Optimizing SNP microarray probe design for high accuracy microbial genotyping

Microarrays to characterize single nucleotide polymorphisms (SNPs) provide a cost-effective and rapid method (under 24 h) to genotype microbes as an alternative to sequencing. We developed a pipeline for SNP discovery and microarray design that scales to 100's of microbial genomes. Here we tested various SNP probe design strategies against 8 sequenced isolates of Bacillus anthracis to compare sequence and microarray data. The best strategy allowed probe length to vary within 32–40 bp to equalize hybridization free energy. This strategy resulted in a call rate of 99.52% and concordance rate of 99.86% for finished genomes. Other probe design strategies averaged substantially lower call rates (94.65–96.41%) and slightly lower concordance rates (99.64–99.80%). These rates were lower for draft than finished genomes, consistent with higher incidence of sequencing errors and gaps. Highly accurate SNP calls were possible in complex soil and blood backgrounds down to 1000 copies, and moderately accurate SNP calls down to 100 spiked copies. The closest genome to the spiked strain was correctly identified at only 10 spiked copies. Discrepancies between sequence and array data did not alter the SNP-based phylogeny, regardless of the probe design strategy, indicating that SNP arrays can accurately place unsequenced isolates on a phylogeny.     

Randomized probe selection algorithm for microarray design

DNA microarray technology, originally developed to measure the level of gene expression, has become one of the most widely used tools in genomic study. The crux of microarray design lies in how to select a unique probe that distinguishes a given genomic sequence from other sequences. Due to its significance, probe selection attracts a lot of attention. Various probe selection algorithms have been developed in recent years. Good probe selection algorithms should produce a small number of candidate probes. Efficiency is also crucial because the data involved are usually huge. Most existing algorithms are usually not sufficiently selective and quite a large number of probes are returned. We propose a new direction to tackle the problem and give an efficient algorithm based on randomization to select a small set of probes and demonstrate that such a small set of probes is sufficient to distinguish each sequence from all the other sequences. Based on the algorithm, we have developed probe selection software RandPS, which runs efficiently in practice. The software is available on our website (http://www.csc.liv.ac.uk/ approximately cindy/RandPS/RandPS.htm). We test our algorithm via experiments on different genomes (Escherichia coli, Saccharamyces cerevisiae, etc.) and our algorithm is able to output unique probes for most of the genes efficiently. The other genes can be identified by a combination of at most two probes.     

PhylArray: phylogenetic probe design algorithm for microarray

MOTIVATION: Microbial diversity is still largely unknown in most environments, such as soils. In order to get access to this microbial 'black-box', the development of powerful tools such as microarrays are necessary. However, the reliability of this approach relies on probe efficiency, in particular sensitivity, specificity and explorative power, in order to obtain an image of the microbial communities that is close to reality. RESULTS: We propose a new probe design algorithm that is able to select microarray probes targeting SSU rRNA at any phylogenetic level. This original approach, implemented in a program called 'PhylArray', designs a combination of degenerate and non-degenerate probes for each target taxon. Comparative experimental evaluations indicate that probes designed with PhylArray yield a higher sensitivity and specificity than those designed by conventional approaches. Applying the combined PhyArray/GoArrays strategy helps to optimize the hybridization performance of short probes. Finally, hybridizations with environmental targets have shown that the use of the PhylArray strategy can draw attention to even previously unknown bacteria.     

Evaluating oligonucleotide properties for DNA microarray probe design

Most current microarray oligonucleotide probe design strategies are based on probe design factors (PDFs), which include probe hybridization free energy (PHFE), probe minimum folding energy (PMFE), dimer score, hairpin score, homology score and complexity score. The impact of these PDFs on probe performance was evaluated using four sets of microarray comparative genome hybridization (aCGH) data, which included two array manufacturing methods and the genomes of two species. Since most of the hybridizing DNA is equimolar in CGH data, such data are ideal for testing the general hybridization properties of almost all candidate oligonucleotides. In all our data sets, PDFs related to probe secondary structure (PMFE, hairpin score and dimer score) are the most significant factors linearly correlated with probe hybridization intensities. PHFE, homology and complexity score are correlating significantly with probe specificities, but in a non-linear fashion. We developed a new PDF, pseudo probe binding energy (PPBE), by iteratively fitting dinucleotide positional weights and dinucleotide stacking energies until the average residue sum of squares for the model was minimized. PPBE showed a better correlation with probe sensitivity and a better specificity than all other PDFs, although training data are required to construct a PPBE model prior to designing new oligonucleotide probes. The physical properties that are measured by PPBE are as yet unknown but include a platform-dependent component. A practical way to use these PDFs for probe design is to set cutoff thresholds to filter out bad quality probes. Programs and correlation parameters from this study are freely available to facilitate the design of DNA microarray oligonucleotide probes.     

Mprobe 2.0: computer-aided probe design for oligonucleotide microarray

DNA chips have proven to be effective tools in detecting gene expression levels. Compared with DNA chips using complementary DNA as probes, oligonucleotide microarrays using oligonucleotides as probes have attracted great attention because of their well known advantages. The design of gene-specific probes for each target is essential to the development of oligonucleotide microarrays. We have previously reported the development of a probe design software termed Mprobe 1.0. Here, we present a new version of this software, termed Mprobe 2.0. Several new features are included in Mprobe 2.0. Firstly, a paradox-based sequence database management system has been developed and integrated into the software, which consequently allows interoperability with sequences in GenBank, EMBL, and FASTA formats. Secondly, in contrast to setting a fixed threshold for the secondary structure of probes in Mprobe 1.0 and other related software, Mprobe 2.0 employs a different method. After parameters such as GC type, probe melting temperature and GC contents have been evaluated, candidate probes are sorted by the free energy from high to low value, followed by specificity analysis. Thirdly, Mprobe 2.0 provides users with substantial parameter options in the visual mode. Mprobe 2.0 possesses an easier interface for users to manage sequences annotated in different formats and design the optimal probes for oligonucleotide microarrays and other applications. AVAILABILITY: The program is free for non-commercial users and can be downloaded from the web page http://www.biosun.org.cn/mprobe/ CONTACT: Wuju Li (wujuli@yahoo.com or liwj@nic.bmi.ac.cn).     

Modeling nonlinearity in dilution design microarray data

MOTIVATION: Dilution design (Mixed tissue RNA) has been utilized by some researchers to evaluate and assess the performance of multiple microarray platforms. Current microarray data analysis approaches assume that the quantified signal intensities are linearly related to the expression of the corresponding genes in the sample. However, there are sources of nonlinearity in microarray expression measurements. Such nonlinearity study in the expressions of the RNA mixtures provides a new way to analyze gene expression data, and we argue that the nonlinearity can reveal novel information for microarray data analysis. Therefore, we proposed a statistical model, called proportion model, which is based on the linear regression analysis. To approximately quantify the nonlinearity in the dilution design, a new calibration, beta ratio (BR) was derived from the proportion model. Furthermore, a new adjusted fold change (adj-FC) was proposed to predict the true FC without nonlinearity, in particular for large FC. RESULTS: We applied our method to one microarray dilution dataset. The experimental results indicated that, to some extent, there are global biases comparing with the linear assumption for the significant genes. Further analysis of those highly expressed genes with significant nonlinearity revealed some promising results, e.g. 'poison' effect was discovered for some genes in RNA mixtures. The adj-FCs of those genes with 'poison' effect, indicate that the nonlinearity can be also caused by the inherent feature of the genes besides signal noise and technical variation. Moreover, when percentage of overlapping genes (POG) was used as a cross-platform consistency measure, adj-FC outperformed simple fold change to show that Affymetrix and Illumina platforms are consistent. AVAILABILITY: The R codes which implements all described methods, and some Supplementary material, are freely available from http://www.utdallas.edu/~ying.liu/BetaRatio.htm     

Genomics and microarray for detection and diagnostics

Genomics provided biomedical scientists an inventory of all genes and sequences present in a living being. This provides an unique opportunity to the scientists to predict and study biological functions of these genes. The changes in the gene expression regulated by genomic sequences therefore reflect changes in the molecular processes working in a cell or tissue in response to external factors including exposure to toxic compounds and pathogens. Microarray offers a biotechnological revolution with the help of DNA chemistry, silicon chip technology and optics to be used to monitor gene expression for thousands of genes in one single experiment. Briefly, 20,000 to 100,000 unique DNA molecules get applied by a robot to the surface of silicon wafers (approximately the size of a microscope slide). Using a single microarray experiment, the expression level of 20,000 to 100,000 genes will be examined in one single experiment. Genomics and microarray have a significant role and impact on the design and development of modern detection and diagnostic tools in several different ways. Microarray tools are now used on regular basis for monitoring gene expression of large number of genes and also frequently applied to DNA sequence analysis, immunology, genotyping, and molecular diagnosing. For diagnostics, these tools can be used to distinguish and differentiate between different DNA fragments that differ by as little as a single nucleotide polymorphism (SNP). These microarrays can be divided based on the gene density spots that will be high density (>10,000 spots) per slide, medium (< 1000 > 100) and low density (< 100). High-density arrays have proven to be very useful in disease diagnosis especially in diagnosis and classification of different types of cancers. These microarray tools hold tremendous potential for pathogen detection, which will be comprised, of unique sets of genes (also referred to as "signatures") able to unambiguously identify the species and strain of pathogens of interest.     

A short-oligonucleotide microarray that allows improved detection of gastrointestinal tract microbial communities

Background  

The human gastrointestinal (GI) tract contains a diverse collection of bacteria, most of which are unculturable by conventional microbiological methods. Increasingly molecular profiling techniques are being employed to examine this complex microbial community. The purpose of this study was to develop a microarray technique based on 16S ribosomal gene sequences for rapidly monitoring the microbial population of the GI tract.     

New C-band protocol by heat denaturation in the presence of formamide

C-banding techniques detect the presence of constitutive heterochromatin, which is usually located in centromeric regions of chromosomes in the majority of analysed species. The common method for C-banding used over the last 30 years involves treatment with a mild alkali barium hydroxide 5% Ba(OH)2 at 50 degrees C for 5-15 min and subsequent incubation in salt solution (2 x SSC at 60 degrees C for 1 h). We here present a new, easy and reliable technique for C-banding, which basically involves heat denaturation of chromosomal DNA in the presence of formamide and incubation in 2 x SSC at room temperature.     

Oligonucleotide microarrays in microbial diagnostics

Oligonucleotide microarrays offer a fast, high-throughput alternative for the parallel detection of microbes from virtually any sample. The application potential spreads across most sectors of life sciences, including environmental microbiology and microbial ecology; human, veterinary, food and plant diagnostics; water quality control; industrial microbiology, and so on. The past two years have witnessed a rapid increase of research in this field. Many alternative techniques were developed and validated as seen in 'proof-of-concept' articles. Publications reporting on the application of oligonucleotide microarray technology for microbial diagnostics in microbiology driven projects have just started to appear. Current and future technical and bioinformatics developments will inevitably improve the potential of this technology further.     

Use of a three-color cDNA microarray platform to measure and control support-bound probe for improved data quality and reproducibility

Construction methodologies for cDNA microarrays lack the ability to determine array integrity prior to hybridization, leaving the array itself a source of uncontrolled experimental variation. We solved this problem through development of a three-color cDNA array platform whereby printed probes are tagged with fluorescein and are compatible with Cy3 and Cy5 target labeling dyes when using confocal laser scanners possessing narrow bandwidths. Here we use this approach to: (i) develop a tracking system to monitor the printing of probe plates at predicted coordinates; (ii) define the quantity of immobilized probe necessary for quality hybridized array data to establish pre-hybridization array selection criteria; (iii) investigate factors that influence probe availability for hybridization; and (iv) explore the feasibility of hybridized data filtering using element fluorescein intensity. A direct and significant relationship (R² = 0.73, P < 0.001) between pre-hybridization average fluorescein intensity and subsequent hybridized replicate consistency was observed, illustrating that data quality can be improved by selecting arrays that meet defined pre-hybridization criteria. Furthermore, we demonstrate that our three-color approach provides a means to filter spots possessing insufficient bound probe from hybridized data sets to further improve data quality. Collectively, this strategy will improve microarray data and increase its utility as a sensitive screening tool.     

Qualified predictions for microarray and proteomics pattern diagnostics with confidence machines

We focus on the problem of prediction with confidence and describe a recently developed learning algorithm called transductive confidence machine for making qualified region predictions. Its main advantage, in comparison with other classifiers, is that it is well-calibrated, with number of prediction errors strictly controlled by a given predefined confidence level. We apply the transductive confidence machine to the problems of acute leukaemia and ovarian cancer prediction using microarray and proteomics pattern diagnostics, respectively. We demonstrate that the algorithm performs well, yielding well-calibrated and informative predictions whilst maintaining a high level of accuracy.     

7-Aminoactinomycin as a fluorescent probe for DNA unwinding and denaturation

A fluorescent analogue of antibiotic actinomycin D, 7-aminoactinomycin D (7AAMD), which is widely used in molecular biology, was shown by steady-state, polarization, and phase fluorescent spectroscopy to bind primarily in the unwound regions of DNA with concomitant increase in its emission intensity. The maximum emission intensity of 7AAMD is observed for denatured DNA. Thus, 7AAMD may serve as a good indicator of DNA unwinding, denaturation, and fragmentation.     

Microfluidic lab-on-a-chip for microbial identification on a DNA microarray

A lab-on-a-chip for the rapid identification of microbial species has been developed for a water monitoring system. We employed highly parallel DNA microarrays for the direct profiling of microbial populations in a sample. For the integration and minimization of the DNA microarray protocols for bacterial identification, rRNA was selected as a target nucleotide for probe:target hybridization. In order to hybridize target rRNA onto the probe oligonucleotide, intact rRNA extracted fromE. coli rRNA was fragmented via chemical techniques in the lab-on-a-chip platform. The size of fragmented rRNA was less than 400 base pairs, which was confirmed by polyacrylamide gel electrophoresis. The fragmented rRNA was also labeled using fluorescent chemicals. The lab-on-a-chip for fragmentation and labeling includes a PDMS chaotic mixer for efficient mixing, operated by flow pressure. In addition, the fragmented rRNA was hybridized successfully on a DNA microarray with sample recirculation on a microfluidic platform. Our fragmentation and labeling technique will have far-reaching applications, which require rapid but complicated chemical genetic material processing on a lab-on-a-chip platform.     

On the reliability of probe diagnostics in RF plasma

The errors of probe diagnostics of the resting or slow-moving plasma of Q-machines or dc discharges are shown to lie typically within a range of ±(20–30)%. Problems of probe diagnostics of RF plasma and modern approaches to their solving are considered. The objectivity of probe diagnostics of RF plasma is established by the mutual agreement between the electron energy distribution functions measured in the same experiment using the Langmuir probes and the method of relative intensities of spectral lines.     

Metabolic engineering for improved microbial pentose fermentation

Global concern over the depletion of fossil fuel reserves, and the detrimental impact that combustion of these materials has on the environment, is focusing attention on initiatives to create sustainable approaches for the production and use of biofuels from various biomass substrates. The development of a low-cost, safe and eco-friendly process for the utilization of renewable resources to generate value-added products with biotechnological potential as well as robust microorganisms capable of efficient fermentation of all types of sugars are essential to underpin the economic production of biofuels from biomass feedstocks. Saccharomyces cerevisiae, the most established fermentation yeast used in large scale bioconversion strategies, does not however metabolise the pentose sugars, xylose and arabinose and bioengineering is required for introduction of efficient pentose metabolic pathways and pentose sugar transport proteins for bioconversion of these substrates. Our approach provided a basis for future experiments that may ultimately lead to the development of industrial S. cerevisiae strains engineered to express pentose metabolising proteins from thermophilic fungi living on decaying plant material and here we expand our original article and discuss the strategies implemented to improve pentose fermentation.