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MzML2ISA & nmrML2ISA: generating enriched ISA-Tab metadata files from metabolomics XML data.


ABSTRACT: Submission to the MetaboLights repository for metabolomics data currently places the burden of reporting instrument and acquisition parameters in ISA-Tab format on users, who have to do it manually, a process that is time consuming and prone to user input error. Since the large majority of these parameters are embedded in instrument raw data files, an opportunity exists to capture this metadata more accurately. Here we report a set of Python packages that can automatically generate ISA-Tab metadata file stubs from raw XML metabolomics data files. The parsing packages are separated into mzML2ISA (encompassing mzML and imzML formats) and nmrML2ISA (nmrML format only). Overall, the use of mzML2ISA & nmrML2ISA reduces the time needed to capture metadata substantially (capturing 90% of metadata on assay and sample levels), is much less prone to user input errors, improves compliance with minimum information reporting guidelines and facilitates more finely grained data exploration and querying of datasets.mzML2ISA & nmrML2ISA are available under version 3 of the GNU General Public Licence at https://github.com/ISA-tools. Documentation is available from http://2isa.readthedocs.io/en/latest/.reza.salek@ebi.ac.uk or isatools@googlegroups.com.Supplementary data are available at Bioinformatics online.

SUBMITTER: Larralde M 

PROVIDER: S-EPMC5870861 | biostudies-other | 2017 Aug

REPOSITORIES: biostudies-other

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mzML2ISA & nmrML2ISA: generating enriched ISA-Tab metadata files from metabolomics XML data.

Larralde Martin M   Lawson Thomas N TN   Weber Ralf J M RJM   Moreno Pablo P   Haug Kenneth K   Rocca-Serra Philippe P   Viant Mark R MR   Steinbeck Christoph C   Salek Reza M RM  

Bioinformatics (Oxford, England) 20170801 16


<h4>Summary</h4>Submission to the MetaboLights repository for metabolomics data currently places the burden of reporting instrument and acquisition parameters in ISA-Tab format on users, who have to do it manually, a process that is time consuming and prone to user input error. Since the large majority of these parameters are embedded in instrument raw data files, an opportunity exists to capture this metadata more accurately. Here we report a set of Python packages that can automatically genera  ...[more]

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