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Sort-seq under the hood: implications of design choices on large-scale characterization of sequence-function relations

Overview of attention for article published in BMC Genomics, March 2016
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About this Attention Score

  • In the top 25% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (87th percentile)

Mentioned by

blogs
2 blogs
twitter
2 tweeters

Citations

dimensions_citation
39 Dimensions

Readers on

mendeley
115 Mendeley
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1 CiteULike
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Title
Sort-seq under the hood: implications of design choices on large-scale characterization of sequence-function relations
Published in
BMC Genomics, March 2016
DOI 10.1186/s12864-016-2533-5
Pubmed ID
Authors

Neil Peterman, Erel Levine

Abstract

Sort-seq is an effective approach for simultaneous activity measurements in a large-scale library, combining flow cytometry, deep sequencing, and statistical inference. Such assays enable the characterization of functional landscapes at unprecedented scale for a wide-reaching array of biological molecules and functionalities in vivo. Applications of sort-seq range from footprinting to establishing quantitative models of biological systems and rational design of synthetic genetic elements. Nearly as diverse are implementations of this technique, reflecting key design choices with extensive impact on the scope and accuracy the results. Yet how to make these choices remains unclear. Here we investigate the effects of alternative sort-seq designs and inference methods on the information output using mathematical formulation and simulations. We identify key intrinsic properties of any system of interest with practical implications for sort-seq assays, depending on the experimental goals. The fluorescence range and cell-to-cell variability specify the number of sorted populations needed for quantitative measurements that are precise and unbiased. These factors also indicate cases where an enrichment-based approach that uses a single sorted population can offer satisfactory results. These predications of our model are corroborated using re-analysis of published data. We explore implications of these results for quantitative modeling and library design. Sort-seq assays can be streamlined by reducing the number of sorted populations, saving considerable resources. Simple preliminary experiments can guide optimal experiment design, minimizing cost while maintaining the maximal information output and avoiding latent biases. These insights can facilitate future applications of this highly adaptable technique.

Twitter Demographics

The data shown below were collected from the profiles of 2 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 115 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United Kingdom 2 2%
United States 2 2%
Unknown 111 97%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 32 28%
Researcher 21 18%
Student > Bachelor 19 17%
Student > Master 12 10%
Professor 4 3%
Other 9 8%
Unknown 18 16%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 38 33%
Agricultural and Biological Sciences 26 23%
Engineering 7 6%
Chemistry 7 6%
Chemical Engineering 5 4%
Other 12 10%
Unknown 20 17%

Attention Score in Context

This research output has an Altmetric Attention Score of 13. 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 05 April 2022.
All research outputs
#2,095,247
of 21,055,026 outputs
Outputs from BMC Genomics
#657
of 10,202 outputs
Outputs of similar age
#36,321
of 279,616 outputs
Outputs of similar age from BMC Genomics
#1
of 1 outputs
Altmetric has tracked 21,055,026 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 90th percentile: it's in the top 10% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 10,202 research outputs from this source. They receive a mean Attention Score of 4.6. This one has done particularly well, scoring higher than 93% 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 279,616 tracked outputs that were published within six weeks on either side of this one in any source. This one has done well, scoring higher than 87% of its contemporaries.
We're also able to compare this research output to 1 others from the same source and published within six weeks on either side of this one. This one has scored higher than all of them