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Short DNA sequence patterns accurately identify broadly active human enhancers

Overview of attention for article published in BMC Genomics, July 2017
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Title
Short DNA sequence patterns accurately identify broadly active human enhancers
Published in
BMC Genomics, July 2017
DOI 10.1186/s12864-017-3934-9
Pubmed ID
Authors

Laura L. Colbran, Ling Chen, John A. Capra

Abstract

Enhancers are DNA regulatory elements that influence gene expression. There is substantial diversity in enhancers' activity patterns: some enhancers drive expression in a single cellular context, while others are active across many. Sequence characteristics, such as transcription factor (TF) binding motifs, influence the activity patterns of regulatory sequences; however, the regulatory logic through which specific sequences drive enhancer activity patterns is poorly understood. Recent analysis of Drosophila enhancers suggested that short dinucleotide repeat motifs (DRMs) are general enhancer sequence features that drive broad regulatory activity. However, it is not known whether the regulatory role of DRMs is conserved across species. We performed a comprehensive analysis of the relationship between short DNA sequence patterns, including DRMs, and human enhancer activity in 38,538 enhancers across 411 different contexts. In a machine-learning framework, the occurrence patterns of short sequence motifs accurately predicted broadly active human enhancers. However, DRMs alone were weakly predictive of broad enhancer activity in humans and showed different enrichment patterns than in Drosophila. In general, GC-rich sequence motifs were significantly associated with broad enhancer activity, and consistent with this enrichment, broadly active human TFs recognize GC-rich motifs. Our results reveal the importance of specific sequence motifs in broadly active human enhancers, demonstrate the lack of evolutionary conservation of the role of DRMs, and provide a computational framework for investigating the logic of enhancer sequences.

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 39 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Italy 1 3%
Canada 1 3%
Unknown 37 95%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 12 31%
Researcher 6 15%
Student > Bachelor 5 13%
Other 4 10%
Professor 3 8%
Other 5 13%
Unknown 4 10%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 16 41%
Agricultural and Biological Sciences 13 33%
Computer Science 3 8%
Physics and Astronomy 1 3%
Medicine and Dentistry 1 3%
Other 1 3%
Unknown 4 10%
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 22 August 2017.
All research outputs
#15,866,607
of 23,577,654 outputs
Outputs from BMC Genomics
#6,808
of 10,777 outputs
Outputs of similar age
#179,955
of 284,241 outputs
Outputs of similar age from BMC Genomics
#133
of 223 outputs
Altmetric has tracked 23,577,654 research outputs across all sources so far. This one is in the 22nd percentile – i.e., 22% of other outputs scored the same or lower than it.
So far Altmetric has tracked 10,777 research outputs from this source. They receive a mean Attention Score of 4.7. This one is in the 28th percentile – i.e., 28% of its peers scored the same or lower than it.
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We're also able to compare this research output to 223 others from the same source and published within six weeks on either side of this one. This one is in the 33rd percentile – i.e., 33% of its contemporaries scored the same or lower than it.