ABSTRACT
The share of variable renewable energy (VRE) is forecasted to increase in the energy sector to meet decarbonization targets and/or reduce their dependence on fossil fuels. The modeling of future power system scenarios is crucial to assess the role of different flexibility options, including low-carbon technologies. The data presented here support the research article "The role of energy storage in Great Britain's future power system: focus on hydrogen and biomass". These data include updated parameters, inputs, equations, biomass resource potential and biomass demand to balance bio-power and bio-hydrogen requirements. The Future Renewable Energy Performance into the Power System Model (FEPPS), a rule-based model that includes flexibility and stability constraints, has been used, and the hourly results of future scenarios by 2030 and 2040 are provided. Researchers, policymakers, and investors could use this paper as these data provide insights into the role of different technologies (including hydrogen and biomass) in power generation, system flexibility, decarbonization and costs.
ABSTRACT
BACKGROUND: Models for the simulation of metabolic networks require the accurate prediction of enzyme function. Based on a genomic sequence, enzymatic functions of gene products are today mainly predicted by sequence database searching and operon analysis. Other methods can support these techniques: We have developed an automatic method "BrEPS" that creates highly specific sequence patterns for the functional annotation of enzymes. RESULTS: The enzymes in the UniprotKB are identified and their sequences compared against each other with BLAST. The enzymes are then clustered into a number of trees, where each tree node is associated with a set of EC-numbers. The enzyme sequences in the tree nodes are aligned with ClustalW. The conserved columns of the resulting multiple alignments are used to construct sequence patterns. In the last step, we verify the quality of the patterns by computing their specificity. Patterns with low specificity are omitted and recomputed further down in the tree. The final high-quality patterns can be used for functional annotation. We ran our protocol on a recent Swiss-Prot release and show statistics, as well as a comparison to PRIAM, a probabilistic method that is also specialized on the functional annotation of enzymes. We determine the amount of true positive annotations for five common microorganisms with data from BRENDA and AMENDA serving as standard of truth. BrEPS is almost on par with PRIAM, a fact which we discuss in the context of five manually investigated cases. CONCLUSIONS: Our protocol computes highly specific sequence patterns that can be used to support the functional annotation of enzymes. The main advantages of our method are that it is automatic and unsupervised, and quite fast once the patterns are evaluated. The results show that BrEPS can be a valuable addition to the reconstruction of metabolic networks.
