↓ Skip to main content

Genome-wide identification of Drosophila dorso-ventral enhancers by differential histone acetylation analysis

Overview of attention for article published in Genome Biology, September 2016
Altmetric Badge

About this Attention Score

  • Average Attention Score compared to outputs of the same age

Mentioned by

twitter
4 X users
facebook
1 Facebook page

Citations

dimensions_citation
58 Dimensions

Readers on

mendeley
107 Mendeley
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
Genome-wide identification of Drosophila dorso-ventral enhancers by differential histone acetylation analysis
Published in
Genome Biology, September 2016
DOI 10.1186/s13059-016-1057-2
Pubmed ID
Authors

Nina Koenecke, Jeff Johnston, Bjoern Gaertner, Malini Natarajan, Julia Zeitlinger

Abstract

Drosophila dorso-ventral (DV) patterning is one of the best-understood regulatory networks to date, and illustrates the fundamental role of enhancers in controlling patterning, cell fate specification, and morphogenesis during development. Histone acetylation such as H3K27ac is an excellent marker for active enhancers, but it is challenging to obtain precise locations for enhancers as the highest levels of this modification flank the enhancer regions. How to best identify tissue-specific enhancers in a developmental system de novo with a minimal set of data is still unclear. Using DV patterning as a test system, we develop a simple and effective method to identify tissue-specific enhancers de novo. We sample a broad set of candidate enhancer regions using data on CREB-binding protein co-factor binding or ATAC-seq chromatin accessibility, and then identify those regions with significant differences in histone acetylation between tissues. This method identifies hundreds of novel DV enhancers and outperforms ChIP-seq data of relevant transcription factors when benchmarked with mRNA expression data and transgenic reporter assays. These DV enhancers allow the de novo discovery of the relevant transcription factor motifs involved in DV patterning and contain additional motifs that are evolutionarily conserved and for which the corresponding transcription factors are expressed in a DV-biased fashion. Finally, we identify novel target genes of the regulatory network, implicating morphogenesis genes as early targets of DV patterning. Taken together, our approach has expanded our knowledge of the DV patterning network even further and is a general method to identify enhancers in any developmental system, including mammalian development.

X Demographics

X Demographics

The data shown below were collected from the profiles of 4 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 107 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 1 <1%
Switzerland 1 <1%
Unknown 105 98%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 28 26%
Researcher 15 14%
Student > Bachelor 13 12%
Student > Master 10 9%
Professor > Associate Professor 8 7%
Other 18 17%
Unknown 15 14%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 41 38%
Agricultural and Biological Sciences 35 33%
Computer Science 6 6%
Neuroscience 3 3%
Pharmacology, Toxicology and Pharmaceutical Science 2 2%
Other 5 5%
Unknown 15 14%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 3. 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 06 October 2016.
All research outputs
#14,388,865
of 25,374,917 outputs
Outputs from Genome Biology
#3,817
of 4,467 outputs
Outputs of similar age
#170,928
of 330,837 outputs
Outputs of similar age from Genome Biology
#45
of 54 outputs
Altmetric has tracked 25,374,917 research outputs across all sources so far. This one is in the 42nd percentile – i.e., 42% of other outputs scored the same or lower than it.
So far Altmetric has tracked 4,467 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 27.6. This one is in the 13th percentile – i.e., 13% 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 330,837 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 47th percentile – i.e., 47% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 54 others from the same source and published within six weeks on either side of this one. This one is in the 14th percentile – i.e., 14% of its contemporaries scored the same or lower than it.