Evaluation of time profile reconstruction from complex two-color microarray designs

<p>Abstract</p> <p>Background</p> <p>As an alternative to the frequently used "reference design" for two-channel microarrays, other designs have been proposed. These designs have been shown to be more profitable from a theoretical point of view (more replicates of the conditions of interest for the same number of arrays). However, the interpretation of the measurements is less straightforward and a reconstruction method is needed to convert the observed ratios into the genuine profile of interest (e.g. a time profile). The potential advantages of using these alternative designs thus largely depend on the success of the profile reconstruction. Therefore, we compared to what extent different linear models agree with each other in reconstructing expression ratios and corresponding time profiles from a complex design.</p> <p>Results</p> <p>On average the correlation between the estimated ratios was high, and all methods agreed with each other in predicting the same profile, especially for genes of which the expression profile showed a large variance across the different time points. Assessing the similarity in profile shape, it appears that, the more similar the underlying principles of the methods (model and input data), the more similar their results. Methods with a dye effect seemed more robust against array failure. The influence of a different normalization was not drastic and independent of the method used.</p> <p>Conclusion</p> <p>Including a dye effect such as in the methods lmbr_dye, anovaFix and anovaMix compensates for residual dye related inconsistencies in the data and renders the results more robust against array failure. Including random effects requires more parameters to be estimated and is only advised when a design is used with a sufficient number of replicates. Because of this, we believe lmbr_dye, anovaFix and anovaMix are most appropriate for practical use.</p

An integrative, multi-scale, genome-wide model reveals the phenotypic landscape of Escherichia coli.

Given the vast behavioral repertoire and biological complexity of even the simplest organisms, accurately predicting phenotypes in novel environments and unveiling their biological organization is a challenging endeavor. Here, we present an integrative modeling methodology that unifies under a common framework the various biological processes and their interactions across multiple layers. We trained this methodology on an extensive normalized compendium for the gram-negative bacterium Escherichia coli, which incorporates gene expression data for genetic and environmental perturbations, transcriptional regulation, signal transduction, and metabolic pathways, as well as growth measurements. Comparison with measured growth and high-throughput data demonstrates the enhanced ability of the integrative model to predict phenotypic outcomes in various environmental and genetic conditions, even in cases where their underlying functions are under-represented in the training set. This work paves the way toward integrative techniques that extract knowledge from a variety of biological data to achieve more than the sum of their parts in the context of prediction, analysis, and redesign of biological systems

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

Genome-scale resources for Thermoanaerobacterium saccharolyticum

Background Thermoanaerobacterium saccharolyticum is a hemicellulose-degrading thermophilic anaerobe that was previously engineered to produce ethanol at high yield. A major project was undertaken to develop this organism into an industrial biocatalyst, but the lack of genome information and resources were recognized early on as a key limitation. Results Here we present a set of genome-scale resources to enable the systems level investigation and development of this potentially important industrial organism. Resources include a complete genome sequence for strain JW/SL-YS485, a genome-scale reconstruction of metabolism, tiled microarray data showing transcription units, mRNA expression data from 71 different growth conditions or timepoints and GC/MS-based metabolite analysis data from 42 different conditions or timepoints. Growth conditions include hemicellulose hydrolysate, the inhibitors HMF, furfural, diamide, and ethanol, as well as high levels of cellulose, xylose, cellobiose or maltodextrin. The genome consists of a 2.7 Mbp chromosome and a 110 Kbp megaplasmid. An active prophage was also detected, and the expression levels of CRISPR genes were observed to increase in association with those of the phage. Hemicellulose hydrolysate elicited a response of carbohydrate transport and catabolism genes, as well as poorly characterized genes suggesting a redox challenge. In some conditions, a time series of combined transcription and metabolite measurements were made to allow careful study of microbial physiology under process conditions. As a demonstration of the potential utility of the metabolic reconstruction, the OptKnock algorithm was used to predict a set of gene knockouts that maximize growth-coupled ethanol production. The predictions validated intuitive strain designs and matched previous experimental results. Conclusion These data will be a useful asset for efforts to develop T. saccharolyticum for efficient industrial production of biofuels. The resources presented herein may also be useful on a comparative basis for development of other lignocellulose degrading microbes, such as Clostridium thermocellum. Electronic supplementary material The online version of this article (doi:10.1186/s12918-015-0159-x) contains supplementary material, which is available to authorized users

Meta Analysis of Gene Expression Data within and Across Species

Since the second half of the 1990s, a large number of genome-wide analyses have been described that study gene expression at the transcript level. To this end, two major strategies have been adopted, a first one relying on hybridization techniques such as microarrays, and a second one based on sequencing techniques such as serial analysis of gene expression (SAGE), cDNA-AFLP, and analysis based on expressed sequence tags (ESTs). Despite both types of profiling experiments becoming routine techniques in many research groups, their application remains costly and laborious. As a result, the number of conditions profiled in individual studies is still relatively small and usually varies from only two to few hundreds of samples for the largest experiments. More and more, scientific journals require the deposit of these high throughput experiments in public databases upon publication. Mining the information present in these databases offers molecular biologists the possibility to view their own small-scale analysis in the light of what is already available. However, so far, the richness of the public information remains largely unexploited. Several obstacles such as the correct association between ESTs and microarray probes with the corresponding gene transcript, the incompleteness and inconsistency in the annotation of experimental conditions, and the lack of standardized experimental protocols to generate gene expression data, all impede the successful mining of these data. Here, we review the potential and difficulties of combining publicly available expression data from respectively EST analyses and microarray experiments. With examples from literature, we show how meta-analysis of expression profiling experiments can be used to study expression behavior in a single organism or between organisms, across a wide range of experimental conditions. We also provide an overview of the methods and tools that can aid molecular biologists in exploiting these public data

Strategioita toksikogenomidata-analyysien standardisoinnin ja robustisuuden parantamiseksi

Toxicology is the scientific pursuit of identifying and classifying the toxic effect of a substance, as well as exploration and understanding of the adverse effects due to toxic exposure. The modern toxicological efforts have been driven by the human industrial exploits in the production of engineered substances with advanced interdisciplinary scientific collaborations. These engineered substances must be carefully tested to ensure public safety. This task is now more challenging than ever with the employment of new classes of chemical compounds, such as the engineered nanomaterials. Toxicological paradigms have been redefined over the decades to be more agile, versatile, and sensitive. On the other hand, the design of toxicological studies has become more complex, and the interpretation of the results is more challenging. Toxicogenomics offers a wealth of data to estimate the gene regulation by inspection of the alterations of many biomolecules (such as DNA, RNA, proteins, and metabolites). The response of functional genes can be used to infer the toxic effects on the biological system resulting in acute or chronic adverse effects. However, the dense data from toxicogenomics studies is difficult to analyze, and the results are difficult to interpret. Toxicogenomic evidence is still not completely integrated into the regulatory framework due to these drawbacks. Nanomaterial properties such as particle size, shape, and structure increase complexity and unique challenges to Nanotoxicology. This thesis presents the efforts in the standardization of toxicogenomics data by showcasing the potential of omics in nanotoxicology and providing easy to use tools for the analysis, and interpretation of omics data. This work explores two main themes: i) omics experimentation in nanotoxicology and investigation of nanomaterial effect by analysis of the omics data, and ii) the development of analysis pipelines as easy to use tools that bring advanced analytical methods to general users. In this work, I explored a potential solution that can ensure effective interpretability and reproducibility of omics data and related experimentation such that an independent researcher can interpret it thoroughly. DNA microarray technology is a well-established research tool to estimate the dynamics of biological molecules with high throughput. The analysis of data from these assays presents many challenges as the study designs are quite complex. I explored the challenges of omics data processing and provided bioinformatics solutions to standardize this process. The responses of individual molecules to a given exposure is only partially informative and more sophisticated models, disentangling the complex networks of dynamic molecular interactions, need to be explored. An analytical solution is presented in this thesis to tackle down the challenge of producing robust interpretations of molecular dynamics in biological systems. It allows exploring the substructures in molecular networks underlying mechanisms of molecular adaptation to exposures. I also present here a multi-omics approach to defining the mechanism of action for human cell lines exposed to nanomaterials. All the methodologies developed in this project for omics data processing and network analysis are implemented as software solutions that are designed to be easily accessible also by users with no expertise in bioinformatics. Our strategies are also developed in an effort to standardize omics data processing and analysis and to promote the use of omics-based evidence in chemical risk assessment.Toxicology is the scientific pursuit of identifying and classifying the toxic effect of a substance, as well as exploration and understanding of the adverse effects due to toxic exposure. The modern toxicological efforts have been driven by the human industrial exploits in the production of engineered substances with advanced interdisciplinary scientific collaborations. These engineered substances must be carefully tested to ensure public safety. This task is now more challenging than ever with the employment of new classes of chemical compounds, such as the engineered nanomaterials. Toxicological paradigms have been redefined over the decades to be more agile, versatile, and sensitive. On the other hand, the design of toxicological studies has become more complex, and the interpretation of the results is more challenging. Toxicogenomics offers a wealth of data to estimate the gene regulation by inspection of the alterations of many biomolecules (such as DNA, RNA, proteins, and metabolites). The response of functional genes can be used to infer the toxic effects on the biological system resulting in acute or chronic adverse effects. However, the dense data from toxicogenomics studies is difficult to analyze, and the results are difficult to interpret. Toxicogenomic evidence is still not completely integrated into the regulatory framework due to these drawbacks. Nanomaterial properties such as particle size, shape, and structure increase complexity and unique challenges to Nanotoxicology. This thesis presents the efforts in the standardization of toxicogenomics data by showcasing the potential of omics in nanotoxicology and providing easy to use tools for the analysis, and interpretation of omics data. This work explores two main themes: i) omics experimentation in nanotoxicology and investigation of nanomaterial effect by analysis of the omics data, and ii) the development of analysis pipelines as easy to use tools that bring advanced analytical methods to general users. In this work, I explored a potential solution that can ensure effective interpretability and reproducibility of omics data and related experimentation such that an independent researcher can interpret it thoroughly. DNA microarray technology is a well-established research tool to estimate the dynamics of biological molecules with high throughput. The analysis of data from these assays presents many challenges as the study designs are quite complex. I explored the challenges of omics data processing and provided bioinformatics solutions to standardize this process. The responses of individual molecules to a given exposure is only partially informative and more sophisticated models, disentangling the complex networks of dynamic molecular interactions, need to be explored. An analytical solution is presented in this thesis to tackle down the challenge of producing robust interpretations of molecular dynamics in biological systems. It allows exploring the substructures in molecular networks underlying mechanisms of molecular adaptation to exposures. I also present here a multi-omics approach to defining the mechanism of action for human cell lines exposed to nanomaterials. All the methodologies developed in this project for omics data processing and network analysis are implemented as software solutions that are designed to be easily accessible also by users with no expertise in bioinformatics. Our strategies are also developed in an effort to standardize omics data processing and analysis and to promote the use of omics-based evidence in chemical risk assessment

Practical Approaches to Biological Network Discovery

This dissertation addresses a current outstanding problem in the field of systems biology, which is to identify the structure of a transcriptional network from high-throughput experimental data. Understanding of the connectivity of a transcriptional network is an important piece of the puzzle, which relates the genotype of an organism to its phenotypes. An overwhelming number of computational approaches have been proposed to perform integrative analyses on large collections of high-throughput gene expression datasets to infer the structure of transcriptional networks. I put forth a methodology by which these tools can be evaluated and compared against one another to better understand their strengths and weaknesses. Next I undertake the task of utilizing high-throughput datasets to learn new and interesting network biology in the pathogenic fungus Cryptococcus neoformans. Finally I propose a novel computational method for mapping out transcriptional networks that unifies two orthogonal strategies for network inference. I apply this method to map out the transcriptional network of Saccharomyces cerevisiae and demonstrate how network inference results can complement chromatin immunoprecipitation: ChIP) experiments, which directly probe the binding events of transcriptional regulators. Collectively, my contributions improve both the accessibility and practicality of network inference methods

Meta-analysis of muscle transcriptome data using the MADMuscle database reveals biologically relevant gene patterns

<p>Abstract</p> <p>Background</p> <p>DNA microarray technology has had a great impact on muscle research and microarray gene expression data has been widely used to identify gene signatures characteristic of the studied conditions. With the rapid accumulation of muscle microarray data, it is of great interest to understand how to compare and combine data across multiple studies. Meta-analysis of transcriptome data is a valuable method to achieve it. It enables to highlight conserved gene signatures between multiple independent studies. However, using it is made difficult by the diversity of the available data: different microarray platforms, different gene nomenclature, different species studied, etc.</p> <p>Description</p> <p>We have developed a system tool dedicated to muscle transcriptome data. This system comprises a collection of microarray data as well as a query tool. This latter allows the user to extract similar clusters of co-expressed genes from the database, using an input gene list. Common and relevant gene signatures can thus be searched more easily. The dedicated database consists in a large compendium of public data (more than 500 data sets) related to muscle (skeletal and heart). These studies included seven different animal species from invertebrates (<it>Drosophila melanogaster, Caenorhabditis elegans</it>) and vertebrates (<it>Homo sapiens, Mus musculus, Rattus norvegicus, Canis familiaris, Gallus gallus</it>). After a renormalization step, clusters of co-expressed genes were identified in each dataset. The lists of co-expressed genes were annotated using a unified re-annotation procedure. These gene lists were compared to find significant overlaps between studies.</p> <p>Conclusions</p> <p>Applied to this large compendium of data sets, meta-analyses demonstrated that conserved patterns between species could be identified. Focusing on a specific pathology (Duchenne Muscular Dystrophy) we validated results across independent studies and revealed robust biomarkers and new pathways of interest. The meta-analyses performed with MADMuscle show the usefulness of this approach. Our method can be applied to all public transcriptome data.</p