Literature DB >> 19236607

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.

Jan Hannemann1, Hendrik Poorter, Björn Usadel, Oliver E Bläsing, Alex Finck, Francois Tardieu, Owen K Atkin, Thijs Pons, Mark Stitt, Yves Gibon.   

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 (http://www.codeplex.com/XeO) 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.

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Year:  2009        PMID: 19236607     DOI: 10.1111/j.1365-3040.2009.01964.x

Source DB:  PubMed          Journal:  Plant Cell Environ        ISSN: 0140-7791            Impact factor:   7.228


  13 in total

1.  The developmental dynamics of the maize leaf transcriptome.

Authors:  Pinghua Li; Lalit Ponnala; Neeru Gandotra; Lin Wang; Yaqing Si; S Lori Tausta; Tesfamichael H Kebrom; Nicholas Provart; Rohan Patel; Christopher R Myers; Edwin J Reidel; Robert Turgeon; Peng Liu; Qi Sun; Timothy Nelson; Thomas P Brutnell
Journal:  Nat Genet       Date:  2010-10-31       Impact factor: 38.330

2.  Robin: an intuitive wizard application for R-based expression microarray quality assessment and analysis.

Authors:  Marc Lohse; Adriano Nunes-Nesi; Peter Krüger; Axel Nagel; Jan Hannemann; Federico M Giorgi; Liam Childs; Sonia Osorio; Dirk Walther; Joachim Selbig; Nese Sreenivasulu; Mark Stitt; Alisdair R Fernie; Björn Usadel
Journal:  Plant Physiol       Date:  2010-04-13       Impact factor: 8.340

3.  Impact of the carbon and nitrogen supply on relationships and connectivity between metabolism and biomass in a broad panel of Arabidopsis accessions.

Authors:  Ronan Sulpice; Zoran Nikoloski; Hendrik Tschoep; Carla Antonio; Sabrina Kleessen; Abdelhalim Larhlimi; Joachim Selbig; Hirofumi Ishihara; Yves Gibon; Alisdair R Fernie; Mark Stitt
Journal:  Plant Physiol       Date:  2013-03-20       Impact factor: 8.340

4.  A software tool for the input and management of phenotypic data using personal digital assistants and other mobile devices.

Authors:  Karin Köhl; Jürgen Gremmels
Journal:  Plant Methods       Date:  2015-04-07       Impact factor: 4.993

5.  Optimizing experimental procedures for quantitative evaluation of crop plant performance in high throughput phenotyping systems.

Authors:  Astrid Junker; Moses M Muraya; Kathleen Weigelt-Fischer; Fernando Arana-Ceballos; Christian Klukas; Albrecht E Melchinger; Rhonda C Meyer; David Riewe; Thomas Altmann
Journal:  Front Plant Sci       Date:  2015-01-20       Impact factor: 5.753

Review 6.  Fortune telling: metabolic markers of plant performance.

Authors:  Olivier Fernandez; Maria Urrutia; Stéphane Bernillon; Catherine Giauffret; François Tardieu; Jacques Le Gouis; Nicolas Langlade; Alain Charcosset; Annick Moing; Yves Gibon
Journal:  Metabolomics       Date:  2016-09-15       Impact factor: 4.290

7.  Ribosome and transcript copy numbers, polysome occupancy and enzyme dynamics in Arabidopsis.

Authors:  Maria Piques; Waltraud X Schulze; Melanie Höhne; Björn Usadel; Yves Gibon; Johann Rohwer; Mark Stitt
Journal:  Mol Syst Biol       Date:  2009-10-13       Impact factor: 11.429

Review 8.  Data standards can boost metabolomics research, and if there is a will, there is a way.

Authors:  Philippe Rocca-Serra; Reza M Salek; Masanori Arita; Elon Correa; Saravanan Dayalan; Alejandra Gonzalez-Beltran; Tim Ebbels; Royston Goodacre; Janna Hastings; Kenneth Haug; Albert Koulman; Macha Nikolski; Matej Oresic; Susanna-Assunta Sansone; Daniel Schober; James Smith; Christoph Steinbeck; Mark R Viant; Steffen Neumann
Journal:  Metabolomics       Date:  2015-11-17       Impact factor: 4.290

9.  COordination of Standards in MetabOlomicS (COSMOS): facilitating integrated metabolomics data access.

Authors:  Reza M Salek; Steffen Neumann; Daniel Schober; Jan Hummel; Kenny Billiau; Joachim Kopka; Elon Correa; Theo Reijmers; Antonio Rosato; Leonardo Tenori; Paola Turano; Silvia Marin; Catherine Deborde; Daniel Jacob; Dominique Rolin; Benjamin Dartigues; Pablo Conesa; Kenneth Haug; Philippe Rocca-Serra; Steve O'Hagan; Jie Hao; Michael van Vliet; Marko Sysi-Aho; Christian Ludwig; Jildau Bouwman; Marta Cascante; Timothy Ebbels; Julian L Griffin; Annick Moing; Macha Nikolski; Matej Oresic; Susanna-Assunta Sansone; Mark R Viant; Royston Goodacre; Ulrich L Günther; Thomas Hankemeier; Claudio Luchinat; Dirk Walther; Christoph Steinbeck
Journal:  Metabolomics       Date:  2015-05-26       Impact factor: 4.290

10.  Field-omics-understanding large-scale molecular data from field crops.

Authors:  Erik Alexandersson; Dan Jacobson; Melané A Vivier; Wolfram Weckwerth; Erik Andreasson
Journal:  Front Plant Sci       Date:  2014-06-20       Impact factor: 5.753

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