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Where Have All the Interactions Gone? Estimating the Coverage of Two-Hybrid Protein Interaction Maps

Overview of attention for article published in PLoS Computational Biology, November 2007
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Title
Where Have All the Interactions Gone? Estimating the Coverage of Two-Hybrid Protein Interaction Maps
Published in
PLoS Computational Biology, November 2007
DOI 10.1371/journal.pcbi.0030214
Pubmed ID
Authors

Hailiang Huang, Bruno M Jedynak, Joel S Bader

Abstract

Yeast two-hybrid screens are an important method for mapping pairwise physical interactions between proteins. The fraction of interactions detected in independent screens can be very small, and an outstanding challenge is to determine the reason for the low overlap. Low overlap can arise from either a high false-discovery rate (interaction sets have low overlap because each set is contaminated by a large number of stochastic false-positive interactions) or a high false-negative rate (interaction sets have low overlap because each misses many true interactions). We extend capture-recapture theory to provide the first unified model for false-positive and false-negative rates for two-hybrid screens. Analysis of yeast, worm, and fly data indicates that 25% to 45% of the reported interactions are likely false positives. Membrane proteins have higher false-discovery rates on average, and signal transduction proteins have lower rates. The overall false-negative rate ranges from 75% for worm to 90% for fly, which arises from a roughly 50% false-negative rate due to statistical undersampling and a 55% to 85% false-negative rate due to proteins that appear to be systematically lost from the assays. Finally, statistical model selection conclusively rejects the Erdös-Rényi network model in favor of the power law model for yeast and the truncated power law for worm and fly degree distributions. Much as genome sequencing coverage estimates were essential for planning the human genome sequencing project, the coverage estimates developed here will be valuable for guiding future proteomic screens. All software and datasets are available in and , -, and -, and are also available from our Web site, http://www.baderzone.org.

Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 147 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 5 3%
Germany 3 2%
United Kingdom 3 2%
Netherlands 2 1%
Australia 1 <1%
Austria 1 <1%
France 1 <1%
Belgium 1 <1%
Argentina 1 <1%
Other 2 1%
Unknown 127 86%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 47 32%
Researcher 34 23%
Student > Master 18 12%
Student > Bachelor 12 8%
Other 6 4%
Other 16 11%
Unknown 14 10%
Readers by discipline Count As %
Agricultural and Biological Sciences 82 56%
Biochemistry, Genetics and Molecular Biology 27 18%
Computer Science 11 7%
Chemistry 3 2%
Neuroscience 2 1%
Other 7 5%
Unknown 15 10%
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 07 January 2008.
All research outputs
#17,302,400
of 25,394,764 outputs
Outputs from PLoS Computational Biology
#7,481
of 8,964 outputs
Outputs of similar age
#141,359
of 165,924 outputs
Outputs of similar age from PLoS Computational Biology
#33
of 40 outputs
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