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Quantifying noise in mass spectrometry and yeast two-hybrid protein interaction detection experiments

Overview of attention for article published in Journal of The Royal Society Interface, September 2015
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Title
Quantifying noise in mass spectrometry and yeast two-hybrid protein interaction detection experiments
Published in
Journal of The Royal Society Interface, September 2015
DOI 10.1098/rsif.2015.0573
Pubmed ID
Authors

Annibale, A, Coolen, A C C, Planell-Morell, N

Abstract

Protein interaction networks (PINs) are popular means to visualize the proteome. However, PIN datasets are known to be noisy, incomplete and biased by the experimental protocols used to detect protein interactions. This paper aims at understanding the connection between true protein interactions and the protein interaction datasets that have been obtained using the most popular experimental techniques, i.e. mass spectronomy and yeast two-hybrid. We start from the observation that the adjacency matrix of a PIN, i.e. the binary matrix which defines, for every pair of proteins in the network, whether or not there is a link, has a special form, that we call separable. This induces precise relationships between the moments of the degree distribution (i.e. the average number of links that a protein in the network has, its variance, etc.) and the number of short loops (i.e. triangles, squares, etc.) along the links of the network. These relationships provide powerful tools to test the reliability of datasets and hint at the underlying biological mechanism with which proteins and complexes recruit each other.

Twitter Demographics

The data shown below were collected from the profiles of 4 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 4 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United Kingdom 1 25%
Unknown 3 75%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 2 50%
Researcher 1 25%
Student > Postgraduate 1 25%
Professor > Associate Professor 1 25%
Readers by discipline Count As %
Agricultural and Biological Sciences 3 75%
Biochemistry, Genetics and Molecular Biology 1 25%
Medicine and Dentistry 1 25%

Attention Score in Context

This research output has an Altmetric Attention Score of 2. 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 06 November 2015.
All research outputs
#5,233,892
of 9,771,259 outputs
Outputs from Journal of The Royal Society Interface
#1,162
of 1,661 outputs
Outputs of similar age
#114,322
of 236,521 outputs
Outputs of similar age from Journal of The Royal Society Interface
#40
of 58 outputs
Altmetric has tracked 9,771,259 research outputs across all sources so far. This one is in the 43rd percentile – i.e., 43% of other outputs scored the same or lower than it.
So far Altmetric has tracked 1,661 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 20.3. This one is in the 26th percentile – i.e., 26% of its peers scored the same or lower than it.
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 236,521 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 47th percentile – i.e., 47% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 58 others from the same source and published within six weeks on either side of this one. This one is in the 25th percentile – i.e., 25% of its contemporaries scored the same or lower than it.