Better genechip microarray layouts by combining probe placement and embedding

The microarray layout problem is a generalization of the border length minimization problem, and asks to distribute oligonucleotide probes on a microarray and to determine their embeddings in the deposition sequence in such a way that the overall quality of the resulting synthesized probes is maximized. Because of its inherent computational complexity, it is traditionally attacked in several phases: partitioning, placement, and re-embedding. We present the first algorithm, Greedy+, that combines placement and embedding and that results in improved layouts in terms of border length and conflict index (a more realistic measure of probe quality), both on arrays of random probes and on existing Affymetrix GeneChip arrays. We also present a detailed study on the layouts of the latest GeneChip arrays, and show how Greedy+ can further improve layout quality by as much as 12% in terms of border length and 35% in terms of conflict index.     

lumi: a pipeline for processing Illumina microarray

Illumina microarray is becoming a popular microarray platform. The BeadArray technology from Illumina makes its preprocessing and quality control different from other microarray technologies. Unfortunately, most other analyses have not taken advantage of the unique properties of the BeadArray system, and have just incorporated preprocessing methods originally designed for Affymetrix microarrays. lumi is a Bioconductor package especially designed to process the Illumina microarray data. It includes data input, quality control, variance stabilization, normalization and gene annotation portions. In specific, the lumi package includes a variance-stabilizing transformation (VST) algorithm that takes advantage of the technical replicates available on every Illumina microarray. Different normalization method options and multiple quality control plots are provided in the package. To better annotate the Illumina data, a vendor independent nucleotide universal identifier (nuID) was devised to identify the probes of Illumina microarray. The nuID annotation packages and output of lumi processed results can be easily integrated with other Bioconductor packages to construct a statistical data analysis pipeline for Illumina data. Availability: The lumi Bioconductor package, www.bioconductor.org     

Characterization and simulation of cDNA microarray spots using a novel mathematical model

Background  

The quality of cDNA microarray data is crucial for expanding its application to other research areas, such as the study of gene regulatory networks. Despite the fact that a number of algorithms have been suggested to increase the accuracy of microarray gene expression data, it is necessary to obtain reliable microarray images by improving wet-lab experiments. As the first step of a cDNA microarray experiment, spotting cDNA probes is critical to determining the quality of spot images.     

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.     

DNA microarray probe preparation by gel isolation nested PCR

To develop a simplified method that can rapidly prepare DNA microarray probes in a massive scale, a lambda phage genomic DNA-fragments library was constructed for the microarray-probes collection. Four methods of DNA band recovery from the first PCR products were tested and compared. The DNA microarray probes were collected by a novel method of nested PCR that was mediated by gel isolation of the first PCR products. This method was named GIN-PCR. The probes that were prepared by this GIN-PCR technique were used as subjects to fabricate a DNA microarray. The results showed that a wooden toothpick was superior to the other 3 methods, since this technique can steadily transfer the DNA bands as the template of the second PCR after the first PCR. A group of probes were successfully collected and DNA microarrays were constructed using these probes. Hybridization results demonstrated that this technique of DNA recovery and probe preparation was rapid, efficient, and effective. We developed a cost-effective and less labor-intensive method for DNA microarray probe preparation by nested PCR that is mediated by wooden toothpick transfer of the DNA bands in the gel after electrophoresis.     

GenXHC: a probabilistic generative model for cross-hybridization compensation in high-density genome-wide microarray data

MOTIVATION: Microarray designs containing millions to hundreds of millions of probes that tile entire genomes are currently being released. Within the next 2 months, our group will release a microarray data set containing over 12,000,000 microarray measurements taken from 37 mouse tissues. A problem that will become increasingly significant in the upcoming era of genome-wide exon-tiling microarray experiments is the removal of cross-hybridization noise. We present a probabilistic generative model for cross-hybridization in microarray data and a corresponding variational learning method for cross-hybridization compensation, GenXHC, that reduces cross-hybridization noise by taking into account multiple sources for each mRNA expression level measurement, as well as prior knowledge of hybridization similarities between the nucleotide sequences of microarray probes and their target cDNAs. RESULTS: The algorithm is applied to a subset of an exon-resolution genome-wide Agilent microarray data set for chromosome 16 of Mus musculus and is found to produce statistically significant reductions in cross-hybridization noise. The denoised data is found to produce enrichment in multiple gene ontology-biological process (GO-BP) functional groups. The algorithm is found to outperform robust multi-array analysis, another method for cross-hybridization compensation.     

Ultrasonic approach to enhance signal-to-noise in three dimensional microarray

Three-dimensional polyacrylamide gel microarray (3-D gel microarray) has a strong adsorption for hybridzation probes with fluorescent label, resulting in high fluorescent background noise. The background noise has hampered the broad application of the microarray in scientific research. A low-frequency ultrasound of 40 kHz has been successfully employed to efficiently remove the nonspecifically bound fluorescent probes from the 3-D gel microarray. The method can significantly enhance the signal to noise ratio of the 3-D gel microarray and make it work better in single-nucleotide polymorphism genotyping.     

Multipathogen oligonucleotide microarray for environmental and biodefense applications

Food-borne pathogens are a major health problem. The large and diverse number of microbial pathogens and their virulence factors has fueled interest in technologies capable of detecting multiple pathogens and multiple virulence factors simultaneously. Some of these pathogens and their toxins have potential use as bioweapons. DNA microarray technology allows the simultaneous analysis of thousands of sequences of DNA in a relatively short time, making it appropriate for biodefense and for public health uses. This paper describes methods for using DNA microarrays to detect and analyze microbial pathogens. The FDA-1 microarray was developed for the simultaneous detection of several food-borne pathogens and their virulence factors including Listeria spp., Campylobacter spp., Staphylococcus aureus enterotoxin genes and Clostridium perfringens toxin genes. Three elements were incorporated to increase confidence in the microarray detection system: redundancy of genes, redundancy of oligonucleotide probes (oligoprobes) for a specific gene, and quality control oligoprobes to monitor array spotting and target DNA hybridization. These elements enhance the reliability of detection and reduce the chance of erroneous results due to the genetic variability of microbes or technical problems with the microarray. The results presented demonstrate the potential of oligonucleotide microarrays for detection of environmental and biodefense relevant microbial pathogens.     

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.     

Analysis of crucial factors resulting in microarray hybridization failure

The factors that affect the formation and stability of DNA/DNA duplexes are complicated and still mostly unknown. In this study attempts were made to look for the crucial factor affecting hybridization failure in DNA microarray assays. A comprehensive range of factors were investigated simultaneously using a 25-mer oligonucleotide Potyvirus microarray. These included steric hindrance, direct/indirect labelling types, distance of a probe to the fluorescent labelling end, target (the DNA fragment used to hybridize with microarray probes) strand types either single strand or double strand, probes without mismatch and with different numbers of mismatch nucleotides (up to 36%) and different mismatch locations (5' end, centre and 3' end), probe GC content and T(m), secondary structures of probes and targets, different target lengths (0.277 kb to ~1.3 kb) and concentrations (0.1-30 nM). The results showed that whilst most of these known factors were unlikely to be the main causes of failed hybridization, there was strong evidence suggesting that the viral amplicon target structure is the most crucial factor. However, computing predicted target secondary structures by Mfold showed no correlation with the hybridization results. One explanation is that the predicted target secondary structures are different from the real structures. Here we postulate that the real target structure might be a combination of secondary structures resulting in a three-dimensional structure from exposure to three types of sub-structures: (1) a completely exposed linear structure to allow probes access for the successful hybridization and showing strong fluorescent signals; (2) a partially exposed structure to allow unstable binding and showing weak fluorescent signals; (3) a closed structure resulting in failed hybridization. These results are very important for microarray based studies as they not only provide an explanation for some current controversial results, but also provide potential resolution for the future studies. Due to the lack of available software for predicting the true target structure, development of microarrays should conduct an initial oligonucleotide probe selection procedure and those probes with capacity to hybridize with the target should be considered for the microarray development.     

A novel strategy for microarray quality control using Bayesian networks

MOTIVATION: High-throughput microarray technologies enable measurements of the expression levels of thousands of genes in parallel. However, microarray printing, hybridization and washing may create substantial variability in the quality of the data. As erroneous measurements may have a drastic impact on the results by disturbing the normalization schemes and by introducing expression patterns that lead to incorrect conclusions, it is crucial to discard low quality observations in the early phases of a microarray experiment. A typical microarray experiment consists of tens of thousands of spots on a microarray, making manual extraction of poor quality spots impossible. Thus, there is a need for a reliable and general microarray spot quality control strategy. RESULTS: We suggest a novel strategy for spot quality control by using Bayesian networks, which contain many appealing properties in the spot quality control context. We illustrate how a non-linear least squares based Gaussian fitting procedure can be used in order to extract features for a spot on a microarray. The features we used in this study are: spot intensity, size of the spot, roundness of the spot, alignment error, background intensity, background noise, and bleeding. We conclude that Bayesian networks are a reliable and useful model for microarray spot quality assessment. SUPPLEMENTARY INFORMATION: http://sigwww.cs.tut.fi/TICSP/SpotQuality/.     

问号钩端螺旋体基因组DNA芯片的研制

目的 研制并初步评估问号钩端螺旋体(简称钩体)赖型赖株的基因组DNA芯片。方法 利用Primegens引物设计软件筛选出问号钩体赖型赖株全基因组中的特异性基因进行引物设计。对成功设计出相应引物的3 290个基因用聚合酶链反应方法进行扩增,以纯化后的产物点样制备芯片。并用双色荧光杂交策略对芯片质量进行了初步平估。结果 共获得3 290个基因产物用于点样。参考株自身杂交实验结果表明:该芯片有较高的点一致性、信噪比和较低的假阳性率。结论 成功制备了包含问号钩体赖型赖株3 290个目的基因的基因组DNA芯片,并可用于基于该芯片的问号钩体比较基因组学的研究。     

Sequence overlap between autosomal and sex-linked probes on the Illumina HumanMethylation27 microarray

The Illumina Infinium HumanMethylation27 BeadChip (Illumina 27k) microarray is a high-throughput platform capable of interrogating the human DNA methylome. In a search for autosomal sex-specific DNA methylation using this microarray, we discovered autosomal CpG loci showing significant methylation differences between the sexes. However, we found that the majority of these probes cross-reacted with sequences from sex chromosomes. Moreover, we determined that 6-10% of the microarray probes are non-specific and map to highly homologous genomic sequences. Using probes targeting different CpGs that are exact duplicates of each other, we investigated the precision of these repeat measurements and concluded that the overall precision of this microarray is excellent. In addition, we identified a small number of probes targeting CpGs that include single-nucleotide polymorphisms. Overall, our findings address several technical issues associated with the Illumina 27k microarray that, once considered, will enhance the analysis and interpretation of data generated from this platform.     

GeneCruiser: a web service for the annotation of microarray data

SUMMARY: GeneCruiser is a web service allowing users to annotate their genomic data by mapping microarray feature identifiers to gene identifiers from databases, such as UniGene, while providing links to web resources, such as the UCSC Genome Browser. It relies on a regularly updated database that retrieves and indexes the mappings between microarray probes and genomic databases. Genes are identified using the Life Sciences Identifier standard. AVAILABILITY: GeneCruiser is freely available in the following forms: Web service and Web application, http://www.genecruiser.org; GenePattern, GeneCruiser access has been integrated into our microarray analysis platform, GenePattern. http://www.genepattern.org.     

等Tm检测探针寡核苷酸芯片在p53突变热点检测中的应用

用等长探针检测基因的点突变,不同GC含量探针的碱基错分辨率很难均一。尝试利用探针近似等Tm的原则设计、制备了检测抑癌基因p53外显子7中密码子245、248、249单碱基突变及缺失的寡核苷酸芯片。实验得到较好的碱基错配分辨率,检测不同位点的碱基错配分辨率较为一致,芯片检测结果与测序结果一致。实验结果为制备检测p53常见热点突变的寡核酸芯片奠定了基础。     

Automatic registration of microarray images. II. Hexagonal grid

MOTIVATION: In the first part of this paper the author presented an efficient, robust and completely automated algorithm for spot and block indexing in microarray images with rectangular grids. Although the rectangular grid is currently the most common type of grouping the probes on microarray slides, there is another microarray technology based on bundles of optical fibers where the probes are packed in hexagonal grids. The hexagonal grid provides both advantages and drawbacks over the standard rectangular packing and of course requires adaptation and/or modification of the algorithm of spot indexing presented in the first part of the paper. RESULTS: In the second part of the paper the author presents a version of the spot indexing algorithm adapted for microarray images with spots packed in hexagonal structures. The algorithm is completely automated, works with hexagonal grids of different types and with different parameters of grid spacing and rotation as well as spot sizes. It can successfully trace the local and global distortions of the grid, including non-orthogonal transformations. Similar to the algorithm from part I, it scales linearly with the grid size, the time complexity is O(M), where M is total number of grid points in hexagonal grid. The algorithm has been tested both on CCD and scanned images with spot expression rates as low as 2%. The processing time of an image with about 50 000 hex grid points was less than a second. For images with high expression rates ( approximately 90%) the registration time is even smaller, around a quarter of a second. Supplementary information: http://fleece.ucsd.edu/~vit/Registration_Supplement.pdf