Evidence map›Paper›PMID 30462270›Full record

ArticleNucleic acids research2019

Start-Stop Assembly: a functionally scarless DNA assembly system optimized for metabolic engineering.

George M Taylor, Paweł M Mordaka, John T Heap

Abstract read
In one paragraph

Article in Nucleic acids research, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 57 papers.

0numbers the graph read from it
0cells of the map it votes in
57citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

57 citing papers in PubMed.

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  8. Proceedings of the National Academy of Sciences of the United States of America · 2026
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors.

George M TaylorImperial College Centre for Synthetic Biology, Department of Life Sciences, Imperial College London, London SW7 2AZ, UK.
Paweł M MordakaImperial College Centre for Synthetic Biology, Department of Life Sciences, Imperial College London, London SW7 2AZ, UK.
John T HeapImperial College Centre for Synthetic Biology, Department of Life Sciences, Imperial College London, London SW7 2AZ, UK.

Funding

Biotechnology and Biological Sciences Research Council BB/ M002454/1
6 · The paper itself

Abstract

DNA assembly allows individual DNA constructs or libraries to be assembled quickly and reliably. Most methods are either: (i) Modular, easily scalable and suitable for combinatorial assembly, but leave undesirable 'scar' sequences; or (ii) bespoke (non-modular), scarless but less suitable for construction of combinatorial libraries. Both have limitations for metabolic engineering. To overcome this trade-off we devised Start-Stop Assembly, a multi-part, modular DNA assembly method which is both functionally scarless and suitable for combinatorial assembly. Crucially, 3 bp overhangs corresponding to start and stop codons are used to assemble coding sequences into expression units, avoiding scars at sensitive coding sequence boundaries. Building on this concept, a complete DNA assembly framework was designed and implemented, allowing assembly of up to 15 genes from up to 60 parts (or mixtures); monocistronic, operon-based or hybrid configurations; and a new streamlined assembly hierarchy minimizing the number of vectors. Only one destination vector is required per organism, reflecting our optimization of the system for metabolic engineering in diverse organisms. Metabolic engineering using Start-Stop Assembly was demonstrated by combinatorial assembly of carotenoid pathways in Escherichia coli resulting in a wide range of carotenoid production and colony size phenotypes indicating the intended exploration of design space.

Indexed as

CarotenoidsCloning, MolecularDNAEscherichia coliGenetic VectorsMetabolic EngineeringMetabolic Networks and PathwaysCarotenoidsDNA

Identifiers

PMID30462270
PMCPMC6379671

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.