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Your search keyword '"Hannemann, Jan"' showing total 3 results
Start Over Author "Hannemann, Jan" Topic bioinformatics
3 results on '"Hannemann, Jan"'

2. Xeml Lab: a tool that supports the design of experiments at a graphical interface and generates computer-readable metadata files, which capture information about genotypes, growth conditions, environmental perturbations and sampling strategy.

Author

HANNEMANN, JAN, POORTER, HENDRIK, USADEL, BJÖRN, BLÄSING, OLIVER E., FINCK, ALEX, TARDIEU, FRANCOIS, ATKIN, OWEN K., PONS, THIJS, STITT, MARK, and GIBON, YVES

Subjects
*ARABIDOPSIS thaliana, *GENETIC polymorphisms, *PLANT proteins, *PHYSIOLOGICAL control systems, *METAPHYSICS, *GENOTYPE-environment interaction, *PLANT growth, *ECOLOGICAL disturbances
Abstract

Data mining depends on the ability to access machine-readable metadata that describe genotypes, environmental conditions, and sampling times and strategy. This article presents Xeml Lab. The Xeml Interactive Designer provides an interactive graphical interface at which complex experiments can be designed, and concomitantly generates machine-readable metadata files. It uses a new eXtensible Mark-up Language (XML)-derived dialect termed XEML. Xeml Lab includes a new ontology for environmental conditions, called Xeml Environment Ontology. However, to provide versatility, it is designed to be generic and also accepts other commonly used ontology formats, including OBO and OWL. A review summarizing important environmental conditions that need to be controlled, monitored and captured as metadata is posted in a Wiki ( ) to promote community discussion. The usefulness of Xeml Lab is illustrated by two meta-analyses of a large set of experiments that were performed with Arabidopsis thaliana during 5 years. The first reveals sources of noise that affect measurements of metabolite levels and enzyme activities. The second shows that Arabidopsis maintains remarkably stable levels of sugars and amino acids across a wide range of photoperiod treatments, and that adjustment of starch turnover and the leaf protein content contribute to this metabolic homeostasis. [ABSTRACT FROM AUTHOR]

Published
2009
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3. Robin: An intuitive wizard application for R-based expression microarray quality assessment and analysis

Author

Liam Childs, Dirk Walther, Marc Lohse, Nese Sreenivasulu, Sonia Osorio, Björn Usadel, Mark Stitt, Axel Nagel, Peter Krüger, Federico M. Giorgi, Alisdair R. Fernie, Adriano Nunes-Nesi, Jan Hannemann, Joachim Selbig, Lohse, Marc, Nunes-Nesi, Adriano, Kruger, Peter, Nagel, Axel, Hannemann, Jan, Giorgi, Federico M., Childs, Liam, Osorio, Sonia, Walther, Dirk, Selbig, Joachim, Sreenivasulu, Nese, Stitt, Mark, Fernie, Alisdair R., and Usadel, Bjorn

Subjects
Java, Bioinformatics, Computer science, Physiology, Plant Science, Bioconductor, User-Computer Interface, Software, Documentation, Solanum lycopersicum, Genetic, Genetics, Oligonucleotide Array Sequence Analysis, computer.programming_language, business.industry, Gene Expression Profiling, Computational Biology, Usability, Wizard, Expression (mathematics), Workflow, business, Software engineering, computer
Abstract

The wide application of high-throughput transcriptomics using microarrays has generated a plethora of technical platforms, data repositories, and sophisticated statistical analysis methods, leaving the individual scientist with the problem of choosing the appropriate approach to address a biological question. Several software applications that provide a rich environment for microarray analysis and data storage are available (e.g. GeneSpring, EMMA2), but these are mostly commercial or require an advanced informatics infrastructure. There is a need for a noncommercial, easy-to-use graphical application that aids the lab researcher to find the proper method to analyze microarray data, without this requiring expert understanding of the complex underlying statistics, or programming skills. We have developed Robin, a Java-based graphical wizard application that harnesses the advanced statistical analysis functions of the R/BioConductor project. Robin implements streamlined workflows that guide the user through all steps of two-color, single-color, or Affymetrix microarray analysis. It provides functions for thorough quality assessment of the data and automatically generates warnings to notify the user of potential outliers, low-quality chips, or low statistical power. The results are generated in a standard format that allows ready use with both specialized analysis tools like MapMan and PageMan and generic spreadsheet applications. To further improve user friendliness, Robin includes both integrated help and comprehensive external documentation. To demonstrate the statistical power and ease of use of the workflows in Robin, we present a case study in which we apply Robin to analyze a two-color microarray experiment comparing gene expression in tomato (Solanum lycopersicum) leaves, flowers, and roots.

Published
2010

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