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A simple integrated agarose microchip-based substrate is reported for surface enhanced Raman spectroscopy. The substrate was evaluated for the determination of sodium thiocyanate in milk. The limit of detection was 5 × 10−7 grams per milliliter. The microarray design allowed the synchronous processing of many samples. The reported procedure is green and practical for on-site determination of sodium thiocyanate in milk with potential for additional applications.

Serotyping Escherichia coli is a cumbersome and complex procedure due to the existence of large numbers of O- and H-antigen types. It can also be unreliable, as many Shiga toxin-producing E. coli (STEC) strains isolated from fresh produce cannot be typed by serology or have only partial serotypes. The FDA E. coli identification (FDA-ECID) microarray, designed for characterizing pathogenic E. coli , contains a molecular serotyping component, which was evaluated here for its efficacy. Analysis of a panel of 75 reference E. coli strains showed that the array correctly identified the O and H types in 97% and 98% of the strains, respectively. Comparative analysis of 73 produce STEC strains showed that serology and the array identified 37% and 50% of the O types, respectively, and that the array was able to identify 16 strains that could not be O serotyped. Furthermore, the array identified the H types of 97% of the produce STEC strains compared to 65% by serology, including six strains that were mistyped by serology. These results show that the array is an effective alternative to serology in serotyping environmental E. coli isolates.

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.

In this paper, we consider several variations of the following basic tiling problem: given a sequence of real numbers with two size-bound parameters, we want to find a set of tiles of maximum total weight such that each tiles satisfies the size bounds. A solution to this problem is important to a number of computational biology applications such as selecting genomic DNA fragments for PCR-based amplicon microarrays and performing homology searches with long sequence queries. Our goal is to design efficient algorithms with linear or near-linear time and space in the normal range of parameter values for these problems. For this purpose, we first discuss the solution to a basic online interval maximum problem via a sliding-window approach and show how to use this solution in a nontrivial manner for many of the tiling problems introduced. We also discuss NP-hardness results and approximation algorithms for generalizing our basic tiling problem to higher dimensions. Finally, computational results from applying our tiling algorithms to genomic sequences of five model eukaryotes are reported.

Microarrays provide a versatile platform for utilizing information from the Human Genome Project to benefit human health. This article reviews the ways in which microarray technology may be used in breast cancer research. Its diverse applications include monitoring chromosome gains and losses, tumour classification, drug discovery and development, DNA resequencing, mutation detection and investigating the mechanism of tumour development.

The manufacture and use of a whole-genome microarray is a complex process and it is essential that all data surrounding the process is stored, is accessible and can be easily associated with the data generated following hybridization and scanning. As part of a program funded by the Wellcome Trust, the Bacterial Microarray Group at St. George's Hospital Medical School (BμG@S) will generate whole-genome microarrays for 12 bacterial pathogens for use in collaboration with specialist research groups. BμG@S will collaborate with these groups at all levels, including the experimental design, methodology and analysis. In addition, we will provide informatic support in the form of a database system (BμG@Sbase). BμG@Sbase will provide access through a web interface to the microarray design data and will allow individual users to store their data in a searchable, secure manner. Tools developed by BμG@S in collaboration with specific research groups investigating analysis methodology will also be made available to those groups using the arrays and submitting data to BμG@Sbase.

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.

This paper explores the design problem of selecting a small subset of clones from a large pool for creation of a microarray plate. A new kernel based unsupervised feature selection method using the Hilbert---Schmidt independence criterion ( hsic ) is presented and evaluated on three microarray datasets: the Alon colon cancer dataset, the van 't Veer breast cancer dataset, and a multiclass cancer of unknown primary dataset. The experiments show that subsets selected by the hsic resulted in equivalent or betterperformance than supervised feature selection, with the added benefit that the subsets are not target specific.

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.

Expressed sequence tags (ESTs) provides a scope viewer of transcribed gene in organisms in depth. In this work, we establish a web-based system, called Bio301. Bio301 is an integrated system for computational analysis of expressed sequence tags (EST) and microarray design decision support. It provides EST cleaning processes, BLAST similarity analysis, gene ontology (GO) retrieval, GO browsing, statistics and recommend cDNA/EST clone list for microarray design. Furthermore, the Web interface of Bio301 is easy for users to manipulate and manage EST data like a virtual lab on Internet.

DNA duplex stability on oligonucleotide microarray was calculated using recently developed electrostatic theory of on-array hybridization thermodynamics. In this method, the first step is to finding the enthalpy and entropy of duplex formation in solution. This standard calculation was done with nearest-neighbor scheme and on-line software. Next the defined parameters and the array's single characteristic, the surface density of probes, are used to predict on-array duplex melting behavior. Reasonable accords of calculated and experimental melting curves for in situ synthesized microfluidic array were observed. The proposed method could be useful in microarray design and hybridization optimization. However, lack of melting curve measurements for different microarray platforms makes more experiments desirable to determine the method's accuracy.

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.

We discuss our concerns regarding the reliability of data generated by spotted cDNA microarrays. Two types of error we highlight are cross-hybridization artifact due to sequence homologies and sequence errors in the cDNA used for spotting on microarrays. We feel that statisticians who analyze microarray data should be aware of these sources of unreliability intrinsic to cDNA microarray design and use.

Microarray analysis provides simultaneous measure of gene expression levels in biological experiments. One difficulty of microarray analysis is the uncertainty evoked by large amount of instability due to various kinds of systemic and random fluctuations that can degrade the analysis. This thesis develops a dependency representation on the spot of a microarray design that we called consigned correlation pattern. A pattern distance is applied to evaluate a set of microarray data that can also identify spot groupings. A prediction rule from the spot signals is designed to predict the class of a microarray that can identify irrelevant range of the spot signal values. The advantage of such an approach is that global interdependency information is incorporated to evaluate the stability of the spot signals in the microarray design. Experiments using a relatively large set of microarray samples with a small number of spots on genes from six food-born bacterial strains are performed. The results yield interesting gene inter-relationships indicated by the spot signals. The groupings of the spot signals also reflect the effectiveness of the designed probes in identifying the bacterial strains. The prediction rule is found to be less susceptible to data with a large amount of irrelevant signals.

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.

DNA microarrays or DNA chips are a rapidly developing technology in the domain of nucleic acid analytics. They facilitate rapid, parallel, cheap and highly specific and sensitive assays for the analysis of biodiversity or genetic profiles. Thousands of reactions with short nucleic acid molecules (so called oligonucleotides or oligos) have to be optimized, and parameters and criteria have to be considered. This chip design or configuration, is the subject of this work.The oligos are designed to detect the presence of a given set of target sequences. Provided that an oligo gives a signal by hybridization and the target sequence is in the sample, this signal is called true positive. The detection of non target sequences, e.g. human DNA within a virus assay, is called false positive signal or cross hybridization. A statistic on these types of signals determine the specificity and sensitivity of the oligos or a whole oligo set. Another criterion is the hybridization efficiency, which is influenced by a lot of properties of the oligo and the target sequence, like secondary structures. To detect and cover all variations of highly variable virus genomes it is necessary to design a combinatorily optimized oligo set. This work shows three approaches for this set cover problem , considering the above mentioned criteria and properties of the oligos or the whole set. These are a modified greedy search, a combination of gradient descent and competition and genetic algorithms.A web based bioinformatics system optiNA optimal Nucleic Acids was developed that enables the design of combinatorily optimized oligo sets. It reduces the development process to a few days, there is the possibility to process large amounts of sequence data and work with a lot of parameters and conditions and elements of the hybridization protocol can be used to parametrize optiNA. The error-prone manual work with large amounts of data is unnecessary.

Large intergenic non-coding RNAs (lincRNAs) are emerging as key regulators of diverse cellular processes, yet determining the function of individual lincRNAs remains challenging. Recently, more than 8,000 human lincRNAs were annotated and cataloged from more than 4 billion RNA-Seq reads across 24 tissues and cell types by scientists at the Broad Institute of MIT and Harvard. Data from this project indicates that lincRNA expression is highly tissue-specific as compared to protein coding gene expression. As researchers continue to investigate the function of lincRNAs, there is a need for tools that can rapidly and accurately measure the expression of the recently annotated lincRNAs along with mRNA expression. We have previously developed and recently updated the content of the human SurePrint G3 microarrays so that they are comprised of all known protein-coding mRNAs and lincRNAs, to enable systematic profiling and simultaneous detection of coding and non-coding gene expression from a single sample. To demonstrate the utility of the new microarray design we used low nanogram amounts RNA from matched tumor and adjacent normal tissues to produce cyanine-labeled cRNA. The labeled cRNA was applied to the microarrays to detect differences in coding and non-coding gene expression profiles. Using the GeneSpring GX software we are able to identify differentially expressed lincRNAs and protein-coding RNAs in the tumor and normal samples in less than two days. Comparisons of probe signals from technical replicate samples demonstrated high reproducibility with wide dynamic ranges and high sensitivity. Data from the microarrays correlates well with whole transcriptome sequencing of the same matched tumor/normal samples. Using this approach we show that lincRNA expression coincides with key genes known to regulate biological processes involved in cancer progression and this work demonstrates how profiling mRNA and lincRNA from matched tumor and adjacent normal samples can enable researchers to further define the role of lincRNAs in gene regulation. Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 103rd Annual Meeting of the American Association for Cancer Research; 2012 Mar 31-Apr 4; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2012;72(8 Suppl):Abstract nr 1256. doi:1538-7445.AM2012-1256

In this study, we have developed a meander-shaped dynamic microfluidic technology that allows us to pair two different types of microbeads in a trapping site. The dynamic microfluidic technology comprises implemented modifications of a conventional dynamic microarray design such as: (i) the combination of a meander-shaped by-pass channel and a trapping channel with a hydrodynamic trapping site and (ii) line-symmetrical formation of the by-pass and trapping channels. Using these modifications, we have successfully trapped different types of sample in one trapping site, and constructed an array of paired beads of different type such as polystyrene beads or hydrogel beads made of agarose, collagen or alginate. We found that this meander-shaped dynamic microfluidic technology is applicable for the observation of interactions between the paired beads such as molecular diffusion.

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.