Microarray reviews 2006

Reviews of microarray experimental design and data analysis techniques published on 2006.

1: Methods Inf Med. 2006;45 Suppl 1:91-103.

Computational approaches to analysis of DNA microarray data.

Quackenbush J.

Objectives: To review the current state of the art in computational methods for
the analysis of DNA microarray data. Methods: The review considers methods of
microarray data collection, transformation and representation, comparisons and predictions of gene expression from the data, their mechanistic analysis, related systems biology, and the application of clustering techniques. Results:
Functional genomics approaches have greatly increased the rate at which data on biological systems is generated, leading to corresponding challenges in
analyzing the data through advanced computational techniques. The paper compares and contrasts the application of computational clustering for discovery, comparison, and prediction of gene expression classes, together with their evaluation and relation to mechanistic analyses of biological systems. Conclusion: Methods for assaying gene expression levels by DNA microarray experiments produce considerably more data than other techniques, and require a wide variety of computational techniques for identifying patterns of expression
that may be biologically significant. These will have to be verified and
validated by comparison to results from other methods, integrated with other
systems data, and provide the feedback for further experimentation for testing
mechanistic or other biological hypotheses.

2: Curr Mol Med. 2006 Sep;6(6):695-701.

Network theory to understand microarray studies of complex diseases.

Benson M, Breitling R.


Complex diseases, such as allergy, diabetes and obesity depend on altered
interactions between multiple genes, rather than changes in a single causal
gene. DNA microarray studies of a complex disease often implicate hundreds of genes in the pathogenesis. This indicates that many different mechanisms and pathways are involved. How can we understand such complexity? How can hypotheses be formulated and tested? One approach is to organize the data in network models and to analyze these in a top-down manner. Globally, networks in nature are often characterized by a small number of highly connected nodes, while the majority of nodes have few connections. The highly connected nodes serve as hubs that affect many other nodes. Such hubs have key roles in the network. In yeast cells, for example, deletion of highly connected proteins is associated with increased lethality, compared to deletion of less connected proteins. This suggests the biological relevance of networks. Moving down in the network structure, there may be sub-networks or modules with specific functions. These modules may be further dissected to analyze individual nodes. In the context of DNA microarray studies of complex diseases, gene-interaction networks may contain modules of co-regulated or interacting genes that have distinct biological functions. Such modules may be linked to specific gene polymorphisms, transcription factors, cellular functions and disease mechanisms. Genes that are reliably active only in the context of their modules can be considered markers
for the activity of the modules and may thus be promising candidates for
biomarkers or therapeutic targets. This review aims to give an introduction to
network theory and how it can be applied to microarray studies of complex
diseases.

3: Physiol Genomics. 2006 Sep 19; [Epub ahead of print]

A review of microarray experimental design strategies for genetical genomics
studies.

Rosa GJ, de Leon N, Rosa A.

Genetical genomics approaches provide a powerful tool for studying the genetic
mechanisms governing variation in complex traits. By combining information on phenotypic traits, pedigree structure, molecular markers and gene expression, such studies can be used for estimating heritability of mRNA transcript abundances, for mapping expression quantitative trait loci (eQTL), and for inferring regulatory gene networks. Microarray experiments, however, can be extremely costly and time consuming, which may limit sample sizes and statistical power. Thus it is crucial to optimize experimental designs by carefully choosing the subjects to be assayed, within a selective profiling
approach, and by cautiously controlling systematic factors affecting the system.
Also, a rigorous strategy should be used for allocating mRNA samples across
assay batches, slides and dye labeling, so that effects of interest are not
confounded with nuisance factors. In this presentation, we review some selective profiling strategies for genetical genomics studies, including the selection of individuals for increased genetic dissimilarity and for a higher number of recombination events. Efficient designs for studying epistasis are also discussed, as well as experiments for inferring heritability of transcriptional levels. It is shown that solving an optimal design problem generally requires a numerical implementation, and that the optimality criteria should be intimately
related to the goals of the experiment, such as the estimation of additive,
dominance and interacting effects, localizing putative eQTL, or inferring
genetical and environmental variance components associated with transcriptional
abundances. Key words: optimal design, selective phenotyping, transcriptional
profiling, gene expression, eQTL.

4: Expert Opin Biol Ther. 2006 Aug;6(8):833-8.

Antibody microarray-based oncoproteomics.

Borrebaeck CA.

The driving force behind oncoproteomics is the belief that certain protein
signatures or patterns exist that are associated with a particular malignancy.
If so, the correlation of clinical parameters with defined protein expression patterns would allow us to predict disease progression and perhaps even postulate improved therapeutic modalities. The technological challenges to achieve these goals are significant, as the human proteome is not defined. No
general methodological approach exists today, and human cancer can, furthermore, be divided into several disease subgroups. One potential solution to finding cancer-associated protein signatures is the emerging technology of affinity proteomics. This approach addresses some of the shortcomings of traditional proteomics and combines it with the power of microarrays. The present review
focuses on the role of antibody microarrays in oncoproteomics and its potential to provide a truly proteome-wide analytical approach.

5: Curr Opin Biotechnol. 2006 Aug;17(4):422-30. Epub 2006 Jul 12.

DNA microarray technologies for measuring protein-DNA interactions.

Bulyk ML.

DNA-binding proteins have key roles in many cellular processes, including
transcriptional regulation and replication. Microarray-based technologies permit the high-throughput identification of binding sites and enable the functional roles of these binding proteins to be elucidated. In particular, microarray readout either of chromatin immunoprecipitated DNA-bound proteins (ChIP-chip) or of DNA adenine methyltransferase fusion proteins (DamID) enables the identification of in vivo genomic target sites of proteins. A complementary approach to analyse the in vitro binding of proteins directly to double-stranded DNA microarrays (protein binding microarrays; PBMs), permits rapid characterization of their DNA binding site sequence specificities. Recent advances in DNA microarray synthesis technologies have facilitated the definition of DNA-binding sites at much higher resolution and coverage, and
advances in these and emerging technologies will further increase the
efficiencies of these exciting new approaches.

6: Ultrastruct Pathol. 2006 May-Jun;30(3):209-19.

DNA microarray applications in functional genomics.

Jares P.

The successful completion of the Human Genome Project and the achievement of
similar goals in other species have generated a huge amount of free available information about the genomic sequence of different organisms, opening the door to a postgenome era where new challenges arise. One of the most ambitious objectives of this new period, addressed by the emerging discipline of
functional genomics, attempts to understand the genome and the products it encodes for, and how these gene products interact to produce complex living organisms. This new era is also characterized by the development of new
technologies, which have produced genomic tools indispensable for understanding how gene products are regulated in normal and diseased conditions on a global genome scale. One of these technologies is DNA microarrays, turned into a very
popular tool in the last years. Although the most common use of DNA microarrays
is gene expression profiling, scientists have successfully used them for
multiple applications, including genotyping, sequencing, DNA copy number
analysis, and DNA-protein interactions, among others. In summary, DNA
microarrays are changing the way biomedicine and other disciplines are
addressing different biological questions and will allow the translation of genome research to the clinic.

7: NeuroRx. 2006 Jul;3(3):373-83.

The microarray data analysis process: from raw data to biological significance.

Olson NE.

Despite advances in microarray technology that have led to increased
reproducibility and substantial reductions in the cost of microarrays, the successful use of this technology is still elusive for many researchers, and
microarray data analysis in particular presents a substantial bottleneck for
many biomedical researchers. There are many reasons for this, including the
expense of and a lack of adequate training in the use of analysis software. An additional reason is that microarray data analysis has largely been treated in the past as a set of separate steps, with the majority of emphasis being placed on statistical analysis and visualization of the data. For many biomedical researchers determining the biological significance of the data has been the greatest challenge and in the last several years more emphasis has been placed on this aspect of the analysis process. Despite this broadening of the scope of
analysis there are still several aspects of the process that continue to be
neglected, including additional related and interdependent aspects, such as
experimental design, data accessibility, and platform selection. Though not
traditionally thought of as integral to the data analysis process, these factors
have profound effects on the analysis process. This article will discuss the
importance of these additional aspects, as well as statistical analysis and
determination of biological significance of microarray data. A summary of
currently available software options will also be presented with a focus on the aspects discussed.

8: Radiat Res. 2006 Jun;165(6):745-8.

Some statistical issues in microarray gene expression data.

Mayo MS, Gajewski BJ, Morris JS.

In this paper we discuss some of the statistical issues that should be
considered when conducting experiments involving microarray gene expression
data. We discuss statistical issues related to preprocessing the data as well as the analysis of the data. Analysis of the data is discussed in three contexts: class comparison, class prediction and class discovery. We also review the methods used in two studies that are using microarray gene expression to assess the effect of exposure to radiofrequency (RF) fields on gene expression. Our intent is to provide a guide for radiation researchers when conducting studies
involving microarray gene expression data.

9: Comb Chem High Throughput Screen. 2006 Jun;9(5):365-80.

Microarray technology as a universal tool for high-throughput analysis of
biological systems.

Sobek J, Bartscherer K, Jacob A, Hoheisel JD, Angenendt P.

Over the last years microarray technology has become one of the principal platform technologies for the high-throughput analysis of biological systems. Starting with the construction of first DNA microarrays in the 1990s, microarray technology has flourished in the last years and many different new formats have been developed. Peptide and protein microarrays are now applied for the elucidation of interaction partners, modification sites and enzyme substrates. Antibody microarrays are envisaged to be of high importance for the high-throughput determination of protein abundances in translational profiling approaches. First cell microarrays have been constructed to transform microarray technology from an in vitro technology to an in vivo functional analysis tool. All of these approaches share a common prerequisite: the solid support on which they are generated. The demands on this solid support are thereby as manifold as
the applications themselves. This review is aimed to display the recent
developments in surface chemistry and derivatization, and to summarize the
latest developments in the different application areas of microarray technology.

10: Brief Funct Genomic Proteomic. 2006 May 10; [Epub ahead of print]

Data merging for integrated microarray and proteomic analysis.

Waters KM, Pounds JG, Thrall BD.

The functioning of even a simple biological system is much more complicated than the sum of its genes, proteins and metabolites. A premise of systems biology is that molecular profiling will facilitate the discovery and characterization of important disease pathways. However, as multiple levels of effector pathway regulation appear to be the norm rather than the exception, a significant challenge presented by high-throughput genomics and proteomics technologies is
the extraction of the biological implications of complex data. Thus, integration of heterogeneous types of data generated from diverse global technology platforms represents the first challenge in developing the necessary foundational databases needed for predictive modelling of cell and tissue responses. Given the apparent difficulty in defining the correspondence between gene expression and protein abundance measured in several systems to date, how do we make sense of these data and design the next experiment? In this review, we highlight current approaches and challenges associated with integration and
analysis of heterogeneous data sets, focusing on global analysis obtained from high-throughput technologies.

11: Brief Bioinform. 2006 Mar;7(1):37-47.

Propagating uncertainty in microarray data analysis.

Rattray M, Liu X, Sanguinetti G, Milo M, Lawrence ND.

Microarray technology is associated with many sources of experimental
uncertainty. In this review we discuss a number of approaches for dealing with
this uncertainty in the processing of data from microarray experiments. We focus here on the analysis of high-density oligonucleotide arrays, such as the popular Affymetrix GeneChip array, which contain multiple probes for each target. This set of probes can be used to determine an estimate for the target concentration and can also be used to determine the experimental uncertainty associated with this measurement. This measurement uncertainty can then be propagated through the downstream analysis using probabilistic methods. We give examples showing how these credibility intervals can be used to help identify differential expression, to combine information from replicated experiments and to improve the performance of principal component analysis.

12: Methods Mol Biol. 2006;323:359-66.

Statistical issues in microarray data analysis.

Rensink WA, Hazen SP.

Microarrays provide the ability to quantitatively measure the abundance of
specific RNA transcripts through sample hybridization to a solid-state grid of
oligonucleotides or amplicons. The prospect of measuring the entire
transcriptome is extremely alluring, but as with any experiment, it should be
met with caution and great consideration. The level of confidence we can assign to the results depends on the skill at which the experiment is conducted, the quality of the experimental design and subsequent analysis, and, most important,
the power in the study. Any microarray experiment consists of several
components: (1) carrying out an appropriately designed (replicated) plant experiment; (2) array processing, which includes several steps of data
acquisition and normalization; and (3) analysis of expression data to identify
differentially expressed genes and overall patterns of expression. Numerous
software packages are available to assist in performing these steps and it is not our intent to provide a software users manual or a statistical review. It is our intent to provide a brief user's explanation of these various components and present the commonly used methods.

13: Expert Rev Mol Diagn. 2006 May;6(3):295-306.

DualChip microarray as a new tool in cancer research.

Gillet JP, de Longueville F, Remacle J.

Over the last 5 years, the emergence of gene expression profiling using
high-density DNA microarrays led to a better understanding of tumor development and identified new prognostic markers. However, high-density microarrays failed
to leap from the researcher's bench to the clinical practice due to their cost,
data management and lack of standardization. DualChip low-density DNA microarrays were developed as a new flexible tool that is able to reliably
quantify the expression of a limited number of genes of clinical relevance. This review will illustrate how DualChip technology can be applied to tumor diagnosis and tumor-acquired drug resistance.

14: Clin Exp Pharmacol Physiol. 2006 May-Jun;33(5-6):496-503.

DNA microarray technology for target identification and validation.

Jayapal M, Melendez AJ.

1. Microarrays, a recent development, provide a revolutionary platform to
analyse thousands of genes at once. They have enormous potential in the study of
biological processes in health and disease and, perhaps, microarrays have become crucial tools in diagnostic applications and drug discovery. 2. Microarray based studies have provided the essential impetus for biomedical experiments, such as identification of disease-causing genes in malignancies and regulatory genes in the cell cycle mechanism. Microarrays can identify genes for new and unique potential drug targets, predict drug responsiveness for individual patients and, finally, initiate gene therapy and prevention strategies. 3. The present article
reviews the principles and technological concerns, as well as the steps involved
in obtaining and analysing of data. Furthermore, applications of microarray
based experiments in drug target identifications and validation strategies are discussed. 4. To exemplify how this tool can be useful, in the present review we provide an overview of some of the past and potential future aspects of microarray technology and present a broad overview of this rapidly growing field.

15: Methods Mol Biol. 2006;316:111-57.

Standardization of microarray and pharmacogenomics data.

Husser CS, Buchhalter JR, Raffo OS, Shabo A, Brown SH, Lee KE, Elkin PL.

This chapter provides a bottom-up perspective on bioinformatics data standards, beginning with a historical perspective on biochemical nomenclature standards. Various file format standards were soon developed to convey increasingly complex and voluminous data that nomenclature alone could not effectively organize without additional structure and annotation. As areas of biochemistry and molecular biology have become more integral to the practice of modern medicine, broader data representation models have been created, from corepresentation of genomic and clinical data as a framework for drug research and discovery to the modeling of genotyping and pharmacogenomic therapy within the broader process of
the delivery of health care.

16: Methods Mol Biol. 2006;316:35-48.

From microarray to biological networks: Analysis of gene expression profiles.

Wu X, Dewey TG.

Powerful new methods, such as expression profiles using cDNA arrays, have been
used to monitor changes in gene expression levels as a result of a variety of metabolic, xenobiotic, or pathogenic challenges. This potentially vast quantity of data enables, in principle, the dissection of the complex genetic networks that control the patterns and rhythms of gene expression in the cell. Here we present a general approach to developing dynamic models for analyzing time series of whole-genome expression. The parameters in the model show the influence of one gene expression level on another and are calculated using singular value decomposition as a means of inverting noisy and near-singular matrices. Correlative networks can then be generated based on these parameters
with a simple threshold approach. We also demonstrate how dynamic models can be used in conjunction with cluster analysis to analyze microarray time series. Using the parameters from the dynamic model as a metric, two-way hierarchical clustering could be performed to visualize how influencing genes affect the expression levels of responding genes. Application of these approaches is demonstrated using gene expression data in yeast cell cycle.

17: Methods Mol Biol. 2006;316:13-33.

Basic microarray analysis: strategies for successful experiments.

Ness SA.

Microarrays offer a powerful approach to the analysis of gene expression that
can be used for a wide variety of experimental purposes. However, several types of microarray platforms are available. In addition, microarray experiments are expensive and generate complicated data sets that can be difficult to interpret. Success with microarray approaches requires a sound experimental design and a coordinated and appropriate use of statistical tools. Here, the advantages and
pitfalls of utilizing microarrays are discussed, as are practical strategies to help novice users succeed with this method that can empower them with the
ability to assay changes in gene expression at the whole-genome level.

18: Physiol Genomics. 2006 May 16;25(3):355-63. Epub 2006 Mar 22.

Microarray analysis of gene expression: considerations in data mining and statistical treatment.

Verducci JS, Melfi VF, Lin S, Wang Z, Roy S, Sen CK.

DNA microarray represents a powerful tool in biomedical discoveries. Harnessing the potential of this technology depends on the development and appropriate use of data mining and statistical tools. Significant current advances have made microarray data mining more versatile. Researchers are no longer limited to default choices that generate suboptimal results. Conflicting results in repeated experiments can be resolved through attention to the statistical
details. In the current dynamic environment, there are many choices and
potential pitfalls for researchers who intend to incorporate microarrays as a
research tool. This review is intended to provide a simple framework to
understand the choices and identify the pitfalls. Specifically, this review
article discusses the choice of microarray platform, preprocessing raw data, differential expression and validation, clustering, annotation and functional characterization of genes, and pathway construction in light of emergent concepts and tools.

19: Methods. 2006 Apr;38(4):312-6.

The flow cytometric analysis of cytokines using multi-analyte fluorescence
microarray technology.

Hill HR, Martins TB.

Cytokines, which are small peptides that act as hormones of the immune system,
affect cells throughout the body in a variety of different ways. These cellular signaling molecules often have synergistic or opposing effects on various cell types and often different cytokines have overlapping activities. There is great advantage, therefore, to be able to assess a pattern of cytokine responses in certain inflammatory, autoimmune, transplant or immunodeficiency states. This is
one of the major advantages of the new particle-based flow cytometric assays,
which have become available. We have employed such assays to analyze up to 10
different cytokines in cultured supernatants of stimulated mononuclear cells and in as little as 75 microL of serum from patients with a variety of different disorders. In developing these assays and validating them for use in our esoteric reference laboratory (ARUP Laboratories), we have found that a variety of heterophile antibodies can lead to both false positive and false negative results. This review will describe the development of our multi-analyte cytokine assays and document the interference derived from heterophile antibodies. Lastly, we will point out various procedures that we have utilized to include internal controls directly in the assays, which allow one to routinely detect these interfering antibodies, as well as methods we have developed to circumvent the interference posed by these antibodies.

20: Comb Chem High Throughput Screen. 2006 Mar;9(3):203-12.

Microarray: a versatile platform for high-throughput functional proteomics.

Hu Y, Uttamchandani M, Yao SQ.

The advent of microarray technologies has dramatically accelerated the
functional study of proteins, including enzymes (catalomics) in a proteome.
Herein, we review recent advances and exciting new developments of microarrays
in high-throughput functional proteomics.

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