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Functional optimization of gene clusters by combinatorial design and assembly

Overview of attention for article published in Nature Biotechnology, November 2014
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About this Attention Score

  • In the top 5% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (95th percentile)
  • Above-average Attention Score compared to outputs of the same age and source (58th percentile)

Mentioned by

1 news outlet
1 policy source
27 tweeters
1 Wikipedia page


131 Dimensions

Readers on

395 Mendeley
5 CiteULike
Functional optimization of gene clusters by combinatorial design and assembly
Published in
Nature Biotechnology, November 2014
DOI 10.1038/nbt.3063
Pubmed ID

Michael J Smanski, Swapnil Bhatia, Dehua Zhao, YongJin Park, Lauren B A Woodruff, Georgia Giannoukos, Dawn Ciulla, Michele Busby, Johnathan Calderon, Robert Nicol, D Benjamin Gordon, Douglas Densmore, Christopher A Voigt


Large microbial gene clusters encode useful functions, including energy utilization and natural product biosynthesis, but genetic manipulation of such systems is slow, difficult and complicated by complex regulation. We exploit the modularity of a refactored Klebsiella oxytoca nitrogen fixation (nif) gene cluster (16 genes, 103 parts) to build genetic permutations that could not be achieved by starting from the wild-type cluster. Constraint-based combinatorial design and DNA assembly are used to build libraries of radically different cluster architectures by varying part choice, gene order, gene orientation and operon occupancy. We construct 84 variants of the nifUSVWZM operon, 145 variants of the nifHDKY operon, 155 variants of the nifHDKYENJ operon and 122 variants of the complete 16-gene pathway. The performance and behavior of these variants are characterized by nitrogenase assay and strand-specific RNA sequencing (RNA-seq), and the results are incorporated into subsequent design cycles. We have produced a fully synthetic cluster that recovers 57% of wild-type activity. Our approach allows the performance of genetic parts to be quantified simultaneously in hundreds of genetic contexts. This parallelized design-build-test-learn cycle, which can access previously unattainable regions of genetic space, should provide a useful, fast tool for genetic optimization and hypothesis testing.

Twitter Demographics

The data shown below were collected from the profiles of 27 tweeters who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 395 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 18 5%
Germany 4 1%
United Kingdom 4 1%
Canada 4 1%
Denmark 2 <1%
China 2 <1%
France 1 <1%
Chile 1 <1%
Belgium 1 <1%
Other 1 <1%
Unknown 357 90%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 138 35%
Researcher 91 23%
Student > Master 48 12%
Student > Bachelor 40 10%
Student > Doctoral Student 18 5%
Other 60 15%
Readers by discipline Count As %
Agricultural and Biological Sciences 194 49%
Biochemistry, Genetics and Molecular Biology 100 25%
Engineering 35 9%
Unspecified 19 5%
Chemistry 18 5%
Other 29 7%

Attention Score in Context

This research output has an Altmetric Attention Score of 30. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 18 December 2017.
All research outputs
of 11,373,241 outputs
Outputs from Nature Biotechnology
of 6,213 outputs
Outputs of similar age
of 248,428 outputs
Outputs of similar age from Nature Biotechnology
of 70 outputs
Altmetric has tracked 11,373,241 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 96th percentile: it's in the top 5% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 6,213 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 21.8. This one has done well, scoring higher than 86% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 248,428 tracked outputs that were published within six weeks on either side of this one in any source. This one has done particularly well, scoring higher than 95% of its contemporaries.
We're also able to compare this research output to 70 others from the same source and published within six weeks on either side of this one. This one has gotten more attention than average, scoring higher than 58% of its contemporaries.