Over the last decade, the introduction of microarray technology has had a profound impact on gene expression research. The publication of studies with dissimilar or altogether contradictory results, obtained using different microarray platforms to analyze identical RNA samples, has raised concerns about the reliability of this technology. The MicroArray Quality Control (MAQC) project was initiated to address these concerns, as well as other performance and data analysis issues. Expression data on four titration pools from two distinct reference RNA samples were generated at multiple test sites using a variety of microarray-based and alternative technology platforms. Here we describe the experimental design and probe mapping efforts behind the MAQC project. We show intraplatform consistency across test sites as well as a high level of interplatform concordance in terms of genes identified as differentially expressed. This study provides a resource that represents an important first step toward establishing a framework for the use of microarrays in clinical and regulatory settings.

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The MicroArray Quality Control (MAQC) project shows inter- and intraplatform reproducibility of gene expression measurements

Microarray-based expression profiling experiments typically use either a one-color or a two-color design to measure mRNA abundance. The validity of each approach has been amply demonstrated. Here we provide a simultaneous comparison of results from one- and two-color labeling designs, using two independent RNA samples from the Microarray Quality Control (MAQC) project, tested on each of three different microarray platforms. The data were evaluated in terms of reproducibility, specificity, sensitivity and accuracy to determine if the two approaches provide comparable results. For each of the three microarray platforms tested, the results show good agreement with high correlation coefficients and high concordance of differentially expressed gene lists within each platform. Cumulatively, these comparisons indicate that data quality is essentially equivalent between the one- and two-color approaches and strongly suggest that this variable need not be a primary factor in decisions regarding experimental microarray design.

http://w1.iata.csic.es/IATA/usct/geno/Doc/Bibliografia/2006%20Patterson%20TA%20et%20al%20MAQC%20project.pdf

Performance comparison of one-color and two-color platforms within the Microarray Quality Control (MAQC) project

To validate and extend the findings of the MicroArray Quality Control (MAQC) project, a biologically relevant toxicogenomics data set was generated using 36 RNA samples from rats treated with three chemicals (aristolochic acid, riddelliine and comfrey) and each sample was hybridized to four microarray platforms The MAQC project assessed concordance in intersite and cross-platform comparisons and the impact of gene selection methods on the reproducibility of profiling data in terms of differentially expressed genes using distinct reference RNA samples The real-world toxicogenomic data set reported here showed high concordance in intersite and cross-platform comparisons Further, gene lists generated by fold-change ranking were more reproducible than those obtained by t-test P value or Significance Analysis of Microarrays Finally, gene lists generated by fold-change ranking with a nonstringent P-value cutoff showed increased consistency in Gene Ontology terms and pathways, and hence the biological impact of chemical exposure could be reliably deduced from all platforms analyzed

Rat toxicogenomic study reveals analytical consistency across microarray platforms

The Comparative Toxicogenomics Database (CTD) is a curated database that promotes understanding about the effects of environmental chemicals on human health. Biocurators at CTD manually curate chemical–gene interactions, chemical–disease relationships and gene–disease relationships from the literature. This strategy allows data to be integrated to construct chemical–gene–disease networks. CTD is unique in numerous respects: curation focuses on environmental chemicals; interactions are manually curated; interactions are constructed using controlled vocabularies and hierarchies; additional gene attributes (such as Gene Ontology, taxonomy and KEGG pathways) are integrated; data can be viewed from the perspective of a chemical, gene or disease; results and batch queries can be downloaded and saved; and most importantly, CTD acts as both a knowledgebase (by reporting data) and a discovery tool (by generating novel inferences). Over 116 000 interactions between 3900 chemicals and 13 300 genes have been curated from 270 species, and 5900 gene–disease and 2500 chemical–disease direct relationships have been captured. By integrating these data, 350 000 gene–disease relationships and 77 000 chemical–disease relationships can be inferred. This wealth of chemical–gene–disease information yields testable hypotheses for understanding the effects of environmental chemicals on human health. CTD is freely available at http://ctd.mdibl.org.

/pdf/comparative-toxicogenomics-database-a-knowledgebase-and-2fz3x5dmra.pdf

Comparative Toxicogenomics Database: a knowledgebase and discovery tool for chemical–gene–disease networks

Biofuels derived from algal lipids represent an opportunity to dramatically impact the global energy demand for transportation fuels. Systems biology analyses of oleaginous algae could greatly accelerate the commercialization of algal-derived biofuels by elucidating the key components involved in lipid productivity and leading to the initiation of hypothesis-driven strain-improvement strategies. However, higher-level systems biology analyses, such as transcriptomics and proteomics, are highly dependent upon available genomic sequence data, and the lack of these data has hindered the pursuit of such analyses for many oleaginous microalgae. In order to examine the triacylglycerol biosynthetic pathway in the unsequenced oleaginous microalga, Chlorella vulgaris, we have established a strategy with which to bypass the necessity for genomic sequence information by using the transcriptome as a guide. Our results indicate an upregulation of both fatty acid and triacylglycerol biosynthetic machinery under oil-accumulating conditions, and demonstrate the utility of a de novo assembled transcriptome as a search model for proteomic analysis of an unsequenced microalga.

/pdf/examination-of-triacylglycerol-biosynthetic-pathways-via-de-4057bfwjdz.pdf

Examination of Triacylglycerol Biosynthetic Pathways via De Novo Transcriptomic and Proteomic Analyses in an Unsequenced Microalga

The mapping of the human genome and the determination of corresponding gene functions, pathways, and biological mechanisms are driving the emergence of the new research fields of toxicogenomics and systems toxicology. Many technological advances such as microarrays are enabling this paradigm shift that indicates an unprecedented advancement in the methods of understanding the expression of toxicity at the molecular level. At the National Center for Toxicological Research (NCTR) of the U.S. Food and Drug Administration, core facilities for genomic, proteomic, and metabonomic technologies have been established that use standardized experimental procedures to support centerwide toxicogenomic research. Collectively, these facilities are continuously producing an unprecedented volume of data. NCTR plans to develop a toxicoinformatics integrated system (TIS) for the purpose of fully integrating genomic, proteomic, and metabonomic data with the data in public repositories as well as conventional (Italic)in vitro(/Italic) and (Italic)in vivo(/Italic) toxicology data. The TIS will enable data curation in accordance with standard ontology and provide or interface a rich collection of tools for data analysis and knowledge mining. In this article the design, practical issues, and functions of the TIS are discussed through presenting its prototype version, ArrayTrack, for the management and analysis of DNA microarray data. ArrayTrack is logically constructed of three linked components: a) a library (LIB) that mirrors critical data in public databases; b) a database (MicroarrayDB) that stores microarray experiment information that is Minimal Information About a Microarray Experiment (MIAME) compliant; and c) tools (TOOL) that operate on experimental and public data for knowledge discovery. Using ArrayTrack, we can select an analysis method from the TOOL and apply the method to selected microarray data stored in the MicroarrayDB; the analysis results can be linked directly to gene information in the LIB.

ArrayTrack--supporting toxicogenomic research at the U.S. Food and Drug Administration National Center for Toxicological Research.

A robust bioinformatics capability is widely acknowledged as central to realizing the promises of toxicogenomics. Successful application of toxicogenomic approaches, such as DNA microarray, inextricably relies on appropriate data management, the ability to extract knowledge from massive amounts of data and the availability of functional information for data interpretation. At the FDA's National Center for Toxicological Research (NCTR), we are developing a public microarray data management and analysis software, called ArrayTrack. ArrayTrack is Minimum Information About a Microarray Experiment (MIAME) supportive for storing both microarray data and experiment parameters associated with a toxicogenomics study. A quality control mechanism is implemented to assure the fidelity of entered expression data. ArrayTrack also provides a rich collection of functional information about genes, proteins and pathways drawn from various public biological databases for facilitating data interpretation. In addition, several data analysis and visualization tools are available with ArrayTrack, and more tools will be available in the next released version. Importantly, gene expression data, functional information and analysis methods are fully integrated so that the data analysis and interpretation process is simplified and enhanced. ArrayTrack is publicly available online and the prospective user can also request a local installation version by contacting the authors.

Development of public toxicogenomics software for microarray data management and analysis.

ArrayTrack - Supporting Toxicogenomic Research at the FDA?s National Center for Toxicological Research (NCTR)

cDNA arrays allow quantitative measurement of expression levels for thousands of genes simultaneously. The measurements are affected by many sources of variation, and substantial improvements in the precision of estimatedeffects accompany adjustments for these effects. Two generic nuisance variations, one associated with the magnitude of expression and the other associated with array location, are common in data from filter arrays. Procedures, like normalization using lowess regression, are effective at reducing variation associated with magnitude, and they have been widely adopted. However, variation associated with location has received less attention. Here, a simple, but effective method based on localized median is expounded for dealing with these nuisance effects, and its properties are discussed. The proposed methodology handles location-dependent variation ("splotches") and magnitude-dependent variation (background and/or saturation) effectively. The procedure is related to lowess when implemented to adjust magnitude-dependent variation, and it performs similarly. The proposed methodology is illustrated with data from the National Center for Toxicological Research (NCTR), where treatment differences in levels of mRNA from rat hepatocytes were assessed using 3 3 P-labeled samples hybridized to cDNA spotted arrays. Normalizing intensities by the median-of-subsets removes systematic variation associated with the location of a gene on the array and/or the level of its expression. This procedure is easy to implement using iteratively reweighted least-squares algorithms. Although less sophisticated than lowess, this procedure works nearly as well for normalizing intensities based upon their magnitude. Unlike lowess, it can adjust for location-dependent effects.

Dan Casciano

Papers

ArrayTrack--supporting toxicogenomic research at the U.S. Food and Drug Administration National Center for Toxicological Research.

Development of public toxicogenomics software for microarray data management and analysis.

ArrayTrack - Supporting Toxicogenomic Research at the FDA?s National Center for Toxicological Research (NCTR)

Median-of-Subsets Normalization of Intensities for cDNA Array Data