Evidence map›Paper›PMID 32661437›Full record

ArticleNature biotechnology2020

Automated design of thousands of nonrepetitive parts for engineering stable genetic systems.

Ayaan Hossain, Eriberto Lopez, Sean M Halper, Daniel P Cetnar, Alexander C Reis, Devin Strickland, Eric Klavins, Howard M Salis

Abstract read
PubMed Publisher
In one paragraph

Article in Nature biotechnology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 69 papers.

0numbers the graph read from it
0cells of the map it votes in
69citing 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

69 citing papers in PubMed.

  1. Systematic optimization ofEngineering microbiology · 2026
    Review
  2. Article
  3. Technologies for engineering repetitive DNA.Quantitative biology (Beijing, China) · 2026
    Review
  4. Article
  5. Article
  6. Article
  7. The Quantum Optimization Benchmarking Library.Nature computational science · 2026
    Article
  8. Article
  9. Article
  10. Article
  11. Review
  12. Article
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  20. Synthesis of large single-transcript pathways from oligonucleotide pools: Design of STARBURST, an autobioluminescent reporter.Proceedings of the National Academy of Sciences of the United States of America · 2025
    Article

9 more citing papers are in PubMed but not listed here.

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

8 authors.

Ayaan HossainBioinformatics and Genomics, Pennsylvania State University, University Park, PA, USA.ORCID http://orcid.org/0000-0002-4781-5501
Eriberto LopezDepartment of Electrical & Computer Engineering, University of Washington, Seattle, WA, USA.ORCID http://orcid.org/0000-0001-6182-1716
Sean M HalperDepartment of Chemical Engineering, Pennsylvania State University, University Park, PA, USA.
Daniel P CetnarDepartment of Chemical Engineering, Pennsylvania State University, University Park, PA, USA.
Alexander C ReisDepartment of Chemical Engineering, Pennsylvania State University, University Park, PA, USA.
Devin StricklandDepartment of Electrical & Computer Engineering, University of Washington, Seattle, WA, USA.
Eric KlavinsDepartment of Electrical & Computer Engineering, University of Washington, Seattle, WA, USA.
Howard M SalisBioinformatics and Genomics, Pennsylvania State University, University Park, PA, USA. salis@psu.edu.ORCID http://orcid.org/0000-0003-0411-4390

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Engineered genetic systems are prone to failure when their genetic parts contain repetitive sequences. Designing many nonrepetitive genetic parts with desired functionalities remains a difficult challenge with high computational complexity. To overcome this challenge, we developed the Nonrepetitive Parts Calculator to rapidly generate thousands of highly nonrepetitive genetic parts from specified design constraints, including promoters, ribosome-binding sites and terminators. As a demonstration, we designed and experimentally characterized 4,350 nonrepetitive bacterial promoters with transcription rates that varied across a 820,000-fold range, and 1,722 highly nonrepetitive yeast promoters with transcription rates that varied across a 25,000-fold range. We applied machine learning to explain how specific interactions controlled the promoters' transcription rates. We also show that using nonrepetitive genetic parts substantially reduces homologous recombination, resulting in greater genetic stability.

Indexed as

Genetic EngineeringAutomationBacteriaBase SequenceNucleosomesPromoter Regions, GeneticSaccharomyces cerevisiaeTranscription, GeneticNucleosomes

Identifiers

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

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.