Stekel, D. Microarray bioinformatics

Microarray technology offers biologists the chance to measurethe expression levels of tens of thousands of mRNA species simultaneously,by quantifying fluorescence levels of dye-labelled mRNAs boundto their complementary targets on a glass slide. The design,execution and analysis of microarray experiments requires awide range of practical, computing and statistical knowledge.Gaining the necessary background information from the primaryliterature would be time consuming, hence the availability ofseveral text books in this field. According to the back cover,Dov Stekel’s book sets out to be a     

Chasing the dream: plant EST microarrays

DNA microarray technology is poised to make an important contribution to the field of plant biology. Stimulated by recent funding programs, expressed sequence tag sequencing and microarray production either has begun or is being contemplated for most economically important plant species. Although the DNA microarray technology is still being refined, the basic methods are well established. The real challenges lie in data analysis and data management. To fully realize the value of this technology, centralized databases that are capable of storing microarray expression data and managing information from a variety of sources will be needed. These information resources are under development and will help usher in a new era in plant functional genomics.     

Label-free detection methods for protein microarrays

With the growth of the "-omics" such as functional genomics and proteomics, one of the foremost challenges in biotechnologies has become the development of novel methods to monitor biological process and acquire the information of biomolecular interactions in a systematic manner. To fully understand the roles of newly discovered genes or proteins, it is necessary to elucidate the functions of these molecules in their interaction network. Microarray technology is becoming the method of choice for such a task. Although protein microarray can provide a high throughput analytical platform for protein profiling and protein-protein interaction, most of the current reports are limited to labeled detection using fluorescence or radioisotope techniques. These limitations deflate the potential of the method and prevent the technology from being adapted in a broader range of proteomics applications. In recent years, label-free analytical approaches have gone through intensified development and have been coupled successfully with protein microarray. In many examples of label-free study, the microarray has not only offered the high throughput detection in real time, but also provided kinetics information as well as in situ identification. This article reviews the most significant label-free detection methods for microarray technology, including surface plasmon resonance imaging, atomic force microscope, electrochemical impedance spectroscopy and MS and their applications in proteomics research.     

DNA microarray analyses in higher plants

DNA microarrays were originally devised and described as a convenient technology for the global analysis of plant gene expression. Over the past decade, their use has expanded enormously to cover all kingdoms of living organisms. At the same time, the scope of applications of microarrays has increased beyond expression analyses, with plant genomics playing a leadership role in the on-going development of this technology. As the field has matured, the rate-limiting step has moved from that of the technical process of data generation to that of data analysis. We currently face major problems in dealing with the accumulating datasets, not simply with respect to how to archive, access, and process the huge amounts of data that have been and are being produced, but also in determining the relative quality of the different datasets. A major recognized concern is the appropriate use of statistical design in microarray experiments, without which the datasets are rendered useless. A vigorous area of current research involves the development of novel statistical tools specifically for microarray experiments. This article describes, in a necessarily selective manner, the types of platforms currently employed in microarray research and provides an overview of recent activities using these platforms in plant biology.     

Gene expression signatures in primary immunodeficiencies: the experience from human disease and mouse models

Extensive research on molecular genetics in recent decades has provided a wealth of information regarding the underlying mechanisms of primary immunodeficiency diseases. The microarray technology has made its entry into the molecular biology research area and hereby enabled signature expression profiling of whole species genomes. Perhaps no other methodological approach has transformed molecular biology more in recent years than the use of microarrays. Microarray technology has led the way from studies of the individual biological functions of a few related genes, proteins or, at best, pathways towards more global investigations of cellular activity. The development of this technology immediately yielded new and interesting information, and has produced more data than can be currently dealt with. It has also helped to realize that even a 'horizontally exhaustive' molecular analysis is insufficient. Applications of this tool in primary immunodeficiency studies have generated new information, which has led to a better understanding of the underlying basic biology of the diseases. Also, the technology has been used as an exploratory tool to disease genes in immunodeficiency diseases of unknown cause as in the case of the CD3Delta-chain and the MAPBPIP deficiency. For X-linked agammaglobulinemia, the technique has provided better understanding of the genes influenced by Btk. There is considerable hope that the microarray technology will lead to a better understanding of disease processes and the molecular phenotypes obtained from microarray experiments may represent a new tool for diagnosis of the disease.     

Gene expression profiling: methodological challenges,results, and prospects for addiction research

This review describes the current methods used to profile gene expression. These methods include microarrays, spotted arrays, serial analysis of gene expression (SAGE), and massive parallel signature sequencing (MPSS). Methodological and statistical problems in interpreting microarray and spotted array experiments are also discussed. Methods and formats such as minimum information about microarray experiments (MIAME) needed to share gene expression data are described. The last part of the review provides an overview of the application of gene-expression profiling technology to substance abuse research and discusses future directions.     

The implications of microarray technology for animal use in scientific research

Microarray technology has the potential to affect the number of laboratory animals used, the severity of animal experiments, and the development of non-animal alternatives in several areas scientific research. Microarrays can contain hundreds or thousands of microscopic spots of DNA, immobilised on a solid support, and their use enables global patterns of gene expression to be determined in a single experiment. This technology is being used to improve our understanding of the operation of biological systems during health and disease, and their responses to chemical insults. Although it is impossible to predict with certainty any future trends regarding animal use, microarray technology might not initially reduce animal use, as is often claimed to be the case. The accelerated pace of research as a result of the use of microarrays could increase overall animal use in basic and applied biological research, by increasing the numbers of interesting genes identified for further analysis, and the number of potential targets for drug development. Each new lead will require further evaluation i n studies that could involve animals. In toxicity testing, microarray studies could lead to increases in animal studies, if further confirmatory and other studies are performed. However, before such technology can be used more extensively, several technical problems need to be overcome, and the relevance of the data to biological processes needs to be assessed. Were microarray technology to be used in the manner envisaged by its protagonists, there need to be efforts to increase the likelihood that its application will create new opportunities for reducing, refining and replacing animal use. This comment is a critical assessment of the possible implications of the application of microarray technology on animal experimentation in various research areas, and makes some recommendations for maximising the application of the Three Rs.     

Genomic Portraits of the Nervous System in Health and Disease

As the human genome project moves toward its goal of sequencing the entire human genome, gene expression profiling by DNA microarray technology is being employed to rapidly screen genes for biological information. In this review, we will introduce DNA microarray technology, outline the basic experimental paradigms and data analysis methods, and then show with some examples how gene expression profiling can be applied to the study of the central nervous system in health and disease.     

Proteome microarray technology and application: higher,wider, and deeper

ABSTRACT

Introduction: Protein microarray is a powerful tool for both biological study and clinical research. The most useful features of protein microarrays are their miniaturized size (low reagent and sample consumption), high sensitivity and their capability for parallel/high-throughput analysis. The major focus of this review is functional proteome microarray.

Areas covered: For proteome microarray, this review will discuss some recently constructed proteome microarrays and new concepts that have been used for constructing proteome microarrays and data interpretation in past few years, such as PAGES, M-NAPPA strategy, VirD technology, and the first protein microarray database. this review will summarize recent proteomic scale applications and address the limitations and future directions of proteome microarray technology.

Expert opinion: Proteome microarray is a powerful tool for basic biological and clinical research. It is expected to see improvements in the currently used proteome microarrays and the construction of more proteome microarrays for other species by using traditional strategies or novel concepts. It is anticipated that the maximum number of features on a single microarray and the number of possible applications will be increased, and the information that can be obtained from proteome microarray experiments will more in-depth in the future.     

Combining literature text mining with microarray data: advances for system biology modeling

A huge amount of important biomedical information is hidden in the bulk of research articles in biomedical fields. At the same time, the publication of databases of biological information and of experimental datasets generated by high-throughput methods is in great expansion, and a wealth of annotated gene databases, chemical, genomic (including microarray datasets), clinical and other types of data repositories are now available on the Web. Thus a current challenge of bioinformatics is to develop targeted methods and tools that integrate scientific literature, biological databases and experimental data for reducing the time of database curation and for accessing evidence, either in the literature or in the datasets, useful for the analysis at hand. Under this scenario, this article reviews the knowledge discovery systems that fuse information from the literature, gathered by text mining, with microarray data for enriching the lists of down and upregulated genes with elements for biological understanding and for generating and validating new biological hypothesis. Finally, an easy to use and freely accessible tool, GeneWizard, that exploits text mining and microarray data fusion for supporting researchers in discovering gene-disease relationships is described.     

Consistency,comprehensiveness, and compatibility of pathway databases

Background  

It is necessary to analyze microarray experiments together with biological information to make better biological inferences. We investigate the adequacy of current biological databases to address this need.     

Positional artifacts in microarrays: experimental verification and construction of COP, an automated detection tool

Microarray technology is currently one of the most widely-used technologies in biology. Many studies focus on inferring the function of an unknown gene from its co-expressed genes. Here, we are able to show that there are two types of positional artifacts in microarray data introducing spurious correlations between genes. First, we find that genes that are close on the microarray chips tend to have higher correlations between their expression profiles. We call this the 'chip artifact'. Our calculations suggest that the carry-over during the printing process is one of the major sources of this type of artifact, which is later confirmed by our experiments. Based on our experiments, the measured intensity of a microarray spot contains 0.1% (for fully-hybridized spots) to 93% (for un-hybridized ones) of noise resulting from this artifact. Secondly, we, for the first time, show that genes that are close on the microtiter plates in microarray experiments also tend to have higher correlations. We call this the 'plate artifact'. Both types of artifacts exist with different severity in all cDNA microarray experiments that we analyzed. Therefore, we develop an automated web tool-COP (COrrelations by Positional artifacts) to detect these artifacts in microarray experiments. COP has been integrated with the microarray data normalization tool, ExpressYourself, which is available at http://bioinfo.mbb.yale.edu/ExpressYourself/. Together, the two can eliminate most of the common noises in microarray data.     

Breaking the waves: improved detection of copy number variation from microarray-based comparative genomic hybridization

Background

Large-scale high throughput studies using microarray technology have established that copy number variation (CNV) throughout the genome is more frequent than previously thought. Such variation is known to play an important role in the presence and development of phenotypes such as HIV-1 infection and Alzheimer's disease. However, methods for analyzing the complex data produced and identifying regions of CNV are still being refined.

Results

We describe the presence of a genome-wide technical artifact, spatial autocorrelation or 'wave', which occurs in a large dataset used to determine the location of CNV across the genome. By removing this artifact we are able to obtain both a more biologically meaningful clustering of the data and an increase in the number of CNVs identified by current calling methods without a major increase in the number of false positives detected. Moreover, removing this artifact is critical for the development of a novel model-based CNV calling algorithm - CNVmix - that uses cross-sample information to identify regions of the genome where CNVs occur. For regions of CNV that are identified by both CNVmix and current methods, we demonstrate that CNVmix is better able to categorize samples into groups that represent copy number gains or losses.

Conclusion

Removing artifactual 'waves' (which appear to be a general feature of array comparative genomic hybridization (aCGH) datasets) and using cross-sample information when identifying CNVs enables more biological information to be extracted from aCGH experiments designed to investigate copy number variation in normal individuals.     

Studying the functional genomics of stress responses in loblolly pine with the Expresso microarray experiment management system

Conception, design, and implementation of cDNA microarray experiments present a variety of bioinformatics challenges for biologists and computational scientists. The multiple stages of data acquisition and analysis have motivated the design of Expresso, a system for microarray experiment management. Salient aspects of Expresso include support for clone replication and randomized placement; automatic gridding, extraction of expression data from each spot, and quality monitoring; flexible methods of combining data from individual spots into information about clones and functional categories; and the use of inductive logic programming for higher-level data analysis and mining. The development of Expresso is occurring in parallel with several generations of microarray experiments aimed at elucidating genomic responses to drought stress in loblolly pine seedlings. The current experimental design incorporates 384 pine cDNAs replicated and randomly placed in two specific microarray layouts. We describe the design of Expresso as well as results of analysis with Expresso that suggest the importance of molecular chaperones and membrane transport proteins in mechanisms conferring successful adaptation to long-term drought stress.     

Novel and simple transformation algorithm for combining microarray data sets

Background  

With microarray technology, variability in experimental environments such as RNA sources, microarray production, or the use of different platforms, can cause bias. Such systematic differences present a substantial obstacle to the analysis of microarray data, resulting in inconsistent and unreliable information. Therefore, one of the most pressing challenges in the field of microarray technology is how to integrate results from different microarray experiments or combine data sets prior to the specific analysis.     

DNA microarrays for functional plant genomics

DNA microarray technology is a key element in today's functional genomics toolbox. The power of the method lies in miniaturization, automation and parallelism permitting large-scale and genome-wide acquisition of quantitative biological information from multiple samples. DNA microarrays are currently fabricated and assayed by two main approaches involving either in situ synthesis of oligonucleotides (`oligonucleotide microarrays') or deposition of pre-synthesized DNA fragments (`cDNA microarrays') on solid surfaces. To date, the main applications of microarrays are in comprehensive, simultaneous gene expression monitoring and in DNA variation analyses for the identification and genotyping of mutations and polymorphisms. Already at a relatively early stage of its application in plant science, microarrays are being utilized to examine a range of biological issues including the circadian clock, plant defence, environmental stress responses, fruit ripening, phytochrome A signalling, seed development and nitrate assimilation. Novel insights are obtained into the molecular mechanisms co-ordinating metabolic pathways, regulatory and signalling networks. Exciting new information will be gained in the years to come not only from genome-wide expression analyses on a few model plant species, but also from extensive studies of less thoroughly studied species on a more limited scale. The value of microarray technology to our understanding of living processes will depend both on the amount of data to be generated and on its clever exploration and integration with other biological knowledge arising from complementary functional genomics tools for `profiling' the genome, proteome, metabolome and phenome.