Rapid 40 kb genome construction from 52 parts
Pryor, J. M.; Potapov, V.; Pokhrel, N.; Lohman, G. J. S.
Show abstract
Large DNA constructs (>10 kb), including small genomes and artificial chromosomes, are invaluable tools for genetic engineering and vaccine development. However, the manufacture of these constructs is laborious. To address this problem, we applied new design insights and modified protocols to Golden Gate assembly. While this methodology is routinely used to assemble 5-10 DNA parts in one-step, we found that optimized assembly permitted >50 DNA fragments to be faithfully assembled in a single reaction. We applied these insights to genome construction, carrying out rapid assembly of the 40 kb T7 bacteriophage genome from 52 parts and recovering infectious phage particles after cellular transformation. The new Golden Gate assembly protocols and design principles described here can be applied to rapidly engineer a wide variety of large and complex assembly targets.
Matching journals
The top 4 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- Amplified DNA Heterogeneity Assessment with Oxford Nanopore Sequencing Applied to Cell Free Expression Templates 95%
- MultiGreen: A multiplexing architecture for GreenGate cloning 95%
- GreenGate 2.0: backwards compatible addons for assembly of complex transcriptional units and their stacking with GreenGate 94%
Similar papers in this journal
Similar papers in this journal
Similar papers in this journal
Similar papers in this journal
- The pAblo·pCasso self-curing vector toolset for unconstrained cytidine and adenine base-editing in Pseudomonas species 94%
- ConSeqUMI, an error-free nanopore sequencing pipeline to identify and extract individual nucleic acid molecules from heterogeneous samples 93%
- Nanopore sequencing undergoes catastrophic sequence failure at inverted duplicated DNA sequences 93%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.