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Genome and network visualization facilitates the analyses of the effects of drugs and mutations on protein-protein and drug-protein networks

Overview of attention for article published in BMC Bioinformatics, March 2016
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  • Good Attention Score compared to outputs of the same age (73rd percentile)
  • Above-average Attention Score compared to outputs of the same age and source (64th percentile)

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9 X users

Citations

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5 Dimensions

Readers on

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26 Mendeley
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3 CiteULike
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Title
Genome and network visualization facilitates the analyses of the effects of drugs and mutations on protein-protein and drug-protein networks
Published in
BMC Bioinformatics, March 2016
DOI 10.1186/s12859-016-0908-x
Pubmed ID
Authors

Arnaud Céol, Lisette G. G. C. Verhoef, Mark Wade, Heiko Muller

Abstract

Biologists generally interrogate genomics data using web-based genome browsers that have limited analytical potential. New generation genome browsers such as the Integrated Genome Browser (IGB) have largely overcome this limitation and permit customized analyses to be implemented using plugins. We illustrate the use of a plugin for IGB that exploits advanced visualization techniques to integrate the analysis of genomics data with network and structural approaches. We show how visualization technologies that combine both genomics and network biology can facilitate the selection of the key amino acid contacts from protein-protein and protein-drug interactions. Starting from the MDM2-P53 interaction, which is a high-value target for cancer therapy, and Nutlin, the parent small molecule of an MDM2 antagonist that is currently in clinical trials, we show that this method can be generalized to analyze how drugs and mutations can interfere with both protein-protein and drug-protein networks. We illustrate this point by two additional use-cases exploring the molecular basis of tamoxifen side effects and of drug resistance in chronic myeloid leukemia patients. Combined network and structure biology approaches provide key insights into both the genetic and the edgetic roles of variants in diseases. 3D interactomes facilitate the identification of disease-relevant interactions that can then be specifically targeted by drugs. Recent advances in molecular interaction and structure visualization tools have greatly simplified the mapping of mutated residues to molecular interaction interfaces. Such approaches can now also be integrated with genome visualization tools to enable comparative analyses of interaction contacts.

X Demographics

X Demographics

The data shown below were collected from the profiles of 9 X users who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 26 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 26 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 6 23%
Researcher 5 19%
Student > Master 4 15%
Student > Bachelor 1 4%
Professor 1 4%
Other 3 12%
Unknown 6 23%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 5 19%
Agricultural and Biological Sciences 4 15%
Computer Science 4 15%
Psychology 3 12%
Environmental Science 1 4%
Other 2 8%
Unknown 7 27%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 6. 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 12 March 2016.
All research outputs
#6,102,679
of 24,293,076 outputs
Outputs from BMC Bioinformatics
#2,136
of 7,511 outputs
Outputs of similar age
#80,540
of 303,307 outputs
Outputs of similar age from BMC Bioinformatics
#45
of 128 outputs
Altmetric has tracked 24,293,076 research outputs across all sources so far. This one has received more attention than most of these and is in the 74th percentile.
So far Altmetric has tracked 7,511 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.5. This one has gotten more attention than average, scoring higher than 71% 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 303,307 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 73% of its contemporaries.
We're also able to compare this research output to 128 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 64% of its contemporaries.