The design of biocatalytic reaction systems is highly complex due to the dependency of the estimated kinetic parameters on the enzyme, the reaction conditions, and the modelling method. Consequently, reproducibility of enzymatic experiments and re-usability of enzymatic data are challenging. We developed the XML–based markup language EnzymeML to enable storage and exchange of enzymatic data such as reaction conditions, time course of substrate and product, kinetic parameters, and kinetic model, thus making enzymatic data findable, accessible, interoperable, and reusable (FAIR). The feasibility and usefulness of the EnzymeML toolbox is demonstrated in six scenarios, where data and metadata of different enzymatic reactions are collected and analysed. EnzymeML serves as a seamless communication channel between experimental platforms, electronic lab notebooks, tools for modelling of enzyme kinetics, publication platforms, and enzymatic reaction databases. EnzymeML is open, transparent, and invites the community to contribute. All documents and codes are freely available at https://enzymeml.org/.

SVDSS combines ideas from traditional mapping-based and assembly-based SV detection algorithms with the novel mapping-free Substring-free Sample-specific Strings (SFS) framework to achieve a 15% recall improvement over state-of-the-art methods in detecting SVs in repetitive regions of the genome.

Ultraflat graphene membrane is developed here as the supporting film for cryo-EM specimen preparation, which improves the image quality of vitrified samples and enables the high-resolution reconstruction of small macromolecules (<100KD).