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FOCS: a novel method for analyzing enhancer and gene activity patterns infers an extensive enhancer–promoter map

Overview of attention for article published in Genome Biology (Online Edition), May 2018
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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)

Mentioned by

twitter
45 tweeters

Citations

dimensions_citation
38 Dimensions

Readers on

mendeley
107 Mendeley
citeulike
2 CiteULike
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Title
FOCS: a novel method for analyzing enhancer and gene activity patterns infers an extensive enhancer–promoter map
Published in
Genome Biology (Online Edition), May 2018
DOI 10.1186/s13059-018-1432-2
Pubmed ID
Authors

Tom Aharon Hait, David Amar, Ron Shamir, Ran Elkon

Abstract

Recent sequencing technologies enable joint quantification of promoters and their enhancer regions, allowing inference of enhancer-promoter links. We show that current enhancer-promoter inference methods produce a high rate of false positive links. We introduce FOCS, a new inference method, and by benchmarking against ChIA-PET, HiChIP, and eQTL data show that it results in lower false discovery rates and at the same time higher inference power. By applying FOCS to 2630 samples taken from ENCODE, Roadmap Epigenomics, FANTOM5, and a new compendium of GRO-seq samples, we provide extensive enhancer-promotor maps ( http://acgt.cs.tau.ac.il/focs ). We illustrate the usability of our maps for deriving biological hypotheses.

Twitter Demographics

The data shown below were collected from the profiles of 45 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 107 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 107 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 29 27%
Researcher 23 21%
Student > Master 16 15%
Student > Bachelor 7 7%
Student > Postgraduate 5 5%
Other 15 14%
Unknown 12 11%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 35 33%
Agricultural and Biological Sciences 31 29%
Medicine and Dentistry 8 7%
Computer Science 6 6%
Neuroscience 2 2%
Other 7 7%
Unknown 18 17%

Attention Score in Context

This research output has an Altmetric Attention Score of 23. 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 05 May 2019.
All research outputs
#1,134,764
of 19,042,601 outputs
Outputs from Genome Biology (Online Edition)
#1,063
of 3,796 outputs
Outputs of similar age
#29,412
of 289,883 outputs
Outputs of similar age from Genome Biology (Online Edition)
#1
of 1 outputs
Altmetric has tracked 19,042,601 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 94th percentile: it's in the top 10% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 3,796 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 26.9. This one has gotten more attention than average, scoring higher than 72% 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 289,883 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 1 others from the same source and published within six weeks on either side of this one. This one has scored higher than all of them