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Identification of regulatory modules in genome scale transcription regulatory networks

Overview of attention for article published in BMC Systems Biology, December 2017
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Title
Identification of regulatory modules in genome scale transcription regulatory networks
Published in
BMC Systems Biology, December 2017
DOI 10.1186/s12918-017-0493-2
Pubmed ID
Authors

Qi Song, Ruth Grene, Lenwood S. Heath, Song Li

Abstract

In gene regulatory networks, transcription factors often function as co-regulators to synergistically induce or inhibit expression of their target genes. However, most existing module-finding algorithms can only identify densely connected genes but not co-regulators in regulatory networks. We have developed a new computational method, CoReg, to identify transcription co-regulators in large-scale regulatory networks. CoReg calculates gene similarities based on number of common neighbors of any two genes. Using simulated and real networks, we compared the performance of different similarity indices and existing module-finding algorithms and we found CoReg outperforms other published methods in identifying co-regulatory genes. We applied CoReg to a large-scale network of Arabidopsis with more than 2.8 million edges and we analyzed more than 2,300 published gene expression profiles to charaterize co-expression patterns of gene moduled identified by CoReg. We identified three types of modules in the Arabidopsis network: regulator modules, target modules and intermediate modules. Regulator modules include genes with more than 90% edges as out-going edges; Target modules include genes with more than 90% edges as incoming edges. Other modules are classified as intermediate modules. We found that genes in target modules tend to be highly co-expressed under abiotic stress conditions, suggesting this network struture is robust against perturbation. Our analysis shows that the CoReg is an accurate method in identifying co-regulatory genes in large-scale networks. We provide CoReg as an R package, which can be applied in finding co-regulators in any organisms with genome-scale regulatory network data.

Twitter Demographics

The data shown below were collected from the profiles of 2 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 35 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 35 100%

Demographic breakdown

Readers by professional status Count As %
Student > Master 10 29%
Unspecified 5 14%
Researcher 5 14%
Student > Ph. D. Student 4 11%
Professor 3 9%
Other 8 23%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 12 34%
Unspecified 8 23%
Agricultural and Biological Sciences 7 20%
Computer Science 3 9%
Engineering 2 6%
Other 3 9%

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 19 December 2017.
All research outputs
#9,838,551
of 12,319,220 outputs
Outputs from BMC Systems Biology
#738
of 1,022 outputs
Outputs of similar age
#247,377
of 346,238 outputs
Outputs of similar age from BMC Systems Biology
#35
of 53 outputs
Altmetric has tracked 12,319,220 research outputs across all sources so far. This one is in the 11th percentile – i.e., 11% of other outputs scored the same or lower than it.
So far Altmetric has tracked 1,022 research outputs from this source. They receive a mean Attention Score of 3.4. This one is in the 12th percentile – i.e., 12% of its peers scored the same or lower than it.
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We're also able to compare this research output to 53 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.