The Special Interest Group Data Infrastructure offers a forum to interested working groups that want to set up or further develop an RDM infrastructure at working group or institute level. We invite you to a monthly SIGDIUS seminar, to which we invite internal and external experts for presentations and discussions. SIGDIUS members will have the opportunity to exchange their experiences with concrete RDM infrastructures.
We cordially invite all interested parties to our next meeting on 20 January 2021 at 2pm. Due to the current situation, this seminar will be held as an online seminar. For participation, please send an e-mail to Juergen.Pleiss@itb.uni-stuttgart.de with the subject line "SIGDIUS seminar 20.01."
Jürgen Pleiss (Institute of Biochemistry and Technical Biochemistry/SimTech, University of Stuttgart):
"F.A.I.R. data management in biocatalysis"
Enzyme catalysis provides a powerful toolbox for novel, sustainable synthesis routes and innovative solutions for bio-based chemistry. A comprehensive biochemical characterization of the desired enzyme-catalyzed reaction is essential and provides the basis for enzyme engineering and process development. Standardization of reporting of enzymatic data and metadata is considered as pivotal to accelerating bioprocess development and reducing costs. Meta-research studies suggest the lack of standardization to report and share experimental protocols, results, and data as one of the causes of the reproducibility crisis in the biomedical sciences. As first steps for the standardized reporting of enzyme function data, the enzymology and biocatalysis community has established the Standards for Reporting Enzymology Data (STRENDA) Guidelines, the STRENDA DB as a public database to make enzymatic data findable and accessible, and the XML–based data exchange format EnzymeML to make enzymatic data interoperable and reusable.
Jan Range (Institute of Biochemistry and Technical Biochemistry, University of Stuttgart):
"EnzymeML - a data exchange format for biocatalysis and enzymology"
Data management remains an ongoing challenge in biocatalysis. Modern analysis methods such as machine learning evolved to achieve peak performances beyond human capabilities in many fields but biocatalysis. The lack of a standardized data format hinders data scientists to acquire large quantities of data and thus maximize algorithmic performance. EnzymeML proposes a solution to store meta- as well as experimental data following STRENDA recommendations and fulfilling the FAIR data principles. EnzymeML is based on Systems Biology Markup Language (SBML) and was extended to match the needs of biocatalysis and enzymology experiments. In addition, a software was written in Java and Python for reading, writing and editing EnzymeML files as well as an interface for Machine Learning applications. This presentation will highlight the role of EnzymeML in an automated workflow system, starting from experiment over modelling up to tracking and storing of acquired data using a newly developed REST API – Seamless from the beginning till the end.