CARMAweb: comprehensive R- and bioconductor-based web service for microarray data analysis

CARMAweb (Comprehensive R-based Microarray Analysis web service) is a web application designed for the analysis of microarray data. CARMAweb performs data preprocessing (background correction, quality control and normalization), detection of differentially expressed genes, cluster analysis, dimension reduction and visualization, classification, and Gene Ontology-term analysis. This web application accepts raw data from a variety of imaging software tools for the most widely used microarray platforms: Affymetrix GeneChips, spotted two-color microarrays and Applied Biosystems (ABI) microarrays. R and packages from the Bioconductor project are used as an analytical engine in combination with the R function Sweave, which allows automatic generation of analysis reports. These report files contain all R commands used to perform the analysis and guarantee therefore a maximum transparency and reproducibility for each analysis. The web application is implemented in Java based on the latest J2EE (Java 2 Enterprise Edition) software technology. CARMAweb is freely available at

The phenazine pyocyanin is a terminal signalling factor in the quorum sensing network of Pseudomonas aeruginosa

Certain members of the fluorescent pseudomonads produce and secrete phenazines. These heterocyclic, redox-active compounds are toxic to competing organisms, and the cause of these antibiotic effects has been the focus of intense research efforts. It is largely unknown, however, how pseudomonads themselves respond to – and survive in the presence of – these compounds. Using Pseudomonas aeruginosa DNA microarrays and quantitative RT-PCR, we demonstrate that the phenazine pyocyanin elicits the upregulation of genes/operons that function in transport [such as the resistance-nodulation-cell division (RND) efflux pump MexGHI-OpmD] and possibly in redox control (such as PA2274, a putative flavin-dependant monooxygenase), and downregulates genes involved in ferric iron acquisition. Strikingly, mexGHI-opmD and PA2274 were previously shown to be regulated by the PA14 quorum sensing network that controls the production of virulence factors (including phenazines). Through mutational analysis, we show that pyocyanin is the physiological signal for the upregulation of these quorum sensing-controlled genes during stationary phase and that the response is mediated by the transcription factor SoxR. Our results implicate phenazines as signalling molecules in both P. aeruginosa PA14 and PAO1

Region based gene expression via reanalysis of publicly available microarray data sets.

A DNA microarray is a high-throughput technology used to identify relative gene expression. One of the most widely used platforms is the Affymetrix® GeneChip® technology which detects gene expression levels based on probe sets composed of a set of twenty-five nucleotide probes designed to hybridize with specific gene targets. Given a particular Affymetrix® GeneChip® platform, the design of the probes is fixed. However, the method of analysis is dynamic in nature due to the ability to annotate and group probes into uniquely defined groupings. This is particularly important since publicly available repositories of microarray datasets, such as ArrayExpress and NCBI’s Gene Expression Omnibus (GEO) have made millions of samples readily available to be reanalyzed computationally without the need for new biological experiments. One way in which the analysis can dynamically change is by correcting the mapping between probe sets and targets by creating custom Chip Description Files (CDFs) to arrange which probes belong to which probe set based on the latest genomic information or specific annotations of interest. Since default probe sets in Affymetrix® GeneChip® platforms are specific for a gene, transcript or exon, the analyses are then limited to profile differential expression at the gene, transcript or individual exon level. However, it has been revealed that untranslated regions (UTRs) of mRNA have important impacts on the regulation of proteins. We therefore developed a new probe mapping protocol that addresses three issues of Affymetrix® GeneChip® data analyses: removing nonspecific probes, updating probe target mapping based on the latest genome information and grouping the probes into region (UTR, individual exon), gene and transcript level targets of interest to support a better understanding of the effect of UTRs and individual exons on gene expression levels. Furthermore, we developed an R package, affyCustomCdf, for users to dynamically create custom CDFs. The affyCustomCdf tool takes annotations in a General/Gene Transfer Format File (GTF), aligns probes to gene annotations via Nested Containment List (NCList) indexing and generates a custom Chip Description File (CDF) to regroup probes into probe sets based on a region (UTR and individual exon), transcript or gene level. Our results indicate that removing probes that no longer align to the genome without mismatches or align to multiple locations can help to reduce false-positive differential expression, as can removal of probes in regions overlapping multiple genes. Moreover, our method based on regions can detect changes that would have been missed by analysis based on gene and transcript. It also allows for a better understanding of 3’ UTR dynamics through the reanalysis of publicly available data

Non-parametric algorithms for evaluating gene expression in cancer using DNA microarray technology

Microarray technology has transformed the field of cancer biology by enabling the simultaneous evaluation of tens of thousands mRNA expression levels in a single experiment. This technology has been applied to medical science in order to find gene expression markers that cluster diseased and normal tissues, genes affected by treatments, and gene network interactions. All methods of microarray data analysis can be summarized as a study of differential gene expression. This study addresses three questions, 1) the roles of selectively expressed genes for the classification of cancer, 2) issues of accounting for both experimental and biological noise, and 3) issues of comparing data derived from different research groups using the Affymetrix GeneChipTM platform. A key finding of this study is that selectively expressed genes are very powerful when used for disease classification. A model was designed to reduce noise and eliminate false positives from true results. With this approach, data from different research groups can be integrated to increase information and enable a better understanding of cancer

Microarray analysis of relative gene expression stability for selection of internal reference genes in the rhesus macaque brain

Abstract Background Normalization of gene expression data refers to the comparison of expression values using reference standards that are consistent across all conditions of an experiment. In PCR studies, genes designated as "housekeeping genes" have been used as internal reference genes under the assumption that their expression is stable and independent of experimental conditions. However, verification of this assumption is rarely performed. Here we assess the use of gene microarray analysis to facilitate selection of internal reference sequences with higher expression stability across experimental conditions than can be expected using traditional selection methods. We recently demonstrated that relative gene expression from qRT-PCR data normalized using GAPDH, ALG9 and RPL13A expression values mirrored relative expression using quantile normalization in Robust Multichip Analysis (RMA) on the Affymetrix® GeneChip® rhesus Macaque Genome Array. Having shown that qRT-PCR and Affymetrix® GeneChip® data from the same hormone replacement therapy (HRT) study yielded concordant results, we used quantile-normalized gene microarray data to identify the most stably expressed among probe sets for prospective internal reference genes across three brain regions from the HRT study and an additional study of normally menstruating rhesus macaques (cycle study). Gene selection was limited to 575 previously published human "housekeeping" genes. Twelve animals were used per study, and three brain regions were analyzed from each animal. Gene expression stabilities were determined using geNorm, NormFinder and BestKeeper software packages. Results Sequences co-annotated for ribosomal protein S27a (RPS27A), and ubiquitin were among the most stably expressed under all conditions and selection criteria used for both studies. Higher annotation quality on the human GeneChip® facilitated more targeted analysis than could be accomplished using the rhesus GeneChip®. In the cycle study, multiple probe sets annotated for actin, gamma 1 (ACTG1) showed high signal intensity and were among the most stably expressed. Conclusions Using gene microarray analysis, we identified genes showing high expression stability under various sex-steroid environments in different regions of the rhesus macaque brain. Use of quantile-normalized microarray gene expression values represents an improvement over traditional methods of selecting internal reference genes for PCR analysis

Integrating data from heterogeneous DNA microarray platforms

DNA microarrays are one of the most used technologies for gene expression measurement. However, there are several distinct microarray platforms, from different manufacturers, each with its own measurement protocol, resulting in data that can hardly be compared or directly integrated. Data integration from multiple sources aims to improve the assertiveness of statistical tests, reducing the data dimensionality problem. The integration of heterogeneous DNA microarray platforms comprehends a set of tasks that range from the re-annotation of the features used on gene expression, to data normalization and batch effect elimination. In this work, a complete methodology for gene expression data integration and application is proposed, which comprehends a transcript-based re-annotation process and several methods for batch effect attenuation. The integrated data will be used to select the best feature set and learning algorithm for a brain tumor classification case study. The integration will consider data from heterogeneous Agilent and Affymetrix platforms, collected from public gene expression databases, such as The Cancer Genome Atlas and Gene Expression Omnibus.The authors thank the FCT Strategic Project of UID/BIO/04469/2013 unit, the project RECI/BBBEBI/0179/2012 (FCOMP-01-0124-FEDER-027462) and the project BioInd - Biotechnology and Bioengineering for improved Industrial and Agro-Foodprocesses”, REF.NORTE-07-0124FEDER-000028 Co-funded by the Programa Operacional Regional do Norte (ON.2 O Novo Norte), QREN, FEDER

Meta-analysis of archived DNA microarrays identifies genes regulated by hypoxia and involved in a metastatic phenotype in cancer cells

<p>Abstract</p> <p>Background</p> <p>Metastasis is a major cancer-related cause of death. Recent studies have described metastasis pathways. However, the exact contribution of each pathway remains unclear. Another key feature of a tumor is the presence of hypoxic areas caused by a lack of oxygen at the center of the tumor. Hypoxia leads to the expression of pro-metastatic genes as well as the repression of anti-metastatic genes. As many Affymetrix datasets about metastasis and hypoxia are publicly available and not fully exploited, this study proposes to re-analyze these datasets to extract new information about the metastatic phenotype induced by hypoxia in different cancer cell lines.</p> <p>Methods</p> <p>Affymetrix datasets about metastasis and/or hypoxia were downloaded from GEO and ArrayExpress. AffyProbeMiner and GCRMA packages were used for pre-processing and the Window Welch <it>t </it>test was used for processing. Three approaches of meta-analysis were eventually used for the selection of genes of interest.</p> <p>Results</p> <p>Three complementary approaches were used, that eventually selected 183 genes of interest. Out of these 183 genes, 99, among which the well known <it>JUNB</it>, <it>FOS </it>and <it>TP63</it>, have already been described in the literature to be involved in cancer. Moreover, 39 genes of those, such as <it>SERPINE1 </it>and <it>MMP7</it>, are known to regulate metastasis. Twenty-one genes including <it>VEGFA </it>and <it>ID2 </it>have also been described to be involved in the response to hypoxia. Lastly, DAVID classified those 183 genes in 24 different pathways, among which 8 are directly related to cancer while 5 others are related to proliferation and cell motility. A negative control composed of 183 random genes failed to provide such results. Interestingly, 6 pathways retrieved by DAVID with the 183 genes of interest concern pathogen recognition and phagocytosis.</p> <p>Conclusion</p> <p>The proposed methodology was able to find genes actually known to be involved in cancer, metastasis and hypoxia and, thus, we propose that the other genes selected based on the same methodology are of prime interest in the metastatic phenotype induced by hypoxia.</p

Design and Evaluation of Oligonucleotide Microarrays for the Detection of Bovine Pathogens

Two microarray designs were developed and produced to screen for multiple bovine pathogens commonly found in the cattle industry today. The first microarray was designed, built, and processed in-house using conventional material and equipment and targeted Pasteurella multocida, Manheimia haemolytica, Histophilus somni, and Arcanobacterium pyogenes. For each pathogen, 12 perfect-match oligonucleotide probes, which were also designed in-house, targeted different sections of the respective 16S ribosomal genes, and were coupled with 12 corresponding mismatched probes for background. These arrays were able to produce distinct hybridization patterns for each pathogen that were easily visible without the need for computer analysis. However, the need for PCR amplification of the 16S gene prior to hybridization motivated us to explore more efficient array options. The second designed microarray, a custom Affymetrix GeneChip, targeted Escherichia coli, Salmonella typhimurium, and Salmonella dublin in addition to the previously mentioned pathogens and was more successful in overall performance than the in-house arrays. In addition to the 16S gene, oligonucleotide probes targeted other genes (from 2 to \u3e4500, depending on whether the genome was sequenced) that were unique to each pathogen. This array also differed from the in-house arrays in that mismatched probes were not designed. The different probe sets performed at different detection limits as P. multocida, A. pyogenes, S. typhimurium, and S. dublin were detected with as little as 250ng of hybridized genomic DNA (gDNA), while M. haemolytica, H. somni, and E. coli required as much as 1μg gDNA. These pathogens were also spiked into bovine tissue to simulate multiorgan infections in which they were individually detected with the microarray design