Protein Arrays in Functional Genome Research

Whole-genome analyses become more and more necessary for pharmaceutical research. DNA chip hybridizations are an important tool for monitoring gene expression profiles during diseases or medical treatment. However, drug target identification and validation as well as an increasing number of antibodies and other polypeptides tested as potential drugs produce an increasing demand for genome-wide functional assays. Protein arrays are an important step into this direction. Peptide arrays and protein expression libraries are useful for the identification of antibodies and for epitope mapping. Antibody arrays allow protein quantification, protein binding studies, and protein phosphorylation assays. Tissue micro-arrays give a detailed information about the localization of macromolecules. More complex interactions can be addressed in cells spotted in array format. Finally, microfluidics chips enable us to describe the communication between cells in a tissue. In this review, possibilities, limitations and chances of different protein array techniques are discussed.     

RefPlus: an R package extending the RMA Algorithm

RMA has become a widely used methodology to pre-process Affymetrix gene expression microarrays. A limitation of RMA is that the calculated probeset intensities change when a set of microarrays is re-pre-processed after the inclusion of additional microarrays into the analysis set. Here we report the availability of the RefPlus package containing functions to perform the Extrapolation Strategy and Extrapolation Averaging algorithms which address these issues. AVAILABILITY: The software is implemented in the R language and can be downloaded from the Bioconductor project website (http://www.bioconductor.org). SUPPLEMENTARY INFORMATION: Further details of the workings and evaluation of these functions are given in the documentation available on the Bioconductor website.     

High-throughput polymorphism screening and genotyping with high-density oligonucleotide arrays

A highly reliable and efficient technology has been developed for high-throughput DNA polymorphism screening and large-scale genotyping. Photolithographic synthesis has been used to generate miniaturized, high-density oligonucleotide arrays. Dedicated instrumentation and software have been developed for array hybridization, fluorescent detection, and data acquisition and analysis. Specific oligonucleotide probe arrays have been designed to rapidly screen human STSs, known genes and full-length cDNAs. This has led to the identification of several thousand biallelic single-nucleotide polymorphisms (SNPs). Meanwhile, a rapid and robust method has been developed for genotyping these SNPs using oligonucleotide arrays. Each allele of an SNP marker is represented on the array by a set of perfect match and mismatch probes. Prototype genotyping chips have been produced to detect 400, 600 and 3000 of these SNPs. Based on the preliminary results, using oligonucleotide arrays to genotype several thousand polymorphic loci simultaneously appears feasible.     

Automated DNA chip annotation tables at IFOM: the importance of synchronisation and cross-referencing of sequence databases

The increasing popularity of DNA chip technology for the study of gene expression is producing, for each experiment, a sizable quantity of numerical data to analyse and an accompanying large number of gene identifiers that should be associated with the relevant biological annotation. We describe here a website at IFOM (FIRC Institute of Molecular Oncology) where we release regularly updated annotation tables for the most used Affymetrix oligonucleotide DNA chips and for the whole Research Genetics 46K clone collection for cDNA arrays. These tables are synchronised with every new release of the mouse and human UniGene databases (NCBI; National Center for Biotechnology Information), allowing fast and easy preliminary annotation of DNA array experiments. We also report some comparative evidence about the importance of biological database synchronisation and cross-references in the process of generating annotation tables for DNA chips.     

A summarization approach for Affymetrix GeneChip data using a reference training set from a large, biologically diverse database

Background  

Many of the most popular pre-processing methods for Affymetrix expression arrays, such as RMA, gcRMA, and PLIER, simultaneously analyze data across a set of predetermined arrays to improve precision of the final measures of expression. One problem associated with these algorithms is that expression measurements for a particular sample are highly dependent on the set of samples used for normalization and results obtained by normalization with a different set may not be comparable. A related problem is that an organization producing and/or storing large amounts of data in a sequential fashion will need to either re-run the pre-processing algorithm every time an array is added or store them in batches that are pre-processed together. Furthermore, pre-processing of large numbers of arrays requires loading all the feature-level data into memory which is a difficult task even with modern computers. We utilize a scheme that produces all the information necessary for pre-processing using a very large training set that can be used for summarization of samples outside of the training set. All subsequent pre-processing tasks can be done on an individual array basis. We demonstrate the utility of this approach by defining a new version of the Robust Multi-chip Averaging (RMA) algorithm which we refer to as refRMA.     

DNA芯片制作原理及其杂交信号检测方法

文章讨论了DNA芯片的制作原理和杂交信号的检测方法。依其结构,DNA芯片可分为两种形式,DNA阵列和寡核苷酸微芯片。DNA芯片的制作方法主要有光导原位合成法和自动化点样法。DNA芯片与标记的探针或DNA样品杂交,并通过探测杂交信号谱型来实现DNA序列或基因表达的分析。适应于DNA芯片的发展,同时出现了许多新型的杂交信号检测方法。主要有激光荧光扫描显微镜、激光扫描共焦显微镜、结合使用CCD相机的荧光显微镜、光纤生物传感器、化学发生法、光激发磷光物质存储屏法、光散射法等。     

Summaries of Affymetrix GeneChip probe level data

High density oligonucleotide array technology is widely used in many areas of biomedical research for quantitative and highly parallel measurements of gene expression. Affymetrix GeneChip arrays are the most popular. In this technology each gene is typically represented by a set of 11–20 pairs of probes. In order to obtain expression measures it is necessary to summarize the probe level data. Using two extensive spike-in studies and a dilution study, we developed a set of tools for assessing the effectiveness of expression measures. We found that the performance of the current version of the default expression measure provided by Affymetrix Microarray Suite can be significantly improved by the use of probe level summaries derived from empirically motivated statistical models. In particular, improvements in the ability to detect differentially expressed genes are demonstrated.     

A comparison of normalization methods for high density oligonucleotide array data based on variance and bias

MOTIVATION: When running experiments that involve multiple high density oligonucleotide arrays, it is important to remove sources of variation between arrays of non-biological origin. Normalization is a process for reducing this variation. It is common to see non-linear relations between arrays and the standard normalization provided by Affymetrix does not perform well in these situations. RESULTS: We present three methods of performing normalization at the probe intensity level. These methods are called complete data methods because they make use of data from all arrays in an experiment to form the normalizing relation. These algorithms are compared to two methods that make use of a baseline array: a one number scaling based algorithm and a method that uses a non-linear normalizing relation by comparing the variability and bias of an expression measure. Two publicly available datasets are used to carry out the comparisons. The simplest and quickest complete data method is found to perform favorably. AVAILABILITY: Software implementing all three of the complete data normalization methods is available as part of the R package Affy, which is a part of the Bioconductor project http://www.bioconductor.org. SUPPLEMENTARY INFORMATION: Additional figures may be found at http://www.stat.berkeley.edu/~bolstad/normalize/index.html     

DNA芯片技术研究进展

DNA芯片技术是近年来发展迅速的生物高技术 .其基本过程是采用寡核苷酸原位合成或显微打印手段 ,将大量探针片段有序地固化于支持物如硅芯片的表面 ,然后与扩增、标记的生物样品杂交 ,通过对杂交信号的检测分析 ,即可得出样品的遗传信息 .该技术不仅可以对遗传信息进行定性、定量分析 ,而且扩展到基因组研究和基因诊断等方面的应用 .尽管目前在硬件和软件上还面临一些困难 ,但其发展和应用的前景广阔 .     

Maskless fabrication of light-directed oligonucleotide microarrays using a digital micromirror array.

Oligonucleotide microarrays, also called "DNA chips," are currently made by a light-directed chemistry that requires a large number of photolithographic masks for each chip. Here we describe a maskless array synthesizer (MAS) that replaces the chrome masks with virtual masks generated on a computer, which are relayed to a digital micromirror array. A 1:1 reflective imaging system forms an ultraviolet image of the virtual mask on the active surface of the glass substrate, which is mounted in a flow cell reaction chamber connected to a DNA synthesizer. Programmed chemical coupling cycles follow light exposure, and these steps are repeated with different virtual masks to grow desired oligonucleotides in a selected pattern. This instrument has been used to synthesize oligonucleotide microarrays containing more than 76,000 features measuring 16 microm 2. The oligonucleotides were synthesized at high repetitive yield and, after hybridization, could readily discriminate single-base pair mismatches. The MAS is adaptable to the fabrication of DNA chips containing probes for thousands of genes, as well as any other solid-phase combinatorial chemistry to be performed in high-density microarrays.     

Evaluation of normalization procedures for oligonucleotide array data based on spiked cRNA controls

Background  

Affymetrix oligonucleotide arrays simultaneously measure the abundances of thousands of mRNAs in biological samples. Comparability of array results is necessary for the creation of large-scale gene expression databases. The standard strategy for normalizing oligonucleotide array readouts has practical drawbacks. We describe alternative normalization procedures for oligonucleotide arrays based on a common pool of known biotin-labeled cRNAs spiked into each hybridization.     

Fundamentals of DNA hybridization arrays for gene expression analysis

DNA hybridization arrays [also known as macroarrays, microarrays and/or high-density oligonucleotide arrays (Gene Chips)] bring gene expression analysis to a genomic scale by permitting investigators to simultaneously examine changes in the expression of literally thousands of genes. For hybridization arrays, the general approach is to immobilize gene-specific sequences (probes) on a solid state matrix (nylon membranes, glass microscope slides, silicon/ceramic chips). These sequences are then queried with labeled copies of nucleic acids from biological samples (targets). The underlying theory is that the greater the expression of a gene, the greater the amount of labeled target, and hence, the greater output signal. In spite of the simplicity of the experimental design, there are at least four different platforms and several different approaches to processing and labeling the biological samples. Moreover, investigators must also determine whether they will utilize commercially available arrays or generate their own. This review will cover the status of the hybridization array field with an eye toward underlying principles and available technologies. Future developments and technological trends will also be evaluated.     

Nucleotide sequence changes between Streptococcus pneumoniae R6 and D39 strains determined by an oligonucleotide hybridization DNA sequencing technology

The Gram-positive pathogen Streptococcus pneumoniae, which can be responsible for serious cases of pneumonia and meningitis, has been intensely studied for almost 100 years. Many of the key experiments have been performed in two strains; the non-pathogenic S. pneumoniae R6 and its pathogenic progenitor, S. pneumoniae D39. Whereas the genomic sequence of the R6 strain has been published, there is relatively little genomic information available on D39. Since R6 was derived from D39, we wished to explore the utility of a new technology, Comparative Genome Sequencing, which uses a set of custom oligonucleotide arrays to compare DNA sequences between similar strains. We report here the nucleotide polymorphisms identified between the R6 strain and D39 based on an R6 sequencing array. During the process, we were also able to confirm all of the high confidence changes reported by the oligonucleotide array chip by sequencing the region in the genome around the changes identified with the genome hybridization chip. We also discuss the potential impact of some of the amino acid changes found between these two widely used strains of pneumococci.     

生物信息学在基因芯片中的应用

生物信息学和基因芯片是生命科学研究领域中的两种新方法和新技术,生物信息学与基因芯片密切相关,生物信息学促进了基因芯片的研究与应用,而基因芯片则丰富了生物信息学的研究内容。本论文探讨生物信息学在基因芯片中的应用,将生物信息学方法运用到高密度基因芯片设计和芯片实验数据管理及分析。从信息学的角度提出基因芯片设计准则,提出寡核苷酸探针的优化设计方法,将该方法运用于再测序型芯片和基因表达型芯片的设计,在此基础上研制出高密度基因芯片设计软件系统和实验结果分析系统。     

Transcriptional signatures of normal human mammary epithelial cells in response to benzo[a]pyrene exposure: a comparison of three microarray platforms

Microarrays are used to study gene expression in a variety of biological systems. A number of different platforms have been developed, but few studies exist that have directly compared the performance of one platform with another. The goal of this study was to determine array variation by analyzing the same RNA samples with three different array platforms. Using gene expression responses to benzo[a]pyrene exposure in normal human mammary epithelial cells (NHMECs), we compared the results of gene expression profiling using three microarray platforms: photolithographic oligonucleotide arrays (Affymetrix), spotted oligonucleotide arrays (Amersham), and spotted cDNA arrays (NCI). While most previous reports comparing microarrays have analyzed pre-existing data from different platforms, this comparison study used the same sample assayed on all three platforms, allowing for analysis of variation from each array platform. In general, poor correlation was found with corresponding measurements from each platform. Each platform yielded different gene expression profiles, suggesting that while microarray analysis is a useful discovery tool, further validation is needed to extrapolate results for broad use of the data. Also, microarray variability needs to be taken into consideration, not only in the data analysis but also in specific probe selection for each array type.     

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.