Vasily Sharov

Microarrays have emerged as the premier tool for studying gene expression on a genomic scale. Advances in the precision of array printers and scanners as well as improved laboratory protocols (11) allow for assays of tremendous complexity and scope. Scientists seeking to harness the potential of this technique are often challenged by the large quantities of data produced. Well-designed, user-friendly software is the key to tracking, integrating, qualifying, and ultimately deriving scientific insight from the experimental results. In support of our ongoing work in microarray analysis of gene expression, we developed a suite of software that allow users in the laboratory to capture, manage, and analyze effectively data from DNA microarray experiments. The TM4 suite of tools consist of four major applications, Microarray Data Manager (MADAM), TIGR_Spotfinder, Microarray Data Analysis System (MIDAS), and Multiexperiment Viewer (MeV), as well as a Minimal Information About a Microarray Experiment (MIAME)-compliant MySQL database, all of which are freely available to the scientific research community at http://www.tigr.org/software. Although these software tools were developed for spotted two-color arrays, many of the components can be easily adapted to work with single-color formats such as filter arrays and GeneChips (Affymetrix, Santa Clara, CA, USA). Three of the TM4 applications, MADAM, MIDAS, and MeV, were developed in Java and have been tested on Microsoft Windows, Linux , Unix , and MacOS X platforms; TIGR Spotfinder was written in C/C++ and runs only on Windows systems. The TM4 software system represents a comprehensive, extensible, open-source, and freely available collection of tools that we believe will be of use to a wide range of laboratories conducting microarray experiments. We further hope that by providing source code along with the executable software, we can encourage others to develop new analysis methods and utilities that will further enhance the capabilities of this software system.

BACKGROUND: 'Fold-change' cutoffs have been widely used in microarray assays to identify genes that are differentially expressed between query and reference samples. More accurate measures of differential expression and effective data-normalization strategies are required to identify high-confidence sets of genes with biologically meaningful changes in transcription. Further, the analysis of a large number of expression profiles is facilitated by a common reference sample, the construction of which must be carefully addressed. RESULTS: We carried out a series of 'self-self' hybridizations in which aliquots of the same RNA sample were labeled separately with Cy3 and Cy5 fluorescent dyes and co-hybridized to the same microarray. From this, we can analyze the intensity-dependent behavior of microarray data, define a statistically significant measure of differential expression that exploits the structure of the fluorescent signals, and measure the inherent reproducibility of the technique. We also devised a simple procedure for identifying and eliminating low-quality data for replicates within and between slides. We examine the properties required of a universal reference RNA sample and show how pooling a small number of samples with a diverse representation of expressed genes can outperform more complex mixtures as a reference sample. CONCLUSION: Analysis of cell-line samples can identify systematic structure in measured gene-expression levels. A general procedure for analyzing cDNA microarray data is proposed and validated. We show that pooled reference samples should be based not only on the expression of individual genes in each cell line but also on the expression levels of genes within cell lines.

Competence for genetic transformation in Streptococcus pneumoniae is coordinated by the competence-stimulating peptide (CSP), which induces a sudden and transient appearance of competence during exponential growth in vitro. Models of this quorum-sensing mechanism have proposed sequential expression of several regulatory genes followed by induction of target genes encoding DNA-processing-pathway proteins. Although many genes required for transformation are known to be expressed only in response to CSP, the relative timing of their expression has not been established. Overlapping expression patterns for the genes cinA and comD (G. Alloing, B. Martin, C. Granadel, and J. P. Claverys, Mol. Microbiol. 29:75-83, 1998) suggest that at least two distinct regulatory mechanisms may underlie the competence cycle. DNA microarrays were used to estimate mRNA levels for all known competence operons during induction of competence by CSP. The known competence regulatory operons, comAB, comCDE, and comX, exhibited a low or zero initial (uninduced) signal, strongly increased expression during the period between 5 and 12 min after CSP addition, and a decrease nearly to original values by 15 min after initiation of exposure to CSP. The remaining competence genes displayed a similar expression pattern, but with an additional delay of approximately 5 min. In a mutant defective in ComX, which may act as an alternate sigma factor to allow expression of the target competence genes, the same regulatory genes were induced, but the other competence genes were not. Finally, examination of the expression of 60 candidate sites not previously associated with competence identified eight additional loci that could be induced by CSP.

BACKGROUND: DNA microarray assays typically compare two biological samples and present the results of those comparisons gene-by-gene as the logarithm base two of the ratio of the measured expression levels for the two samples. RESULTS: Because of the fixed dynamic range of fluorescence and other detection systems, there is a limit to the range of comparisons that can be made using any array technology, and this must be taken into account when interpreting the results of any such analysis. CONCLUSIONS: The dynamic range of microarray data collection systems results in limits in the comparative analyses that can be derived from such measurements and suggests that optimal results can be obtained by making measurements that avoid the boundaries of that dynamic range.