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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 (96th percentile)
  • Above-average Attention Score compared to outputs of the same age and source (61st percentile)

Mentioned by

1 news outlet
1 policy source
27 tweeters
5 patents
1 Wikipedia page


143 Dimensions

Readers on

340 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 340 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 12 4%
Canada 4 1%
United Kingdom 4 1%
Germany 3 <1%
Denmark 2 <1%
France 1 <1%
China 1 <1%
Belgium 1 <1%
Netherlands 1 <1%
Other 1 <1%
Unknown 310 91%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 110 32%
Researcher 80 24%
Student > Master 40 12%
Student > Bachelor 36 11%
Student > Doctoral Student 18 5%
Other 56 16%
Readers by discipline Count As %
Agricultural and Biological Sciences 159 47%
Biochemistry, Genetics and Molecular Biology 86 25%
Engineering 29 9%
Unspecified 19 6%
Chemistry 14 4%
Other 33 10%

Attention Score in Context

This research output has an Altmetric Attention Score of 37. 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 10 July 2018.
All research outputs
of 11,969,825 outputs
Outputs from Nature Biotechnology
of 6,316 outputs
Outputs of similar age
of 263,658 outputs
Outputs of similar age from Nature Biotechnology
of 71 outputs
Altmetric has tracked 11,969,825 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 6,316 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 24.0. This one has done well, scoring higher than 87% 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 263,658 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 96% of its contemporaries.
We're also able to compare this research output to 71 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 61% of its contemporaries.