Literature DB >> 33808985

The mwtab Python Library for RESTful Access and Enhanced Quality Control, Deposition, and Curation of the Metabolomics Workbench Data Repository.

Christian D Powell1,2,3,4, Hunter N B Moseley2,3,4,5.   

Abstract

The Metabolomics Workbench (MW) is a public scientific data repository consisting of experimental data and metadata from metabolomics studies collected with mass spectroscopy (MS) and nuclear magnetic resonance (NMR) analyses. MW has been constantly evolving; updating its 'mwTab' text file format, adding a JavaScript Object Notation (JSON) file format, implementing a REpresentational State Transfer (REST) interface, and nearly quadrupling the number of datasets hosted on the repository within the last three years. In order to keep up with the quickly evolving state of the MW repository, the 'mwtab' Python library and package have been continuously updated to mirror the changes in the 'mwTab' and JSONized formats and contain many new enhancements including methods for interacting with the MW REST interface, enhanced format validation features, and advanced features for parsing and searching for specific metabolite data and metadata. We used the enhanced format validation features to evaluate all available datasets in MW to facilitate improved curation and FAIRness of the repository. The 'mwtab' Python package is now officially released as version 1.0.1 and is freely available on GitHub and the Python Package Index (PyPI) under a Clear Berkeley Software Distribution (BSD) license with documentation available on ReadTheDocs.

Entities:  

Keywords:  data deposition; data validation; metabolomics workbench; python package

Year:  2021        PMID: 33808985      PMCID: PMC8000456          DOI: 10.3390/metabo11030163

Source DB:  PubMed          Journal:  Metabolites        ISSN: 2218-1989


  14 in total

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Authors:  Andrey Smelter; Hunter N B Moseley
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3.  Metabolomics Workbench: An international repository for metabolomics data and metadata, metabolite standards, protocols, tutorials and training, and analysis tools.

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Journal:  Nucleic Acids Res       Date:  2015-10-13       Impact factor: 16.971

4.  HMDB 4.0: the human metabolome database for 2018.

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Journal:  Nucleic Acids Res       Date:  2018-01-04       Impact factor: 16.971

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Review 6.  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

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Journal:  Nucleic Acids Res       Date:  2007-11-04       Impact factor: 16.971

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Journal:  Nucleic Acids Res       Date:  2019-01-08       Impact factor: 16.971

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  2 in total

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Authors:  Flávia da Silva Zandonadi; Emerson Andrade Ferreira Dos Santos; Mariana Silveira Marques; Alessandra Sussulini
Journal:  Adv Exp Med Biol       Date:  2022       Impact factor: 3.650

Review 2.  A Current Encyclopedia of Bioinformatics Tools, Data Formats and Resources for Mass Spectrometry Lipidomics.

Authors:  Nils Hoffmann; Gerhard Mayer; Canan Has; Dominik Kopczynski; Fadi Al Machot; Dominik Schwudke; Robert Ahrends; Katrin Marcus; Martin Eisenacher; Michael Turewicz
Journal:  Metabolites       Date:  2022-06-23
  2 in total

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