SEVA
SEVA provides a standardized plasmid vector architecture and repository (SEVA-DB 3.0) for selecting, assembling, and analyzing plasmid constructs to deconstruct and reconstruct complex prokaryotic phenotypes.
Key Features:
- Plasmid repository: SEVA-DB 3.0 maintains an expanded, canonical collection of SEVA plasmid vectors for genetic and genomic engineering of prokaryotes.
- SBOL availability: Canonical SEVA plasmids are provided in machine-readable SBOL (Synthetic Biology Open Language) format to enable interoperability with other platforms.
- Construct visualization: Integrated bioinformatic tools enable visualization of plasmid constructs.
- In silico DNA manipulation: Integrated tools support virtual manipulation of DNA segments within constructs.
- Plasmid behavior simulation: The resource supports simulations of plasmid behavior under various in vivo conditions.
Scientific Applications:
- Vector selection and assembly: Selection of optimal plasmid vectors for deconstructing and reconstructing complex prokaryotic phenotypes.
- Non-standard bacterial species engineering: Extending synthetic biology approaches to non-standard bacterial species.
- Chassis programming: Genetically programming new prokaryotic chassis.
- Research and biotechnology: Support for fundamental research and biotechnological applications in genetic engineering and synthetic biology.
Methodology:
Integration of bioinformatic tools for construct visualization and virtual DNA-segment manipulation; provision of plasmid sequences in SBOL format; and simulation of plasmid behavior under various in vivo conditions.
Topics
Details
- Added:
- 1/14/2020
- Last Updated:
- 1/16/2021
Operations
Publications
Martínez-García E, Goñi-Moreno A, Bartley B, McLaughlin J, Sánchez-Sampedro L, Pascual del Pozo H, Prieto Hernández C, Marletta AS, De Lucrezia D, Sánchez-Fernández G, Fraile S, de Lorenzo V. SEVA 3.0: an update of the Standard European Vector Architecture for enabling portability of genetic constructs among diverse bacterial hosts. Nucleic Acids Research. 2019;48(D1):D1164-D1170. doi:10.1093/nar/gkz1024. PMID:31740968. PMCID:PMC7018797.