EnzymeML is a flexible data model for biocatalysis and enzymology capable of completely describing an experiment in a machine-readable format. To handle and integrate EnzymeML in your database, we provide our software solution PyEnzyme, which can be either installed locally or reached via several endpoints of our REST-API.
PyEnzyme as well as our REST-API provide an interface to validate incoming EnzymeML documents to comply with the minimum requirements necessary for a successful upload to your database. Besides that, we also offer a complete validation report, such that you can provide all the important information to your user if a dataset does not comply. With our long-term partner database SABIO-RK we already established a workflow to perform validation and subsequent upload to the database. The validation is performed in conjunction with a template describing all mandatory as well as optional EnzymeML fields required for a successful upload.
To integrate EnzymeML validation into your database, please follow our instructions:
Dataverse installations provide a modern and modular approach to databases, offering flexibility and stability at the same time. Hence, we developed a Dataverse-compliant metadata-block schema, such that you can easily integrate EnzymeML into your Dataverse installation. In collaboration with the FoKUS-Team from the University of Stuttgart, we already established a seamless process chain from an EnzymeML archive to a dataverse entry. Our REST-API offers an automatic upload to your dataverse in conjunction with our validation interface.
Do you provide a dataverse installation and want to integrate EnzymeML? Please feel free to contact our support to assist you.