Application of COMPOCHIP Microarray to Investigate the Bacterial Communities of Different Composts

A microarray spotted with 369 different 16S rRNA gene probes specific to microorganisms involved in the degradation process of organic waste during composting was developed. The microarray was tested with pure cultures, and of the 30,258 individual probe-target hybridization reactions performed, there were only 188 false positive (0.62%) and 22 false negative signals (0.07%). Labeled target DNA was prepared by polymerase chain reaction amplification of 16S rRNA genes using a Cy5-labeled universal bacterial forward primer and a universal reverse primer. The COMPOCHIP microarray was applied to three different compost types (green compost, manure mix compost, and anaerobic digestate compost) of different maturity (2, 8, and 16 weeks), and differences in the microorganisms in the three compost types and maturity stages were observed. Multivariate analysis showed that the bacterial composition of the three composts was different at the beginning of the composting process and became more similar upon maturation. Certain probes (targeting Sphingobacterium, Actinomyces, Xylella/Xanthomonas/ Stenotrophomonas, Microbacterium, Verrucomicrobia, Planctomycetes, Low G + C and Alphaproteobacteria) were more influential in discriminating between different composts. Results from denaturing gradient gel electrophoresis supported those of microarray analysis. This study showed that the COMPOCHIP array is a suitable tool to study bacterial communities in composts

Pigeons: a novel GUI software for analysing and parsing high density heterologous oligonucleotide microarray probe level data

Genomic DNA-based probe selection by using high density oligonucleotide arrays has recently been applied to heterologous species (Xspecies). With the advent of this new approach, researchers are able to study the genome and transcriptome of a non-model or an underutilised crop species through current state-of-the-art microarray platforms. However, a software package with a graphical user interface (GUI) to analyse and parse the oligonucleotide probe pair level data is still lacking when an experiment is designed on the basis of this cross species approach. A novel computer program called Pigeons has been developed for customised array data analysis to allow the user to import and analyse Affymetrix GeneChip® probe level data through XSpecies. One can determine empirical boundaries for removing poor probes based on genomic hybridisation of the test species to the Xspecies array, followed by making a species-specific Chip Description File (CDF) file for transcriptomics in the heterologous species, or Pigeons can be used to examine an experimental design to identify potential Single-Feature Polymorphisms (SFPs) at the DNA or RNA level. Pigeons is also focused around visualization and interactive analysis of the datasets. The software with its manual (the current release number version 1.2.1) is freely available at the website of the Nottingham Arabidopsis Stock Centre (NASC)

Tool for the identification of differentially expressed genes using a user-defined threshold

Microarray and 2D gel experiments are used for the large scale measurement, and comparison of gene expression. Since these experiments generate large and complex amounts of data, a great challenge the researcher faces is trying to find ways to analyze this data. This paper focuses on the tool DiffExpress, which was designed to make the gene expression analysis process easier. One of the main features of DiffExpress is the user defined threshold which allows users to set their personal restriction of the expression change at which genes are differentially expressed. DiffExpress also makes use of graphs such as the Scatter Plot, Box and Whisker Plot and Volcano Plot for easier visualization of data

Transcriptional adaptation of Mycobacterium tuberculosis within macrophages: Insights into the phagosomal environment

Little is known about the biochemical environment in phagosomes harboring an infectious agent. To assess the state of this organelle we captured the transcriptional responses of Mycobacterium tuberculosis (MTB) in macrophages from wild-type and nitric oxide (NO) synthase 2–deficient mice before and after immunologic activation. The intraphagosomal transcriptome was compared with the transcriptome of MTB in standard broth culture and during growth in diverse conditions designed to simulate features of the phagosomal environment. Genes expressed differentially as a consequence of intraphagosomal residence included an interferon � – and NO-induced response that intensifies an iron-scavenging program, converts the microbe from aerobic to anaerobic respiration, and induces a dormancy regulon. Induction of genes involved in the activation and �-oxidation of fatty acids indicated that fatty acids furnish carbon and energy. Induction of �E-dependent, sodium dodecyl sulfate–regulated genes and genes involved in mycolic acid modification pointed to damage and repair of the cell envelope. Sentinel genes within the intraphagosomal transcriptome were induced similarly by MTB in the lungs of mice. The microbial transcriptome thus served as a bioprobe of the MTB phagosomal environment

E-Predict: a computational strategy for species identification based on observed DNA microarray hybridization patterns

DNA microarrays may be used to identify microbial species present in environmental and clinical samples. However, automated tools for reliable species identification based on observed microarray hybridization patterns are lacking. We present an algorithm, E-Predict, for microarray-based species identification. E-Predict compares observed hybridization patterns with theoretical energy profiles representing different species. We demonstrate the application of the algorithm to viral detection in a set of clinical samples and discuss its relevance to other metagenomic applications

SWISS MADE: Standardized WithIn Class Sum of Squares to Evaluate Methodologies and Dataset Elements

Contemporary high dimensional biological assays, such as mRNA expression microarrays, regularly involve multiple data processing steps, such as experimental processing, computational processing, sample selection, or feature selection (i.e. gene selection), prior to deriving any biological conclusions. These steps can dramatically change the interpretation of an experiment. Evaluation of processing steps has received limited attention in the literature. It is not straightforward to evaluate different processing methods and investigators are often unsure of the best method. We present a simple statistical tool, Standardized WithIn class Sum of Squares (SWISS), that allows investigators to compare alternate data processing methods, such as different experimental methods, normalizations, or technologies, on a dataset in terms of how well they cluster a priori biological classes. SWISS uses Euclidean distance to determine which method does a better job of clustering the data elements based on a priori classifications. We apply SWISS to three different gene expression applications. The first application uses four different datasets to compare different experimental methods, normalizations, and gene sets. The second application, using data from the MicroArray Quality Control (MAQC) project, compares different microarray platforms. The third application compares different technologies: a single Agilent two-color microarray versus one lane of RNA-Seq. These applications give an indication of the variety of problems that SWISS can be helpful in solving. The SWISS analysis of one-color versus two-color microarrays provides investigators who use two-color arrays the opportunity to review their results in light of a single-channel analysis, with all of the associated benefits offered by this design. Analysis of the MACQ data shows differential intersite reproducibility by array platform. SWISS also shows that one lane of RNA-Seq clusters data by biological phenotypes as well as a single Agilent two-color microarray

Molecular analysis of endocrine disruption in hornyhead turbot at wastewater outfalls in southern california using a second generation multi-species microarray.

Sentinel fish hornyhead turbot (Pleuronichthysverticalis) captured near wastewater outfalls are used for monitoring exposure to industrial and agricultural chemicals of ~ 20 million people living in coastal Southern California. Although analyses of hormones in blood and organ morphology and histology are useful for assessing contaminant exposure, there is a need for quantitative and sensitive molecular measurements, since contaminants of emerging concern are known to produce subtle effects. We developed a second generation multi-species microarray with expanded content and sensitivity to investigate endocrine disruption in turbot captured near wastewater outfalls in San Diego, Orange County and Los Angeles California. Analysis of expression of genes involved in hormone [e.g., estrogen, androgen, thyroid] responses and xenobiotic metabolism in turbot livers was correlated with a series of phenotypic end points. Molecular analyses of turbot livers uncovered altered expression of vitellogenin and zona pellucida protein, indicating exposure to one or more estrogenic chemicals, as well as, alterations in cytochrome P450 (CYP) 1A, CYP3A and glutathione S-transferase-α indicating induction of the detoxification response. Molecular responses indicative of exposure to endocrine disruptors were observed in field-caught hornyhead turbot captured in Southern California demonstrating the utility of molecular methods for monitoring environmental chemicals in wastewater outfalls. Moreover, this approach can be adapted to monitor other sites for contaminants of emerging concern in other fish species for which there are few available gene sequences

Gene expression data analysis using novel methods: Predicting time delayed correlations and evolutionarily conserved functional modules

Microarray technology enables the study of gene expression on a large scale. One of the main challenges has been to devise methods to cluster genes that share similar expression profiles. In gene expression time courses, a particular gene may encode transcription factor and thus controlling several genes downstream; in this case, the gene expression profiles may be staggered, indicating a time-delayed response in transcription of the later genes. The standard clustering algorithms consider gene expression profiles in a global way, thus often ignoring such local time-delayed correlations. We have developed novel methods to capture time-delayed correlations between expression profiles: (1) A method using dynamic programming and (2) CLARITY, an algorithm that uses a local shape based similarity measure to predict time-delayed correlations and local correlations. We used CLARITY on a dataset describing the change in gene expression during the mitotic cell cycle in Saccharomyces cerevisiae. The obtained clusters were significantly enriched with genes that share similar functions, reflecting the fact that genes with a similar function are often co-regulated and thus co-expressed. Time-shifted as well as local correlations could also be predicted using CLARITY. In datasets, where the expression profiles of independent experiments are compared, the standard clustering algorithms often cluster according to all conditions, considering all genes. This increases the background noise and can lead to the missing of genes that change the expression only under particular conditions. We have employed a genetic algorithm based module predictor that is capable to identify group of genes that change their expression only in a subset of conditions. With the aim of supplementing the Ustilago maydis genome annotation, we have used the module prediction algorithm on various independent datasets from Ustilago maydis. The predicted modules were cross-referenced in various Saccharomyces cerevisiae datasets to check its evolutionarily conservation between these two organisms. The key contributions of this thesis are novel methods that explore biological information from DNA microarray data

Generalization of DNA microarray dispersion properties: microarray equivalent of t-distribution

BACKGROUND: DNA microarrays are a powerful technology that can provide a wealth of gene expression data for disease studies, drug development, and a wide scope of other investigations. Because of the large volume and inherent variability of DNA microarray data, many new statistical methods have been developed for evaluating the significance of the observed differences in gene expression. However, until now little attention has been given to the characterization of dispersion of DNA microarray data. RESULTS: Here we examine the expression data obtained from 682 Affymetrix GeneChips(® )with 22 different types and we demonstrate that the Gaussian (normal) frequency distribution is characteristic for the variability of gene expression values. However, typically 5 to 15% of the samples deviate from normality. Furthermore, it is shown that the frequency distributions of the difference of expression in subsets of ordered, consecutive pairs of genes (consecutive samples) in pair-wise comparisons of replicate experiments are also normal. We describe a consecutive sampling method, which is employed to calculate the characteristic function approximating standard deviation and show that the standard deviation derived from the consecutive samples is equivalent to the standard deviation obtained from individual genes. Finally, we determine the boundaries of probability intervals and demonstrate that the coefficients defining the intervals are independent of sample characteristics, variability of data, laboratory conditions and type of chips. These coefficients are very closely correlated with Student's t-distribution. CONCLUSION: In this study we ascertained that the non-systematic variations possess Gaussian distribution, determined the probability intervals and demonstrated that the K(α )coefficients defining these intervals are invariant; these coefficients offer a convenient universal measure of dispersion of data. The fact that the K(α )distributions are so close to t-distribution and independent of conditions and type of arrays suggests that the quantitative data provided by Affymetrix technology give "true" representation of physical processes, involved in measurement of RNA abundance. REVIEWERS: This article was reviewed by Yoav Gilad (nominated by Doron Lancet), Sach Mukherjee (nominated by Sandrine Dudoit) and Amir Niknejad and Shmuel Friedland (nominated by Neil Smalheiser)