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X Demographics
Mendeley readers
Attention Score in Context
Title |
Relating genes to function: identifying enriched transcription factors using the ENCODE ChIP-Seq significance tool
|
---|---|
Published in |
Bioinformatics, June 2013
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DOI | 10.1093/bioinformatics/btt316 |
Pubmed ID | |
Authors |
Raymond K. Auerbach, Bin Chen, Atul J. Butte |
Abstract |
Biological analysis has shifted from identifying genes and transcripts to mapping these genes and transcripts to biological functions. The ENCODE Project has generated hundreds of ChIP-Seq experiments spanning multiple transcription factors and cell lines for public use, but tools for a biomedical scientist to analyze these data are either non-existent or tailored to narrow biological questions. We present the ENCODE ChIP-Seq Significance Tool, a flexible web application leveraging public ENCODE data to identify enriched transcription factors in a gene or transcript list for comparative analyses. |
X Demographics
The data shown below were collected from the profiles of 13 X users who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
Country | Count | As % |
---|---|---|
United States | 9 | 69% |
Norway | 1 | 8% |
Netherlands | 1 | 8% |
Switzerland | 1 | 8% |
Unknown | 1 | 8% |
Demographic breakdown
Type | Count | As % |
---|---|---|
Scientists | 9 | 69% |
Members of the public | 4 | 31% |
Mendeley readers
The data shown below were compiled from readership statistics for 116 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
Country | Count | As % |
---|---|---|
United States | 5 | 4% |
United Kingdom | 4 | 3% |
Germany | 1 | <1% |
Switzerland | 1 | <1% |
Italy | 1 | <1% |
Hungary | 1 | <1% |
China | 1 | <1% |
France | 1 | <1% |
Unknown | 101 | 87% |
Demographic breakdown
Readers by professional status | Count | As % |
---|---|---|
Researcher | 36 | 31% |
Student > Ph. D. Student | 19 | 16% |
Student > Master | 11 | 9% |
Professor | 9 | 8% |
Other | 8 | 7% |
Other | 27 | 23% |
Unknown | 6 | 5% |
Readers by discipline | Count | As % |
---|---|---|
Agricultural and Biological Sciences | 58 | 50% |
Biochemistry, Genetics and Molecular Biology | 27 | 23% |
Medicine and Dentistry | 10 | 9% |
Computer Science | 7 | 6% |
Nursing and Health Professions | 1 | <1% |
Other | 5 | 4% |
Unknown | 8 | 7% |
Attention Score in Context
This research output has an Altmetric Attention Score of 21. 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 19 September 2017.
All research outputs
#1,786,568
of 25,373,627 outputs
Outputs from Bioinformatics
#992
of 12,808 outputs
Outputs of similar age
#14,832
of 207,874 outputs
Outputs of similar age from Bioinformatics
#19
of 214 outputs
Altmetric has tracked 25,373,627 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 92nd percentile: it's in the top 10% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 12,808 research outputs from this source. They typically receive more attention than average, with a mean Attention Score of 8.0. This one has done particularly well, scoring higher than 92% 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 207,874 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 92% of its contemporaries.
We're also able to compare this research output to 214 others from the same source and published within six weeks on either side of this one. This one has done particularly well, scoring higher than 91% of its contemporaries.