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Functional assessment of human enhancer activities using whole-genome STARR-sequencing

Overview of attention for article published in Genome Biology (Online Edition), November 2017
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  • In the top 25% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (89th percentile)
  • Average Attention Score compared to outputs of the same age and source

Mentioned by

twitter
35 tweeters

Citations

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59 Dimensions

Readers on

mendeley
129 Mendeley
citeulike
1 CiteULike
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Title
Functional assessment of human enhancer activities using whole-genome STARR-sequencing
Published in
Genome Biology (Online Edition), November 2017
DOI 10.1186/s13059-017-1345-5
Pubmed ID
Authors

Yuwen Liu, Shan Yu, Vineet K. Dhiman, Tonya Brunetti, Heather Eckart, Kevin P. White

Abstract

Genome-wide quantification of enhancer activity in the human genome has proven to be a challenging problem. Recent efforts have led to the development of powerful tools for enhancer quantification. However, because of genome size and complexity, these tools have yet to be applied to the whole human genome.  In the current study, we use a human prostate cancer cell line, LNCaP as a model to perform whole human genome STARR-seq (WHG-STARR-seq) to reliably obtain an assessment of enhancer activity. This approach builds upon previously developed STARR-seq in the fly genome and CapSTARR-seq techniques in targeted human genomic regions. With an improved library preparation strategy, our approach greatly increases the library complexity per unit of starting material, which makes it feasible and cost-effective to explore the landscape of regulatory activity in the much larger human genome. In addition to our ability to identify active, accessible enhancers located in open chromatin regions, we can also detect sequences with the potential for enhancer activity that are located in inaccessible, closed chromatin regions. When treated with the histone deacetylase inhibitor, Trichostatin A, genes nearby this latter class of enhancers are up-regulated, demonstrating the potential for endogenous functionality of these regulatory elements. WHG-STARR-seq provides an improved approach to current pipelines for analysis of high complexity genomes to gain a better understanding of the intricacies of transcriptional regulation.

Twitter Demographics

The data shown below were collected from the profiles of 35 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 129 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 129 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 39 30%
Researcher 21 16%
Student > Bachelor 17 13%
Student > Master 10 8%
Professor 6 5%
Other 18 14%
Unknown 18 14%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 56 43%
Agricultural and Biological Sciences 30 23%
Medicine and Dentistry 7 5%
Computer Science 5 4%
Neuroscience 4 3%
Other 5 4%
Unknown 22 17%

Attention Score in Context

This research output has an Altmetric Attention Score of 18. 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 26 November 2017.
All research outputs
#1,152,505
of 15,922,434 outputs
Outputs from Genome Biology (Online Edition)
#1,176
of 3,414 outputs
Outputs of similar age
#42,968
of 411,881 outputs
Outputs of similar age from Genome Biology (Online Edition)
#125
of 241 outputs
Altmetric has tracked 15,922,434 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 92nd percentile: it's in the top 10% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 3,414 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 25.7. This one has gotten more attention than average, scoring higher than 65% 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 411,881 tracked outputs that were published within six weeks on either side of this one in any source. This one has done well, scoring higher than 89% of its contemporaries.
We're also able to compare this research output to 241 others from the same source and published within six weeks on either side of this one. This one is in the 48th percentile – i.e., 48% of its contemporaries scored the same or lower than it.