Independence and reproducibility across microarray platforms

Microarrays have been widely used for the analysis of gene expression, but the issue of reproducibility across platforms has yet to be fully resolved. To address this apparent problem, we compared gene expression between two microarray platforms: the short oligonucleotide Affymetrix Mouse Genome 430 2.0 GeneChip and a spotted cDNA array using a mouse model of angiotensin II-induced hypertension. RNA extracted from treated mice was analyzed using Affymetrix and cDNA platforms and then by quantitative RT-PCR (qRT-PCR) for validation of specific genes. For the 11,710 genes present on both arrays, we assessed the relative impact of experimental treatment and platform on measured expression and found that biological treatment had a far greater impact on measured expression than did platform for more than 90% of genes, a result validated by qRT-PCR. In the small number of cases in which platforms yielded discrepant results, qRT-PCR generally did not confirm either set of data, suggesting that sequence-specific effects may make expression predictions difficult to make using any technique.     

ProbeMatchDB--a web database for finding equivalent probes across microarray platforms and species

SUMMARY: ProbeMatchDB is a web-based database designed to facilitate the search of EST/cDNA sequences or STS markers that can be used to represent the same gene across different microarray platforms and species. It can be used for finding equivalent EST clones in the Research Genetics sequence verified clone set based on results from Affymetirx GeneChips. It will also help to identify probes representing orthologous genes across human, mouse and rat on different microarray platforms. AVAILABILITY: The database is accessible at http://brainarray.mhri.med.umich.edu/MARRAY/BC_ASP/brainarray.htm by clicking the 'Query ProbeMatchDB' link.     

Drosophila microarray platforms.

The advent of high throughput microarrays and the complete sequencing of the Drosophila melanogaster genome have enabled global gene expression analysis in this powerful genetic model organism. Currently, researchers are using three main Drosophila array platform types, with elements composed of cDNA amplicons, oligonucleotides (short and long) or genomic amplicons. This paper provides a broad overview of these platforms.     

Multiple-laboratory comparison of microarray platforms

Microarray technology is a powerful tool for measuring RNA expression for thousands of genes at once. Various studies have been published comparing competing platforms with mixed results: some find agreement, others do not. As the number of researchers starting to use microarrays and the number of cross-platform meta-analysis studies rapidly increases, appropriate platform assessments become more important. Here we present results from a comparison study that offers important improvements over those previously described in the literature. In particular, we noticed that none of the previously published papers consider differences between labs. For this study, a consortium of ten laboratories from the Washington, DC-Baltimore, USA, area was formed to compare data obtained from three widely used platforms using identical RNA samples. We used appropriate statistical analysis to demonstrate that there are relatively large differences in data obtained in labs using the same platform, but that the results from the best-performing labs agree rather well.     

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.     

Evaluation of gene expression measurements from commercial microarray platforms

Multiple commercial microarrays for measuring genome-wide gene expression levels are currently available, including oligonucleotide and cDNA, single- and two-channel formats. This study reports on the results of gene expression measurements generated from identical RNA preparations that were obtained using three commercially available microarray platforms. RNA was collected from PANC-1 cells grown in serum-rich medium and at 24 h following the removal of serum. Three biological replicates were prepared for each condition, and three experimental replicates were produced for the first biological replicate. RNA was labeled and hybridized to microarrays from three major suppliers according to manufacturers’ protocols, and gene expression measurements were obtained using each platform’s standard software. For each platform, gene targets from a subset of 2009 common genes were compared. Correlations in gene expression levels and comparisons for significant gene expression changes in this subset were calculated, and showed considerable divergence across the different platforms, suggesting the need for establishing industrial manufacturing standards, and further independent and thorough validation of the technology.     

Two efficient polymeric chemical platforms for oligonucleotide microarray preparation

In this report we describe two robust procedures for oligonucleotide microarray preparation based on polymeric coatings. The proposed chemical approaches include: 1) a glass functionalisation step with appropriate silanes (gamma-aminopropyltriethoxysilane-APTES or 3-glycidoxypropyltrimethoxysilane-GOPS), 2) a coating step using polymers (poly-L-Lysine or poly(acrylic acid-co-acrylamide) copolymer) covalently bound to the modified glass and 3) a surface activation step to allow for the attachment of amino-modified oligonucleotides. Results obtained using these chemistries in oligo microarray preparation show: 1) an overall high loading capacity and availability to hybridisation against targets, 2) a good uniformity, 3) resistance to consecutive probing/ stripping cycles, 4) stability to thermal cycles, 5) effectiveness in hybridisation-mediated mutation detection procedures and 6) the possibility to perform enzymatic reactions, such as ligation.     

Classification across gene expression microarray studies

Background  

The increasing number of gene expression microarray studies represents an important resource in biomedical research. As a result, gene expression based diagnosis has entered clinical practice for patient stratification in breast cancer. However, the integration and combined analysis of microarray studies remains still a challenge. We assessed the potential benefit of data integration on the classification accuracy and systematically evaluated the generalization performance of selected methods on four breast cancer studies comprising almost 1000 independent samples. To this end, we introduced an evaluation framework which aims to establish good statistical practice and a graphical way to monitor differences. The classification goal was to correctly predict estrogen receptor status (negative/positive) and histological grade (low/high) of each tumor sample in an independent study which was not used for the training. For the classification we chose support vector machines (SVM), predictive analysis of microarrays (PAM), random forest (RF) and k-top scoring pairs (kTSP). Guided by considerations relevant for classification across studies we developed a generalization of kTSP which we evaluated in addition. Our derived version (DV) aims to improve the robustness of the intrinsic invariance of kTSP with respect to technologies and preprocessing.     

Importance of randomization in microarray experimental designs with Illumina platforms

Measurements of gene expression from microarray experiments are highly dependent on experimental design. Systematic noise can be introduced into the data at numerous steps. On Illumina BeadChips, multiple samples are assayed in an ordered series of arrays. Two experiments were performed using the same samples but different hybridization designs. An experiment confounding genotype with BeadChip and treatment with array position was compared to another experiment in which these factors were randomized to BeadChip and array position. An ordinal effect of array position on intensity values was observed in both experiments. We demonstrate that there is increased rate of false-positive results in the confounded design and that attempts to correct for confounded effects by statistical modeling reduce power of detection for true differential expression. Simple analysis models without post hoc corrections provide the best results possible for a given experimental design. Normalization improved differential expression testing in both experiments but randomization was the most important factor for establishing accurate results. We conclude that lack of randomization cannot be corrected by normalization or by analytical methods. Proper randomization is essential for successful microarray experiments.     

Evaluation of DNA microarray results with quantitative gene expression platforms

We have evaluated the performance characteristics of three quantitative gene expression technologies and correlated their expression measurements to those of five commercial microarray platforms, based on the MicroArray Quality Control (MAQC) data set. The limit of detection, assay range, precision, accuracy and fold-change correlations were assessed for 997 TaqMan Gene Expression Assays, 205 Standardized RT (Sta)RT-PCR assays and 244 QuantiGene assays. TaqMan is a registered trademark of Roche Molecular Systems, Inc. We observed high correlation between quantitative gene expression values and microarray platform results and found few discordant measurements among all platforms. The main cause of variability was differences in probe sequence and thus target location. A second source of variability was the limited and variable sensitivity of the different microarray platforms for detecting weakly expressed genes, which affected interplatform and intersite reproducibility of differentially expressed genes. From this analysis, we conclude that the MAQC microarray data set has been validated by alternative quantitative gene expression platforms thus supporting the use of microarray platforms for the quantitative characterization of gene expression.     

Meta-analysis combines affymetrix microarray results across laboratories

With microarray technology becoming more prevalent in recent years, it is now common for several laboratories to employ the same microarray technology to identify differentially expressed genes that are related to the same phenomenon in the same species. Although experimental specifics may be similar, each laboratory will typically produce a slightly different list of statistically significant genes, which calls into question the validity of each gene list (i.e. which list is best). A statistically-based meta-analytic approach to microarray analysis systematically combines results from the different laboratories to provide a single estimate of the degree of differential expression for each gene. This approach provides a more precise view of genes that are of significant interest, while simultaneously allowing for differences between laboratories. The widely-used Affymetrix oligonucleotide array and its software are of particular interest because the results are naturally suited to a meta-analysis. A simulation model based on the Affymetrix platform is developed to examine the adaptive nature of the meta-analytic approach and to illustrate the utility of such an approach in combining microarray results across laboratories.     

Biomarker discovery using protein microarray technology platforms: antibody-antigen complex profiling

Protein microarrays represent an important new tool in proteomic systems biology. This review focuses on the contributions of protein microarrays to the discovery of novel disease biomarkers through antibody-based assays. Of particular interest is the use of protein microarrays for immune response profiling, through which a disease-specific antibody repertoire may be defined. The antigens and antibodies revealed by these studies are useful for clinical assay development, with enormous potential to aid in diagnosis, prognosis, disease staging and treatment selection. The discovery and characterization of novel biomarkers specifically tailored to disease type and stage are expected to enable personalized medicine by facilitating preventative medicine, predictive diagnostics and individualized curative therapies.     

Biomarker discovery using protein microarray technology platforms: antibody-antigen complex profiling

Protein microarrays represent an important new tool in proteomic systems biology. This review focuses on the contributions of protein microarrays to the discovery of novel disease biomarkers through antibody-based assays. Of particular interest is the use of protein microarrays for immune response profiling, through which a disease-specific antibody repertoire may be defined. The antigens and antibodies revealed by these studies are useful for clinical assay development, with enormous potential to aid in diagnosis, prognosis, disease staging and treatment selection. The discovery and characterization of novel biomarkers specifically tailored to disease type and stage are expected to enable personalized medicine by facilitating preventative medicine, predictive diagnostics and individualized curative therapies.     

Standardizing global gene expression analysis between laboratories and across platforms

To facilitate collaborative research efforts between multi-investigator teams using DNA microarrays, we identified sources of error and data variability between laboratories and across microarray platforms, and methods to accommodate this variability. RNA expression data were generated in seven laboratories, which compared two standard RNA samples using 12 microarray platforms. At least two standard microarray types (one spotted, one commercial) were used by all laboratories. Reproducibility for most platforms within any laboratory was typically good, but reproducibility between platforms and across laboratories was generally poor. Reproducibility between laboratories increased markedly when standardized protocols were implemented for RNA labeling, hybridization, microarray processing, data acquisition and data normalization. Reproducibility was highest when analysis was based on biological themes defined by enriched Gene Ontology (GO) categories. These findings indicate that microarray results can be comparable across multiple laboratories, especially when a common platform and set of procedures are used.     

Three microarray platforms: an analysis of their concordance in profiling gene expression

Background

Bacillus cereus, Bacillus anthracis and Bacillus thuringiensis belong to the genetically close-knit Bacillus cereus sensu lato group, a family of rod-shaped Gram-positive bacteria. pAW63 is the first conjugative plasmid from the B. cereus group to be completely sequenced.

Results

The 71,777 bp nucleotide sequence of pAW63 reveals a modular structure, including a 42 kb tra region encoding homologs of the Type IV secretion systems components VirB11, VirB4 and VirD4, as well as homologs of Gram-positive conjugation genes from Enterococcus, Lactococcus, Listeria, Streptococcus and Staphylococcus species. It also firmly establishes the existence of a common backbone between pAW63, pXO2 from Bacillus anthracis and pBT9727 from the pathogenic Bacillus thuringiensis serovar konkukian strain 97-27. The alignment of these three plasmids highlights the presence of well conserved segments, in contrast to distinct regions of high sequence plastiCity. The study of their specific differences has provided a three-point reference framework that can be exploited to formulate solid hypotheses concerning the functionalities and the molecular evolution of these three closely related plasmids. This has provided insight into the chronology of their divergence, and led to the discovery of two Type II introns on pAW63, matching copies of the mobile element IS231L in different loci of pXO2 and pBT9727, and the identification on pXO2 of a 37 kb pathogeniCity island (PAI) containing the anthrax capsule genes.

Conclusion

The complete sequence determination of pAW63 has led to a functional map of the plasmid yielding insights into its conjugative apparatus, which includes T4SS-like components, as well as its resemblance to other large plasmids of Gram-positive bacteria. Of particular interest is the extensive homology shared between pAW63 and pXO2, the second virulence plasmid of B. anthracis, as well as pBT9727 from the pathogenic strain B. thuringiensis serovar konkukian strain 97-27.