The mwtab Python Library for RESTful Access and Enhanced Quality Control, Deposition, and Curation of the Metabolomics Workbench Data Repository.
The mwtab Python Library for RESTful Access and Enhanced Quality Control, Deposition, and Curation of the Metabolomics Workbench Data Repository.
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mwtab Python 库,用于 RESTful 访问和增强代谢组学工作台数据存储库的质量控制、沉积和管理。
DOI:
10.3390/metabo11030163
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发表时间:
2021-03-12
期刊:
影响因子:
4.1
通讯作者:
Moseley HNB
中科院分区:
文献类型:
--
作者:
Powell CD;Moseley HNB
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.
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影响因子:
8.6
作者:
Heller SR;McNaught A;Pletnev I;Stein S;Tchekhovskoi D
通讯作者:
Tchekhovskoi D
影响因子:
9.8
作者:
Spicer RA;Salek R;Steinbeck C
通讯作者:
Steinbeck C
影响因子:
14.9
作者:
Kim S;Chen J;Cheng T;Gindulyte A;He J;He S;Li Q;Shoemaker BA;Thiessen PA;Yu B;Zaslavsky L;Zhang J;Bolton EE
通讯作者:
Bolton EE
影响因子:
14.9
作者:
Kanehisa M;Araki M;Goto S;Hattori M;Hirakawa M;Itoh M;Katayama T;Kawashima S;Okuda S;Tokimatsu T;Yamanishi Y
通讯作者:
Yamanishi Y
影响因子:
--
作者:
Pundir S;Magrane M;Martin MJ;O'Donovan C;UniProt Consortium
通讯作者:
UniProt Consortium