↓ Skip to main content

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

Overview of attention for article published in Genome Biology, November 2017
Altmetric Badge

About this Attention Score

  • 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
34 X users

Citations

dimensions_citation
100 Dimensions

Readers on

mendeley
156 Mendeley
citeulike
1 CiteULike
You are seeing a free-to-access but limited selection of the activity Altmetric has collected about this research output. Click here to find out more.
Title
Functional assessment of human enhancer activities using whole-genome STARR-sequencing
Published in
Genome Biology, 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.

X Demographics

X Demographics

The data shown below were collected from the profiles of 34 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 156 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 156 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 41 26%
Researcher 24 15%
Student > Bachelor 19 12%
Student > Master 14 9%
Professor 6 4%
Other 19 12%
Unknown 33 21%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 64 41%
Agricultural and Biological Sciences 31 20%
Medicine and Dentistry 8 5%
Computer Science 5 3%
Neuroscience 4 3%
Other 7 4%
Unknown 37 24%
Attention Score in Context

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
#2,060,426
of 25,382,440 outputs
Outputs from Genome Biology
#1,743
of 4,468 outputs
Outputs of similar age
#44,775
of 445,134 outputs
Outputs of similar age from Genome Biology
#36
of 57 outputs
Altmetric has tracked 25,382,440 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 91st percentile: it's in the top 10% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 4,468 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 27.6. This one has gotten more attention than average, scoring higher than 60% 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 445,134 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 57 others from the same source and published within six weeks on either side of this one. This one is in the 36th percentile – i.e., 36% of its contemporaries scored the same or lower than it.