Metabolomics

Dataset Information

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MetaDB a Data Processing Workflow in Untargeted MS-Based Metabolomics Experiments

ABSTRACT: This dataset reports the UPLC-QTof MS untargeted analysis of Vitis vinifera L. leaves, collected from Italy (Trentino) and Germany (Mecklenburg West-Pomerania), from two fungus-resistant grape varieties (PIWI), Regent and Phoenix. For each variety, 40 leaves were sampled from 10 plants (4 leaves/plant) in Italy, and other 40 with the same process in Germany. The leaves from each plant were homogenized and extracted separately, in the same day, under a randomized order. A quality control (QC) sample was prepared by pooling a small aliquot from each sample.

The aim of the project, was to use this sample/data set as an illustrative example for the use the pipeline MetaDB (https://github.com/rmylonas/MetaDB). MetaDB has been developed in order to combine, with a user-friendly web based, different bioinformatic tools used in metabolomics, which takes care a) metadata organization, b) creation of randomized sequences including QC sample, c) data quality evaluation, d) data storage organization, e) data analysis and f) submission to public repositories.

INSTRUMENT(S): SYNAPT HDMS (Waters)

SUBMITTER: Panagiotis Arapitsas 

PROVIDER: MTBLS137 | MetaboLights | 2014-11-09

REPOSITORIES: MetaboLights

Json Xml

Dataset's files

Source:
Action DRS
MTBLS137 Other
FILES Other
a_MTBLS137_grape_leaves_metabolite_profiling_mass_spectrometry.txt Txt
i_Investigation.txt Txt
s_MTBLS137.txt Txt
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Publications

MetaDB a Data Processing Workflow in Untargeted MS-Based Metabolomics Experiments.

Franceschi Pietro P   Mylonas Roman R   Shahaf Nir N   Scholz Matthias M   Arapitsas Panagiotis P   Masuero Domenico D   Weingart Georg G   Carlin Silvia S   Vrhovsek Urska U   Mattivi Fulvio F   Wehrens Ron R  

Frontiers in bioengineering and biotechnology 20141216

Due to their sensitivity and speed, mass-spectrometry based analytical technologies are widely used to in metabolomics to characterize biological phenomena. To address issues like metadata organization, quality assessment, data processing, data storage, and, finally, submission to public repositories, bioinformatic pipelines of a non-interactive nature are often employed, complementing the interactive software used for initial inspection and visualization of the data. These pipelines often are c  ...[more]

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