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Determination and Inference of Eukaryotic Transcription Factor Sequence Specificity

Overview of attention for article published in Cell, September 2014
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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 (94th percentile)
  • Above-average Attention Score compared to outputs of the same age and source (55th percentile)

Citations

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847 Dimensions

Readers on

mendeley
891 Mendeley
citeulike
9 CiteULike
Title
Determination and Inference of Eukaryotic Transcription Factor Sequence Specificity
Published in
Cell, September 2014
DOI 10.1016/j.cell.2014.08.009
Pubmed ID
Authors

Matthew T. Weirauch, Ally Yang, Mihai Albu, Atina G. Cote, Alejandro Montenegro-Montero, Philipp Drewe, Hamed S. Najafabadi, Samuel A. Lambert, Ishminder Mann, Kate Cook, Hong Zheng, Alejandra Goity, Harm van Bakel, Jean-Claude Lozano, Mary Galli, Mathew G. Lewsey, Eryong Huang, Tuhin Mukherjee, Xiaoting Chen, John S. Reece-Hoyes, Sridhar Govindarajan, Gad Shaulsky, Albertha J.M. Walhout, François-Yves Bouget, Gunnar Ratsch, Luis F. Larrondo, Joseph R. Ecker, Timothy R. Hughes

Abstract

Transcription factor (TF) DNA sequence preferences direct their regulatory activity, but are currently known for only ∼1% of eukaryotic TFs. Broadly sampling DNA-binding domain (DBD) types from multiple eukaryotic clades, we determined DNA sequence preferences for >1,000 TFs encompassing 54 different DBD classes from 131 diverse eukaryotes. We find that closely related DBDs almost always have very similar DNA sequence preferences, enabling inference of motifs for ∼34% of the ∼170,000 known or predicted eukaryotic TFs. Sequences matching both measured and inferred motifs are enriched in chromatin immunoprecipitation sequencing (ChIP-seq) peaks and upstream of transcription start sites in diverse eukaryotic lineages. SNPs defining expression quantitative trait loci in Arabidopsis promoters are also enriched for predicted TF binding sites. Importantly, our motif "library" can be used to identify specific TFs whose binding may be altered by human disease risk alleles. These data present a powerful resource for mapping transcriptional networks across eukaryotes.

Twitter Demographics

The data shown below were collected from the profiles of 44 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 891 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 11 1%
France 6 <1%
Germany 5 <1%
Spain 4 <1%
Chile 2 <1%
Mexico 2 <1%
United Kingdom 2 <1%
Sweden 2 <1%
Brazil 1 <1%
Other 10 1%
Unknown 846 95%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 260 29%
Researcher 188 21%
Student > Master 94 11%
Student > Bachelor 85 10%
Professor > Associate Professor 41 5%
Other 134 15%
Unknown 89 10%
Readers by discipline Count As %
Agricultural and Biological Sciences 421 47%
Biochemistry, Genetics and Molecular Biology 265 30%
Computer Science 36 4%
Medicine and Dentistry 20 2%
Neuroscience 11 1%
Other 45 5%
Unknown 93 10%

Attention Score in Context

This research output has an Altmetric Attention Score of 27. 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 29 December 2020.
All research outputs
#886,747
of 17,364,317 outputs
Outputs from Cell
#3,373
of 15,571 outputs
Outputs of similar age
#11,914
of 209,161 outputs
Outputs of similar age from Cell
#65
of 145 outputs
Altmetric has tracked 17,364,317 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 15,571 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 42.4. This one has done well, scoring higher than 78% 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 209,161 tracked outputs that were published within six weeks on either side of this one in any source. This one has done particularly well, scoring higher than 94% of its contemporaries.
We're also able to compare this research output to 145 others from the same source and published within six weeks on either side of this one. This one has gotten more attention than average, scoring higher than 55% of its contemporaries.