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Microarrays are widely used in gene expression studies, and custom expression microarrays are popular to monitor expression changes of a customer-defined set of genes. However, the complexity of transcriptomes uncovered recently make custom expression microarray design a non-trivial task. Pervasive transcription and alternative processing of transcripts generate a wealth of interweaved transcripts that requires well-considered probe design strategies and is largely neglected in existing approaches. We developed the web server CEM-Designer that facilitates microarray platform independent design of custom expression microarrays for complex transcriptomes. CEM-Designer covers (i) the collection and generation of a set of unique target sequences from different sources and (ii) the selection of a set of sensitive and specific probes that optimally represents the target sequences. Probe design itself is left to third party software to ensure that probes meet provider-specific constraints. CEM-Designer is available at http://designpipeline.bioinf.uni-leipzig.de.

In this report we examine the limitations of existing microarray immunoassays and investigate how best to optimize them using theoretical and experimental approaches. Derived from DNA technology, microarray immunoassays present a major technological challenge with much greater physicochemical complexity. A key physicochemical limitation of the current generation of microarray immunoassays is a strong dependence of antibody microspot kinetics on the mass flux to the spot as was reported by us previously. In this report we analyze, theoretically and experimentally, the effects of microarray design parameters (incubation vessel geometry, incubation time, stirring, spot size, antibody-binding site density, etc.) on microspot reaction kinetics and sensitivity. Using a two-compartment model, the quantitative descriptors of the microspot reaction were determined for different incubation and microarray design conditions. This analysis revealed profound mass transport limitations in the observed kinetics, which may be slowed down as much as hundreds of times compared with the solution kinetics. The data obtained were considered with relevance to microspot assay diffusional and adsorptive processes, enabling us to validate some of the underlying principles of the antibody microspot reaction mechanism and provide guidelines for optimal microspot immunoassay design. For an assay optimized to maximize the reaction velocity on a spot, we demonstrate sensitivities in the am and low fm ranges for a system containing a representative sample of antigen-antibody pairs. In addition, a separate panel of low abundance cytokines in blood plasma was detected with remarkably high signal-to-noise ratios.

Background The probe percent bound value, calculated using multi-state equilibrium models of solution hybridization, is shown to be useful in understanding the hybridization behavior of microarray probes having 50 nucleotides, with and without mismatches. These longer oligonucleotides are in widespread use on microarrays, but there are few controlled studies of their interactions with mismatched targets compared to 25-mer based platforms. Principal Findings 50-mer oligonucleotides with centrally placed single, double and triple mismatches were spotted on an array. Over a range of target concentrations it was possible to discriminate binding to perfect matches and mismatches, and the type of mismatch could be predicted accurately in the concentration midrange (100 pM to 200 pM) using solution hybridization modeling methods. These results have implications for microarray design, optimization and analysis methods. Conclusions Our results highlight the importance of incorporating biophysical factors in both the design and the analysis of microarrays. Use of the probe “percent bound” value predicted by equilibrium models of hybridization is confirmed to be important for predicting and interpreting the behavior of long oligonucleotide arrays, as has been shown for short oligonucleotide arrays.

The neoglycolipid (NGL) technology for generating lipid-linked oligosaccharide probes has been shown to be an extremely useful tool for detection and characterization of bioactive oligosaccharides that are extremely minor components (Feizi and Child 1994). As the affinities of most carbohydrate-protein interactions are thought to be very low, and the oligosaccharide portions that play a role in biological function are generally believed to be of a small region ranging from monosaccharide to decasaccharide, the neoglycolipid technology is eminently adaptable for microarray design for high-throughput detection and specificity assignments of carbohydrate-protein interactions.

Assessing bacterial flora composition appears to be of increasing importance to fields as diverse as physiology, development, medicine, epidemiology, the environment, and the food industry. We report here the development and validation of an original microarray strategy that allows analysis of the phylogenic composition of complex bacterial mixtures. The microarray contains ∼9,500 feature elements targeting 16S rRNA gene-specific regions. Probe design was performed by selecting oligonucleotide sequences specific to each node of the seven levels of the bacterial phylogenetic tree (domain, phylum, class, order, family, genus, and species). This approach, based on sequence information, allows analysis of the bacterial contents of complex bacterial mixtures to detect both known and unknown microorganisms. The presence of unknown organisms can be suspected and mapped on the phylogenetic tree, indicating where to refine analysis. Initial proof-of-concept experiments were performed on oral bacterial communities. Our results show that this hierarchical approach can reveal minor changes (≤1%) in gingival flora content when samples collected in individuals from similar geographical origins are compared.

A remarkable feature of the Yeast Knockout strain collection is the presence of two unique 20mer TAG sequences in almost every strain. In principle, the relative abundances of strains in a complex mixture can be profiled swiftly and quantitatively by amplifying these sequences and hybridizing them to microarrays, but TAG microarrays have not been widely used. Here, we introduce a TAG microarray design with sophisticated controls and describe a robust method for hybridizing high concentrations of dye-labeled TAGs in single-stranded form. We also highlight the importance of avoiding PCR contamination and provide procedures for detection and eradication. Validation experiments using these methods yielded false positive (FP) and false negative (FN) rates for individual TAG detection of 3–6% and 15–18%, respectively. Analysis demonstrated that cross-hybridization was the chief source of FPs, while TAG amplification defects were the main cause of FNs. The materials, protocols, data and associated software described here comprise a suite of experimental resources that should facilitate the use of TAG microarrays for a wide variety of genetic screens.

Two-colour microarrays are used to study differential gene expression on a large scale. Experimental planning can help reduce the chances of wrong inferences about whether genes are differentially expressed. Previous research on this problem has focused on minimising estimation errors (according to variance-based criteria such as A-optimality) on the basis of optimistic assumptions about the system studied. In this paper, we propose a novel planning criterion to evaluate existing plans for microarray experiments. The proposed criterion is 'Generalised-A Optimality' that is based on realistic assumptions that include bias errors. Using Generalised-A Optimality, the reference-design approach is likely to yield greater estimation accuracy in specific situations in which loop designs had previously seemed superior. However, hybrid designs are likely to offer higher estimation accuracy than reference, loop and interwoven designs having the same number of samples and slides. These findings are supported by data from both simulated and real microarray experiments.

Background Microarray is a high-throughput technology to study expression of thousands of genes in parallel. A critical aspect of microarray production is the design aimed at space optimization while maximizing the number of gene probes and their replicates to be spotted.

Background DNA microarrays provide the ability to interrogate multiple genes in a single experiment and have revolutionized genomic research. However, the microarray technology suffers from various forms of biases and relatively low reproducibility. A particular source of false data has been described, in which non-random placement of gene probes on the microarray surface is associated with spurious correlations between genes. Results In order to assess the prevalence of this effect and better understand its origins, we applied an autocorrelation analysis of the relationship between chromosomal position and expression level to a database of over 2000 individual yeast microarray experiments. We show that at least 60% of these experiments exhibit spurious chromosomal position-dependent gene correlations, which nonetheless appear in a stochastic manner within each experimental dataset. Using computer simulations, we show that large spatial biases caused in the microarray hybridization step and independently of printing procedures can exclusively account for the observed spurious correlations, in contrast to previous suggestions. Our data suggest that such biases may generate more than 15% false data per experiment. Importantly, spatial biases are expected to occur regardless of microarray design and over a wide range of microarray platforms, organisms and experimental procedures. Conclusions Spatial biases comprise a major source of noise in microarray studies; revision of routine experimental practices and normalizations to account for these biases may significantly and comprehensively improve the quality of new as well as existing DNA microarray data.