Oligonucleotide microarray data mining: search for age-dependent gene expression

Information on gene expression in colon tumors versus normal human colon was recently generated by an oligonucleotide microarray study. We used the associated database to search for genes that display age-dependent variations in expression. Statistically significant evidence was obtained that such genes are present in both the tumor and normal tissue databases. Besides the analysis of all genes included in the database, three subsets of genes were analyzed separately: genes controlled by p53, and genes coding for ribosomal proteins and for nuclear-encoded mitochondrial proteins. Among the genes controlled by p53 some show an age-dependent change in expression in tumor tissues, in the sense compatible with an activation of p53 at higher age. A decreased expression of some ribosomal genes at advanced age was detected both in tumor and normal tissues. No significant age-dependent expression could be detected for genes encoding mitochondrial proteins.     

Transcriptional signatures of normal human mammary epithelial cells in response to benzo[a]pyrene exposure: a comparison of three microarray platforms

Microarrays are used to study gene expression in a variety of biological systems. A number of different platforms have been developed, but few studies exist that have directly compared the performance of one platform with another. The goal of this study was to determine array variation by analyzing the same RNA samples with three different array platforms. Using gene expression responses to benzo[a]pyrene exposure in normal human mammary epithelial cells (NHMECs), we compared the results of gene expression profiling using three microarray platforms: photolithographic oligonucleotide arrays (Affymetrix), spotted oligonucleotide arrays (Amersham), and spotted cDNA arrays (NCI). While most previous reports comparing microarrays have analyzed pre-existing data from different platforms, this comparison study used the same sample assayed on all three platforms, allowing for analysis of variation from each array platform. In general, poor correlation was found with corresponding measurements from each platform. Each platform yielded different gene expression profiles, suggesting that while microarray analysis is a useful discovery tool, further validation is needed to extrapolate results for broad use of the data. Also, microarray variability needs to be taken into consideration, not only in the data analysis but also in specific probe selection for each array type.     

Comparison of gene expression measurements from cDNA and 60-mer oligonucleotide microarrays

As the data generated by microarray technology continue to amass, it is necessary to compare and combine gene expression data from different platforms. To evaluate the performance of cDNA and long oligonucleotide (60-mer) arrays, we generated gene expression profiles for two cancer cell lines and compared the data between the two platforms. All 6182 unique genes represented on both platforms were included in the analysis. A limited correlation (r = 0.4708) was obtained and the difference in measurement of low-expression genes was considered to contribute to the limited correlation. Further restriction of the data set to differentially expressed genes detected in cDNA microarrays (1205 genes) and oligonucleotide arrays (1325 genes) showed modest correlations of 0.7076 and 0.6441 between the two platforms. Quantitative real-time PCR measurements of a set of 10 genes showed better correlation with oligonucleotide arrays. Our results demonstrate that there is substantial variation in the data generated from cDNA and 60-mer oligonucleotide arrays. Although general agreement was observed in measurements of differentially expressed genes, we suggest that data from different platforms could not be directly amassed.     

Automated DNA chip annotation tables at IFOM: the importance of synchronisation and cross-referencing of sequence databases

The increasing popularity of DNA chip technology for the study of gene expression is producing, for each experiment, a sizable quantity of numerical data to analyse and an accompanying large number of gene identifiers that should be associated with the relevant biological annotation. We describe here a website at IFOM (FIRC Institute of Molecular Oncology) where we release regularly updated annotation tables for the most used Affymetrix oligonucleotide DNA chips and for the whole Research Genetics 46K clone collection for cDNA arrays. These tables are synchronised with every new release of the mouse and human UniGene databases (NCBI; National Center for Biotechnology Information), allowing fast and easy preliminary annotation of DNA array experiments. We also report some comparative evidence about the importance of biological database synchronisation and cross-references in the process of generating annotation tables for DNA chips.     

DGEM--a microarray gene expression database for primary human disease tissues

Gene expression patterns can reflect gene regulations in human tissues under normal or pathologic conditions. Gene expression profiling data from studies of primary human disease samples are particularly valuable since these studies often span many years in order to collect patient clinical information and achieve a large sample size. Disease-to-Gene Expression Mapper (DGEM) provides a beneficial community resource to access and analyze these data; it currently includes Affymetrix oligonucleotide array datasets for more than 40 human diseases and 1400 samples. The data are normalized to the same scale and stored in a relational database. A statistical-analysis pipeline was implemented to identify genes abnormally expressed in disease tissues or genes whose expressions are associated with clinical parameters such as cancer patient survival. Data-mining results can be queried through a web-based interface at http://dgem.dhcp.iupui.edu/. The query tool enables dynamic generation of graphs and tables that are further linked to major gene and pathway resources that connect the data to relevant biology, including Entrez Gene and Kyoto Encyclopedia of Genes and Genomes (KEGG). In summary, DGEM provides scientists and physicians a valuable tool to study disease mechanisms, to discover potential disease biomarkers for diagnosis and prognosis, and to identify novel gene targets for drug discovery. The source code is freely available for non-profit use, on request to the authors.     

Expression Profiling with Oligonucleotide Arrays: Technologies and Applications for Neurobiology

DNA microarrays have been used in applications ranging from the assignment of gene function to analytical uses in prognostics. However, the detection sensitivity, cross hybridization, and reproducibility of these arrays can affect experimental design and data interpretation. Moreover, several technologies are available for fabrication of oligonucleotide microarrays. We review these technologies and performance attributes and, with data sets generated from human brain RNA, present statistical tools and methods to analyze data quality and to mine and visualize the data. Our data show high reproducibility and should allow an investigator to discern biological and regional variability from differential expression. Although we have used brain RNA as a model system to illustrate some of these points, the oligonucleotide arrays and methods employed in this study can be used with cell lines, tissue sections, blood, and other fluids. To further demonstrate this point, we provide data generated from total RNA sample sizes of 200 ng.     

Monitoring cellular responses to Listeria monocytogenes with oligonucleotide arrays

Listeria monocytogenes is a pathogenic intracellular microorganism whose infection induces pleiotropic biological changes associated with host cell gene expression regulation. Here we define the gene expression profiles of the human promyelocytic THP1 cell line before and after L. monocytogenes infection. Gene expression was measured on a large scale via oligonucleotide microarrays with probe sets corresponding to 6,800 human genes. We assessed and discussed the reproducibility of the hybridization signatures. In addition to oligonucleotide arrays, we also performed the large scale gene expression measurement with two high-density membranes, assaying for 588 and 18,376 human genes, respectively. This work allowed the reproducible identification of 74 up-regulated RNAs and 23 down-regulated RNAs as a consequence of L. monocytogenes infection of THP1. The reliability of these data was reinforced by performing independent infections. Some of these detected RNAs were consistent with previous results, while some newly identified RNAs encode gene products that may play key roles in L. monocytogenes infection. These findings will undoubtedly enhance the understanding of L. monocytogenes molecular physiology and may help identify new therapeutic targets.     

Microarray standard data set and figures of merit for comparing data processing methods and experiment designs

MOTIVATION: There is a very large and growing level of effort toward improving the platforms, experiment designs, and data analysis methods for microarray expression profiling. Along with a growing richness in the approaches there is a growing confusion among most scientists as to how to make objective comparisons and choices between them for different applications. There is a need for a standard framework for the microarray community to compare and improve analytical and statistical methods. RESULTS: We report on a microarray data set comprising 204 in-situ synthesized oligonucleotide arrays, each hybridized with two-color cDNA samples derived from 20 different human tissues and cell lines. Design of the approximately 24 000 60mer oligonucleotides that report approximately 2500 known genes on the arrays, and design of the hybridization experiments, were carried out in a way that supports the performance assessment of alternative data processing approaches and of alternative experiment and array designs. We also propose standard figures of merit for success in detecting individual differential expression changes or expression levels, and for detecting similarities and differences in expression patterns across genes and experiments. We expect this data set and the proposed figures of merit will provide a standard framework for much of the microarray community to compare and improve many analytical and statistical methods relevant to microarray data analysis, including image processing, normalization, error modeling, combining of multiple reporters per gene, use of replicate experiments, and sample referencing schemes in measurements based on expression change. AVAILABILITY/SUPPLEMENTARY INFORMATION: Expression data and supplementary information are available at http://www.rii.com/publications/2003/HE_SDS.htm