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DEEPN as an Approach for Batch Processing of Yeast 2-Hybrid Interactions

Overview of attention for article published in Cell Reports, September 2016
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Article details
Title
DEEPN as an Approach for Batch Processing of Yeast 2-Hybrid Interactions
Published in
Cell Reports, September 2016
DOI 10.1016/j.celrep.2016.08.095
Pubmed ID
Authors
Abstract

We adapted the yeast 2-hybrid assay to simultaneously uncover multiple transient protein interactions within a single screen by using a strategy termed DEEPN (dynamic enrichment for evaluation of protein networks). This approach incorporates high-throughput DNA sequencing and computation to follow competition among a plasmid population encoding interacting partners. To demonstrate the capacity of DEEPN, we identify a wide range of ubiquitin-binding proteins, including interactors that we verify biochemically. To demonstrate the specificity of DEEPN, we show that DEEPN allows simultaneous comparison of candidate interactors across multiple bait proteins, allowing differential interactions to be identified. This feature was used to identify interactors that distinguish between GTP- and GDP-bound conformations of Rab5.

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Mendeley demographics

Mendeley demographics

The data shown below were compiled from readership statistics for 46 Mendeley readers of this research output. Click here to see the associated Mendeley record.
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Geographical breakdown

Geographical breakdown
Country Count As %
United States 1 2%
United Kingdom 1 2%
Unknown 44 96%

Demographic breakdown

Readers by professional status
Readers by professional status Count As %
Student > Ph. D. Student 8 17%
Researcher 7 15%
Student > Bachelor 6 13%
Student > Master 4 9%
Other 3 7%
Other 8 17%
Unknown 10 22%
Readers by discipline
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 15 33%
Agricultural and Biological Sciences 10 22%
Arts and Humanities 2 4%
Pharmacology, Toxicology and Pharmaceutical Science 2 4%
Computer Science 2 4%
Other 5 11%
Unknown 10 22%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 1. 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 04 October 2016.
All research outputs
#22,690,735
of 27,776,330 outputs
Outputs from Cell Reports
#13,702
of 14,264 outputs
Outputs of similar age
#243,834
of 316,273 outputs
Outputs of similar age from Cell Reports
#284
of 291 outputs
Altmetric has tracked 27,776,330 research outputs across all sources so far. This one is in the 10th percentile – i.e., 10% of other outputs scored the same or lower than it.
So far Altmetric has tracked 14,264 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 29.0. This one is in the 2nd percentile – i.e., 2% of its peers scored the same or lower than it.
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We're also able to compare this research output to 291 others from the same source and published within six weeks on either side of this one. This one is in the 1st percentile – i.e., 1% of its contemporaries scored the same or lower than it.