Mayday - integrative analytics for expression data

<p>Abstract</p> <p>Background</p> <p>DNA Microarrays have become the standard method for large scale analyses of gene expression and epigenomics. The increasing complexity and inherent noisiness of the generated data makes visual data exploration ever more important. Fast deployment of new methods as well as a combination of predefined, easy to apply methods with programmer's access to the data are important requirements for any analysis framework. Mayday is an open source platform with emphasis on visual data exploration and analysis. Many built-in methods for clustering, machine learning and classification are provided for dissecting complex datasets. Plugins can easily be written to extend Mayday's functionality in a large number of ways. As Java program, Mayday is platform-independent and can be used as Java WebStart application without any installation. Mayday can import data from several file formats, database connectivity is included for efficient data organization. Numerous interactive visualization tools, including box plots, profile plots, principal component plots and a heatmap are available, can be enhanced with metadata and exported as publication quality vector files.</p> <p>Results</p> <p>We have rewritten large parts of Mayday's core to make it more efficient and ready for future developments. Among the large number of new plugins are an automated processing framework, dynamic filtering, new and efficient clustering methods, a machine learning module and database connectivity. Extensive manual data analysis can be done using an inbuilt R terminal and an integrated SQL querying interface. Our visualization framework has become more powerful, new plot types have been added and existing plots improved.</p> <p>Conclusions</p> <p>We present a major extension of Mayday, a very versatile open-source framework for efficient micro array data analysis designed for biologists and bioinformaticians. Most everyday tasks are already covered. The large number of available plugins as well as the extension possibilities using compiled plugins and ad-hoc scripting allow for the rapid adaption of Mayday also to very specialized data exploration. Mayday is available at <url>http://microarray-analysis.org</url>.</p

Constraining nonlinear time series modeling with the metabolic theory of ecology

Forecasting the response of ecological systems to environmental change is a critical challenge for sustainable management. The metabolic theory of ecology (MTE) posits scaling of biological rates with temperature, but it has had limited application to population dynamic forecasting. Here we use the temperature dependence of the MTE to constrain empirical dynamic modeling (EDM), an equation-free nonlinear machine learning approach for forecasting. By rescaling time with temperature and modeling dynamics on a “metabolic time step,” our method (MTE-EDM) improved forecast accuracy in 18 of 19 empirical ectotherm time series (by 19% on average), with the largest gains in more seasonal environments. MTE-EDM assumes that temperature affects only the rate, rather than the form, of population dynamics, and that interacting species have approximately similar temperature dependence. A review of laboratory studies suggests these assumptions are reasonable, at least approximately, though not for all ecological systems. Our approach highlights how to combine modern data-driven forecasting techniques with ecological theory and mechanistic understanding to predict the response of complex ecosystems to temperature variability and trends

A method and tool for ‘cradle to grave’ embodied energy and carbon impacts of UK buildings in compliance with the new TC350 standards

As operational impacts from buildings are reduced, embodied impacts are increasing. However, the latter are seldom calculated in the UK; when they are, they tend to be calculated after the building has been constructed, or are underestimated by considering only the initial materials stage. In 2010, the UK Government recommended that a standard methodology for calculating embodied impacts of buildings be developed for early stage design decisions. This was followed in 2011–12 by the publication of the European TC350 standards defining the ‘cradle to grave’ impact of buildings and products through a process Life Cycle Analysis. This paper describes a new whole life embodied carbon and energy of buildings (ECEB) tool, designed as a usable empirical-based approach for early stage design decisions for UK buildings. The tool complies where possible with the TC350 standards. Initial results for a simple masonry construction dwelling are given in terms of the percentage contribution of each life cycle stage. The main difficulty in obtaining these results is found to be the lack of data, and the paper suggests that the construction and manufacturing industries now have a responsibility to develop new data in order to support this task

Your space or mine? : Mapping self in time

Peer reviewedPublisher PD

The science of decadence

In the nineteenth century, the concept of decadence was not solely of aesthetic interest but had a number of scientific applications. Decadence itself is an organic metaphor, extending the natural processes of decline and decay to societies and the arts. Rather than rejecting nature outright, decadent authors readily embraced new scientific theories that changed the way people thought about the natural world. The pessimism of nineteenth-century science stemmed from the brutal world of industrial capitalism in which it was developed. Decadent writers then incorporated both scientific ideas and language into a literary style obsessed with decay and decline. Finally, science returned to decadent literature to pathologize certain modes of artistic expression as yet another sign that certain types of individuals were ‘degenerate’. Three key scientific theories of the nineteenth century underpin the decadent fixation on decline, decay, and degeneration: uniformitarianism, evolution, and the conservation of energy. All three theories identify impermanence in natural structures previously believed to be permanent and stable

Diacylglycerol triggers Rim101 pathway dependent necrosis in yeast: a model for lipotoxicity

The loss of lipid homeostasis can lead to lipid overload and is associated with a variety of disease states. However, little is known as to how the disruption of lipid regulation or lipid overload affects cell survival. In this study we investigated how excess diacylglycerol (DG), a cardinal metabolite suspected to mediate lipotoxicity, compromises the survival of yeast cells. We reveal that increased DG achieved by either genetic manipulation or pharmacological administration of 1,2-dioctanoyl-sn-glycerol (DOG) triggers necrotic cell death. The toxic effects of DG are linked to glucose metabolism and require a functional Rim101 signaling cascade involving the Rim21 dependent sensing complex and activation of a calpain-like protease. The Rim101 cascade is an established pathway that triggers a transcriptional response to alkaline or lipid stress. We propose that the Rim101 pathway senses DG-induced lipid perturbation and conducts a signaling response that either facilitates cellular adaptation or triggers lipotoxic cell death. Using established models of lipotoxicity i.e. high fat diet in Drosophila and palmitic acid administration in cultured human endothelial cells, we present evidence that the core mechanism underlying this calpain-dependent lipotoxic cell death pathway is phylogenetically conserved

Analyse und Visualisierung von Genexpressionsdaten

Today, gene expression data is acquired with increasing speed with increasing quality and depth. High throughput technologies like DNA microarrays and next generation sequencing technologies have led to a rising pace of new discoveries in the biomedical field. These technologies are complemented by high throughput pipelines for proteomics and metabolomics profiling. Altogether, vast amounts of primary measured data, complementary data from other omics and meta information from many sources is available for researchers. This data needs to be jointly analyzed and visualized in context of external data and meta information. In this thesis, new tools and concepts are introduced for the purpose of visualizing gene expression data in the context of meta information and complementary data from other "omics" experiments. First, the application of generic visualization tools to resequencing microarrays, which are used for finding mutations in single genes is discussed. For this final step of gene expression analysis, an application called ResqMi, ("Resequencing using Microarrays") is presented that allows to use generic and adapted visualization tools on resequencing microarrays, in order to improve quality control, data analysis and revision of problematic base calls. The focus of this work is on the visualization of gene expression data. Here, new tools for the visualization of gene expression data in the context of meta information from processing results and external sources, like functional annotations are introduced. For the visualization of clustered gene expression data, profile logos extend the concept of sequence logos to expression data. Chromograms and tag clouds, tools for visualizing different properties of collections of nominal data are applied in combination in order to explore temporal, spatial and other patters in annotations of gene expression data. Furthermore, enhanced tabular views of summarized gene annotations and genes ranked by statistical values are discussed for comparative visualization of textual and numeric meta data. Graph based visualizations of gene expression and meta data are more generic and investigated in greater detail. Most tools for visualizing biological pathways do not make full use of gene expression or meta information data. Here, a variety of ways to include gene expression data into biological network visualizations is investigated and implemented, based both on the node rendering and the layout of the graph. This allows dense, high dimensional visualizations. Specialized tools that are optimized for working with pathways in KEGG and BioPax formats, are presented as well as MGV (the Mayday Graph Viewer), a general tool for visualizing a wide range of biological networks that offers a full range of options within a rich, extensible user interface. Options for integration and creation of network data, data organization and analysis within the graph framework are investigated. MGV furthermore incorporates tools for integrating data from several datasets, which allows to combine multiple "omics" data in one visualization. With dynamic groups that can contain nodes with data from all sources, cross dataset analyses can be performed. Further applications include the integration of metabolomics data, clustering comparisons and the visualization of gene models.Die heutigen Methoden der Genexpressionsanalyse erlauben die Datenerfassung mit zunehmender Geschwindigkeit und Qualität. Hochdurchsatzverfahren wie DNA-Microarrays und Sequenzierungsverfahren der zweiten Generation haben zahlreiche neue Entdeckungen ermöglicht. Zusammen mit Ergebnissen aus Verfahren der Proteom- und Metabolomanalyse stehen große Datenmengen zur Verfügung, zusätzlich ergänzt durch viele Annotationen und Metadaten. Im Rahmen von Genexpressionsstudien müssen diese Daten oft mit visuellen Methoden untersucht und analysiert werden. In dieser Arbeit werden dazu neue Methoden und Konzepte für die Visualisierung von Genexpressionsdaten im Kontext von Metainformationen und Ergebnissen anderer Technologien vorgestellt. Zunächst werden Standardvisualisierungsmethoden für Resequenzierungs-Microarrays diskutiert. Zum Zwecke der Anwendung von generischen und angepassten Visualisierungsmethoden auf entsprechende Daten wird das Programm ResqMi ("Resequencing using Microarrays") vorgestellt. ResqMi bietet neue Möglichkeiten für die Qualitätskontrolle, Analyse und Nachbearbeitung der Daten. Der Fokus dieser Arbeit liegt auf der Visualisierung von Genexpressionsdaten. Zunächst werden einige Visualisierungsmethoden für Genexpressionsdaten im Kontext von Metainformationen aus Prozessierungsergebnissen und externen Quellen - etwa Funktionsannotationen - vorgestellt. Zur Darstellung von geclusterten Genexpressionsprofilen werden Profillogos verwendet, die das Konzept der Sequenzlogos für die Darstellung von Expressionsdaten erweitern. Chromogramme und Tag Clouds, Visualisierungstools für verschiedene Aspekte von nominalen Daten, werden hier kombiniert genutzt, um Muster in Annotationen von Genexpressionsdaten zu finden. Außerdem werden zur Visualisierung von nominalen und ordinalen Metainformationen geeignete erweiterte tabellenartige Ansichten verwendet: die Term Pyramid bzw. Probe Rank Plots. Die graphenbasierte Visualisierung von Genexpressionsdaten ist generischer und bietet viele zusätzliche Möglichkeiten und wird im Weiteren näher verfolgt. Viele Anwendungen für die Visualisierung biologischer Pathways nutzen nicht alle Möglichkeiten für die Darstellung von Genexpressions- und Metadaten. Hier werden verschiedene Möglichkeiten zur Einbeziehung von Genexpressionsdaten in die Darstellung biologischer Daten untersucht. Sowohl spezielle Anwendungen für die Darstellung biologischer Pathways (in KEGG- und BioPax-Format) als auch MGV ("Mayday Graph Viewer") werden vorgestellt. MGV ist ein generisches Tool, das die Visualisierung vieler verschiedener biologischer Netzwerke mit vielen Optionen innerhalb einer mächtigen Oberfläche ermöglicht. Verschiedene Strategien für die Erzeugung, Strukturierung und Analyse innerhalb dieses Programmes werden vorgestellt. Außerdem wird die Integration von Daten aus unterschiedlichen Studien und Technologien in MGV untersucht. Dynamische Gruppen von Knoten, die mit Daten aus verschiedenen Quellen angereichert sind, sind die Ausgangsbasis für datensatzübergreifende Analysen. Weitere Anwendungen umfassen unter anderem die Analyse von Metabolomik-Daten, der Vergleich von Clusterings und die Visualisierung von Genmodellen