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)

PROVIDER: MTBLS137 | MetaboLights | 2014-11-09

REPOSITORIES: MetaboLights

Dataset's files

Source:
Action DRS
L001_BLANK_RP_POS01.CDF Other
L001_Blank_RP_pos.mzML Mzml
L002_QC_RP_POS01.CDF Other
L002_QC_RP_pos.mzML Mzml
L003_QC_RP_pos.mzML Mzml
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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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