Microarray results: how accurate are they?

Techniques, procedures, and applications in host genetic analysis

This chapter overviews genetic techniques' fundamentals and methodological features, including different approaches, analyses, and applications that have contributed to advancing health and disease. The aim is to describe laboratory methodologies and analyses employed to understand the genetic landscape of different biological contexts, from conventional techniques to cutting-edge technologies. Besides describing detailed aspects of the polymerase chain reaction (PCR) and derived types as one of the principles for many novel techniques, we also discuss microarray analysis, next-generation sequencing, and genome editing technologies such as transcription activator-like effector nucleases (TALENs) and the clustered regularly interspaced short palindromic repeats (CRISPR) and CRISPR-associated (Cas) systems. These techniques study several phenotypes, ranging from autoimmune disorders to viral diseases. The significance of integrating diverse genetic methodologies and tools to understand host genetics comprehensively and addressing the ethical, legal, and social implications (ELSI) associated with using genetic information is highlighted. Overall, the methods, procedures, and applications in host genetic analysis provided in this chapter furnish researchers and practitioners with a roadmap for navigating the dynamic landscape of host-genome interactions.

Deep Learning-Based Prediction of Alzheimer's Disease Using Microarray Gene Expression Data

Alzheimer's disease is a genetically complex disorder, and microarray technology provides valuable insights into it. However, the high dimensionality of microarray datasets and small sample sizes pose challenges. Gene selection techniques have emerged as a promising solution to this challenge, potentially revolutionizing AD diagnosis. The study aims to investigate deep learning techniques, specifically neural networks, in predicting Alzheimer's disease using microarray gene expression data. The goal is to develop a reliable predictive model for early detection and diagnosis, potentially improving patient care and intervention strategies. This study employed gene selection techniques, including Singular Value Decomposition (SVD) and Principal Component Analysis (PCA), to pinpoint pertinent genes within microarray datasets. Leveraging deep learning principles, we harnessed a Convolutional Neural Network (CNN) as our classifier for Alzheimer's disease (AD) prediction. Our approach involved the utilization of a seven-layer CNN with diverse configurations to process the dataset. Empirical outcomes on the AD dataset underscored the effectiveness of the PCA-CNN model, yielding an accuracy of 96.60% and a loss of 0.3503. Likewise, the SVD-CNN model showcased remarkable accuracy, attaining 97.08% and a loss of 0.2466. These results accentuate the potential of our method for gene dimension reduction and classification accuracy enhancement by selecting a subset of pertinent genes. Integrating gene selection methodologies with deep learning architectures presents a promising framework for elevating AD prediction and promoting precision medicine in neurodegenerative disorders. Ongoing research endeavors aim to generalize this approach for diverse applications, explore alternative gene selection techniques, and investigate a variety of deep learning architectures.

Generating weighted and thresholded gene coexpression networks using signed distance correlation

Even within well-studied organisms, many genes lack useful functional annotations. One way to generate such functional information is to infer biological relationships between genes or proteins, using a network of gene coexpression data that includes functional annotations. Signed distance correlation has proved useful for the construction of unweighted gene coexpression networks. However, transforming correlation values into unweighted networks may lead to a loss of important biological information related to the intensity of the correlation. Here we introduce a principled method to construct weighted gene coexpression networks using signed distance correlation. These networks contain weighted edges only between those pairs of genes whose correlation value is higher than a given threshold. We analyse data from different organisms and find that networks generated with our method based on signed distance correlation are more stable and capture more biological information compared to networks obtained from Pearson correlation. Moreover, we show that signed distance correlation networks capture more biological information than unweighted networks based on the same metric. While we use biological data sets to illustrate the method, the approach is general and can be used to construct networks in other domains. Code and data are available on https://github.com/javier-pardodiaz/sdcorGCN.

Protein Sequencing, One Molecule at a Time

Despite tremendous gains over the past decade, methods for characterizing proteins have generally lagged behind those for nucleic acids, which are characterized by extremely high sensitivity, dynamic range, and throughput. However, the ability to directly characterize proteins at nucleic acid levels would address critical biological challenges such as more sensitive medical diagnostics, deeper protein quantification, large-scale measurement, and discovery of alternate protein isoforms and modifications and would open new paths to single-cell proteomics. In response to this need, there has been a push to radically improve protein sequencing technologies by taking inspiration from high-throughput nucleic acid sequencing, with a particular focus on developing practical methods for single-molecule protein sequencing (SMPS). SMPS technologies fall generally into three categories: sequencing by degradation (e.g., mass spectrometry or fluorosequencing), sequencing by transit (e.g., nanopores or quantum tunneling), and sequencing by affinity (as in DNA hybridization-based approaches). We describe these diverse approaches, which range from those that are already experimentally well-supported to the merely speculative, in this nascent field striving to reformulate proteomics.

Robust gene coexpression networks using signed distance correlation

MOTIVATION

Even within well studied organisms, many genes lack useful functional annotations. One way to generate such functional information is to infer biological relationships between genes/proteins, using a network of gene coexpression data that includes functional annotations. However, the lack of trustworthy functional annotations can impede the validation of such networks. Hence, there is a need for a principled method to construct gene coexpression networks that capture biological information and are structurally stable even in the absence of functional information.

RESULTS

We introduce the concept of signed distance correlation as a measure of dependency between two variables, and apply it to generate gene coexpression networks. Distance correlation offers a more intuitive approach to network construction than commonly used methods such as Pearson correlation and mutual information. We propose a framework to generate self-consistent networks using signed distance correlation purely from gene expression data, with no additional information. We analyse data from three different organisms to illustrate how networks generated with our method are more stable and capture more biological information compared to networks obtained from Pearson correlation or mutual information.

SUPPLEMENTARY INFORMATION

Supplementary Information and code are available at Bioinformatics and https://github.com/javier-pardodiaz/sdcorGCN online.

Current Achievements and Applications of Transcriptomics in Personalized Cancer Medicine

Over the last decades, transcriptome profiling emerged as one of the most powerful approaches in oncology, providing prognostic and predictive utility for cancer management. The development of novel technologies, such as revolutionary next-generation sequencing, enables the identification of cancer biomarkers, gene signatures, and their aberrant expression affecting oncogenesis, as well as the discovery of molecular targets for anticancer therapies. Transcriptomics contribute to a change in the holistic understanding of cancer, from histopathological and organic to molecular classifications, opening a more personalized perspective for tumor diagnostics and therapy. The further advancement on transcriptome profiling may allow standardization and cost reduction of its analysis, which will be the next step for transcriptomics to become a canon of contemporary cancer medicine.

CCR2 improves homing and engraftment of adipose-derived stem cells in dystrophic mice

Background

Dystrophinopathy, a common neuromuscular disorder caused by the absence of dystrophin, currently lacks effective treatments. Systemic transplantation of adipose-derived stem cells (ADSCs) is a promising treatment approach, but its low efficacy remains a challenge. Chemokine system-mediated stem cell homing plays a critical role in systemic transplantation. Here, we investigated whether overexpression of a specific chemokine receptor could improve muscle homing and therapeutic effects of ADSC systemic transplantation in dystrophic mice.

Methods

We analysed multiple microarray datasets from the Gene Expression Omnibus to identify a candidate chemokine receptor and then evaluated the protein expression of target ligands in different tissues and organs of dystrophic mice. The candidate chemokine receptor was overexpressed using the lentiviral system in mouse ADSCs, which were used for systemic transplantation into the dystrophic mice, followed by evaluation of motor function, stem cell muscle homing, dystrophin expression, and muscle pathology.

Results

Chemokine-profile analysis identified C–C chemokine receptor (CCR)2 as the potential target for improving ADSC homing. We found that the levels of its ligands C–C chemokine ligand (CCL)2 and CCL7 were higher in muscles than in other tissues and organs of dystrophic mice. Additionally, CCR2 overexpression improved ADSC migration ability and maintained their multilineage-differentiation potentials. Compared with control ADSCs, transplantation of those overexpressing CCR2 displayed better muscle homing and further improved motor function, dystrophin expression, and muscle pathology in dystrophic mice.

Conclusions

These results demonstrated that CCR2 improved ADSC muscle homing and therapeutic effects following systemic transplantation in dystrophic mice.

Transcriptomic biomarkers for predicting response to neoadjuvant treatment in oesophageal cancer

Oesophageal cancer is a devastating disease with poor outcomes and is the sixth leading cause of cancer death worldwide. In the setting of resectable disease, there is clear evidence that neoadjuvant chemotherapy and chemoradiotherapy result in improved survival. Disappointingly, only 15%-30% of patients obtain a histopathological response to neoadjuvant therapy, often at the expense of significant toxicity. There are no predictive biomarkers in routine clinical use in this setting and the ability to stratify patients for treatment could dramatically improve outcomes. In this review, we aim to outline current progress in evaluating predictive transcriptomic biomarkers for neoadjuvant therapy in oesophageal cancer and discuss the challenges facing biomarker development in this setting. We place these issues in the wider context of recommendations for biomarker development and reporting. The majority of studies focus on messenger RNA (mRNA) and microRNA (miRNA) biomarkers. These studies report a range of different genes involved in a wide variety of pathways and biological processes, and this is explained to a large extent by the different platforms and analysis methods used. Many studies are also vastly underpowered so are not suitable for identifying a candidate biomarker. Multiple molecular subtypes of oesophageal cancer have been proposed, although little is known about how these relate to clinical outcomes. We anticipate that the accumulating wealth of genomic and transcriptomic data and clinical trial collaborations in the coming years will provide unique opportunities to stratify patients in this poor-prognosis disease and recommend that future biomarker development incorporates well-designed retrospective and prospective analyses.

Time Course of Gene Expression Profile in Renal Ischemia and Reperfusion Injury in Mice

Ischemic renal failure is an inflammatory disease that can affect various organs, including the heart. The organ responds to the stimulus and undergoes tissue remodeling that can result in cardiac hypertrophy. This study aimed to characterize the cardiac global gene expression profile in renal ischemia/reperfusion (IR) model using microarray technology. To do that, left kidney ischemia was induced in male C57BL/6 mice for 60 minutes, followed by reperfusion (IR) for 5, 8, 15, or 20 days post ischemia (dpi). Total cardiac tissue RNA was extracted and hybridized to chips with 35,000 mouse genes. The GeneChip Mouse Genome 430 2.0 Array Expression chip (Affymetrix) was used, and CEL files generated were processed with DNA-Chip-Analyzer (dCHIP) software. Subsequent analysis considered only differences among groups of at least 1.2-fold (up or down) expression changes. Analyses of the samples indicated positive modulation of 17,413 genes and 405 pathways and negative modulation of 18,287 genes and 300 pathways. A narrower analysis of genes related to inflammation, metabolism, apoptosis, oxidative stress, and channels/ion transport was performance, and it was correlated with IR injury, corroborating previous data from literature. Renal IR induced a global shift in cardiac tissue gene expression; in particular, genes related to the inflammatory system and cardiomyocyte function were changed. The in-depth study of the cell signaling in the present study could stimulate the development of new therapeutic option to ameliorate the outcome of renal-IR-induced heart damage.

Unraveling the heterogeneity of IgM monoclonal gammopathies: a gene mutational and gene expression study

Immunoglobulin M (IgM) monoclonal gammopathies show considerable variability, involving three different stages of presentation: IgM monoclonal gammopathy of undetermined significance (IgM-MGUS), asymptomatic Waldenström's macroglobulinemia (AWM), and symptomatic WM (SWM). Despite recent findings about the genomic and transcriptomic characteristics of such disorders, we know little about the causes of this clinical heterogeneity or the mechanisms involved in the progression from indolent to symptomatic forms. To clarify these matters, we have performed a gene expression and mutational study in a well-characterized cohort of 69 patients, distinguishing between the three disease presentations in an attempt to establish the relationship with the clinical and biological features of the patients. Results showed that the frequency of genetic alterations progressively increased from IgM-MGUS to AWM and SWM. This means that, in contrast to MYD88 p.L265P and CXCR4 WHIM mutations, present from the beginning of the pathogenesis, most of them would be acquired during the course of the disease. Moreover, the expression study revealed a higher level of expression of genes belonging to the Toll-like receptor (TLR) signaling pathway in symptomatic versus indolent forms, which was also reflected in the disease presentation and prognosis. In conclusion, our findings showed that IgM monoclonal gammopathies present higher mutational burden as the disease progresses, in parallel to the upregulation of relevant pathogenic pathways. This study provides a translational view of the genomic basis of WM pathogenesis.

A novel and versatile dual fluorescent reporter tool for the study of gene expression and regulation in multi- and single copy number

To unravel intricate mechanisms of gene regulation it is imperative to work in physiologically relevant conditions and therefore preferentially in single copy constructs, which are not always easy to manipulate. Such in vivo studies are generally based on enzymatic assays, microarrays, RNA-seq, qRT-PCR, or multicopy reporter gene systems, frequently with β-galactosidase, luciferase or a fluorescent protein as reporter. Each method has its advantages and shortcomings and may require validation. Enzyme assays are generally reliable but may be quite complex, time consuming, and require a (expensive) substrate. Microarrays and RNA-seq provide a genome wide view of gene expression but may rapidly become expensive and time consuming especially for detailed studies with large numbers of mutants, different growth conditions and multiple time points. Multicopy reporter gene systems are handy to generate numerous constructs but may not provide accurate information due to titration effects of trans-acting regulatory elements. Therefore and in spite of the existence of various reporter systems, there is still need for an efficient and user-friendly tool for detailed studies and high throughput screenings. Here we develop and validate a novel and versatile fluorescent reporter tool to study gene regulation in single copy mode that enables real-time measurement. This tool bears two independent fluorescent reporters that allow high throughput screening and standardization, and combines modern efficient cloning methods (multicopy, in vitro manipulation) with classical genetics (in vivo homologous recombination with a stable, self-transmissible episome) to generate multi- and single copy reporter systems. We validate the system with constitutive and differentially regulated promoters and show that the tool can equally be used with heterologous transcription factors. The flexibility and versatility of this dual reporter tool in combination with an easy conversion from a multicopy plasmid to a stable, single copy reporter system makes this system unique and attractive for a variety of applications. Examples are in vivo studies of DNA-binding transcription factors (single copy) or screening of promoter and RBS libraries (multicopy) for synthetic biology purposes.

Challenges for MicroRNA Microarray Data Analysis

Microarray is a high throughput discovery tool that has been broadly used for genomic research. Probe-target hybridization is the central concept of this technology to determine the relative abundance of nucleic acid sequences through fluorescence-based detection. In microarray experiments, variations of expression measurements can be attributed to many different sources that influence the stability and reproducibility of microarray platforms. Normalization is an essential step to reduce non-biological errors and to convert raw image data from multiple arrays (channels) to quality data for further analysis. In general, for the traditional microarray analysis, most established normalization methods are based on two assumptions: (1) the total number of target genes is large enough (>10,000); and (2) the expression level of the majority of genes is kept constant. However, microRNA (miRNA) arrays are usually spotted in low density, due to the fact that the total number of miRNAs is less than 2,000 and the majority of miRNAs are weakly or not expressed. As a result, normalization methods based on the above two assumptions are not applicable to miRNA profiling studies. In this review, we discuss a few representative microarray platforms on the market for miRNA profiling and compare the traditional methods with a few novel strategies specific for miRNA microarrays.

Fatty acid extract from CLA-enriched egg yolks can mediate transcriptome reprogramming of MCF-7 cancer cells to prevent their growth and proliferation

Background

Our previous study showed that fatty acids extract obtained from CLA-enriched egg yolks (EFA-CLA) suppressed the viability of MCF-7 cancer cell line more effectively than extract from non-enriched egg yolks (EFA). In this study, we analysed the effect of EFA-CLA and EFA on transcriptome profile of MCF-7 cells by applying the whole Human Genome Microarray technology.

Results

We found that EFA-CLA and EFA treated cells differentially regulated genes involved in cancer development and progression. EFA-CLA, compared to EFA, positively increased the mRNA expression of TSC2 and PTEN tumor suppressors as well as decreased the expression of NOTCH1, AGPS, GNA12, STAT3, UCP2, HIGD2A, HIF1A, PPKAR1A oncogenes.

Conclusions

We show for the first time that EFA-CLA can regulate genes engaged in AKT/mTOR pathway and inhibiting cell cycle progression. The observed results are most likely achieved by the combined effect of both: incorporated CLA isomers and other fatty acids in eggs organically modified through hens’ diet. Our results suggest that CLA-enriched eggs could be easily available food products with a potential of a cancer chemopreventive agent.

Electronic supplementary material

The online version of this article (doi:10.1186/s12263-016-0537-z) contains supplementary material, which is available to authorized users.

Concordance between RNA-sequencing data and DNA microarray data in transcriptome analysis of proliferative and quiescent fibroblasts

DNA microarrays and RNA sequencing (RNA-seq) are major technologies for performing high-throughput analysis of transcript abundance. Recently, concerns have been raised regarding the concordance of data derived from the two techniques. Using cDNA libraries derived from normal human foreskin fibroblasts, we measured changes in transcript abundance as cells transitioned from proliferative growth to quiescence using both DNA microarrays and RNA-seq. The internal reproducibility of the RNA-seq data was greater than that of the microarray data. Correlations between the RNA-seq data and the individual microarrays were low, but correlations between the RNA-seq values and the geometric mean of the microarray values were moderate. The two technologies had good agreement when considering probes with the largest (both positive and negative) fold change (FC) values. An independent technique, quantitative reverse-transcription PCR (qRT-PCR), was used to measure the FC of 76 genes between proliferative and quiescent samples, and a higher correlation was observed between the qRT-PCR data and the RNA-seq data than between the qRT-PCR data and the microarray data.

Villain of Molecular Biology: Why are we not reproducible in research?

Worldwide, there is an issue of  irreproducibility in life science research. In the USA alone $28 billion per year spent on preclinical research is not reproducible. Within this opinion article, I provide a brief historical account of the discovery of the Watson-Crick DNA model and introduce another neglected model of DNA. This negligence may be one of the fundamental reasons behind irreproducibility in molecular biology research.

A New Combinatorial Optimization Approach for Integrated Feature Selection Using Different Datasets: A Prostate Cancer Transcriptomic Study

Background

The joint study of multiple datasets has become a common technique for increasing statistical power in detecting biomarkers obtained from smaller studies. The approach generally followed is based on the fact that as the total number of samples increases, we expect to have greater power to detect associations of interest. This methodology has been applied to genome-wide association and transcriptomic studies due to the availability of datasets in the public domain. While this approach is well established in biostatistics, the introduction of new combinatorial optimization models to address this issue has not been explored in depth. In this study, we introduce a new model for the integration of multiple datasets and we show its application in transcriptomics.

Methods

We propose a new combinatorial optimization problem that addresses the core issue of biomarker detection in integrated datasets. Optimal solutions for this model deliver a feature selection from a panel of prospective biomarkers. The model we propose is a generalised version of the (α,β)-k-Feature Set problem. We illustrate the performance of this new methodology via a challenging meta-analysis task involving six prostate cancer microarray datasets. The results are then compared to the popular RankProd meta-analysis tool and to what can be obtained by analysing the individual datasets by statistical and combinatorial methods alone.

Results

Application of the integrated method resulted in a more informative signature than the rank-based meta-analysis or individual dataset results, and overcomes problems arising from real world datasets. The set of genes identified is highly significant in the context of prostate cancer. The method used does not rely on homogenisation or transformation of values to a common scale, and at the same time is able to capture markers associated with subgroups of the disease.

Microarray-based RNA profiling of breast cancer: batch effect removal improves cross-platform consistency

Microarray is a powerful technique used extensively for gene expression analysis. Different technologies are available, but lack of standardization makes it challenging to compare and integrate data. Furthermore, batch-related biases within datasets are common but often not tackled. We have analyzed the same 234 breast cancers on two different microarray platforms. One dataset contained known batch-effects associated with the fabrication procedure used. The aim was to assess the significance of correcting for systematic batch-effects when integrating data from different platforms. We here demonstrate the importance of detecting batch-effects and how tools, such as ComBat, can be used to successfully overcome such systematic variations in order to unmask essential biological signals. Batch adjustment was found to be particularly valuable in the detection of more delicate differences in gene expression. Furthermore, our results show that prober adjustment is essential for integration of gene expression data obtained from multiple sources. We show that high-variance genes are highly reproducibly expressed across platforms making them particularly well suited as biomarkers and for building gene signatures, exemplified by prediction of estrogen-receptor status and molecular subtypes. In conclusion, the study emphasizes the importance of utilizing proper batch adjustment methods when integrating data across different batches and platforms.

Identification of phenotype deterministic genes using systemic analysis of transcriptional response

Systemic identification of deterministic genes for different phenotypes is a primary application of high-throughput expression profiles. However, gene expression differences cannot be used when the differences between groups are not significant. Therefore, novel methods incorporating features other than expression differences are required. We developed a promising method using transcriptional response as an operational feature, which is quantified as the correlation between expression levels of pathway genes and target genes of the pathway. We applied this method to identify causative genes associated with chemo-sensitivity to tamoxifen and epirubicin. Genes whose transcriptional response was dysregulated only in the drug-resistant patient group were chosen for in vitro validation in human breast cancer cells. Finally, we discovered two genes responsible for tamoxifen sensitivity and three genes associated with epirubicin sensitivity. The method we propose here can be widely applied to identify deterministic genes for different phenotypes with only minor differences in gene expression levels.

Integrative biological analysis for neuropsychopharmacology

Although advances in psychotherapy have been made in recent years, drug discovery for brain diseases such as schizophrenia and mood disorders has stagnated. The need for new biomarkers and validated therapeutic targets in the field of neuropsychopharmacology is widely unmet. The brain is the most complex part of human anatomy from the standpoint of number and types of cells, their interconnections, and circuitry. To better meet patient needs, improved methods to approach brain studies by understanding functional networks that interact with the genome are being developed. The integrated biological approaches--proteomics, transcriptomics, metabolomics, and glycomics--have a strong record in several areas of biomedicine, including neurochemistry and neuro-oncology. Published applications of an integrated approach to projects of neurological, psychiatric, and pharmacological natures are still few but show promise to provide deep biological knowledge derived from cells, animal models, and clinical materials. Future studies that yield insights based on integrated analyses promise to deliver new therapeutic targets and biomarkers for personalized medicine.

iPSC-derived neurons as a higher-throughput readout for autism: promises and pitfalls

The elucidation of disease etiologies and establishment of robust, scalable, high-throughput screening assays for autism spectrum disorders (ASDs) have been impeded by both inaccessibility of disease-relevant neuronal tissue and the genetic heterogeneity of the disorder. Neuronal cells derived from induced pluripotent stem cells (iPSCs) from autism patients may circumvent these obstacles and serve as relevant cell models. To date, derived cells are characterized and screened by assessing their neuronal phenotypes. These characterizations are often etiology-specific or lack reproducibility and stability. In this review, we present an overview of efforts to study iPSC-derived neurons as a model for autism, and we explore the plausibility of gene expression profiling as a reproducible and stable disease marker.

Transcriptome and proteome research in veterinary science: what is possible and what questions can be asked?

In recent years several technologies for the complete analysis of the transcriptome and proteome have reached a technological level which allows their routine application as scientific tools. The principle of these methods is the identification and quantification of up to ten thousands of RNA and proteins species in a tissue, in contrast to the sequential analysis of conventional methods such as PCR and Western blotting. Due to their technical progress transcriptome and proteome analyses are becoming increasingly relevant in all fields of biological research. They are mainly used for the explorative identification of disease associated complex gene expression patterns and thereby set the stage for hypothesis-driven studies. This review gives an overview on the methods currently available for transcriptome analysis, that is, microarrays, Ref-Seq, quantitative PCR arrays and discusses their potentials and limitations. Second, the most powerful current approaches to proteome analysis are introduced, that is, 2D-gel electrophoresis, shotgun proteomics, MudPIT and the diverse technological concepts are reviewed. Finally, experimental strategies for biomarker discovery, experimental settings for the identification of prognostic gene sets and explorative versus hypothesis driven approaches for the elucidation of diseases associated genes and molecular pathways are described and their potential for studies in veterinary research is highlighted.

Empirical comparison of cross-platform normalization methods for gene expression data

Background

Simultaneous measurement of gene expression on a genomic scale can be accomplished using microarray technology or by sequencing based methods. Researchers who perform high throughput gene expression assays often deposit their data in public databases, but heterogeneity of measurement platforms leads to challenges for the combination and comparison of data sets. Researchers wishing to perform cross platform normalization face two major obstacles. First, a choice must be made about which method or methods to employ. Nine are currently available, and no rigorous comparison exists. Second, software for the selected method must be obtained and incorporated into a data analysis workflow.

Results

Using two publicly available cross-platform testing data sets, cross-platform normalization methods are compared based on inter-platform concordance and on the consistency of gene lists obtained with transformed data. Scatter and ROC-like plots are produced and new statistics based on those plots are introduced to measure the effectiveness of each method. Bootstrapping is employed to obtain distributions for those statistics. The consistency of platform effects across studies is explored theoretically and with respect to the testing data sets.

Conclusions

Our comparisons indicate that four methods, DWD, EB, GQ, and XPN, are generally effective, while the remaining methods do not adequately correct for platform effects. Of the four successful methods, XPN generally shows the highest inter-platform concordance when treatment groups are equally sized, while DWD is most robust to differently sized treatment groups and consistently shows the smallest loss in gene detection. We provide an R package, CONOR, capable of performing the nine cross-platform normalization methods considered. The package can be downloaded at http://alborz.sdsu.edu/conor and is available from CRAN.

Comparative deep transcriptional profiling of four developing oilseeds

Transcriptome analysis based on deep expressed sequence tag (EST) sequencing allows quantitative comparisons of gene expression across multiple species. Using pyrosequencing, we generated over 7 million ESTs from four stages of developing seeds of Ricinus communis, Brassica napus, Euonymus alatus and Tropaeolum majus, which differ in their storage tissue for oil, their ability to photosynthesize and in the structure and content of their triacylglycerols (TAG). The larger number of ESTs in these 16 datasets provided reliable estimates of the expression of acyltransferases and other enzymes expressed at low levels. Analysis of EST levels from these oilseeds revealed both conserved and distinct species-specific expression patterns for genes involved in the synthesis of glycerolipids and their precursors. Independent of the species and tissue type, ESTs for core fatty acid synthesis enzymes maintained a conserved stoichiometry and a strong correlation in temporal profiles throughout seed development. However, ESTs associated with non-plastid enzymes of oil biosynthesis displayed dissimilar temporal patterns indicative of different regulation. The EST levels for several genes potentially involved in accumulation of unusual TAG structures were distinct. Comparison of expression of members from multi-gene families allowed the identification of specific isoforms with conserved function in oil biosynthesis. In all four oilseeds, ESTs for Rubisco were present, suggesting its possible role in carbon metabolism, irrespective of light availability. Together, these data provide a resource for use in comparative and functional genomics of diverse oilseeds. Expression data for more than 350 genes encoding enzymes and proteins involved in lipid metabolism are available at the 'ARALIP' website (http://aralip.plantbiology.msu.edu/).

Comprehensive assessment of array-based platforms and calling algorithms for detection of copy number variants

We have systematically compared copy number variant (CNV) detection on eleven microarrays to evaluate data quality and CNV calling, reproducibility, concordance across array platforms and laboratory sites, breakpoint accuracy and analysis tool variability. Different analytic tools applied to the same raw data typically yield CNV calls with <50% concordance. Moreover, reproducibility in replicate experiments is <70% for most platforms. Nevertheless, these findings should not preclude detection of large CNVs for clinical diagnostic purposes because large CNVs with poor reproducibility are found primarily in complex genomic regions and would typically be removed by standard clinical data curation. The striking differences between CNV calls from different platforms and analytic tools highlight the importance of careful assessment of experimental design in discovery and association studies and of strict data curation and filtering in diagnostics. The CNV resource presented here allows independent data evaluation and provides a means to benchmark new algorithms.

Transcriptomes of the B and T lineages compared by multiplatform microarray profiling

T and B lymphocytes are developmentally and functionally related cells of the immune system, representing the two major branches of adaptive immunity. Although originating from a common precursor, they play very different roles: T cells contribute to and drive cell-mediated immunity, whereas B cells secrete Abs. Because of their functional importance and well-characterized differentiation pathways, T and B lymphocytes are ideal cell types with which to understand how functional differences are encoded at the transcriptional level. Although there has been a great deal of interest in defining regulatory factors that distinguish T and B cells, a truly genomewide view of the transcriptional differences between these two cells types has not yet been taken. To obtain a more global perspective of the transcriptional differences underlying T and B cells, we exploited the statistical power of combinatorial profiling on different microarray platforms, and the breadth of the Immunological Genome Project gene expression database, to generate robust differential signatures. We find that differential expression in T and B cells is pervasive, with the majority of transcripts showing statistically significant differences. These distinguishing characteristics are acquired gradually, through all stages of B and T differentiation. In contrast, very few T versus B signature genes are uniquely expressed in these lineages, but are shared throughout immune cells.

Well differentiated follicular thyroid neoplasia: impact of molecular and technological advances on detection, monitoring and treatment

Our understanding of the molecular mechanisms responsible for follicular thyroid cell oncogenesis has been advanced significantly in recent years. Specific genetic alterations and the molecular pathways they affect have been associated with particular histologic subtypes of well-differentiated thyroid cancer and are now being evaluated for their utility as clinical tools with diagnostic, prognostic and even therapeutic relevance. This paper focuses on the most common and clinically relevant genetic alterations shown to be consistently associated with well-differentiated thyroid carcinoma. We review the impact of recent molecular and technological advances on thyroid cancer standard of care and the practice of clinical medicine.

Cross-platform comparison of microarray-based multiple-class prediction

High-throughput microarray technology has been widely applied in biological and medical decision-making research during the past decade. However, the diversity of platforms has made it a challenge to re-use and/or integrate datasets generated in different experiments or labs for constructing array-based diagnostic models. Using large toxicogenomics datasets generated using both Affymetrix and Agilent microarray platforms, we carried out a benchmark evaluation of cross-platform consistency in multiple-class prediction using three widely-used machine learning algorithms. After an initial assessment of model performance on different platforms, we evaluated whether predictive signature features selected in one platform could be directly used to train a model in the other platform and whether predictive models trained using data from one platform could predict datasets profiled using the other platform with comparable performance. Our results established that it is possible to successfully apply multiple-class prediction models across different commercial microarray platforms, offering a number of important benefits such as accelerating the possible translation of biomarkers identified with microarrays to clinically-validated assays. However, this investigation focuses on a technical platform comparison and is actually only the beginning of exploring cross-platform consistency. Further studies are needed to confirm the feasibility of microarray-based cross-platform prediction, especially using independent datasets.

Hippocampal gene profiling: toward a systems biology of the hippocampus

Transcriptomics and proteomics approaches give a unique perspective for understanding brain and hippocampal functions but also pose unique challenges because of the singular complexity of the nervous system. The proliferation of genome-wide expression studies during the last decade has provided important insight into the molecular underpinnings of brain anatomy, neural plasticity, and neurological diseases. Microarray technology has dominated transcriptomics research, but this situation is rapidly changing with the recent technological advances in high-throughput sequencing. The full potential of transcriptomics in the neurosciences will be achieved as a result of its integration with other "-omics" disciplines as well as the development of novel analytical bioinformatics and systems biology tools for meta-analysis. Here, we review some of the most relevant advances in the gene profiling of the hippocampus, its relationship with proteomics approaches, and the promising perspectives for the future.

Microarray analysis of gene expression during early development: a cautionary overview

The rise of the 'omics' technologies started nearly a decade ago and, among them, transcriptomics has been used successfully to contrast gene expression in mammalian oocytes and early embryos. The scarcity of biological material that early developmental stages provide is the prime reason why the field of transcriptomics is becoming more and more popular with reproductive biologists. The potential to amplify scarce mRNA samples and generate the necessary amounts of starting material enables the relative measurement of RNA abundance of thousands of candidates simultaneously. So far, microarrays have been the most commonly used high-throughput method in this field. Microarray platforms can be found in a wide variety of formats, from cDNA collections to long or short oligo probe sets. These platforms generate large amounts of data that require the integration of comparative RNA abundance values in the physiological context of early development for their full benefit to be appreciated. Unfortunately, significant discrepancies between datasets suggest that direct comparison between studies is difficult and often not possible. We have investigated the sample-handling steps leading to the generation of microarray data produced from prehatching embryo samples and have identified key steps that significantly impact the downstream results. This review provides a discussion on the best methods for the preparation of samples from early embryos for microarray analysis and focuses on the challenges that impede dataset comparisons from different platforms and the reasons why methodological benchmarking performed using somatic cells may not apply to the atypical nature of prehatching development.

An integrated bioinformatics approach to the characterization of influenza A/H5N1 viral sequences by microarray data: Implication for monitoring H5N1 emerging strains and designing appropriate influenza vaccines

In order to characterize A/H5N1 viral sequences, a bioinformatics approach accurately identified viral sequences from discovery of a sequence signature, which provided enough distinctive information for sequence identification. Eight highly pathogenic H5N1 viral isolations were collected from different areas of Thailand between 2003 and 2006, and were used for analysis of H5N1 genotypic testing with a semiconductor-based oligonucleotide microarray. All H5N1 samples and H1N1, H4N8 negative controls were correctly subtyped. Sensitivity of the eight oligonucleotide probes, with optimized cut-offs, ranged from 70% (95% CI 65-75) to 87% (95% CI 84-91), and the corresponding Kappa values ranged from 0.76 (95% CI 0.72-0.80) to 0.86 (95% CI 0.83-0.89). Semi-conductor-based oligonucleotide array and oligonucleotide probes corresponded well when detecting H5N1. After fully correcting the subtype from the result of microarray signal intensity, the microarray output method combined with bioinformatics tools, identified and monitored genetic variations of H5N1. Capability of distinguishing different strains of H5N1 from Thailand was the outstanding feature of this assay. Ninety percent of HA and NA (4/5) genes were sequenced correctly, in accordance with previous examinations performed by classical diagnostic methods. The low-medium-high bioinformatics resolutions were able to predict an epidemic strain of H5N1. This study also showed the advantage of using a large genotypic database to predict the epidemic strain of H5N1. However, the monitoring protocol of this new strain has been recommended for further study with a large-scale sample.

Application of broad-spectrum resequencing microarray for genotyping rhabdoviruses

The rapid and accurate identification of pathogens is critical in the control of infectious disease. To this end, we analyzed the capacity for viral detection and identification of a newly described high-density resequencing microarray (RMA), termed PathogenID, which was designed for multiple pathogen detection using database similarity searching. We focused on one of the largest and most diverse viral families described to date, the family Rhabdoviridae. We demonstrate that this approach has the potential to identify both known and related viruses for which precise sequence information is unavailable. In particular, we demonstrate that a strategy based on consensus sequence determination for analysis of RMA output data enabled successful detection of viruses exhibiting up to 26% nucleotide divergence with the closest sequence tiled on the array. Using clinical specimens obtained from rabid patients and animals, this method also shows a high species level concordance with standard reference assays, indicating that it is amenable for the development of diagnostic assays. Finally, 12 animal rhabdoviruses which were currently unclassified, unassigned, or assigned as tentative species within the family Rhabdoviridae were successfully detected. These new data allowed an unprecedented phylogenetic analysis of 106 rhabdoviruses and further suggest that the principles and methodology developed here may be used for the broad-spectrum surveillance and the broader-scale investigation of biodiversity in the viral world.

Standards affecting the consistency of gene expression arrays in clinical applications

The use of microarray technology to measure gene expression has created optimism for the feasibility of using molecular assessments of tumors routinely in the clinical management of cancer. Gene expression arrays have been pioneers in the development of standards; both for research use and now for clinical application. Some of the existing standards have been driven by the early perception that microarray technology was inconsistent and perhaps unreliable. More recent experimentation has shown that reproducible data can be achieved and clinical standards are beginning to emerge. For the transcriptional assessment of tumors, this means a system that correctly samples a tumor, isolates RNA and processes this for microarray analysis, evaluates the data, and communicates findings in a consistent and timely fashion. The most important standard is to show that a clinically important assessment can be made with microarray data. The standards emerging from work on various parts of the entire process could guide the development of a workable system. However, the final standard for each component of the process depends on the accuracy required when the assay becomes part of the clinical routine: a routine that now includes the molecular evaluation of tumors.

Microarray probes and probe sets

DNA microarrays have gained wide use in biomedical research by simultaneously monitoring the expression levels of a large number of genes. The successful implementation of DNA microarray technologies requires the development of methods and techniques for the fabrication of microarrays, the selection of probes to represent genes, the quantification of hybridization, and data analysis. In this paper, we concentrate on probes that are either spotted or synthesized on the glass slides through several aspects: sources of probes, the criteria for selecting probes, tools available for probe selections, and probes used in commercial microarray chips. We then provide a detailed review of one type of DNA microarray: Affymetrix GeneChips, discuss the need to re-annotate probes, review different methods for regrouping probes into probe sets, and compare various redefinitions through public available datasets.

A white-box approach to microarray probe response characterization: the BaFL pipeline

BACKGROUND

Microarrays depend on appropriate probe design to deliver the promise of accurate genome-wide measurement. Probe design, ideally, produces a unique probe-target match with homogeneous duplex stability over the complete set of probes. Much of microarray pre-processing is concerned with adjusting for non-ideal probes that do not report target concentration accurately. Cross-hybridizing probes (non-unique), probe composition and structure, as well as platform effects such as instrument limitations, have been shown to affect the interpretation of signal. Data cleansing pipelines seldom filter specifically for these constraints, relying instead on general statistical tests to remove the most variable probes from the samples in a study. This adjusts probes contributing to ProbeSet (gene) values in a study-specific manner. We refer to the complete set of factors as biologically applied filter levels (BaFL) and have assembled an analysis pipeline for managing them consistently. The pipeline and associated experiments reported here examine the outcome of comprehensively excluding probes affected by known factors on inter-experiment target behavior consistency.

RESULTS

We present here a 'white box' probe filtering and intensity transformation protocol that incorporates currently understood factors affecting probe and target interactions; the method has been tested on data from the Affymetrix human GeneChip HG-U95Av2, using two independent datasets from studies of a complex lung adenocarcinoma phenotype. The protocol incorporates probe-specific effects from SNPs, cross-hybridization and low heteroduplex affinity, as well as effects from scanner sensitivity, sample batches, and includes simple statistical tests for identifying unresolved biological factors leading to sample variability. Subsequent to filtering for these factors, the consistency and reliability of the remaining measurements is shown to be markedly improved.

CONCLUSIONS

The data cleansing protocol yields reproducible estimates of a given probe or ProbeSet's (gene's) relative expression that translates across datasets, allowing for credible cross-experiment comparisons. We provide supporting evidence for the validity of removing several large classes of probes, and for our approaches for removing outlying samples. The resulting expression profiles demonstrate consistency across the two independent datasets. Finally, we demonstrate that, given an appropriate sampling pool, the method enhances the t-test's statistical power to discriminate significantly different means over sample classes.

New approaches for functional genomic studies in staphylococci

Functional transcriptomics studies have resulted in interesting insights into Staphylococcus aureus diversity and pathogenicity. Here we review the principles, advantages and disadvantages of recent technical developments in the field of transcriptomics and their potential impact on S. aureus research.

Hypoxic transcription gene profiles under the modulation of nitric oxide in nuclear run on-microarray and proteomics

Background

Microarray analysis still is a powerful tool to identify new components of the transcriptosome. It helps to increase the knowledge of targets triggered by stress conditions such as hypoxia and nitric oxide. However, analysis of transcriptional regulatory events remain elusive due to the contribution of altered mRNA stability to gene expression patterns as well as changes in the half-life of mRNAs, which influence mRNA expression levels and their turn over rates. To circumvent these problems, we have focused on the analysis of newly transcribed (nascent) mRNAs by nuclear run on (NRO), followed by microarray analysis.

Results

We identified 196 genes that were significantly regulated by hypoxia, 85 genes affected by nitric oxide and 292 genes induced by the cotreatment of macrophages with both NO and hypoxia. Fourteen genes (Bnip3, Ddit4, Vegfa, Trib3, Atf3, Cdkn1a, Scd1, D4Ertd765e, Sesn2, Son, Nnt, Lst1, Hps6 and Fxyd5) were common to all treatments but with different levels of expression in each group. We observed that 162 transcripts were regulated only when cells were co-treated with hypoxia and NO but not with either treatment alone, pointing to the importance of a crosstalk between hypoxia and NO. In addition, both array and proteomics data supported a consistent repression of hypoxia-regulated targets by NO.

Conclusion

By eliminating the interference of steady state mRNA in gene expression profiling, we obtained a smaller number of significantly regulated transcripts in our study compared to published microarray data and identified previously unknown hypoxia-induced targets. Gene analysis profiling corroborated the interplay between NO- and hypoxia-induced signaling.

Microarrays and high-throughput transcriptomic analysis in species with incomplete availability of genomic sequences

Microarrays produce a measurement of gene expression based on the relative measures of dye intensities that correspond to the amount of target RNA. This technology is fast developing and its application is expanding from Homo sapiens to a wide number of species, where enough information on sequences and annotations exist. Anyway, the number of species for which a dedicated platform exists is not high. The use of heterologous array hybridization, screening for gene expression in one species using an array developed for another one, is still quite frequent, even though cross-species microarray hybridization has raised many arguments. Some methods which are high throughput and do not rely on knowledge of the DNA/RNA sequence exist, namely serial analysis of gene expression (SAGE), Massively Parallel Signature Sequencing (MPSS) and deep sequencing of full transcriptome. Although very powerful, particularly the latter, they are still quite costly and cumbersome methods. In some species where genome sequences are largely unknown, several anonymous sequences are deposited in gene banks as a result of Expressed Sequence Tags (ESTs) sequencing projects. The ESTs databases represent a valuable knowledge that can be exploited with some bioinformatic effort to build species-specific microarrays. We present here a method of high-density in situ synthesized microarrays starting from available EST sequences in, Ovis aries. Our data indicate that the method is very efficient and can be easily extended to other species of which genetic sequences are present in public databases, but neglected so far with advanced devices like microarrays. As a perspective, the approach can be applied also to species of which no sequences are available to date, thanks to high-throughput deep sequencing methods.

Lossless compression of microarray images using image-dependent finite-context models

The use of microarray expression data in state-of-the-art biology has been well established. The widespread adoption of this technology, coupled with the significant volume of data generated per experiment, in the form of images, has led to significant challenges in storage and query retrieval. In this paper, we present a lossless bitplane-based method for efficient compression of microarray images. This method is based on arithmetic coding driven by image-dependent multibitplane finite-context models. It produces an embedded bitstream that allows progressive, lossy-to-lossless decoding. We compare the compression efficiency of the proposed method with three image compression standards (JPEG2000, JPEG-LS, and JBIG) and also with the two most recent specialized methods for microarray image coding. The proposed method gives better results for all images of the test sets and confirms the effectiveness of bitplane-based methods and finite-context modeling for the lossless compression of microarray images.

hag expression in Bacillus subtilis is both negatively and positively regulated by ScoC

In Bacillus subtilis, motility and chemotaxis require the expression of hag, which encodes flagellin. This gene is transcribed by the sigma(D) form of RNA polymerase and is regulated by a group of proteins called transition state regulators (TSRs). Our studies show that hag transcription is negatively regulated by the transition state regulator ScoC, by binding to its promoter. Furthermore, ScoC, indirectly, also positively regulates hag by increasing the availability of sigma(D) by downregulating the levels of the anti-sigma(D)-factor FlgM. We further show that the positive regulation by ScoC predominates over the negative regulation.

Target identification for CNS diseases by transcriptional profiling

Gene expression changes in neuropsychiatric and neurodegenerative disorders, and gene responses to therapeutic drugs, provide new ways to identify central nervous system (CNS) targets for drug discovery. This review summarizes gene and pathway targets replicated in expression profiling of human postmortem brain, animal models, and cell culture studies. Analysis of isolated human neurons implicates targets for Alzheimer's disease and the cognitive decline associated with normal aging and mild cognitive impairment. In addition to tau, amyloid-beta precursor protein, and amyloid-beta peptides (Abeta), these targets include all three high-affinity neurotrophin receptors and the fibroblast growth factor (FGF) system, synapse markers, glutamate receptors (GluRs) and transporters, and dopamine (DA) receptors, particularly the D2 subtype. Gene-based candidates for Parkinson's disease (PD) include the ubiquitin-proteosome system, scavengers of reactive oxygen species, brain-derived neurotrophic factor (BDNF), its receptor, TrkB, and downstream target early growth response 1, Nurr-1, and signaling through protein kinase C and RAS pathways. Increasing variability and decreases in brain mRNA production from middle age to old age suggest that cognitive impairments during normal aging may be addressed by drugs that restore antioxidant, DNA repair, and synaptic functions including those of DA to levels of younger adults. Studies in schizophrenia identify robust decreases in genes for GABA function, including glutamic acid decarboxylase, HINT1, glutamate transport and GluRs, BDNF and TrkB, numerous 14-3-3 protein family members, and decreases in genes for CNS synaptic and metabolic functions, particularly glycolysis and ATP generation. Many of these metabolic genes are increased by insulin and muscarinic agonism, both of which are therapeutic in psychosis. Differential genomic signals are relatively sparse in bipolar disorder, but include deficiencies in the expression of 14-3-3 protein members, implicating these chaperone proteins and the neurotransmitter pathways they support as possible drug targets. Brains from persons with major depressive disorder reveal decreased expression for genes in glutamate transport and metabolism, neurotrophic signaling (eg, FGF, BDNF and VGF), and MAP kinase pathways. Increases in these pathways in the brains of animals exposed to electroconvulsive shock and antidepressant treatments identify neurotrophic and angiogenic growth factors and second messenger stimulation as therapeutic approaches for the treatment of depression.

Downregulation of Ccnd1 and Hes6 in rat hippocampus after chronic exposure to the antidepressant paroxetine

OBJECTIVE

The mechanism of action of antidepressant drugs is not fully understood. Application of genomic methods enables the identification of biochemical pathways that are regulated by antidepressants, and this may provide novel clues to the molecular and cellular actions of these drugs. The present study examined gene expression profiles in the hippocampus of rats exposed to chronic antidepressant treatment.

METHODS

Animals were treated for 12 days with the selective serotonin reuptake inhibitor paroxetine; then, hippocampal ribonucleic acid was recovered, and changes in gene expression were assessed by microarray analysis.

RESULTS

A total of 160 genes that showed differential expression after paroxetine exposure were identified. Using functional relevance and observed fold change as selection criteria, the expression changes in a subset of these genes were confirmed by quantitative polymerase chain reaction.

CONCLUSION

Of this subset, only two genes, cyclin D1 (Ccnd1) and hairy and enhancer of split 6 (Hes6), showed robust and consistent changes in expression. Both genes were downregulated by paroxetine, and both have been previously implicated in neurogenesis. Further investigation of these two genes may provide new insight into the mechanism of action of antidepressants.

Identification of novel neuroendocrine-specific tumour genes

Neuroendocrine tumours (NETs) comprise a heterogenous group of malignancies with an often unpredictable course, and with limited treatment options. Thus, new diagnostic, prognostic, and therapeutic markers are needed. To shed new lights into the biology of NETs, we have by cDNA transcript profiling, sought to identify genes that are either up- or downregulated in NE as compared with non-NE tumour cells. A panel of six NET and four non-NET cell lines were examined, and out of 12 743 genes examined, we studied in detail the 200 most significantly differentially expressed genes in the comparison. In addition to potential new diagnostic markers (NEFM, CLDN4, PEROX2), the results point to genes that may be involved in the tumorigenesis (BEX1, TMEPAI, FOSL1, RAB32), and in the processes of invasion, progression and metastasis (MME, STAT3, DCBLD2) of NETs. Verification by real time qRT–PCR showed a high degree of consistency to the microarray results. Furthermore, the protein expression of some of the genes were examined. The results of our study has opened a window to new areas of research, by uncovering new candidate genes and proteins to be further investigated in the search for new prognostic, predictive, and therapeutic markers in NETs.

BlotBase: a northern blot database

With the availability of high-throughput gene expression analysis, multiple public expression databases emerged, mostly based on microarray expression data. Although these databases are of significant biomedical value, they do hold significant drawbacks, especially concerning the reliability of single gene expression profiles obtained by microarray data. Simultaneously, reliable data on an individual gene's expression are often published as single northern blots in individual publications. These data were not yet available for high-throughput screening. To reduce the gap between high-throughput expression data and individual highly reliable expression data, we designed a novel database "BlotBase", a freely and easily accessible database, currently containing approximately 700 published northern blots of human or mouse origin (http://www.medicalgenomics.org/Databases/BlotBase). As the database is open for public data submission, we expect this database to quickly become a large expression profiling resource, eventually providing higher reliability in high-throughput gene expression analysis. Realizing BlotBase, Pubmed was searched manually and by computer based text mining methods to obtain publications containing northern blot results. Subsequently, northern blots were extracted and expression values of different tissues calculated utilizing Image J. All data were made available through a user friendly web front end. The data may be searched by either full text search or list of available northern blots of a specific tissue. Northern blot expression profiles were displayed by three expression states as well as a bar chart, allowing for automated evaluation. Furthermore, we integrated additional features, e.g. instant access to the corresponding RNA sequence or primer design tools making further expression analysis more convenient. Finally, through a semiautomatic submission system this database was opened to the bioinformatics community.

A dual-probe hybridization method for reducing variability in single nucleotide polymorphism analysis with oligonucleotide microarrays

DNA microarray technology has become powerful and popular in mutation/single nucleotide polymorphism (SNP) discovery and genotyping. However, this method is often associated with considerable signal noise of nonbiological origin that may compromise the data quality and interpretation. To achieve a high degree of reliability, accuracy, and sensitivity in data analysis, an effective normalization method to minimize the technical variability is highly desired. In the current study, a simple and robust normalization method is described. The method is based on introduction of a reference probe coimmobilized with SNP probes on the microarray for a dual-probe hybridization (DPH) reaction. The reference probe is used as an intraspot control for the customized microarrays. Using this method, the interassay coefficient of variation (CV) was reduced significantly by approximately 10%. After DPH normalization, the CVs and ranges of the ratios were reduced by two to five times. The relative magnitudes of variation of different sources were also analyzed by analysis of variance. Glass slides were shown to contribute the most to the variance, whereas sampling and residual errors had relatively modest contribution. The results showed that this DPH-based spot-dependent normalization method is an effective solution for reducing experimental variation associated with microarray genotyping data.

Origin of co-expression patterns in E. coli and S. cerevisiae emerging from reverse engineering algorithms

Background

The concept of reverse engineering a gene network, i.e., of inferring a genome-wide graph of putative gene-gene interactions from compendia of high throughput microarray data has been extensively used in the last few years to deduce/integrate/validate various types of “physical” networks of interactions among genes or gene products.

Results

This paper gives a comprehensive overview of which of these networks emerge significantly when reverse engineering large collections of gene expression data for two model organisms, E.coli and S.cerevisiae, without any prior information. For the first organism the pattern of co-expression is shown to reflect in fine detail both the operonal structure of the DNA and the regulatory effects exerted by the gene products when co-participating in a protein complex. For the second organism we find that direct transcriptional control (e.g., transcription factor–binding site interactions) has little statistical significance in comparison to the other regulatory mechanisms (such as co-sharing a protein complex, co-localization on a metabolic pathway or compartment), which are however resolved at a lower level of detail than in E.coli.

Conclusion

The gene co-expression patterns deduced from compendia of profiling experiments tend to unveil functional categories that are mainly associated to stable bindings rather than transient interactions. The inference power of this systematic analysis is substantially reduced when passing from E.coli to S.cerevisiae. This extensive analysis provides a way to describe the different complexity between the two organisms and discusses the critical limitations affecting this type of methodologies.