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Revisiting operons: an analysis of the landscape of transcriptional units in E. coli

Overview of attention for article published in BMC Bioinformatics, November 2015
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Title
Revisiting operons: an analysis of the landscape of transcriptional units in E. coli
Published in
BMC Bioinformatics, November 2015
DOI 10.1186/s12859-015-0805-8
Pubmed ID
Authors

Xizeng Mao, Qin Ma, Bingqiang Liu, Xin Chen, Hanyuan Zhang, Ying Xu

Abstract

Bacterial operons are considerably more complex than what were thought. At least their components are dynamically rather than statically defined as previously assumed. Here we present a computational study of the landscape of the transcriptional units (TUs) of E. coli K12, revealed by the available genomic and transcriptomic data, providing new understanding about the complexity of TUs as a whole encoded in the genome of E. coli K12. Our main findings include that (i) different TUs may overlap with each other by sharing common genes, giving rise to clusters of overlapped TUs (TUCs) along the genomic sequence; (ii) the intergenic regions in front of the first gene of each TU tend to have more conserved sequence motifs than those of the other genes inside the TU, suggesting that TUs each have their own promoters; (iii) the terminators associated with the 3' ends of TUCs tend to be Rho-independent terminators, substantially more often than terminators of TUs that end inside a TUC; and (iv) the functional relatedness of adjacent gene pairs in individual TUs is higher than those in TUCs, suggesting that individual TUs are more basic functional units than TUCs.

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 59 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 3 5%
Uruguay 1 2%
France 1 2%
Unknown 54 92%

Demographic breakdown

Readers by professional status Count As %
Researcher 12 20%
Student > Ph. D. Student 12 20%
Student > Bachelor 9 15%
Student > Master 8 14%
Student > Doctoral Student 3 5%
Other 5 8%
Unknown 10 17%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 18 31%
Agricultural and Biological Sciences 15 25%
Mathematics 3 5%
Computer Science 3 5%
Physics and Astronomy 2 3%
Other 4 7%
Unknown 14 24%
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 04 November 2015.
All research outputs
#16,177,937
of 24,605,383 outputs
Outputs from BMC Bioinformatics
#5,224
of 7,561 outputs
Outputs of similar age
#163,547
of 290,878 outputs
Outputs of similar age from BMC Bioinformatics
#102
of 155 outputs
Altmetric has tracked 24,605,383 research outputs across all sources so far. This one is in the 32nd percentile – i.e., 32% of other outputs scored the same or lower than it.
So far Altmetric has tracked 7,561 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.5. 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 290,878 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 40th percentile – i.e., 40% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 155 others from the same source and published within six weeks on either side of this one. This one is in the 29th percentile – i.e., 29% of its contemporaries scored the same or lower than it.