Knowledge discovery in gene-expression-microarray data: mining the information output of the genome

Mining Gene Expression Profile with Missing Values: An Integration of Kernel PCA and Robust Singular Values Decomposition

Background:

Gene expression profiling and transcriptomics provide valuable information about the role of genes that are differentially expressed between two or more samples. It is always important and challenging to analyse High-throughput DNA microarray data with a number of missing values under various experimental conditions. </P><P> Objectives: Graphical data visualizations of the expression of all genes in a particular cell provide holistic views of gene expression patterns, which improve our understanding of cellular systems under normal and pathological conditions. However, current visualization methods are sensitive to missing values, which are frequently observed in microarray-based gene expression profiling, potentially affecting the subsequent statistical analyses.

Methods:

We addressed in this study the problem of missing values with respect to different imputation methods using gene expression biplot (GE biplot), one of the most popular gene visualization techniques. The effects of missing values for mining differentially expressed genes in gene expression data were evaluated using four well-known imputation methods: Robust Singular Value Decomposition (Robust SVD), Column Average (CA), Column Median (CM), and K-nearest Neighbors (KNN). Frobenius norm and absolute distances were used to measure the accuracy of the methods.

Results:

Three numerical experiments were performed using simulated data (i) and publicly available colon cancer (ii) and leukemia data (iii) to analyze the performance of each method. The results showed that CM and KNN performed better than Robust SVD and CA for identifying the index gene profile in the biplot visualization in both the simulation study and the colon cancer and leukemia microarray datasets.

Conclusion:

The impact of missing values on the GE biplot was smaller when the data matrix was imputed by KNN than by CM. This study concluded that KNN performed satisfactorily in generating a GE biplot in the presence of missing values in microarray data.

Searching the principal genes for neural differentiation of mouse ES cells by factorizing eigengenes of clusters

A time-series microarray experiment is useful to study the changes in the expression of a large number of genes over time. Many methods for clustering genes using gene expression profiles have been suggested, but it is not easy to interpret the biological significance of the results or utilize these methods for understanding the dynamics of gene regulatory systems. In this study, we introduce an algorithm for readjusting the boundaries of clusters by adopting the advantages of both k-means and singular value decomposition (SVD). In addition, we suggest a methodology for searching the principal genes that can be the most crucial genes in regulation of clusters. We found 34 principal genes from 171 clusters having strong concentratedness in their expression patterns and distinct ranges of oscillatory phases, by using a time-series microarray dataset of mouse embryonic stem (ES) cells after induction of dopaminergic neural differentiation. The biological significance of the principal genes examined in the literature supports the feasibility of our algorithms in that the hierarchy of clusters may lead the manifestation of the phenotypes, e.g., the development of the nervous system.

Discovering dynamic regulatory pathway by applying an auto regressive model to time series DNA microarray data

In this paper we propose a novel method to extract dynamic regulatory pathways from time-series DNA microarray data. To this aim, first a specialized clustering technique is applied that utilizes the available heuristic information about the biological system to form the clusters. Then, an auto regressive (AR) model is applied to model the interactions among all genes and to predict the gene expressions for the next time steps. We tested the proposed method on the eukaryotic cell cycle data. The results indicate that the proposed method can successfully predict the dynamic pathway involved in this biological process.

Bioinformatics for cancer management in the post-genome era

Human cancer is caused by multiple factors, such as genetic predisposition, chronic persistent inflammation, environmental factors, life style, and aging. Dysregulated proliferation, dysregulated adhesion, resistance to apoptosis, resistance to senescence, and resistance to anti-cancer drugs are features of cancer cells. Accumulation of multiple epigenetic changes and genetic alterations of cancer-associated genes during multi-stage carcinogenesis results in more malignant phenotypes. Post-genome science is characterized by omics data related to genome, transcriptome, proteome, metabolome, interactome, and epigenome as well as by high-throughput technology, such as whole-genome tiling oligonucleotide array, array CGH with 32,433 overlapping BAC clones, transcriptome microarray, mass spectrometry, tissue-based expression array, and cell-based transfection array. Benchtop oncology supplies Desktop oncology with large amounts of omics data produced by high-throughput technology. Desktop oncology establishes knowledge on cancer-related biomarkers, such as predisposition markers, diagnostic markers, prognostic markers, and therapeutic markers, by using bioinformatics and human intelligence of experts for data mining and text mining. Bedside oncology applies the knowledge established by Desktop oncology to determine therapeutics for cancer patients. Antibody drugs (Trastuzumab/Herceptin, Cetuximab/Erbitux, Bevacizumab/Avastin, et cetera), small molecule inhibitors for tyrosine kinases (Gefitinib/Iressa, Erlotinib/Tarceva, Imatinib/Gleevec, et cetera), conventional cytotoxic drugs, and anti-hormonal drugs are used for cancer chemotherapy. Biomarker monitoring contributes to therapeutic optional choice and drug dosage determination for cancer patients. Knowledge on biomarkers is feedforwarded from desktop to bedside in the translational research, and then biomarker monitoring is feedbacked from bedside to desktop in the reverse translational research. Desktop oncology is indispensable for cancer research in the post-genome era. Combination of genetic screening for cancer predisposition in the general population and precise selection of therapeutic options during cancer management could contribute to the realization of personalized prevention and to dramatically improve the prognosis of cancer patients in the future.

Transit tumor retrieval preserves RNA fidelity and obviates snap-freezing

Genetic expression profiling is enabling investigators to discover new diagnostic and possibly therapeutic pathways in sarcoma biology. To draw substantial conclusions from these molecular analyses, adequate tissue samples must be accrued. Beyond cohort size, the most variable and limiting aspect of doing gene expression analyses on fresh human tissue is the preservation of labile ribonucleic acids extracted from clinical specimens. We have developed a novel retrieval protocol that is readily amenable to the clinical constraints placed on surgeons and pathologists that minimizes variables that can corrupt ribonucleic acid fidelity. We evaluate critically genomic message integrity of mesenchymal tumors derived from transcontinental inter-institutional collaboration. Intact total ribonucleic acid was isolated and assessed for quality and quantity. Ribosomal RNA integrity was quantified using a bioanalyzer. Ribonucleic acid from 42 mesenchymal tumors was isolated and quantified, with selected samples amplified. The mean ribosomal ratios for collaborative institutions ranged from 1.0 to 1.18. Samples remained at 4 degrees C before processing from 1 to 17 days. Tumors stabilized using this protocol retained total ribonucleic acid integrity suitable for amplification and genomic expression analysis regardless of the institutional source or preprocessing duration, enabling a potential consortium of investigators to collaborate in the expression profiling of sarcomas.

LEVEL OF EVIDENCE

Diagnostic study, Level III-3 (no consistently applied gold standard). See the Guidelines for Authors for a complete description of levels of evidence.

Overexpression of SPARC gene in human gastric carcinoma and its clinic-pathologic significance

Gastric cancer is the second most common cancer in the world and the fifth leading cause of cancer-related death in Taiwan. To improve the survival of gastric cancer patients, biomarkers for early detection and effective anticancer therapy are required. An essential first step is to profile gene expression in gastric cancer and identify genes that are aberrantly expressed, and to do this cDNA microarrays were performed. The clinic–pathologic correlation and prognostic significance of the aberrantly expressed genes were evaluated to identify novel biomarkers of gastric cancer. Fresh surgical samples of tumour tissue and matching noncancerous mucosa were obtained immediately after gastric resection in 43 patients. Secreted Protein, Acidic and Rich in Cysteine (SPARC) (Osteonectins), one of the most highly expressed genes in both intestinal and diffuse gastric cancers in our microarray results, was selected for further study. The overexpression of SPARC was verified using real-time quantitative-reverse transcription–polymerase chain reaction (Q-RT–PCR), Northern blot and immunohistochemical staining. The expression of SPARC in tumour tissues was, on average, 4.27-fold increased (95% CI 2.68–5.85) compared to adjacent noncancerous mucosa (P<0.001). The expression of SPARC was higher in advanced (T2, T3 and T4) cancer compared to the early (T1) cancer (P=0.048) with regard to depth of wall invasion. Higher expression of SPARC was significantly associated with lymph node metastasis (P<0.001), lymphatic invasion (P=0.004) and perineural invasion (P=0.047). Expression of SPARC in patients in stage II and above was significantly higher than those in stage I (P=0.017). The 3-year survival of patients with lower expression of SPARC was significantly better than those with a higher expression (log rank P=0.047). These data indicate the potential of SPARC as a prognostic marker for gastric cancer.

Toxicogenomics and systems toxicology: aims and prospects

Toxicogenomics combines transcript, protein and metabolite profiling with conventional toxicology to investigate the interaction between genes and environmental stress in disease causation. The patterns of altered molecular expression that are caused by specific exposures or disease outcomes have revealed how several toxicants act and cause disease. Despite these success stories, the field faces noteworthy challenges in discriminating the molecular basis of toxicity. We argue that toxicology is gradually evolving into a systems toxicology that will eventually allow us to describe all the toxicological interactions that occur within a living system under stress and use our knowledge of toxicogenomic responses in one species to predict the modes-of-action of similar agents in other species.

Computational Approach to Systems Biology: From Fraction to Integration and Beyond

Systems biology is an approach to understanding the workings of whole biological systems. The various methods used for systems analyses range from experimental to computational. In this paper, we describe basic concepts of systems biology, modeling challenges that arise from the massively parallel interaction among components in biological systems, and what lies beyond integration of modular knowledge.

GeneNote: whole genome expression profiles in normal human tissues

A novel data set, GeneNote (Gene Normal Tissue Expression), was produced to portray complete gene expression profiles in healthy human tissues using the Affymetrix GeneChip HG-U95 set, which includes 62 839 probe-sets. The hybridization intensities of two replicates were processed and analyzed to yield the complete transcriptome for twelve human tissues. Abundant novel information on tissue specificity provides a baseline for past and future expression studies related to diseases. The data is posted in GeneNote (http://genecards.weizmann.ac.il/genenote/), a widely used compendium of human genes (http://bioinfo.weizmann.ac.il/genecards).

Cluster Analysis of Gene Expression Data Based on Self-Splitting and Merging Competitive Learning

Cluster analysis of gene expression data from a cDNA microarray is useful for identifying biologically relevant groups of genes. However, finding the natural clusters in the data and estimating the correct number of clusters are still two largely unsolved problems. In this paper, we propose a new clustering framework that is able to address both these problems. By using the one-prototype-take-one-cluster (OPTOC) competitive learning paradigm, the proposed algorithm can find natural clusters in the input data, and the clustering solution is not sensitive to initialization. In order to estimate the number of distinct clusters in the data, we propose a cluster splitting and merging strategy. We have applied the new algorithm to simulated gene expression data for which the correct distribution of genes over clusters is known a priori. The results show that the proposed algorithm can find natural clusters and give the correct number of clusters. The algorithm has also been tested on real gene expression changes during yeast cell cycle, for which the fundamental patterns of gene expression and assignment of genes to clusters are well understood from numerous previous studies. Comparative studies with several clustering algorithms illustrate the effectiveness of our method.

Toxicogenomic approach for assessing toxicant-related disease

The problems of identifying environmental factors involved in the etiology of human disease and performing safety and risk assessments of drugs and chemicals have long been formidable issues. Three principal components for predicting potential human health risks are: (1) the diverse structure and properties of thousands of chemicals and other stressors in the environment; (2) the time and dose parameters that define the relationship between exposure and disease; and (3) the genetic diversity of organisms used as surrogates to determine adverse chemical effects. The global techniques evolving from successful genomics efforts are providing new exciting tools with which to address these intractable problems of environmental health and toxicology. In order to exploit the scientific opportunities, the National Institute of Environmental Health Sciences has created the National Center for Toxicogenomics (NCT). The primary mission of the NCT is to use gene expression technology, proteomics and metabolite profiling to create a reference knowledge base that will allow scientists to understand mechanisms of toxicity and to be able to predict the potential toxicity of new chemical entities and drugs. A principal scientific objective underpinning the use of microarray analysis of chemical exposures is to demonstrate the utility of signature profiling of the action of drugs or chemicals and to utilize microarray methodologies to determine biomarkers of exposure and potential adverse effects. The initial approach of the NCT is to utilize proof-of-principle experiments in an effort to "phenotypically anchor" the altered patterns of gene expression to conventional parameters of toxicity and to define dose and time relationships in which the expression of such signature genes may precede the development of overt toxicity. The microarray approach is used in conjunction with proteomic techniques to identify specific proteins that may serve as signature biomarkers. The longer-range goal of these efforts is to develop a reference relational database of chemical effects in biological systems (CEBS) that can be used to define common mechanisms of toxicity, chemical and drug actions, to define cellular pathways of response, injury and, ultimately, disease. In order to implement this strategy, the NCT has created a consortium of research organizations and private sector companies to actively collaborative in populating the database with high quality primary data. The evolution of discrete databases to a knowledge base of toxicogenomics will be accomplished through establishing relational interfaces with other sources of information on the structure and activity of chemicals such as that of the National Toxicology Program (NTP) and with databases annotating gene identity, sequence, and function.

Retrieval yield of total and messenger RNA in mesenchymal tissue ex vivo

Mesenchymal neoplasms are a heterogeneous group of tumors comprising more than 200 benign entities and approximately 100 sarcomas. Large intraobserver and interobserver variability mandates improvements in diagnostic criteria. Gene expression microarrays are one tool in an evolving field of technology that permits the screening of tissue for massive amounts of information regarding its genetic composition. Such information may aid clinicians to diagnose and treat sarcomas. Complementary deoxyribonucleic acid microarrays, although very promising, are limited by the fact that messenger ribonucleic, the genetic messenger that permits deoxyribonucleic acid to encode for proteins and is the element retrieved from tumor samples ex vivo, is highly unstable, degrading quite readily. We found that even with optimal retrieval times and processing, total ribonucleic acid extraction from tumor tissue ex vivo is retrieved in adequate amounts to avoid amplification in 23% to 55% (mean 36%) of specimens. The percentage of high-grade tumors that yielded sufficient total ribonucleic acid was significantly higher than low grade and benign tumors. When adequate retrieval is achieved, the quantity and quality of messenger ribonucleic acid is robust. Surgeons, pathologists, and clinical intermediaries must be aware of issues surrounding messenger ribonucleic acid retrieval from surgical specimens to optimize collection.

Developmental biology: an array of new possibilities

Microarrays offer biologists comprehensive and powerful tools to analyze the involvement of genes in developmental processes at an unprecedented scale. Microarrays that employ defined sequences will permit us to elucidate genetic relationships and responses, while those that employ undefined DNA sequences (ESTs, cDNA, or genomic libraries) will help us to discover new genes, relate them to documented gene networks, and examine the way in which genes (and the process that they themselves control) are regulated. With access to broad new avenues of research come strategic and logistical headaches, most of which are embodied in the reams of data that are created over the course of an experiment. The solutions to these problems have provided interesting computational tools, which will allow us to compile huge data sets and to construct a genome-wide view of development. We are on the threshold of a new vista of possibilities where we might consider in comprehensive and yet specific detail, for example, the degree to which diverse organisms utilize similar genetic networks to achieve similar ends.

Genomic DNA standards for gene expression profiling in Mycobacterium tuberculosis

A fundamental problem in DNA microarray analysis is the lack of a common standard to compare the expression levels of different samples. Several normalization protocols have been proposed to overcome variables inherent in this technology. As yet, there are no satisfactory methods to exchange gene expression data among different research groups or to compare gene expression values under different stimulus-response profiles. We have tested a normalization procedure based on comparing gene expression levels to the signals generated from hybridizing genomic DNA (genomic normalization). This procedure was applied to DNA microarrays of Mycobacterium tuberculosis using RNA extracted from cultures growing to the logarithmic and stationary phases. The applied normalization procedure generated reproducible measurements of expression level for 98% of the putative mycobacterial ORFs, among which 5.2% were significantly changed comparing the logarithmic to stationary growth phase. Additionally, analysis of expression levels of a subset of genes by real time PCR technology revealed an agreement in expression of 90% of the examined genes when genomic DNA normalization was applied instead of 29-68% agreement when RNA normalization was used to measure the expression levels in the same set of RNA samples. Further examination of microarray expression levels displayed clusters of genes differentially expressed between the logarithmic, early stationary and late stationary growth phases. We conclude that genomic DNA standards offer advantages over conventional RNA normalization procedures and can be adapted for the investigation of microbial genomes.

RNA-mediated interference as a tool for identifying drug targets

The nematode Caenorhabditis elegans is the first multicellular organism with a fully sequenced genome. As a model organism, C. elegans is playing a special role in functional genomic analyses because it is experimentally tractable on many levels. Moreover, the lessons learned from C. elegans are often applicable across phyla because many of the key biologic processes involved in development and disease have been well conserved. Many global approaches for analysing gene activity are being pursued in C. elegans. RNA-mediated interference (RNAi) is an efficient high-throughput method to disrupt gene function. The basic technique of RNAi involves introducing sequence-specific double-stranded RNA into C. elegans in order to generate a nonheritable, epigenetic knockout of gene function that phenocopies a null mutation in the targeted gene. This technique drastically reduces the time needed to jump from the identification of an interesting gene sequence to achieving an understanding of its function. Thus, RNAi facilitates the high-throughput functional analysis of gene targets identified during drug discovery. RNAi can also help to identify the biochemical mode of action of a drug or pesticide and to identify other genes encoding products that may respond or interact with specific compounds.

Prioritizing the proteome: identifying pharmaceutically relevant targets

Considerable attention is now being placed on prioritizing the proteome as the point of delivery for genomic information. Some of the challenges faced in prioritizing efforts from a pharmaceutical perspective, when presented with an incomplete proteome picture, are described. Examples of pharmaceutically relevant proteins are used to illustrate an informatics-based analysis of the proteome using knowledge of known drug targets. We show how results can be maximized by linking informatics approaches to experimental techniques and describe methods that can be used for prioritization within unprecedented protein families using, for example, single nucleotide polymorphism data and knowledge of disease pathways.

Gene expression profiles during long-term memory consolidation

Changes in gene expression have been postulated to occur during long-term memory (LTM). We used high-density cDNA microarrays to assess changes in gene expression 24 h after rabbit eye blink conditioning. Paired animals were presented with a 400 ms, 1000 Hz, 82 dB tone conditioned stimulus that coterminated with a 100 ms, 60 Hz, 2 mA electrical pulse unconditioned stimulus. Unpaired animals received the same conditioned and unconditioned stimuli but presented in an explicitly unpaired manner. Differences in expression levels between paired and unpaired animals in the hippocampus and cerebellar lobule HVI, two regions activated during eye blink conditioning, indicated the involvement of novel genes as well as the participation of previously implicated genes. Patterns of gene expression were validated by in situ hybridization. Surprisingly, the data suggest that an underlying mechanism of LTM involves widespread decreased, rather than increased, gene expression. These results demonstrate the feasibility and utility of a cDNA microarray system as a tool for dissecting the molecular mechanisms of associative memory.

A streamlined process to phenotypically profile heterologous cDNAs in parallel using yeast cell-based assays

To meet the demands of developing lead drugs for the profusion of human genes being sequenced as part of the human genome project, we developed a high-throughput assay construction method in yeast. A set of optimized techniques allows us to rapidly transfer large numbers of heterologous cDNAs from nonyeast plasmids into yeast expression vectors. These high- or low-copy yeast expression plasmids are then converted quickly into integration-competent vectors for phenotypic profiling of the heterologous gene products. The process was validated first by testing proteins of diverse function, such as p38, poly(ADP-ribose) polymerase-1, and PI 3-kinase, by making active-site mutations and using existing small molecule inhibitors of these proteins. For less well-characterized genes, a novel random mutagenesis scheme was developed that allows a combination selection/screen for mutations that retain full-length expression and yet reverse a growth phenotype in yeast. A broad range of proteins in different functional classes has been profiled, with an average yield for growth interference phenotypes of approximately 30%. The ease of manipulation of the yeast genome affords us the opportunity to approach drug discovery and exploratory biology on a genomic scale and shortens assay development time significantly.

DNA microarrays in neuropsychopharmacology

Recent advances in experimental genomics, coupled with the wealth of sequence information available for a variety of organisms, have the potential to transform the way pharmacological research is performed. At present, high-density DNA microarrays allow researchers to quickly and accurately quantify gene-expression changes in a massively parallel manner. Although now well established in other biomedical fields, such as cancer and genetics research, DNA microarrays have only recently begun to make significant inroads into pharmacology. To date, the major focus in this field has been on the general application of DNA microarrays to toxicology and drug discovery and design. This review summarizes the major microarray findings of relevance to neuropsychopharmacology, as a prelude to the design and analysis of future basic and clinical microarray experiments. The ability of DNA microarrays to monitor gene expression simultaneously in a large-scale format is helping to usher in a post-genomic age, where simple constructs about the role of nature versus nurture are being replaced by a functional understanding of gene expression in living organisms.

Hypoxia-inducible factor 1-dependent induction of intestinal trefoil factor protects barrier function during hypoxia

Mucosal organs such as the intestine are supported by a rich and complex underlying vasculature. For this reason, the intestine, and particularly barrier-protective epithelial cells, are susceptible to damage related to diminished blood flow and concomitant tissue hypoxia. We sought to identify compensatory mechanisms that protect epithelial barrier during episodes of intestinal hypoxia. Initial studies examining T84 colonic epithelial cells revealed that barrier function is uniquely resistant to changes elicited by hypoxia. A search for intestinal-specific, barrier-protective factors revealed that the human intestinal trefoil factor (ITF) gene promoter bears a previously unappreciated binding site for hypoxia-inducible factor (HIF)-1. Hypoxia resulted in parallel induction of ITF mRNA and protein. Electrophoretic mobility shift assay analysis using ITF-specific, HIF-1 consensus motifs resulted in a hypoxia-inducible DNA binding activity, and loading cells with antisense oligonucleotides directed against the alpha chain of HIF-1 resulted in a loss of ITF hypoxia inducibility. Moreover, addition of anti-ITF antibody resulted in a loss of barrier function in epithelial cells exposed to hypoxia, and the addition of recombinant human ITF to vascular endothelial cells partially protected endothelial cells from hypoxia-elicited barrier disruption. Extensions of these studies in vivo revealed prominent hypoxia-elicited increases in intestinal permeability in ITF null mice. HIF-1-dependent induction of ITF may provide an adaptive link for maintenance of barrier function during hypoxia.

An evaluation of the performance of cDNA microarrays for detecting changes in global mRNA expression

The cDNA microarray is one technological approach that has the potential to accurately measure changes in global mRNA expression levels. We report an assessment of an optimized cDNA microarray platform to generate accurate, precise and reliable data consistent with the objective of using microarrays as an acquisition platform to populate gene expression databases. The study design consisted of two independent evaluations with 70 arrays from two different manufactured lots and used three human tissue sources as samples: placenta, brain and heart. Overall signal response was linear over three orders of magnitude and the sensitivity for any element was estimated to be 2 pg mRNA. The calculated coefficient of variation for differential expression for all non-differentiated elements was 12-14% across the entire signal range and did not vary with array batch or tissue source. The minimum detectable fold change for differential expression was 1.4. Accuracy, in terms of bias (observed minus expected differential expression ratio), was less than 1 part in 10 000 for all non-differentiated elements. The results presented in this report demonstrate the reproducible performance of the cDNA microarray technology platform and the methods provide a useful framework for evaluating other technologies that monitor changes in global mRNA expression.

cDNA microarray analysis of adapting bowel after intestinal resection

BACKGROUND/PURPOSE

Studies of the genetic regulation of various physiologic processes have been hampered by methodologies that are limited to the analysis of individual genes. The advent of cDNA microarray technology has permitted the simultaneous screening of numerous genes for alterations in expression. In this study, cDNA microarrays were used to evaluate gene expression changes during the intestinal adaptive response to massive small bowel resection (SBR).

METHODS

Male ICR mice (n = 20) underwent either a 50% SBR or sham operation and then were given either orogastric epidermal growth factor (EGF, 50 microg/kg/d) or saline. After 3 days, cDNA microarray analysis was performed on mRNA extracted from the remnant ileum.

RESULTS

From over 8,700 different genes, the array identified 27 genes that were altered 2-fold or greater after SBR. Small proline-rich protein 2 (sprr2), the gene with the greatest expression change (4.9-fold), was further upregulated by EGF. This gene has never been characterized in the intestine or described in intestinal adaptation.

CONCLUSIONS

cDNA microarray analysis showed enhanced expression of sprr2, a gene not previously known to be involved in the physiology of adaptation after SBR. This technology provides a more rapid and efficient means of dissecting the complex genetic regulation of gut adaptation.

Metabolite profiling for plant functional genomics

Multiparallel analyses of mRNA and proteins are central to today's functional genomics initiatives. We describe here the use of metabolite profiling as a new tool for a comparative display of gene function. It has the potential not only to provide deeper insight into complex regulatory processes but also to determine phenotype directly. Using gas chromatography/mass spectrometry (GC/MS), we automatically quantified 326 distinct compounds from Arabidopsis thaliana leaf extracts. It was possible to assign a chemical structure to approximately half of these compounds. Comparison of four Arabidopsis genotypes (two homozygous ecotypes and a mutant of each ecotype) showed that each genotype possesses a distinct metabolic profile. Data mining tools such as principal component analysis enabled the assignment of "metabolic phenotypes" using these large data sets. The metabolic phenotypes of the two ecotypes were more divergent than were the metabolic phenotypes of the single-loci mutant and their parental ecotypes. These results demonstrate the use of metabolite profiling as a tool to significantly extend and enhance the power of existing functional genomics approaches.

Operomics: molecular analysis of tissues from DNA to RNA to protein

The identification of coding sequences in a number of species, including human in the near future, has ushered in the post-genome era. In this era, technologies are becoming available that allow the profiling of tissues and cell populations at the genomic, transcriptomic and proteomic levels. The molecular analysis of tissues at all three levels has been referred to as operomics. This review covers some basic technologies for operomics and their application to some lymphoid disorders. It is proposed that no one type of analysis is fully informative and that information that can be derived from the different compartments encompassed in operomics is complementary. Prospects for introducing such profiling technologies into the clinical laboratory will depend on their robustness, their user friendliness and the clinical relevance of the added information they provide, which cannot be captured through other technologies in use in the clinical laboratory.

Expression databases--resources for pharmacogenomic R&D

This review aims to outline the primary biological databases that are being generated to understand fundamental biology, identify new drug targets, and to look at compound profiling in a new light. We will give a brief overview of four of the main areas being studied in molecular biology: genomics, pharmacogenomics, pharmacogenetics and proteomics. Looking initially at each data set and some of its potential applications, we will go on to describe some of the potentially enormous advantages gained by fully integrating these data sets.

Current awareness on comparative and functional genomics [bibliography]

In order to keep subscribers up-to-date with the latest developments in their field, this current awareness service is provided by John Wiley & Sons and contains newly-published material on comparative and functional genomics. Each bibliography is divided into 16 sections. 1 Reviews & symposia; 2 General; 3 Large-scale sequencing and mapping; 4 Genome evolution; 5 Comparative genomics; 6 Gene families and regulons; 7 Pharmacogenomics; 8 Large-scale mutagenesis programmes; 9 Functional complementation; 10 Transcriptomics; 11 Proteomics; 12 Protein structural genomics; 13 Metabolomics; 14 Genomic approaches to development; 15 Technological advances; 16 Bioinformatics. Within each section, articles are listed in alphabetical order with respect to author. If, in the preceding period, no publications are located relevant to any one of these headings, that section will be omitted