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An Integrated Regulatory Network Reveals Pervasive Cross-Regulation among Transcription and Splicing Factors

Overview of attention for article published in PLoS Computational Biology, July 2012
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Title
An Integrated Regulatory Network Reveals Pervasive Cross-Regulation among Transcription and Splicing Factors
Published in
PLoS Computational Biology, July 2012
DOI 10.1371/journal.pcbi.1002603
Pubmed ID
Authors

Idit Kosti, Predrag Radivojac, Yael Mandel-Gutfreund

Abstract

Traditionally the gene expression pathway has been regarded as being comprised of independent steps, from RNA transcription to protein translation. To date there is increasing evidence of coupling between the different processes of the pathway, specifically between transcription and splicing. To study the interplay between these processes we derived a transcription-splicing integrated network. The nodes of the network included experimentally verified human proteins belonging to three groups of regulators: transcription factors, splicing factors and kinases. The nodes were wired by instances of predicted transcriptional and alternative splicing regulation. Analysis of the network indicated a pervasive cross-regulation among the nodes; specifically, splicing factors are significantly more connected by alternative splicing regulatory edges relative to the two other subgroups, while transcription factors are more extensively controlled by transcriptional regulation. Furthermore, we found that splicing factors are the most regulated of the three regulatory groups and are subject to extensive combinatorial control by alternative splicing and transcriptional regulation. Consistent with the network results, our bioinformatics analyses showed that the subgroup of kinases have the highest density of predicted phosphorylation sites. Overall, our systematic study reveals that an organizing principle in the logic of integrated networks favor the regulation of regulatory proteins by the specific regulation they conduct. Based on these results, we propose a new regulatory paradigm postulating that gene expression regulation of the master regulators in the cell is predominantly achieved by cross-regulation.

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The data shown below were collected from the profiles of 3 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 72 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United Kingdom 3 4%
United States 3 4%
Germany 2 3%
Korea, Republic of 1 1%
France 1 1%
Luxembourg 1 1%
Unknown 61 85%

Demographic breakdown

Readers by professional status Count As %
Researcher 26 36%
Student > Ph. D. Student 16 22%
Professor 6 8%
Professor > Associate Professor 6 8%
Student > Postgraduate 5 7%
Other 10 14%
Unknown 3 4%
Readers by discipline Count As %
Agricultural and Biological Sciences 30 42%
Biochemistry, Genetics and Molecular Biology 18 25%
Computer Science 9 13%
Mathematics 3 4%
Physics and Astronomy 2 3%
Other 4 6%
Unknown 6 8%
Attention Score in Context

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 16 August 2012.
All research outputs
#16,047,334
of 25,374,647 outputs
Outputs from PLoS Computational Biology
#6,968
of 8,960 outputs
Outputs of similar age
#109,005
of 178,780 outputs
Outputs of similar age from PLoS Computational Biology
#78
of 110 outputs
Altmetric has tracked 25,374,647 research outputs across all sources so far. This one is in the 34th percentile – i.e., 34% of other outputs scored the same or lower than it.
So far Altmetric has tracked 8,960 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 20.4. This one is in the 19th percentile – i.e., 19% 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 178,780 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 36th percentile – i.e., 36% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 110 others from the same source and published within six weeks on either side of this one. This one is in the 26th percentile – i.e., 26% of its contemporaries scored the same or lower than it.