotations and probe design criteria make it difficult to compare gene expression data even from different generations of the same microarray platform.     

Visualization of genomic aberrations using Affymetrix SNP arrays

MOTIVATION: DNA copy number aberrations are frequently found in different types of cancer. Recent developments of microarray-based approaches have broadened the knowledge on number and structure of such aberrations. High-density single nucleotide polymorphism (SNP) microarrays provide an extremely high resolution with up to 500,000 SNPs per genome. Owing to the enormous amount of data the detection of common aberrations in large datasets is a great challenge. We describe a novel open source software tool--IdeogramBrowser--which was specifically designed for use with the Affymetrix SNP arrays. It provides an interactive karyotypic visualization of multiple aberration profiles and direct links to GeneCards. Visualization of consensus regions together with gene representation allows the explorative assessment of the data. AVAILABILITY: IdeogramBrowser and its source code are freely available under a creative commons license and can be obtained from http://www.informatik.uni-ulm.de/ni/staff/HKestler/ideo/. IdeogramBrowser is a platform independent Java application.     

Improving identification of differentially expressed genes by integrative analysis of Affymetrix and Illumina arrays

Together with the widely used Affymetrix microarrays, the recently introduced Illumina platform has become a cost-effective alternative for genome-wide studies. To efficiently use data from both array platforms, there is a pressing need for methods that allow systematic integration of multiple datasets, especially when the number of samples is small. To address these needs, we introduce a meta-analytic procedure for combining Affymetrix and Illumina data in the context of detecting differentially expressed genes between the platforms. We first investigate the effect of different expression change estimation procedures within the platforms on the agreement of the most differentially expressed genes. Using the best estimation methods, we then show the benefits of the integrative analysis in producing reproducible results across bootstrap samples. In particular, we demonstrate its biological relevance in identifying small but consistent changes during T helper 2 cell differentiation.     

VIZARD: analysis of Affymetrix Arabidopsis GeneChip data

SUMMARY: The Affymetrix GeneChip Arabidopsis genome array has proved to be a very powerful tool for the analysis of gene expression in Arabidopsis thaliana, the most commonly studied plant model organism. VIZARD is a Java program created at the University of California, Berkeley, to facilitate analysis of Arabidopsis GeneChip data. It includes several integrated tools for filtering, sorting, clustering and visualization of gene expression data as well as tools for the discovery of regulatory motifs in upstream sequences. VIZARD also includes annotation and upstream sequence databases for the majority of genes represented on the Affymetrix Arabidopsis GeneChip array. AVAILABILITY: VIZARD is available free of charge for educational, research, and not-for-profit purposes, and can be downloaded at http://www.anm.f2s.com/research/vizard/ CONTACT: moseyko@uclink4.berkeley.edu     

EzArray: A web-based highly automated Affymetrix expression array data management and analysis system

Background  

Though microarray experiments are very popular in life science research, managing and analyzing microarray data are still challenging tasks for many biologists. Most microarray programs require users to have sophisticated knowledge of mathematics, statistics and computer skills for usage. With accumulating microarray data deposited in public databases, easy-to-use programs to re-analyze previously published microarray data are in high demand.     

Different effects of the probe summarization algorithms PLIER and RMA on high-level analysis of Affymetrix exon arrays

Background  

Alternative splicing is an important mechanism that increases protein diversity and functionality in higher eukaryotes. Affymetrix exon arrays are a commercialized platform used to detect alternative splicing on a genome-wide scale. Two probe summarization algorithms, PLIER (Probe Logarithmic Intensity Error) and RMA (Robust Multichip Average), are commonly used to compute gene-level and exon-level expression values. However, a systematic comparison of these two algorithms on their effects on high-level analysis of the arrays has not yet been reported.     

Simpleaffy: a BioConductor package for Affymetrix Quality Control and data analysis

SUMMARY: Quality Control is a fundamental aspect of successful microarray data analysis. Simpleaffy is a BioConductor package that provides access to a variety of QC metrics for assessing the quality of RNA samples and of the intermediate stages of sample preparation and hybridization. Simpleaffy also offers fast implementations of popular algorithms for generating expression summaries and detection calls. AVAILABILITY: Simpleaffy can be downloaded from http://www.bioconductor.org. SUPPLEMENTARY INFORMATION: Additional information can be found on the supplementary website located at http://bioinformatics.picr.man.ac.uk.     

GenePicker: replicate analysis of Affymetrix gene expression microarrays

SUMMARY: GenePicker allows efficient analysis of Affymetrix gene expression data performed in replicate, through definition of analysis schemes, data normalization, t-test/ANOVA, Change-Fold Change-analysis and yields lists of differentially expressed genes with high confidence. Comparison of noise and signal analysis schemes allows determining a signal-to-noise ratio in a given experiment. Change Call, Fold Change and Signal mean ratios are used in the analysis. While each parameter alone yields gene lists that contain up to 30% false positives, the combination of these parameters nearly eliminates the false positives as verified by northern blotting, quantitative PCR in numerous independent experiments as well as by the analysis of spike-in data. AVAILABILITY: http://www.ifom-firc.it/RESEARCH/Appl_Bioinfo/tools.html. SUPPLEMENTARY INFORMATION: http://www.ifom-firc.it/RESEARCH/Appl_Bioinfo/tools.html.     

Transformation of expression intensities across generations of Affymetrix microarrays using sequence matching and regression modeling

The utility of previously generated microarray data is severely limited owing to small study size, leading to under-powered analysis, and failure of replication. Multiplicity of platforms and various sources of systematic noise limit the ability to compile existing data from similar studies. We present a model for transformation of data across different generations of Affymetrix arrays, developed using previously published datasets describing technical replicates performed with two generations of arrays. The transformation is based upon a probe set-specific regression model, generated from replicate measurements across platforms, performed using correlation coefficients. The model, when applied to the expression intensities of 5069 shared, sequence-matched probe sets in three different generations of Affymetrix Human oligonucleotide arrays, showed significant improvement in inter generation correlations between sample-wide means and individual probe set pairs. The approach was further validated by an observed reduction in Euclidean distance between signal intensities across generations for the predicted values. Finally, application of the model to independent, but related datasets resulted in improved clustering of samples based upon their biological, as opposed to technical, attributes. Our results suggest that this transformation method is a valuable tool for integrating microarray datasets from different generations of arrays.