MOTIVATION: Cross-platform microarray analysis is an increasingly important research tool, but researchers still lack open source tools for storing, integrating and analyzing large amounts of microarray data obtained from different array platforms. RESULTS: An open source integrated microarray database and analysis suite, WebArrayDB (http://www.webarraydb.org), has been developed that features convenient uploading of data for storage in a MIAME (Minimal Information about a Microarray Experiment) compliant fashion, and allows data to be mined with a large variety of R-based tools, including data analysis across multiple platforms. Different methods for probe alignment, normalization and statistical analysis are included to account for systematic bias. Student's t-test, moderated t-tests, non-parametric tests and analysis of variance or covariance (ANOVA/ANCOVA) are among the choices of algorithms for differential analysis of data. Users also have the flexibility to define new factors and create new analysis models to fit complex experimental designs. All data can be queried or browsed through a web browser. The computations can be performed in parallel on symmetric multiprocessing (SMP) systems or Linux clusters. AVAILABILITY: The software package is available for the use on a public web server (http://www.webarraydb.org) or can be downloaded. are available at Bioinformatics online.

BACKGROUND: Despite the widespread usage of DNA microarrays, questions remain about how best to interpret the wealth of gene-by-gene transcriptional levels that they measure. Recently, methods have been proposed which use biologically defined sets of genes in interpretation, instead of examining results gene-by-gene. Despite a serious limitation, a method based on Fisher's exact test remains one of the few plausible options for gene set analysis when an experiment has few replicates, as is typically the case for prokaryotes. RESULTS: We extend five methods of gene set analysis from use on experiments with multiple replicates, for use on experiments with few replicates. We then use simulated and real data to compare these methods with each other and with the Fisher's exact test (FET) method. As a result of the simulation we find that a method named MAXMEAN-NR, maintains the nominal rate of false positive findings (type I error rate) while offering good statistical power and robustness to a variety of gene set distributions for set sizes of at least 10. Other methods (ABSSUM-NR or SUM-NR) are shown to be powerful for set sizes less than 10. Analysis of three sets of experimental data shows similar results. Furthermore, the MAXMEAN-NR method is shown to be able to detect biologically relevant sets as significant, when other methods (including FET) cannot. We also find that the popular GSEA-NR method performs poorly when compared to MAXMEAN-NR. CONCLUSION: MAXMEAN-NR is a method of gene set analysis for experiments with few replicates, as is common for prokaryotes. Results of simulation and real data analysis suggest that the MAXMEAN-NR method offers increased robustness and biological relevance of findings as compared to FET and other methods, while maintaining the nominal type I error rate.

BACKGROUND: Deficiencies in microarray technology cause unwanted variation in the hybridization signal, obscuring the true measurements of intracellular transcript levels. Here we describe a general method that can improve microarray analysis of toxicant-exposed cells that uses the intrinsic power of transcriptional coupling and toxicant concentration-expression response data. To illustrate this approach, we characterized changes in global gene expression induced in Salmonella typhimurium TA100 by 3-chloro-4-(dichloromethyl)-5-hydroxy-2(5H)-furanone (MX), the primary mutagen in chlorinated drinking water. We used the co-expression of genes within an operon and the monotonic increases or decreases in gene expression relative to increasing toxicant concentration to augment our identification of differentially expressed genes beyond Bayesian-t analysis. RESULTS: Operon analysis increased the number of altered genes by 95% from the list identified by a Bayesian t-test of control to the highest concentration of MX. Monotonic analysis added 46% more genes. A functional analysis of the resulting 448 differentially expressed genes yielded functional changes beyond what would be expected from only the mutagenic properties of MX. In addition to gene-expression changes in DNA-damage response, MX induced changes in expression of genes involved in membrane transport and porphyrin metabolism, among other biological processes. The disruption of porphyrin metabolism might be attributable to the structural similarity of MX, which is a chlorinated furanone, to ligands indigenous to the porphyrin metabolism pathway. Interestingly, our results indicate that the lexA regulon in Salmonella, which partially mediates the response to DNA damage, may contain only 60% of the genes present in this regulon in E. coli. In addition, nanH was found to be highly induced by MX and contains a putative lexA regulatory motif in its regulatory region, suggesting that it may be regulated by lexA. CONCLUSION: Operon and monotonic analyses improved the determination of differentially expressed genes beyond that of Bayesian-t analysis, showing that MX alters cellular metabolism involving pathways other than DNA damage. Because co-expression of similarly functioning genes also occurs in eukaryotes, this method has general applicability for improving analysis of toxicogenomic data.