Regulation of bacterial respiration: Comparison of microarray and comparative genomics data

A comparison was made of the structures of the Fnr and ArcA modulons and regulons. The data on modulon composition were taken from published microarray assays, whereas regulons were characterized using comparative genomic approaches. The regulatory cascade involving Fnr and ArcA contributes greatly to the extension of the Fnr modulon over the Fnr regulon by adding operons of the ArcA modulon. The Fnr and ArcA regulons were shown to contain 26 and 16 operons, respectively. Ten operons had high-score and highly conserved sites for both Fnr and ArcA and were isolated as a so-called core of regulons.     

Visualization-based discovery and analysis of genomic aberrations in microarray data

Background  

Chromosomal copy number changes (aneuploidies) play a key role in cancer progression and molecular evolution. These copy number changes can be studied using microarray-based comparative genomic hybridization (array CGH) or gene expression microarrays. However, accurate identification of amplified or deleted regions requires a combination of visual and computational analysis of these microarray data.     

Extensive genomic diversity in pathogenic Escherichia coli and Shigella Strains revealed by comparative genomic hybridization microarray

Escherichia coli, including the closely related genus Shigella, is a highly diverse species in terms of genome structure. Comparative genomic hybridization (CGH) microarray analysis was used to compare the gene content of E. coli K-12 with the gene contents of pathogenic strains. Missing genes in a pathogen were detected on a microarray slide spotted with 4,071 open reading frames (ORFs) of W3110, a commonly used wild-type K-12 strain. For 22 strains subjected to the CGH microarray analyses 1,424 ORFs were found to be absent in at least one strain. The common backbone of the E. coli genome was estimated to contain about 2,800 ORFs. The mosaic distribution of absent regions indicated that the genomes of pathogenic strains were highly diversified because of insertions and deletions. Prophages, cell envelope genes, transporter genes, and regulator genes in the K-12 genome often were not present in pathogens. The gene contents of the strains tested were recognized as a matrix for a neighbor-joining analysis. The phylogenic tree obtained was consistent with the results of previous studies. However, unique relationships between enteroinvasive strains and Shigella, uropathogenic, and some enteropathogenic strains were suggested by the results of this study. The data demonstrated that the CGH microarray technique is useful not only for genomic comparisons but also for phylogenic analysis of E. coli at the strain level.     

Bayesian meta-analysis models for microarray data: a comparative study

Background

With the growing abundance of microarray data, statistical methods are increasingly needed to integrate results across studies. Two common approaches for meta-analysis of microarrays include either combining gene expression measures across studies or combining summaries such as p-values, probabilities or ranks. Here, we compare two Bayesian meta-analysis models that are analogous to these methods.

Results

Two Bayesian meta-analysis models for microarray data have recently been introduced. The first model combines standardized gene expression measures across studies into an overall mean, accounting for inter-study variability, while the second combines probabilities of differential expression without combining expression values. Both models produce the gene-specific posterior probability of differential expression, which is the basis for inference. Since the standardized expression integration model includes inter-study variability, it may improve accuracy of results versus the probability integration model. However, due to the small number of studies typical in microarray meta-analyses, the variability between studies is challenging to estimate. The probability integration model eliminates the need to model variability between studies, and thus its implementation is more straightforward. We found in simulations of two and five studies that combining probabilities outperformed combining standardized gene expression measures for three comparison values: the percent of true discovered genes in meta-analysis versus individual studies; the percent of true genes omitted in meta-analysis versus separate studies, and the number of true discovered genes for fixed levels of Bayesian false discovery. We identified similar results when pooling two independent studies of Bacillus subtilis. We assumed that each study was produced from the same microarray platform with only two conditions: a treatment and control, and that the data sets were pre-scaled.

Conclusion

The Bayesian meta-analysis model that combines probabilities across studies does not aggregate gene expression measures, thus an inter-study variability parameter is not included in the model. This results in a simpler modeling approach than aggregating expression measures, which accounts for variability across studies. The probability integration model identified more true discovered genes and fewer true omitted genes than combining expression measures, for our data sets.     

Design of a seven-genome Escherichia coli microarray for comparative genomic profiling

We describe the design and evaluate the use of a high-density oligonucleotide microarray covering seven sequenced Escherichia coli genomes in addition to several sequenced E. coli plasmids, bacteriophages, pathogenicity islands, and virulence genes. Its utility is demonstrated for comparative genomic profiling of two unsequenced strains, O175:H16 D1 and O157:H7 3538 (Deltastx(2)::cat) as well as two well-known control strains, K-12 W3110 and O157:H7 EDL933. By using fluorescently labeled genomic DNA to query the microarrays and subsequently analyze common virulence genes and phage elements and perform whole-genome comparisons, we observed that O175:H16 D1 is a K-12-like strain and confirmed that its phi3538 (Deltastx(2)::cat) phage element originated from the E. coli 3538 (Deltastx(2)::cat) strain, with which it shares a substantial proportion of phage elements. Moreover, a number of genes involved in DNA transfer and recombination was identified in both new strains, providing a likely explanation for their capability to transfer phi3538 (Deltastx(2)::cat) between them. Analyses of control samples demonstrated that results using our custom-designed microarray were representative of the true biology, e.g., by confirming the presence of all known chromosomal phage elements as well as 98.8 and 97.7% of queried chromosomal genes for the two control strains. Finally, we demonstrate that use of spatial information, in terms of the physical chromosomal locations of probes, improves the analysis.     

GARBAN: genomic analysis and rapid biological annotation of cDNA microarray and proteomic data

SUMMARY: Genomic Analysis and Rapid Biological ANnotation (GARBAN) is a new tool that provides an integrated framework to analyze simultaneously and compare multiple data sets derived from microarray or proteomic experiments. It carries out automated classifications of genes or proteins according to the criteria of the Gene Ontology Consortium at a level of depth defined by the user. Additionally, it performs clustering analysis of all sets based on functional categories or on differential expression levels. GARBAN also provides graphical representations of the biological pathways in which all the genes/proteins participate. AVAILABILITY: http://garban.tecnun.es.     

Detecting differential expression in microarray data: comparison of optimal procedures

Background  

Many procedures for finding differentially expressed genes in microarray data are based on classical or modified t-statistics. Due to multiple testing considerations, the false discovery rate (FDR) is the key tool for assessing the significance of these test statistics. Two recent papers have generalized two aspects: Storey et al. (2005) have introduced a likelihood ratio test statistic for two-sample situations that has desirable theoretical properties (optimal discovery procedure, ODP), but uses standard FDR assessment; Ploner et al. (2006) have introduced a multivariate local FDR that allows incorporation of standard error information, but uses the standard t-statistic (fdr2d). The relationship and relative performance of these methods in two-sample comparisons is currently unknown.     

CGHMultiArray: exact P-values for multi-array comparative genomic hybridization data

We compute P-values, based on the Wilcoxon test with ties, to compare two conditions with array comparative genomic hybridization data, and we provide a simple interface to export and plot these P-values.     

Regulation of hydrogenase activity in enterobacteria.

Proteus vulgaris, Escherichia coli, and Citrobacter freundii cells were devoid of hydrogenase activity when grown on complex medium or minimal medium plus glucose in the presence of saturating levels of dissolved oxygen. Anaerobically grown cells had appreciable hydrogenase activity. Cells grown anaerobically in the presence of CO (an inhibitor of hydrogenase) or nitrate (an electron acceptor) lacked hydrogenase activity. To make hydrogenase essential for anaerobic growth, cells were grown on fumarate, a nonfermentable carbon source. P. vulgaris and C. freundii evolved H2 gas under these conditions, and the hydrogenase-specific activity was 8 to 10 times greater than that in cells grown on glucose. Cell growth was inhibited by CO, and the cells grew but lacked hydrogenase activity when grown in the presence of nitrate. E. coli grew on fumarate plus H2, and the specific activity was five times greater than that in cells grown on glucose. Thus, hydrogenase activity is inducible and is expressed maximally when the enzyme is essential for cellular growth. Under conditions of growth where the enzyme would not be catalytically active, cells contain little active hydrogenase. Under anaerobic conditions where the enzyme is not essential for growth, the level of hydrogenase activity is intermediate.     

Denoising array-based comparative genomic hybridization data using wavelets

Array-based comparative genomic hybridization (array-CGH) provides a high-throughput, high-resolution method to measure relative changes in DNA copy number simultaneously at thousands of genomic loci. Typically, these measurements are reported and displayed linearly on chromosome maps, and gains and losses are detected as deviations from normal diploid cells. We propose that one may consider denoising the data to uncover the true copy number changes before drawing inferences on the patterns of aberrations in the samples. Nonparametric techniques are particularly suitable for data denoising as they do not impose a parametric model in finding structures in the data. In this paper, we employ wavelets to denoise the data as wavelets have sound theoretical properties and a fast computational algorithm, and are particularly well suited for handling the abrupt changes seen in array-CGH data. A simulation study shows that denoising data prior to testing can achieve greater power in detecting the aberrant spot than using the raw data without denoising. Finally, we illustrate the method on two array-CGH data sets.     

Breakpoint identification and smoothing of array comparative genomic hybridization data

SUMMARY: We describe a tool, called aCGH-Smooth, for the automated identification of breakpoints and smoothing of microarray comparative genomic hybridization (array CGH) data. aCGH-Smooth is written in visual C++, has a user-friendly interface including a visualization of the results and user-defined parameters adapting the performance of data smoothing and breakpoint recognition. aCGH-Smooth can handle array-CGH data generated by all array-CGH platforms: BAC, PAC, cosmid, cDNA and oligo CGH arrays. The tool has been successfully applied to real-life data. AVAILABILITY: aCGH-Smooth is free for researchers at academic and non-profit institutions at http://www.few.vu.nl/~vumarray/.     

Using formal concept analysis for microarray data comparison

Microarray technologies, which can measure tens of thousands of gene expression values simultaneously in a single experiment, have become a common research method for biomedical researchers. Computational tools to analyze microarray data for biological discovery are needed. In this paper, we investigate the feasibility of using formal concept analysis (FCA) as a tool for microarray data analysis. The method of FCA builds a (concept) lattice from the experimental data together with additional biological information. For microarray data, each vertex of the lattice corresponds to a subset of genes that are grouped together according to their expression values and some biological information related to gene function. The lattice structure of these gene sets might reflect biological relationships in the dataset. Similarities and differences between experiments can then be investigated by comparing their corresponding lattices according to various graph measures. We apply our method to microarray data derived from influenza-infected mouse lung tissue and healthy controls. Our preliminary results show the promise of our method as a tool for microarray data analysis.     

Caryoscope: An Open Source Java application for viewing microarray data in a genomic context

Background  

Microarray-based comparative genome hybridization experiments generate data that can be mapped onto the genome. These data are interpreted more easily when represented graphically in a genomic context.     

A comparison of normalization techniques for microRNA microarray data

Normalization of expression levels applied to microarray data can help in reducing measurement error. Different methods, including cyclic loess, quantile normalization and median or mean normalization, have been utilized to normalize microarray data. Although there is considerable literature regarding normalization techniques for mRNA microarray data, there are no publications comparing normalization techniques for microRNA (miRNA) microarray data, which are subject to similar sources of measurement error. In this paper, we compare the performance of cyclic loess, quantile normalization, median normalization and no normalization for a single-color microRNA microarray dataset. We show that the quantile normalization method works best in reducing differences in miRNA expression values for replicate tissue samples. By showing that the total mean squared error are lowest across almost all 36 investigated tissue samples, we are assured that the bias correction provided by quantile normalization is not outweighed by additional error variance that can arise from a more complex normalization method. Furthermore, we show that quantile normalization does not achieve these results by compression of scale.