4.7 Article

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

期刊

BIOINFORMATICS
卷 33, 期 16, 页码 2598-2600

出版社

OXFORD UNIV PRESS
DOI: 10.1093/bioinformatics/btx169

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资金

  1. NERC CASE PhD studentship
  2. GigaScience [NE/L002493/1]
  3. BBSRC [BB/L005077/1, BB/M019985/1, BB/M027635/1]
  4. MRC UK MEDical BIOinformatics partnership [MR/L01632X/1]
  5. PhenoMeNal European Commission's Horizon programme [654241]
  6. Wellcome Trust [202952/Z/16/Z]
  7. Biotechnology and Biological Sciences Research Council [BB/L024152/1] Funding Source: researchfish
  8. Medical Research Council [MR/M009157/1] Funding Source: researchfish
  9. Natural Environment Research Council [1499596, NE/N016777/1] Funding Source: researchfish
  10. BBSRC [BB/L005077/1, BB/L024152/1, BB/M019985/1] Funding Source: UKRI
  11. MRC [MR/M009157/1] Funding Source: UKRI
  12. NERC [NE/N016777/1] Funding Source: UKRI
  13. Wellcome Trust [202952/Z/16/Z] Funding Source: Wellcome Trust

向作者/读者索取更多资源

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 ofmzML2ISA & 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.

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