Toxicogenomics in the assessment of immunotoxicity

Microarray analysis is used for simultaneous measurement of expression of thousands of genes in a given sample and as such extends and deepens our understanding of biological processes. Application of the technique in toxicology is referred to as toxicogenomics. The examples of assessment of immunotoxicity by gene expression profiling presented and discussed here, show that microarray analysis is able to detect known and novel effects of a wide range of immunomodulating agents. Besides the elucidation of mechanisms of action, toxicogenomics is also applied to predict consequences of exposing biological systems to toxic agents. Successful attempts to classify compounds using signature gene expression profiles have been reported. These did, however, not specifically focus on immunotoxicity. Databases containing expression profiles can facilitate the applications of toxicogenomics. Platforms and methodologies for gene expression profiling may vary, however, hampering data compiling across different laboratories. Therefore, attention is paid to standardization of the generation, reporting, and management of microarray data. Obtained gene expression profiles should be anchored to pathological and functional endpoints for correct interpretation of results. These issues are also important when using toxicogenomics in risk assessment. The application of toxicogenomics in evaluation of immunotoxicity is thus not yet without challenges. It already contributes to the understanding of immunotoxic processes and the development of in vitro screening assays, though, and is therefore expected to be of value for mechanistic insight into immunotoxicity and hazard identification of existing and novel compounds.     

Improving missing value estimation in microarray data with gene ontology

MOTIVATION: Gene expression microarray experiments produce datasets with frequent missing expression values. Accurate estimation of missing values is an important prerequisite for efficient data analysis as many statistical and machine learning techniques either require a complete dataset or their results are significantly dependent on the quality of such estimates. A limitation of the existing estimation methods for microarray data is that they use no external information but the estimation is based solely on the expression data. We hypothesized that utilizing a priori information on functional similarities available from public databases facilitates the missing value estimation. RESULTS: We investigated whether semantic similarity originating from gene ontology (GO) annotations could improve the selection of relevant genes for missing value estimation. The relative contribution of each information source was automatically estimated from the data using an adaptive weight selection procedure. Our experimental results in yeast cDNA microarray datasets indicated that by considering GO information in the k-nearest neighbor algorithm we can enhance its performance considerably, especially when the number of experimental conditions is small and the percentage of missing values is high. The increase of performance was less evident with a more sophisticated estimation method. We conclude that even a small proportion of annotated genes can provide improvements in data quality significant for the eventual interpretation of the microarray experiments. AVAILABILITY: Java and Matlab codes are available on request from the authors. SUPPLEMENTARY MATERIAL: Available online at http://users.utu.fi/jotatu/GOImpute.html.     

Pathways to the analysis of microarray data

The development of microarray technology allows the simultaneous measurement of the expression of many thousands of genes. The information gained offers an unprecedented opportunity to fully characterize biological processes. However, this challenge will only be successful if new tools for the efficient integration and interpretation of large datasets are available. One of these tools, pathway analysis, involves looking for consistent but subtle changes in gene expression by incorporating either pathway or functional annotations. We review several methods of pathway analysis and compare the performance of three, the binomial distribution, z scores, and gene set enrichment analysis, on two microarray datasets. Pathway analysis is a promising tool to identify the mechanisms that underlie diseases, adaptive physiological compensatory responses and new avenues for investigation.     

Web Tools for Rice Transcriptome Analyses

Gene expression databases provide profiling data for the expression of thousands of genes to researchers worldwide. Oligonucleotide microarray technology is a useful tool that has been employed to produce gene expression profiles in most species. In rice, there are five genome-wide DNA microarray platforms: NSF 45K, BGI/Yale 60K, Affymetrix, Agilent Rice 44K, and NimbleGen 390K. Presently, more than 1,700 hybridizations of microarray gene expression data are available from public microarray depositing databases such as NCBI gene expression omnibus and Arrayexpress at EBI. More processing or reformatting of public gene expression data is required for further applications or analyses. Web-based databases for expression meta-analyses are useful for guiding researchers in designing relevant research schemes. In this review, we summarize various databases for expression meta-analyses of rice genes and web tools for further applications, such as the development of co-expression network or functional gene network.     

MAPS: a microarray project system for gene expression experiment information and data validation

SUMMARY: MAPS is a MicroArray Project System for management and interpretation of microarray gene expression experiment information and data. Microarray project information is organized to track experiments and results that are: (1) validated by performing analysis on stored replicate gene expression data; and (2) queried according to the biological classifications of genes deposited on microarray chips.     

Software package for automatic microarray image analysis (MAIA)

Although various software solutions are currently available for microarray image analysis, one would still expect to develop algorithms ensuring higher level of intelligence and robustness. We present a fully functional software package for automatic processing of the two-color microarray images including spot localization, quantification and quality control. The developed algorithms aim at making ratio estimates more resistant to array contamination and offer automatic tools to evaluate spot quality. Availability: A demo version of the software can be downloaded from http://bioinfo.curie.fr/projects/maia. A full version is freely available to non-commercial users upon request from the authors.     

MicroCore: mapping genome expression to cell pathways and networks

The MicroCore toolkit is a suite of analysis programs for microarray and proteomics data that is open source and programmed exclusively in Java. MicroCore provides a flexible and extensible environment for the interpretation of functional genomics data through visualization. The first version of the application (downloadable from the MicroCore website: http://www.ucl.ac.uk/oncology/MicroCore/microcore.htm), implements two programs-PIMs (protein interaction maps) and MicroExpress-and is soon to be followed by an extended version which will also feature a fuzzy k-means clustering application and a Java-based R plug-in for microarray analysis. PIMs and MicroExpress provide a simple yet powerful way of graphically relating large quantities of expression data from multiple experiments to cellular pathways and biological processes in a statistically meaningful way.     

In pursuit of effective toxicogenomics

Biological systems exhibit complex responses to xenobiotics varying from generic stress responses to very specific changes closely associated with the mechanism of toxicity. Until recently our view of this complexity was obscured by the simplicity of available analysis tools which allowed determination of only a few genes in any one study. Then genome sequencing and high throughput library screening projects delivered data on the genome sequence of many organisms, and clones were collected and made available to researchers in a previously unparalleled quantity. To exploit this new resource the microarray was developed from its predecessor the dot blot. Further development has expanded the number of clones contained on any one microarray to a point where the expression of many tens of thousands of genes in a biological system can be determined in a short period of time. What these data are revealing is the full complexity of the gene expression response to stimuli such as xenobiotic exposure. Toxicogenomics seeks to use the complexity of this response as a fingerprint or signature characteristic of that xenobiotic exposure. There are though two major experimental challenges that need to be dealt with for toxicogenomics to be successful. The first is technical and relates to the intrinsic difficulties associated with the accurate measurement of gene expression. For microarrays, this problem is multiplied by the number of genes on the microarray itself. To overcome this technical variability correct experimental design is critical. The second challenge concerns the biological system used. What genetic background, time point and dose of xenobiotic should be chosen? For in vitro systems should cell lines or primary cells be used? These factors, and more, could affect the gene expression profile obtained in response to the same xenobiotic exposure. Using both our data and data from public databases these issues are explored in this paper.     

On methods for gene function scoring as a means of facilitating the interpretation of microarray results.

As gene annotation databases continue to evolve and improve, it has become feasible to incorporate the functional and pathway information about genes, available in these databases into the analysis of gene expression data, for a better understanding of the underlying mechanisms. A few methods have been proposed in the literature to formally convert individual gene results into gene function results. In this paper, we will compare the various methods, propose and examine some new ones, and offer a structured approach to incorporating gene function or pathway information into the analysis of expression data. We study the performance of the various methods and also compare them on real data, using a case study from the toxicogenomics area. Our results show that the approaches based on gene function scores yield a different, and functionally more interpretable, array of genes than methods that rely solely on individual gene scores. They also suggest that functional class scoring methods appear to perform better and more consistently than overrepresentation analysis and distributional score methods.     

PlasmoDB: the Plasmodium genome resource. An integrated database providing tools for accessing, analyzing and mapping expression and sequence data (both finished and unfinished)

PlasmoDB (http://PlasmoDB.org) is the official database of the Plasmodium falciparum genome sequencing consortium. This resource incorporates finished and draft genome sequence data and annotation emerging from Plasmodium sequencing projects. PlasmoDB currently houses information from five parasite species and provides tools for cross-species comparisons. Sequence information is also integrated with other genomic-scale data emerging from the Plasmodium research community, including gene expression analysis from EST, SAGE and microarray projects. The relational schemas used to build PlasmoDB [Genomics Unified Schema (GUS) and RNA Abundance Database (RAD)] employ a highly structured format to accommodate the diverse data types generated by sequence and expression projects. A variety of tools allow researchers to formulate complex, biologically based queries of the database. A version of the database is also available on CD-ROM (Plasmodium GenePlot), facilitating access to the data in situations where Internet access is difficult (e.g. by malaria researchers working in the field). The goal of PlasmoDB is to enhance utilization of the vast quantities of data emerging from genome-scale projects by the global malaria research community.     

Missing value imputation for microRNA expression data by using a GO-based similarity measure

Missing values are commonly present in microarray data profiles. Instead of discarding genes or samples with incomplete expression level, missing values need to be properly imputed for accurate data analysis. The imputation methods can be roughly categorized as expression level-based and domain knowledge-based. The first type of methods only rely on expression data without the help of external data sources, while the second type incorporates available domain knowledge into expression data to improve imputation accuracy. In recent years, microRNA (miRNA) microarray has been largely developed and used for identifying miRNA biomarkers in complex human disease studies. Similar to mRNA profiles, miRNA expression profiles with missing values can be treated with the existing imputation methods. However, the domain knowledge-based methods are hard to be applied due to the lack of direct functional annotation for miRNAs. With the rapid accumulation of miRNA microarray data, it is increasingly needed to develop domain knowledge-based imputation algorithms specific to miRNA expression profiles to improve the quality of miRNA data analysis. We connect miRNAs with domain knowledge of Gene Ontology (GO) via their target genes, and define miRNA functional similarity based on the semantic similarity of GO terms in GO graphs. A new measure combining miRNA functional similarity and expression similarity is used in the imputation of missing values. The new measure is tested on two miRNA microarray datasets from breast cancer research and achieves improved performance compared with the expression-based method on both datasets. The experimental results demonstrate that the biological domain knowledge can benefit the estimation of missing values in miRNA profiles as well as mRNA profiles. Especially, functional similarity defined by GO terms annotated for the target genes of miRNAs can be useful complementary information for the expression-based method to improve the imputation accuracy of miRNA array data. Our method and data are available to the public upon request.     

Gene Expression Browser: large-scale and cross-experiment microarray data integration, management, search & visualization

Background

In the last decade, a large amount of microarray gene expression data has been accumulated in public repositories. Integrating and analyzing high-throughput gene expression data have become key activities for exploring gene functions, gene networks and biological pathways. Effectively utilizing these invaluable microarray data remains challenging due to a lack of powerful tools to integrate large-scale gene-expression information across diverse experiments and to search and visualize a large number of gene-expression data points.

Results

Gene Expression Browser is a microarray data integration, management and processing system with web-based search and visualization functions. An innovative method has been developed to define a treatment over a control for every microarray experiment to standardize and make microarray data from different experiments homogeneous. In the browser, data are pre-processed offline and the resulting data points are visualized online with a 2-layer dynamic web display. Users can view all treatments over control that affect the expression of a selected gene via Gene View, and view all genes that change in a selected treatment over control via treatment over control View. Users can also check the changes of expression profiles of a set of either the treatments over control or genes via Slide View. In addition, the relationships between genes and treatments over control are computed according to gene expression ratio and are shown as co-responsive genes and co-regulation treatments over control.

Conclusion

Gene Expression Browser is composed of a set of software tools, including a data extraction tool, a microarray data-management system, a data-annotation tool, a microarray data-processing pipeline, and a data search & visualization tool. The browser is deployed as a free public web service (http://www.ExpressionBrowser.com) that integrates 301 ATH1 gene microarray experiments from public data repositories (viz. the Gene Expression Omnibus repository at the National Center for Biotechnology Information and Nottingham Arabidopsis Stock Center). The set of Gene Expression Browser software tools can be easily applied to the large-scale expression data generated by other platforms and in other species.     

Linking microarray reporters with protein functions

Background  

The analysis of microarray experiments requires accurate and up-to-date functional annotation of the microarray reporters to optimize the interpretation of the biological processes involved. Pathway visualization tools are used to connect gene expression data with existing biological pathways by using specific database identifiers that link reporters with elements in the pathways.