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Unsupervised detection of regulatory gene expression information in different genomic regions enables gene expression ranking

Overview of attention for article published in BMC Bioinformatics, February 2017
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Title
Unsupervised detection of regulatory gene expression information in different genomic regions enables gene expression ranking
Published in
BMC Bioinformatics, February 2017
DOI 10.1186/s12859-017-1497-z
Pubmed ID
Authors

Zohar Zafrir, Tamir Tuller

Abstract

The regulation of all gene expression steps (e.g., Transcription, RNA processing, Translation, and mRNA Degradation) is known to be primarily encoded in different parts of genes and in genomic regions in proximity to genes (e.g., promoters, untranslated regions, coding regions, introns, etc.). However, the entire gene expression codes and the genomic regions where they are encoded are still unknown. Here, we employ an unsupervised approach to estimate the concentration of gene expression codes in different non-coding parts of genes and transcripts, such as introns and untranslated regions, focusing on three model organisms (Escherichia coli, Saccharomyces cerevisiae, and Schizosaccharomyces pombe). Our analyses support the conjecture that regions adjacent to the beginning and end of ORFs and the beginning and end of introns tend to include higher concentration of gene expression information relatively to regions further away. In addition, we report the exact regions with elevated concentration of gene expression codes. Furthermore, we demonstrate that the concentration of these codes in different genetic regions is correlated with the expression levels of the corresponding genes, and with splicing efficiency measurements and meiotic stage gene expression measurements in S. cerevisiae. We suggest that these discoveries improve our understanding of gene expression regulation and evolution; they can also be used for developing improved models of genome/gene evolution and for engineering gene expression in various biotechnological and synthetic biology applications.

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 25 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
China 1 4%
Egypt 1 4%
Unknown 23 92%

Demographic breakdown

Readers by professional status Count As %
Student > Master 7 28%
Researcher 6 24%
Student > Ph. D. Student 4 16%
Student > Bachelor 2 8%
Other 1 4%
Other 1 4%
Unknown 4 16%
Readers by discipline Count As %
Agricultural and Biological Sciences 10 40%
Biochemistry, Genetics and Molecular Biology 5 20%
Mathematics 2 8%
Computer Science 1 4%
Neuroscience 1 4%
Other 1 4%
Unknown 5 20%
Attention Score in Context

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 02 February 2017.
All research outputs
#18,529,032
of 22,950,943 outputs
Outputs from BMC Bioinformatics
#6,341
of 7,308 outputs
Outputs of similar age
#310,706
of 420,361 outputs
Outputs of similar age from BMC Bioinformatics
#106
of 141 outputs
Altmetric has tracked 22,950,943 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 7,308 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.4. This one is in the 5th percentile – i.e., 5% 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 420,361 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 15th percentile – i.e., 15% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 141 others from the same source and published within six weeks on either side of this one. This one is in the 17th percentile – i.e., 17% of its contemporaries scored the same or lower than it.