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EnzymeML-a data exchange format for biocatalysis and enzymology

期刊

FEBS JOURNAL
卷 289, 期 19, 页码 5864-5874

出版社

WILEY
DOI: 10.1111/febs.16318

关键词

biocatalysis; bioinformatics; data exchange; enzymology; FAIR data principles; Python; research data management; Systems Biology Markup Language; XML

资金

  1. Deutsche Forschungsgemeinschaft (DFG) [EXC310, EXC2075]
  2. Biotechnology and Biological Sciences Research Council (BBSRC) [BB/S004955/1]
  3. University of Liverpool
  4. Klaus Tschira Foundation
  5. German Federal Ministry of Education and Research within de.NBI [031L0104A, 031A540]
  6. Projekt DEAL
  7. BBSRC [BB/S004955/1] Funding Source: UKRI

向作者/读者索取更多资源

EnzymeML is an XML-based data exchange format that supports comprehensive documentation of enzymatic data, extended by implementing the STRENDA Guidelines. It supports the scientific community with a standardized data exchange format and integrates software tools through a Python API.
EnzymeML is an XML-based data exchange format that supports the comprehensive documentation of enzymatic data by describing reaction conditions, time courses of substrate and product concentrations, the kinetic model, and the estimated kinetic constants. EnzymeML is based on the Systems Biology Markup Language, which was extended by implementing the STRENDA Guidelines. An EnzymeML document serves as a container to transfer data between experimental platforms, modeling tools, and databases. EnzymeML supports the scientific community by introducing a standardized data exchange format to make enzymatic data findable, accessible, interoperable, and reusable according to the FAIR data principles. An application programming interface in Python supports the integration of software tools for data acquisition, data analysis, and publication. The feasibility of a seamless data flow using EnzymeML is demonstrated by creating an EnzymeML document from a structured spreadsheet or from a STRENDA DB database entry, by kinetic modeling using the modeling platform COPASI, and by uploading to the enzymatic reaction kinetics database SABIO-RK.

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